RU2821748C2 - Methods of diagnosing stroke, effectiveness of therapy, and determining risk of stroke - Google Patents

Abstract

FIELD: medicine; clinical diagnostics.

SUBSTANCE: diagnostic method and a method of determining an individual's risk of developing stroke are proposed, including measuring the expression levels of at least three biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM. A method is proposed for determining whether an individual who has suffered a stroke has responded to therapy, including measuring the expression levels of at least two biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

EFFECT: characterization of patients with ischemic stroke and the development of accompanying biomarkers required for assessing the effectiveness of neuroprotective drugs in patients by identifying a common coding signature for the brain affected by ischemic damage in a sample taken from an individual.

21 cl, 4 dwg, 4 tbl, 2 ex

Description

The present invention relates to blood biomarkers of stroke and their applications, in particular for monitoring the effectiveness of therapy in ischemic stroke, for diagnostic purposes, short- and long-term prognosis, and risk assessment.

Globally, stroke is the second leading cause of death and the third leading cause of disability (McKay & Mensah, 2005. The atlas of heart disease and stroke (1st edition). Geneva: World Health Organization). Focal cerebral ischemia, i.e. ischemic stroke leads to severe and rapidly developing tissue damage at the site of cerebral infarction. After the initial injury, brain cell death progresses slowly, spreading to a heterogeneous zone surrounding the lesion called the “penumbra” (ischemic penumbra) (Astrup et al., 1977. Stroke. 8 (1):51-7). Saving ischemic penumbra cells from irreversible changes mitigates the consequences of disability (Emberson et al., 2014. Lancet. 384(9958): 1929-35).

To date, the only immediate therapeutic treatment is to restore vascular patency through thrombolysis and/or thrombectomy. However, due to severe time constraints for medical intervention (<6 hours for vascular patency and 4 hours 30 minutes for thrombolysis) and the risk of hemorrhagic transformation, such treatments can only be applied to a small percentage of patients with acute ischemic stroke. For example, among all patients presenting to a tertiary care center within 6 hours of stroke onset, it is estimated that only 10.5% will be eligible for thrombectomy based on endovascular parameters according to AHA/ASA criteria (Vanacker et al., 2016. Stroke 47(7):1844-9).

Thus, the development of new treatment strategies that expand the therapeutic window is a critical public health issue. Although many clinical trials have previously failed, neuroprotection, coupled with blood flow restoration, remains a potentially promising strategy to expand the therapeutic window following the introduction of vascular repair techniques. A growing body of evidence suggests that peripheral proteins, nucleic acids, or lipids may be used to confirm the diagnosis of ischemic stroke and monitor disease progression. To date, however, none of this has been implemented in clinical practice (Kim et al, 2013. J Stroke. 15(1):27-37).

Biomarker tests include imaging, chemical or biological tests that can be used to qualitatively assess or quantify the presence or absence of one or more markers indicating the presence, progression or severity of a disease or certain effects of treatment. On the one hand, enormous progress has been made in imaging techniques for the central nervous system (CNS), but on the other hand, technologies for detecting peripheral biological markers are less developed. Determining reliable biomarkers in the blood that characterize diseases or the results of therapy for diseases of the central nervous system is one of the most important problems of modern neurology, since the blood does not come into direct contact with the brain.

The determination of RNA in the blood as a diagnostic marker is a new area, supported by the results of clinical application of this method in the diagnosis of breast cancer, coronary heart disease and infectious diseases (Rothstein & Jickling, 2013. Biomark Med. 7(1) :37-47). In addition, determination of RNA content in the blood can be used as a companion diagnostic to assess the effect of neuroprotective substances in the early stages of clinical trials. Studies on the use of RNA as a diagnostic biomarker in acute ischemic stroke are few and include only a small number of patients, of which only a few were in the acute phase of cerebral infarction (Tang et al., 2006. J Cereb Blood Flow Metab. 26(8):1089- 102; Stamova et al., 2010. Stroke. 41(10):2171-7) to identify coding RNAs. Recently, there has been growing interest in non-coding RNAs as potential biomarkers for stroke as a risk factor, but not in the acute phase of cerebral infarction (Mick et al., 2017. Stroke. 48(4):828-834).

Transcriptomic analysis is already used in many experimental studies of cerebral ischemia to identify changes in gene expression. Most studies using transcriptomic analysis have been performed in rats and mice using focal or global ischemia models (for review, see Cox-Limpens et al., 2014. Brain Res. 1564:85–100). In addition to ischemia, preconditioning has also been used as a tool to study endogenous brain protection in rodents. The results of microarray analysis examining the transcriptome of these rodents showed that during cerebral ischemia there were changes in the expression of immediate early genes, stress response genes, apoptosis genes, signal transduction genes, neurotransmission genes, ion channel genes, inflammatory genes, cytoskeletal genes, ribosomal genes, and neurotrophic factor genes (Schmidt-Kastner et al. 2002. Brain Res Mol Brain Res. 108(1-2):81-93; Büttner et al. 2009. Brain Res. 1252:1-14; Wang et al. 2011 Brain Res 1372:13-21;Wang et al 2012. Lu et al 2004. J Neurosci Res 77(6):843-57. However, to our knowledge, only a small number of experiments have been conducted in primates (Cook et al, 2012. Nature. 483(7388):213-7; Cook & Tymianski, 2012. Neurotherapeutics, 9(2):371-9). Primates are unique models for studying cerebral ischemia due to their anatomical similarity to humans and the high degree of similarity of their genome to the human genome. For example, both primates and humans, unlike rodents, have a non-lissencephalic brain (that is, a brain that has convolutions in the cortex - on the surface of the cerebral hemispheres). Moreover, the cortical transcriptomes of humans and chimpanzees are very similar and vary more between individuals than across regions within a single individual (Khaitovich et al. 2004. Genome Res. 14(8):1462–73).

Four main reasons have been identified for failure to move neuroprotective therapies from animal studies to humans (Moretti et al. 2015. Pharmacol Ther. 146:23-34; Timsit & Menn 2012. Clin Pharmacol Ther. 91(2):327 -32):

i) Low quality of preclinical studies:

For clinical trials, the problem in this area is being gradually addressed by the development of quality scoring criteria for preclinical trials, such as the STAIR (Stroke Therapy Academic Industry Roundtable) criteria, 1999. Stroke. 30(12):2752-8; Fisher et al., 2009. Stroke. 40(6):2244-50); and by designing randomized, blinded human trials (Llovera et al. 2015. Sci Transi Med. 7(299):299ra121).

ii) Lack of accompanying pharmacodynamic biological markers in early clinical trials:

Companion biomarkers are increasingly used for drug development today. To gain confidence in whether an effective response can be achieved in diseased patients at safe doses, a relatively short-term phase Ib clinical trial is conducted in a small number of patients with the relevant disease before the significant investment required by later stages of research. This is the most important application of some pharmacodynamic biomarkers (Zhao et al. 2015. Clin Chem. 61(11):1343-53).

iii) Lack of predictive tomographic biomarkers in phase II human studies to assess penumbra:

As the DAWN study shows, patient selection based on brain imaging to identify those who are suitable for thrombectomy now plays a critical role in penumbra salvage in selected patients (Jovin et al, 2017. Int J Stroke. 12(6) :641-652; Chaisinanunkul et al 2015. Stroke 46(8):2238-43; Nogueira et al 2018. N Engi J Med 378(1):11-21). Selection based on penumbra characteristics has been performed in only a few neuroprotection studies (for review, see Donnan et al. 2009. Lancet Neurol. 8(3):261-9). Today, this new concept may offer a useful avenue for therapeutic intervention (Hillis & Baron, 2015. Front Neurol. 6:85) while awaiting vascular patency or reperfusion.

iv) Inability to restore vascular patency in most patients:

Thrombectomy is becoming the standard treatment during the acute phase of ischemic stroke, although its use needs to be expanded. It is estimated that only 10.5% of patients presenting to a tertiary care center within 6 hours of stroke are endovascularly eligible for thrombectomy based on AHA/ASA criteria (Vanacker et al., 2016. Stroke. 47(7):1844-9).

A better understanding of gene expression profiles in tissue affected by cerebral ischemia may help improve diagnostic methods. In this work, we studied gene expression after the onset of cerebral ischemia by examining the blood and brain. Microarray analysis of gene expression in macaques using blood and brain data showed that samples from animals with and without stroke had different expression profiles, with the majority of highly differentially expressed genes having increased levels of expression in stroke 6 hours after stroke onset. Comparison of differentially expressed genes in the blood and brain revealed a significant degree of overlap in gene expression profiles. To the researchers' surprise, the data suggested a common coding signature between brain and blood in stroke, supporting the development of blood transcriptomics as a tool for biopsy transcriptomic expression profiling to characterize patients with ischemic stroke and develop accompanying biomarkers needed to assess drug efficacy. neuroprotectors in patients.

Brief description of the invention

The present invention relates to a method for diagnosing stroke in an individual, comprising:

i) Determination of a signature (expression profiles of a set of genes) in a sample obtained from an individual by measuring the expression levels of at least two biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and A.D.M.

preferably with the proviso that the at least two biomarkers mentioned above do not consist of DUSP1 and ADM;

ii) Comparison of the signature determined in stage i) with the reference signature; And

iii) Diagnosing an individual as having had a stroke if the expression levels of the previously mentioned at least two biomarkers in the signature are higher than the expression levels of the same at least two biomarkers in the reference signature.

In one embodiment, step i) of this method for diagnosing stroke in an individual includes measuring the expression levels of at least three biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, step i) of this method for diagnosing stroke in an individual includes measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, a reference signature is obtained by measuring biomarker expression levels in a reference group of substantially healthy individuals.

In one embodiment, the method for diagnosing stroke according to the present invention is aimed at differentiating between stroke and stroke mimics.

The present invention also provides a method for determining the likelihood of a stroke survivor's response to treatment.

The method includes:

i) Determining a signature in a sample obtained from a given individual by measuring the expression levels of at least two biomarkers selected from the group consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

preferably with the condition that at least two biomarkers mentioned above do not consist of DUSP1 and ADM

ii) Comparison of the signature determined in stage i) with the reference signature; And

iii) A conclusion that an individual is responding to treatment if the expression levels of the above at least two biomarkers in the signature are lower than the expression levels of the same at least two biomarkers in the reference signature.

In one embodiment, step i) of a subject method for determining the likelihood of a stroke individual's response to treatment involves measuring the expression levels of nine biomarkers selected from PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1, and ADM.

In one embodiment, a reference signature is obtained by measuring biomarker expression levels in a sample taken from the same individual before the start of said treatment.

The present invention relates to a method for determining an individual's risk of stroke, comprising:

i) Determining a signature in a sample obtained from a given individual by measuring the expression levels of at least two biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

preferably with the condition that at least two biomarkers mentioned above do not consist of DUSP1 and ADM

ii) Comparison of the signature determined in stage i) with the reference signature; And

iii) A conclusion that an individual is at risk for stroke if the expression levels of the previously mentioned at least two biomarkers in the signature are higher than the expression levels of the same at least two biomarkers in the reference signature.

In one embodiment, step i) of determining the risk of stroke in an individual includes measuring the expression levels of nine biomarkers selected from PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, a reference signature is obtained by measuring the expression levels of these biomarkers in a reference group of substantially healthy individuals.

In one embodiment, the individual has previously had a stroke, and the method of determining the individual's risk of stroke is directed to determining the individual's risk of having a recurrent stroke.

In one embodiment related to any of the methods of the present invention, the stroke is an ischemic stroke, transient ischemic attack, or hemorrhagic stroke.

In one embodiment related to any of the methods of the present invention, the sample is a blood, plasma or serum sample.

In one embodiment related to any of the methods of the present invention, the sample is not a brain tissue sample.

Definitions

In the present invention, the following terms have the following meanings:

“Stroke” as used herein means any pathological condition resulting from the disruption, reduction or interruption of blood or oxygen flow to any part of the brain. In particular, the term "stroke" includes, but is not limited to, ischemic stroke, transient ischemic attack (TIA), hemorrhagic stroke.

“Ischemic stroke” (or “IS”) is a case of neurological dysfunction caused by focal cerebral ischemia, spinal cord ischemia, or retinal ischemia with signs of acute infarction (Easton et al, 2009. Stroke. 40(6):2276-2293). There are at least four causes of circulatory problems:

(1) Thrombus in a blood vessel;

(2) Thrombus in the dural venous sinuses that drain blood from the brain;

(3) Blockage of a blood vessel by an embolus; and/or

(4) Sudden and sharp drop in blood pressure.

Stroke symptoms can, and often do, persist for more than 24 hours if the patient survives the original disorder.

A “transient ischemic attack” or “TIA,” also called a ministroke, is a transient event of neurological dysfunction caused by focal cerebral ischemia, spinal cord ischemia, or retinal ischemia without evidence of acute infarction. Symptoms of a TIA may initially be similar to those of a stroke, except that they do not last long, usually less than an hour, with a maximum of 24 hours. Even though a TIA is temporary and does not usually cause damage to brain tissue, patients who experience a TIA are advised to seek immediate professional help because of the similarity of symptoms and because a TIA is a risk factor for later occurrence of a TIA. ischemic stroke.

“Hemorrhagic stroke” means a stroke resulting from a rupture of any element of the cerebral vasculature.

Examples of acute neurological disorders that include stroke or encompass the etiology or symptoms observed in stroke are listed above and consist of, but are not limited to, cerebral ischemia or cerebral infarction (including embolic occlusion and thrombolytic occlusion), reperfusion following acute ischemia, perinatal hypoxic-ischemic injuries, cardiac arrest, and intracranial hemorrhages (such as epidural, subdural, subarachnoid and intracerebral).

"Individual" as used herein means a person diagnosed or treated in accordance with the methods of the present invention. Subjects include, but are not limited to, mammals (eg, mice, monkeys, equines, bovines, porcines, canines, felines, and others), preferably primates, most preferably humans. In the context of the present invention, the term “patient” refers generally to a person seeking diagnosis and treatment, or already diagnosed and receiving treatment in accordance with the methods of the present invention.

“Treatment” as used herein means to mitigate a specific condition (eg, stroke), eliminate or reduce the symptoms of a condition (eg, stroke), slow or block the progression of a condition (eg, stroke), and prevent or delay the initial onset of a condition (eg, stroke). (eg, stroke) in an individual, or preventing or delaying the recurrence of a pathological condition (eg, stroke) previously observed in an individual.

“Diagnosis” or “diagnosis” as used herein means the assessment of the development or progression of a pathological condition (eg, stroke). As is known to those skilled in the art, an accurate estimate is feasible for a statistically significant individual, although it is expected to be accurate for 100% of the individuals diagnosed. It is easy for one skilled in the art to establish statistical significance using methods well known in the art, such as confidence interval determination, p-level determination, Student's t test, Mann-Whitney test, and the like. Preferred confidence interval values are 90% or greater, 95% or greater, 97% or greater, 98% or greater, and 99%. Preferred p-level values: 0.1; 0.05; 0.01; 0.005 or 0.0001. Preferably, the diagnostic result of the present invention is accurate for 60 percent or more, 70 percent or more, 80 percent or more, or 90 percent or more of the individuals in the group.

“Prediction” or “prognosis” as used herein means a fact-based guess made regarding the likely outcome for an individual having a pathological condition (eg, stroke) upon completion of a particular treatment or intervention.

“Blood Sample” as used herein means any blood sample obtained or taken from an individual. Collection of blood samples can be performed by methods well known to those skilled in the art. In some embodiments, the blood sample is a whole blood sample, a serum sample, or a blood plasma sample.

"Biomarker" as used herein means a set of gene expression products (eg, mRNA and/or protein) that is associated with damage to brain tissue or nerve cells, and which may correlate with stroke, but preferably does not correlate with other types of damage . Such specific stroke biomarkers isolated in blood by the Applicants include PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM. These specific biomarkers are described in detail below in this document.

In the present description, the name of each of the searched genes means the international name of the corresponding gene as it appears in international databases of gene sequences and protein sequences, in particular, in the database of the Committee on Gene Nomenclature of HUGO, which can be found on the Internet at the following address: http://www.gene.ucl.ac.uk/nomenclature/index.html.

As used herein, the name of each of the various biomarkers sought may also refer to the international name of the corresponding gene as it appears in the international gene and protein sequence databases ENTRE ID, Genbank, TrEMBL or ENSEMBL. Using these international databases, the nucleic acid sequence corresponding to each gene of interest described herein can be determined by one skilled in the art.

"PTG52" as used herein has its common meaning in the art and means the prostaglandin endoperoxide synthase-2 gene (Gene ID: 5743). The typical human amino acid sequence encoded by the "PTGS2" gene is provided as the reference sequence NP_000954.1 (SEQ ID NO: 1) of the National Center for Biotechnology Information (NCBI) data bank. A representative human mRNA sequence of the "PTGS2" gene is provided as reference sequence NM_000963.4 (SEQ ID NO: 2) of the NCBI Data Bank, with the coding sequence (CDS) ranging from residue 134 to residue 1948 of SEQ ID NO: 2.

"HMOX1" as used herein has its common meaning in the art and means the heme oxygenase-1 gene (Gene ID: 3162). A typical human amino acid sequence encoded by the HMOX1 gene is presented as the standard sequence NP_002124.1 (SEQ ID NO: 3) of the NCBI data bank. A typical human mRNA sequence of the "HMOX1" gene is provided as the NCBI reference sequence NM_002133.3 (SEQ ID NO: 4), with the coding sequence (CDS) ranging from residue 79 to residue 945 of SEQ IDNO: 4.

"LDLR" as used herein has its common meaning in the art and refers to the low density lipoprotein receptor gene (Gene ID: 3949). A typical human amino acid sequence encoded by the "LDLR" gene is provided as the standard sequence NP_000518.1 (SEQ ID NO: 5) of the NCBI data bank. A typical human mRNA sequence of the "LDLR" gene is provided as the NCBI reference sequence NM_000527.4 (SEQ ID NO: 6), with the coding sequence (CDS) ranging from residue 188 to residue 2770 of SEQ ID NO: 6.

"HSPA1B" as used herein has its common meaning in the art and refers to the heat shock protein family member A (Hsp70) 1B gene (Gene ID: 3304). A typical human amino acid sequence encoded by the "HSPA1B" gene is provided as the standard sequence NP_005337.2 (SEQ ID NO: 7) of the NCBI data bank. A typical human mRNA sequence of the "HSPA1B" gene is provided as reference sequence NM_005346.5 (SEQ ID NO: 8) of the NCBI Data Bank, with the coding sequence (CDS) ranging from residue 214 to residue 2139 of SEQ ID NO: 8.

"G0S2" as used herein has its common meaning in the art and means the G0/G1 switch 2 protein gene (Gene ID: 50486). A typical human amino acid sequence encoded by the "G0S2" gene is provided as the standard sequence NP_056529.1 (SEQ ID NO: 9) of the NCBI data bank. A typical human mRNA sequence of the "G0S2" gene is provided as reference sequence NM_015714.4 (SEQ ID NO: 10) of the NCBI Data Bank, with the coding sequence (CDS) ranging from residue 171 to residue 482 of SEQ ID NO: 10.

"BAG3" as used herein has its common meaning in the art and means the BCL2-associated athanogen 3 gene (Gene ID: 9531). A typical human amino acid sequence encoded by the "BAG3" gene is provided as the standard sequence NP_004272.2 (SEQ ID NO: 11) of the NCBI data bank. A typical human mRNA sequence of the "BAG3" gene is provided as reference sequence NM_004281.3 (SEQ ID NO: 12) of the NCBI Data Bank, with the coding sequence (CDS) ranging from residue 307 to residue 2034 of SEQ ID NO: 12.

"TM4SF1" as used herein has its common meaning in the art and means the transmembrane 4-L-six family member-1 protein gene (Gene ID: 4075). A typical human amino acid sequence encoded by the "TM4SF1" gene is presented as the standard sequence XP_016861874.1 (SEQ ID NO: 13) of the NCBI data bank. A typical human mRNA sequence of the "TM4SF1" gene is represented by the standard sequence XM_017006385.2 (SEQ ID NO: 14) of the NCBI data bank, with the coding sequence (CDS) ranging from residue 235 to residue 954 of SEQ ID NO: 14.

"DUSP1" as used herein has its common meaning in the art and refers to the dual specificity phosphatase-1 gene (Gene ID: 1843). A typical human amino acid sequence encoded by the "DUSP1" gene is provided as the standard sequence NP_004408.1 (SEQ ID NO: 15) of the NCBI data bank. A typical human mRNA sequence of the "DUSP1" gene is provided as reference sequence NM_004417.4 (SEQ ID NO: 16) of the NCBI Data Bank, with the coding sequence (CDS) ranging from residue 244 to residue 1347 of SEQ ID NO: 16.

"ADM" as used herein has its common meaning in the art and refers to the adrenomedullin gene (Gene ID: 133). A typical human amino acid sequence encoded by the "ADM" gene is provided as the standard sequence NP_001115.1 (SEQ ID NO: 17) of the NCBI data bank. A typical human mRNA sequence of the "ADM" gene is provided as the NCBI reference sequence NM_001124.3 (SEQ ID NO: 18), with the coding sequence (CDS) ranging from residue 179 to residue 736 of SEQ IDNO: 18.

Detailed Description of the Invention

The present invention relates to a stroke signature, wherein the signature comprises biomarkers whose expression levels are specific to or indicative of stroke. Such biomarkers will hereinafter be referred to as “stroke biomarkers.”

In one embodiment, the signature of the present invention is specific or indicative of stroke. In one embodiment, the signature of the present invention is specific or indicative of ischemic stroke, ministroke (also known as transient ischemic attack (TIA)) and/or hemorrhagic stroke (particularly hemorrhagic stroke caused by intracerebral hemorrhage). In one embodiment, the signature of the present invention is specific or indicative of ischemic stroke. In one embodiment, the signature of the present invention is specific or indicative of a transient ischemic attack. In one embodiment, the signature of the present invention is specific or indicative of hemorrhagic stroke (specifically, hemorrhagic stroke caused by intracerebral hemorrhage).

In one embodiment, the signature of the present invention contains or includes at least one stroke biomarker. In one embodiment, the signature of the present invention contains or includes at least two stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least three stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least four stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least five stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least six stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least seven stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least eight stroke biomarkers. In one embodiment, the signature of the present invention contains or includes at least nine stroke biomarkers.

In one embodiment, the stroke biomarkers are selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3 , EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2.

In one embodiment, the stroke biomarkers are selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1, and ADM.

In one embodiment, the stroke biomarkers are selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least one stroke biomarker selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, НМOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least one stroke biomarker selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least one stroke biomarker selected from the group consisting of or consisting of; PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the stroke biomarkers are selected from: contains or includes at least two stroke biomarkers selected from the group consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, NMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least two stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least two stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least two stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, provided that that the at least two biomarkers mentioned above do not consist of DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least three stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, NMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least three stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least three stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least four stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1 ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1. EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least four stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least four stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least five stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least five stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least five stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least six stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NFAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least six stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least six stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least seven stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAGS, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, NMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least seven stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least seven stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1.

In one embodiment, the signature of the present invention contains or includes at least eight stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGSJ, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, NMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least eight stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

In one embodiment, the signature of the present invention contains or includes at least nine stroke biomarkers selected from the group consisting of or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G , RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5 , GEM and G0S2.

In one embodiment, the signature of the present invention contains or includes at least nine stroke biomarkers selected from the group consisting of or consisting of: PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM.

Thus, the present invention also relates to a stroke signature containing or including one or more biomarkers whose expression levels are/are different in an individual affected by stroke and in an essentially healthy individual.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least one stroke biomarker selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least one stroke biomarker selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least two stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature; preferably provided that the at least two biomarkers mentioned above do not consist of DUSP1 and ADM.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least two stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least four stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least four stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least five stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least five stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least six stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least six stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least seven stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least seven stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPAJB, G0S2, BAG3 and TM4SF1 is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least eight stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, a signature of the present invention is specific or indicative of stroke if the expression level of at least nine stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, is increased compared to the reference signature.

In one embodiment, the expression level of the stroke biomarkers of the present invention in a sample obtained from an individual, preferably a body fluid sample, more preferably a blood sample, can be determined using standard protocols known in the art.

In one embodiment, the expression level of the stroke biomarkers of the present invention corresponds to their transcription levels (ie, mRNA expression) or their translation levels (ie, protein expression).

In one embodiment, the expression level of stroke biomarkers in a sample, preferably a body fluid sample, most preferably a blood sample, collected from an individual is assessed by protein level, that is, by translation level. In this embodiment, the signature of the present invention may be referred to as a proteomic signature.

Methods for determining the level of protein in a sample are well known in the art. Examples of such methods include, but are not limited to, immunohistochemistry, multiplex techniques (Luminex), Western blotting, enzyme-linked immunosorbent assay (ELISA), sandwich ELISA, fluorescence-enhanced immunoassay (FLISA), enzyme-linked immunosorbent assay (ELISA), radioimmunoassay ( RIA), mass spectrometry (MS), microarray analysis, etc., or any combination thereof.

Mass spectrometry (MS) can be used to separate different forms of a protein because different forms typically have different masses, which can be separated using mass spectrometry. Accordingly, if one form of a polypeptide or protein is a better biomarker for a disease than another form of such biomarker, mass spectrometry can be used to specifically detect and quantify the useful form. MS may include time-of-flight (TOF) mass spectrometry (e.g., matrix-assisted laser desorption/ionization (MALDI)-TOF MS), surface-enhanced laser desorption/ionization (MELDI)-MS, electrospray ionization-MS , or MC Fourier transform ion cyclotron resonance (FTICRR).

An immunoassay typically involves contacting a sample with a binding partner capable of selectively interacting with a specific biomarker in the sample. In some embodiments, the binding partners are antibodies, such as, for example, monoclonal antibodies or even aptamers. For example, binding can be detected using a competitive immunoassay, a noncompetitive assay system using methods such as Western blot, radioimmunoassay, ELISA, sandwich ELISA, immunoprecipitation assay, precipitation test, diffuse gel precipitation test, immunodiffusion assay , agglutination test, complement fixation assay, immunoradiometric assay, fluorescence immunoassay, protein A immunoassay, immunohistochemical assay, competitive or sandwich ELISA, Western blot, immunohistological assay, immunocytochemical assay, dot blot, fluorescence polarization assay, proximity scintillation assay, time-resolved homogeneous fluorescence analysis, analysis using LAsys and BIAcore systems. The above assays typically involve binding a partner (ie, antibody or aptamer) to a solid support. Solid carriers that may be used in the practice of the present invention include substrates such as nitrocellulose (eg, in the form of a membrane or microtiter plate well), polyvinyl chloride (eg, microtiter plate plates or wells), polystyrene latex (eg, beads or microtiter plates). ), polyvinylidene fluoride, diazotized paper, nylon membranes, activated granules, magnetic sensitive granules, etc.

The multiplex assay may include phage display technology, antibody profiling, or analysis using the Luminex platform. A polypeptide profiling microarray may include analytical microarrays, functional protein microarrays, or reverse phase protein microarrays. In some cases, the profile of polypeptides or proteins can be determined by a proteomic scan (eg, a complete proteomic scan) using a proteomic microarray.

In one embodiment, the expression level of stroke biomarkers is assessed by nucleic acid (i.e., RNA) levels, i.e. at the level of transcription, in a sample, preferably a body fluid sample, more preferably a blood sample, taken from an individual. In this embodiment, the signature according to the present invention may be referred to as a transcriptomic signature.

Methods for assessing the level of transcription of a biomarker are well known in the art. Examples of such methods include, but are not limited to, polymerase chain reaction (PCR), RT-PCR, quantitative reverse transcription PCR, Northern blot, hybridization techniques such as microarrays, and combinations thereof including, but not limited to, amplicon hybridization obtained by RT-PCR, sequencing such as next generation sequencing (NGS) or RNA sequencing (also known as shotgun whole transcriptome sequencing), etc., or any combination thereof.

Common methods usually involve polymerase chain reaction (PCR). For example, US patents US 4,683,202, US 4,683,195, US 4,800,159 and US 4,965,188 describe conventional PCR methods. The PCR method typically uses two oligonucleotide primers that bind to a selected target nucleic acid sequence. Primers used in the present invention include oligonucleotides capable of acting as an initiation point for nucleic acid synthesis within a given target nucleic acid sequence. The primer can be purified from restriction fragments by standard methods or can be produced synthetically. PCR involves the use of a thermostable polymerase. The term "thermostable polymerase" means a polymerase enzyme that is heat stable, i.e. this enzyme catalyzes the formation of primer extension products complementary to the template and is not irreversibly denatured when exposed to elevated temperatures for the time required to cause denaturation of double-stranded template nucleic acids. Thermostable polymerases have been isolated from Thermusfiavus, T. ruber, T. thermophilus, T. aquaticus, T. lacteus, T. rubens, Bacillus stearothermophilus and Methanothermus fervidus. However, polymerases that are not thermostable can also be used in PCR analysis, provided that enzyme losses are replaced. Typically, such a polymerase is Taq polymerase (ie, Thermus aquaticus polymerase).

Quantitative PCR (qPCR) is typically performed in a thermal cycler capable of illuminating each sample with a beam of light at a specified wavelength and detecting the fluorescence emitted by the excited fluorophore. The thermal cycler is also capable of rapidly heating and cooling samples, thereby taking advantage of the physicochemical properties of nucleic acids and thermopolymerase. To detect and measure the amount of amplicon (ie, the amplified target nucleic acid sequence) in a sample, it is necessary to generate a measurable signal that is proportional to the amount of amplified product. All modern detection systems use fluorescent technology. Some of them are non-specific and therefore only detect one target at a time.

In contrast, specific chemical detection methods can distinguish between nonspecific amplification and targeted amplification. These specific methods can be used to make the analysis multiplex, i.e. capable of detecting several different targets within the same study. For example, SYBR® Green I probes, high-resolution melt curve probes, TaqMan® probes, LNA® probes and molecular beam probes can serve this purpose. TaqMan® probes are the most widely used type of probe. They were developed by Roche (Basel, Switzerland) and ABI (Foster City, USA) based on an assay that initially used a radiolabeled probe (Holland et al., 1991. Proc Nati Acad Sci USA. 88 (16): 7276- 80), consisting of a single-stranded probe sequence complementary to one of the amplicon chains. The fluorophore is attached to the 5' end of the probe, and the quencher is attached to the 3' end. The apparatus excites a fluorophore, which transfers its energy to the quencher via fluorescence resonance energy transfer (FRET). Traditionally, the FRET pair is conjugated to FAM as a fluorophore and TAMRA as a quencher. In a well designed probe, FAM does not fluoresce as it transfers its energy to TAMRA. Because TAMRA fluorescence is detectable at a different wavelength than FAM, the background level of FAM is low. The probe binds to the amplicon at each annealing step during PCR. Taq polymerase extends from the primer, which is bound to the amplicon, and replaces the 5' end of the probe, which is then degraded by the 5'-3' exonuclease of Taq polymerase. Digestion continues until the remaining probe melts the amplicon. This process releases the fluorophore and quencher into solution, separating them spatially (compared to when they were held together by the probe). This results in an irreversible increase in FAM fluorescence and a decrease in TAMRA fluorescence.

In some embodiments, the expression level of stroke biomarkers at the level of nucleic acids (i.e., RNA) is determined by RNA sequencing. The term "RNA sequencing" or "transcriptome sequencing" as used refers to the sequencing of RNA (or cDNA) instead of DNA, where the primary goal is usually to measure expression levels, detect fusion transcripts, alternative splicing, and other genomic changes that are better assessed by RNA. RNA sequencing typically involves sequencing the entire transcriptome. As used herein, the term “whole transcriptome sequencing” means the use of high-throughput sequencing technologies to sequence the entire transcriptome to obtain information about the RNA composition of a sample. Whole transcriptome sequencing can be performed using various platforms, such as the Genome Analyzer (Illumina, Inc., San Diego, CA) and the SOLiD™ sequencing system (Life Technologies, Carlsbad, CA). In principle, any platform suitable for sequencing the entire transcriptome can be used. Typically the RNA is extracted and the ribosomal RNA can be removed as described in US Pat. No. 9,005,891. Using commercially available kits such as the ScriptSeq™ M mRNA-Seq Library Preparation Kit (Epicenter Biotechnologies, Madison, WI), targeted single- and paired-end cDNA libraries can be prepared for sequencing. Libraries can also be barcoded for multiplex sequencing using commercially available barcode primers such as RNA-Seq Barcode Primers from Epicenter Biotechnologies (Madison, WI). PCR is then performed to generate second strand cDNA to incorporate barcodes and amplify libraries. Once the libraries have been quantitatively characterized, the sequencing libraries can be sequenced. Nucleic acid sequencing technologies are suitable methods for expression analysis. The principle behind these methods is that the number of times a DNA sequence is found in a sample is directly related to the relative levels of RNA corresponding to that sequence. These methods are sometimes referred to as "digital gene expression" or DGE to reflect the discrete numerical property of the resulting data. Some of the first methods to apply this principle were serial analysis of gene expression (SAGE) and massively parallel sequencing (MPSS). See, for example, Brenner et al. (2000. Nat Biotechnol. 18(6):630-634). Typically, next generation sequencing (or NGS) is used for RNA sequencing. As used herein, the term “next generation sequencing” or “NGS” refers to a relatively new sequencing technique compared to the traditional Sanger sequencing technique. For a review, see Shendure et al. (2008. Nat Biotechol. 26(10):1135-45), incorporated herein by reference. For purposes of the present invention, NGS may include, but is not limited to, parallel cycle sequencing, microelectrophoresis-based sequencing, and hybridization sequencing. In a typical example of next generation sequencing using parallel cycle sequencing techniques, a genomic or cDNA library is first prepared and then common adapters can be ligated to the fragmented genomic DNA or cDNA. Various protocols can be used to generate paired-end tag hopping libraries with controlled distance distributions. PCR creates a matrix of millions of spatially immobilized colonies, or “polonies,” with each polonium consisting of multiple copies of a single shotgun library fragment. Since polonium is attached to a planar matrix, one microliter volume of reagent is sufficient to simultaneously change the properties of the matrix, for example, to hybridize a primer or to carry out enzymatic extension reactions. Detecting the fluorescent tags included in each extension through imaging can be used to obtain sequencing data for all features simultaneously. Successive cycles of repeating enzymatic reactions and imaging can also be used to create a continuous sequence, or read, for each matrix property.

In one embodiment, a determination of whether the expression level of a particular stroke biomarker is elevated in a particular sample, preferably a body fluid sample, more preferably a blood sample, is made by comparison with a reference signature and/or a predetermined reference value.

In one embodiment, the reference signature contains or includes pre-established reference values for each of the stroke biomarkers of interest, preferably at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from group including or consisting of: HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1 , EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MSY, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2, preferably selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, preferably provided that the above at least one or two stroke biomarkers do not consist of DUSP1 and/or ADM.

Typically, the reference signature is either implemented in software, or an overall median or other arithmetic average is obtained from the measurement results.

In one embodiment, the reference signature is derived from a previous measurement of stroke biomarker expression levels of the present invention in a reference sample taken from the same individual, e.g., stroke biomarker expression levels measured one month prior to a subsequent measurement of stroke biomarker expression levels of the invention, preferably six months, more preferably one year or more, before the subsequent measurement; or, for example, expression levels of stroke biomarkers measured before initiation of therapy.

In one embodiment, a reference signature is obtained by measuring the expression levels of stroke biomarkers of the present invention in a control group.

In one embodiment, the reference signature corresponds to a signature obtained from population studies, including, but not limited to, similar individuals having the same age range, individuals belonging to the same or similar ethnic group, having a similar history of cancer, and the like.

In one embodiment, a reference signature is obtained by measuring the expression levels of stroke biomarkers of the present invention in a control sample obtained from one or more substantially healthy individuals. For the purposes of this document, an “essentially healthy individual” has not previously been diagnosed with stroke and has not been identified as having had or been suffering from a stroke.

In one embodiment, the control group comprises substantially healthy individuals, preferably at least 50, more preferably at least 100, more preferably at least 200, and even more preferably at least 500 essentially healthy individuals.

Given multiple samples from controls, the median and/or mean expression level for each stroke biomarker can be calculated, respectively. In conjunction with the results obtained, the stroke biomarker can be tracked as differentially expressed. In one embodiment, the reference signature corresponds to the average expression levels of the stroke biomarkers of the signature of the present invention obtained in a control group. In one embodiment, the reference signature corresponds to the median expression levels of the stroke biomarkers of the signature of the present invention obtained in a control group.

In one embodiment, a reference signature is obtained using algorithms and other statistical and structural classification methods. Samples obtained from the control group are used to determine the average profile (i.e., reference signature) for at least one stroke biomarker, preferably for at least 2, 3, 4, 5, 6, 7, 8 or 9 stroke biomarkers of the present invention.

In one embodiment, a stroke biomarker is considered to be differentially expressed in an individual being assessed for the presence or absence of stroke and in an essentially healthy individual if the range of expression is at least greater than about |1.1|, preferably at least at least greater than about |1.2|, about |1.3|, about 1.4|, about |1.5|, about |1.6|, about 1.7|, about |1.8| , approximately |1.9|, approximately [2.0|, approximately |2.1|, approximately |2.2|, approximately |2.3|, approximately |2.4|, approximately |2.5|, approximately [3.0|, approximately |4.0|, approximately |5.0| or more.

In one embodiment, a stroke biomarker is considered to be differentially expressed in an individual being assessed for the presence or absence of stroke and in an essentially healthy individual if the range of expression is at least greater than about 1.1; preferably at least greater than about 1.2; approximately 1.3; approximately 1.4; approximately 1.5; approximately 1.6; approximately 1.7; approximately 1.8; approximately 1.9; approximately 2.0; approximately 2.1; approximately 2.2; approximately 2.3; approximately 2.4; approximately 2.5; approximately 3.0; approximately 4.0; approximately 5.0 or more.

In one embodiment, a stroke biomarker is considered to be differentially expressed in an individual being assessed for the presence or absence of stroke and in an essentially healthy individual if the log2 of the range of expression is at least greater than about |0.1|, preferably by at least, more than about |0.2|, about |0.3|, about |0.4|, about |0.5|, about |0.6|, about |0.7|, about |0 ,8|, approximately |0.9|, approximately |1.0|, approximately |1.1|, approximately |1.2|, approximately |1.3|, approximately |1.4|, approximately |1, 5|, approximately |1.6|, approximately |1.7|, approximately 1.8|, approximately |1.9|, approximately |2.0|, approximately |2.1|, approximately |2.2| , approximately |2.3| or more.

In one embodiment, a stroke biomarker is considered to be differentially expressed in an individual being assessed for the presence or absence of stroke and in an essentially healthy individual if the log2 of the range of expression is at least greater than about 0.1, preferably at least , greater than about 0.2; approximately 0.3; approximately 0.4; approximately 0.5; approximately 0.6; approximately 0.7; approximately 0.8; approximately 0.9; approximately 1.0; approximately 1.1; approximately 1.2; approximately 1.3; approximately 1.4; approximately 1.5; approximately 1.6; approximately 1.7; approximately 1.8; approximately 1.9; approximately 2.0; approximately 2.1; approximately 2.2; approximately 2.3; or more.

In one embodiment, a stroke biomarker is considered to be differentially expressed in an individual being assessed for the presence or absence of stroke and in an essentially healthy individual if the expression level of said biomarker is at least about 0.5; approximately 1; approximately 1.5; at approximately 2; approximately 2.5; at about 3; approximately 3.5; at about 4; at about 5; at about 6; at about 7; at about 8; at about 9; at about 10; at about 15; at about 20; at about 30; at about 40; about 50 or about 100 times higher than the expression level of the same biomarker determined in an essentially healthy individual, preferably a control group.

In one embodiment, by determining the expression level of more than one stroke biomarker in a sample, preferably a body fluid sample, more preferably a blood sample, obtained from an individual seeking to diagnose the presence or absence of stroke, a score may be calculated that is a composite measure of these expression levels, and which is then compared with a pre-established reference value. In one embodiment, a score greater than a predetermined reference value indicates that the individual is developing or has previously had a stroke.

Typically, the preset reference value is a threshold or cutoff value. Typically, the "threshold value" or "cut-off value" is determined experimentally, empirically, or theoretically. The threshold value can also be chosen arbitrarily based on existing experimental and/or clinical conditions, as understood by one skilled in the relevant art. For example, retrospective quantification of previously collected and properly preserved samples of an individual can be used to establish a reference value. The cut-off value should be determined to achieve optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false-positive and false-negative results). Typically, the optimal sensitivity and specificity (and therefore the cutoff value) can be determined using a receiver operating characteristic curve (ROC curve) based on experimental data. For example, after determining the expression level of a biomarker of the present invention in a control group, algorithmic analysis can be used to statistically process the expression levels determined in the test samples and thereby obtain a classification standard that is meaningful for classifying the samples. The full name of the ROC curve is the receiver operating characteristic curve, which is also known as the sensitivity versus false positive rate curve. It is used mainly in clinical biochemical diagnostic tests. The ROC curve is a composite measure that reflects continuous variables for the proportion of true positives (sensitivity) and the proportion of false positives (1-specificity). It reveals the relationship between sensitivity and specificity using fusion imaging. To calculate a range of sensitivity and specificity values, a number of different cut-off values (threshold or critical values, boundary values between normal and abnormal diagnostic test results) are established as continuous variables. Sensitivity is then used as the vertical coordinate and specificity as the horizontal coordinate to construct the curve. The higher the area under the curve (AUC), the higher the diagnostic accuracy. On the ROC curve, the point closest to the far upper left edge of the coordinate chart is the critical point, having both high sensitivity and high specificity values. The area under the ROC curve (AUC) value ranges from 1.0 to 0.5. When AUC>0.5, the diagnostic result improves as the AUC value approaches 1. When 0.5<AUC<0.7, the accuracy is low. At 0.7<AUC<0.9 the accuracy is moderate. When AUC<0.9, the accuracy is high. This algorithmic method is preferably implemented using a computer. Existing programs or systems can be used to construct the ROC curve, for example: medical statistical software MedCalc 9.2.0.1; SPSS 9.0; ROCKOWER.SAS; DESIGNROC.FOR; MULTIREADER POWER.SAS; CREATE-ROC.SAS; GB STAT VI0.0 (Dynamic Microsystems, Inc., Silver Spring, MD, USA), etc.

The present invention further relates to methods for diagnosing stroke in an individual using the stroke signature of the present invention.

In one embodiment, the stroke diagnostic method of the present invention is particularly well suited for diagnosing ischemic stroke, ministroke (or transient ischemic attack - TIA) and/or hemorrhagic stroke (particularly hemorrhagic stroke caused by intracerebral hemorrhage).

In one embodiment, the stroke diagnostic method of the present invention is particularly well suited for distinguishing a stroke from a stroke mimic, in which some or all of the symptoms of a stroke may be observed, except that it is not a stroke and is not the result of an ischemic brain injury. Without wishing to be bound by any theory, since an elevated biomarker level is a result of stroke, an individual who has a stroke mimic will not have an elevated biomarker level (in other words, since the signature of the present invention is specific or indicative of stroke, an individual experiencing a stroke mimic will not demonstrate a similar signature).

Common stroke mimics include, but are not limited to, migraine, syncope, peripheral vestibular disorders and BPPV (benign paroxysmal positional vertigo), seizures, functional manifestations/anxiety disorder, transient global amnesia, Bell's palsy, peripheral nervous system disease/dysfunction, postural hypotension, tumors, viral diseases, cardiac arrhythmia, multiple sclerosis, drug side effects, hypoglycemia, Parkinson's disease, retinal diseases/ocular pathologies, spinal pathologies, trigeminal neuralgia, urinary tract infections, delirium, motor neuron diseases, subarachnoid hemorrhage, subdural hematoma , and so on. (See Nadarajan et al., 2014. Pract Neurol. 14(1):23-31).

Accordingly, the stroke diagnostic method of the present invention provides the advantage of early and accurate diagnosis of stroke, which may benefit an individual suspected of having a stroke in at least the following aspects:

(1) may reduce the incidence of false diagnoses of stroke, and/or

(2) can limit the extent of tissue damage by providing early and effective treatment to the individual in need of medical attention.

In one embodiment, the method for diagnosing stroke of the present invention includes the step of collecting a sample from an individual who is to be diagnosed as having or not having a stroke.

The term “sample” as used herein generally refers to any sample that can be tested to determine the expression levels of a biomarker, preferably stroke markers, of the present invention.

In one embodiment, the sample is a physiological fluid sample. Examples of body fluids include, but are not limited to, blood, plasma, serum, lymph, ascitic fluid, cystic fluid, urine, bile, mucus, serous fluid, sebaceous gland secretions, nipple exudate, synovial fluid, bronchoalveolar lavage fluid, sputum, amniotic fluid, peritoneal fluid, cerebrospinal fluid, pleural fluid, pericardial fluid, saliva, semen, lacrimal fluid, mucous membrane secretions, sweat, and alveolar macrophages.

In one embodiment, the sample is a blood sample. The term "blood sample" as used herein includes whole blood samples, serum samples and plasma samples.

In one embodiment, the sample is a sample of whole blood, plasma or serum.

In one embodiment, the sample is not a tissue sample from the body. Examples of body tissues include, but are not limited to, brain, muscle, nerves, heart, lung, liver, pancreas, spleen, thymus, esophagus, stomach, intestines, kidney, prostate, testicle, ovary, hair, skin, bone, mammary gland, uterus, bladder and spinal cord.

In one embodiment, the sample is not a brain tissue sample. Therefore, in accordance with this embodiment, the methods of the present invention do not include the step of obtaining a sample of brain tissue from an individual.

In one embodiment, the sample is not a biopsy sample. In one embodiment, the sample is not a sample obtained from a brain biopsy.

In one embodiment, a sample, preferably a body fluid sample, more preferably a blood sample, has been previously collected from the individual, that is, the stroke diagnostic method of the present invention does not include the step of actively collecting a sample from the individual. Therefore, according to this embodiment, the method of the present invention is a non-invasive method, that is, an in vitro method.

In one embodiment, the stroke diagnostic method of the present invention includes the step of detecting a signature of the present invention in the above-mentioned sample, preferably a body fluid sample, more preferably a blood sample of the individual.

Means and methods for determining the signature according to the present invention are described in detail above in this document.

In one embodiment, the signature determination step includes the substep of quantifying the expression level of at least one stroke biomarker, preferably at least 2, 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers of the present invention.

In one embodiment, the expression level of at least one stroke biomarker, preferably at least 2, 3, 4, 5, 6, 7, 8 or 9 stroke biomarkers, is measured using a DNA microarray such that the expression levels of each The stroke biomarker signatures of the present invention were determined simultaneously.

In one embodiment, the expression level of at least one stroke biomarker, preferably at least 2, 3, 4, 5, 6, 7, 8 or 9 stroke biomarkers, is measured using RNA sequencing methods.

In one embodiment, the expression level of at least one stroke biomarker, preferably at least 2, 3, 4, 5, 6, 7, 8 or 9 stroke biomarkers, is measured using a CodeSet. Special sets of codes for a specific panel of markers (eg, for the stroke biomarkers contemplated herein) may be commercially developed. These include, without limitation, nCounter® Custom CodeSets (NanoString) (Malkov et al., 2009. BMC Res Notes. 2:80; Kulkarni, 2011. Curr Protoc Mol Biol. Chapter 25:Unit 25B.10).

In one embodiment, upon determining the expression level for more than one stroke biomarker in the previously mentioned sample, preferably a body fluid sample, more preferably a blood sample, obtained from an individual who is to be diagnosed as having or not having a stroke, a sum of said levels is calculated. expression.

In one embodiment, the stroke diagnostic method of the present invention includes the step of comparing signatures obtained from analyzing a sample, preferably a body fluid sample, more preferably a blood sample, of an individual, with a reference signature as described above.

In one embodiment, the stroke diagnostic method of the present invention includes the step of comparing signatures obtained from analyzing a sample, preferably a body fluid sample, more preferably a blood sample, of an individual, with a predetermined reference value as described above.

In one embodiment, the stroke diagnostic method of the present invention includes the step of comparing the expression levels of stroke biomarkers in a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample of the individual, with the expression levels of stroke biomarkers in a reference signature, as described above.

In one embodiment, the stroke diagnostic method of the present invention includes the step of diagnosing stroke in an individual based on correlating a signature obtained from analyzing a sample, preferably a body fluid sample, more preferably a blood sample, of said individual compared to a reference signature.

In one embodiment, a patient is diagnosed with a stroke when the signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample of the individual, is considered different from the reference signature as defined above.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression range is at least 1, preferably at least 2, 3 , 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least greater than about |1.1|, preferably at least greater than about |1.2| , approximately |1.3|, approximately |1.4|, approximately |1.5|, approximately |1.6|, approximately |1.7|, approximately |1.8|, approximately |1.9|, approximately |2.0|, approximately |2.1|, approximately |2.2|, approximately |2.3|, approximately |2.4|, approximately |2.5|, approximately |3.0|, approximately |4.0|, approximately |5.0|, or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression range is at least 1, preferably at least 2, 3 , 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least greater than about 1.1; preferably at least greater than about 1.2; approximately 1.3; approximately 1.4; approximately 1.5; approximately 1.6; approximately 1.7; approximately 1.8; approximately 1.9; approximately 2.0; approximately 2.1; approximately 2.2; approximately 2.3; approximately 2.4; approximately 2.5; approximately 3.0; approximately 4.0; approximately 5.0 or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the log ₂ of the expression range is at least 1, preferably at least 2 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least greater than about |0.5|, preferably at least greater than about |0. 1|, approximately |0.2|, approximately |0.3|, approximately |0.4|, approximately |0.5|, approximately |0.6|, approximately |0.7|, approximately |0.8 |, approximately |0.9|, approximately |1.0|, approximately |1.1|, approximately |1.2|, approximately |1.3|, approximately |1.4|, approximately |1.5| , approximately |1.6|, approximately |1.7|, approximately |1.8|, approximately |1.9|, approximately |2.0|, approximately |2.1|, approximately |2.2|, approximately |2,3| or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the log ₂ of the expression range is at least 1, preferably at least 2 , 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least greater than about 0.5; preferably at least greater than about 0.1; approximately 0.2; approximately 0.3; approximately 0.4; approximately 0.5; approximately 0.6; approximately 0.7; approximately 0.8; approximately 0.9; approximately 1.0; approximately 1.1; approximately 1.2; approximately 1.3; approximately 1.4; approximately 1.5; approximately 1.6; approximately 1.7; approximately 1.8; approximately 1.9; approximately 2.0; approximately 2.1; approximately 2.2; approximately 2.3 or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression level is at least 1, preferably at least 2, 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least about 0.5; approximately 1; approximately 1.5; at approximately 2; approximately 2.5; at about 3; approximately 3.5; at about 4; at about 5; at about 6; at about 7; at about 8; at about 9; at about 10; at about 15; at about 20; at about 30; at about 40; about 50 or about 100 times the level of expression of the same biomarker determined in an essentially healthy individual, preferably a control group.

In one embodiment, an individual is diagnosed with stroke if the expression level of at least one, preferably at least 2, 3, 4, 5, 6, 7, 8 or 9 stroke biomarkers of the present invention is higher than the level expression of the same stroke biomarker in an essentially healthy individual, preferably a control group.

In one embodiment, an individual is diagnosed with stroke if the sum score calculated for the expression levels of more than one stroke biomarker, preferably at least 2, 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers of the present invention, higher than a predetermined reference value, that is, the sum calculated for the expression levels of the same biomarkers in an essentially healthy individual, preferably a control group.

In one embodiment, the stroke diagnostic method of the present invention includes the following steps:

i) obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of an individual who is to be diagnosed for the presence or absence of a stroke;

ii) identifying the signature of the present invention in said sample;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) diagnosing the individual as having a stroke based on the correlation of the signature with a reference signature, as detailed above.

In one embodiment, the stroke diagnostic method of the present invention includes the following steps:

i) obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of an individual who is to be diagnosed for the presence or absence of a stroke;

ii) determining a signature in said sample, preferably by measuring expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of HSPA1B , NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456 , HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2, preferably with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM ;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) diagnosing the individual as having a stroke based on the correlation of the signature with a reference signature, as detailed above.

In one embodiment, the stroke diagnostic method of the present invention includes the following steps:

i) obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of an individual who is to be diagnosed for the presence or absence of a stroke;

ii) determining a signature in said sample, preferably by measuring the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of PTGS2 , HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, preferably with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM.

iii) comparing the signature determined in step ii) with the reference signature, and

iv) diagnosing the individual as having a stroke based on the correlation of the signature with a reference signature, as detailed above.

In one embodiment, the stroke diagnostic method of the present invention includes the following steps:

i) obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of an individual who is to be diagnosed for the presence or absence of a stroke;

ii) determining a signature in said sample, preferably by measuring the expression levels of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature, and

iv) diagnosing the individual as having a stroke based on the correlation of the signature with a reference signature, as detailed above.

In one embodiment, the stroke diagnostic method of this invention includes the following steps:

i) obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of an individual who is to be diagnosed for the presence or absence of a stroke;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) diagnosing the individual as having a stroke based on the correlation of the signature with a reference signature, as detailed above.

In one embodiment, the stroke diagnostic method of this invention includes the following steps:

i) obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of an individual who is to be diagnosed for the presence or absence of a stroke;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) diagnosing the individual as having a stroke based on the correlation of the signature with a reference signature, as detailed above.

In one embodiment, the stroke diagnostic method of the present invention includes the steps of i) determining the expression level of at least one biomarker selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM in a sample preferably a body fluid sample, more preferably a blood sample obtained from the individual ii) comparing the level determined in step i) with a pre-established reference value, and iii) making a diagnosis of stroke when the expression level determined in step i ) above a preset reference value.

In one embodiment, a reference signature is determined or obtained using data from a control group, preferably a control group comprising at least one substantially healthy individual.

In one embodiment, the stroke diagnostic method of the present invention is applied to an individual who exhibits symptoms of stroke but has not undergone routine testing to rule out all possible causes of stroke.

In one embodiment, the stroke diagnostic method of the present invention may be part of a routine battery of tests performed on an individual who exhibits symptoms of stroke, including, but not limited to, loss of vision in one eye, weakness in one arm or leg, weakness in one side body, lightheadedness, dizziness, double vision, weakness on both sides of the body, difficulty speaking, slurred speech, or loss of coordination.

In one embodiment, the stroke diagnostic method of the present invention may be implemented as an adjunct to other diagnostic tools, which include, but are not limited to, computed tomography (CT) and magnetic resonance imaging (MRI).

The present invention further relates to a method for determining whether an individual suffering from a stroke is responding to therapy. The present invention also relates to a method for determining whether an individual suffering from a stroke is responding during or after completion of therapy.

In one embodiment, a method for determining whether an individual suffering from stroke is responding to therapy includes the steps of i) determining the expression level of at least one biomarker of the present invention selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, ii) comparing the expression level determined in step i) with a pre-established reference value, and iii) judging whether the individual is responding if the level determined in step i) is lower preset reference value.

In one embodiment, a method of determining whether an individual suffering from a stroke is responding to therapy includes the steps of collecting a sample, preferably a body fluid sample, more preferably a blood sample, from the individual; determining in said sample a signature of the present invention, and comparing said signature with a reference signature. These stages have been described in detail above in the context of the stroke diagnostic method and should be applied to the present method accordingly.

In one embodiment, a method for determining whether a patient suffering from a stroke is responding to therapy includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) identifying in said sample the signature of the present invention;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) inferring whether the individual has a response based on the signature's correlation with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient suffering from a stroke is responding to therapy includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of HSPA1B , NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456 , HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) inferring whether the individual has a response based on the signature's correlation with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient suffering from a stroke is responding to therapy includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of PTGS2 , HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) inferring whether the individual has a response based on the signature's correlation with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient suffering from a stroke is responding to therapy includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining a signature in said sample, preferably by measuring the expression level of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) inferring whether the individual has a response based on the signature's correlation with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient suffering from a stroke is responding to therapy includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression level of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) inferring whether the individual has a response based on the signature's correlation with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient suffering from a stroke is responding to therapy includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression level of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) inferring whether the individual has a response based on the signature's correlation with the reference signature, as detailed above.

In one embodiment, the reference signature is determined or derived from prior measurement of stroke biomarker expression levels of the present invention in a reference sample taken from the same individual, for example, stroke biomarker expression levels measured before initiation of therapy.

In one embodiment, a response is inferred from the individual if the signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of the individual is considered different from the reference signature as described above.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression range is at least 1, preferably at least 2, 3 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least less than about |1.1|, preferably at least less than about |1.2 |, approximately |1.3|, approximately |1.4|, approximately |1.5|, approximately |1.6|, approximately |1.7|, approximately |1.8|, approximately |1.9| , approximately |2.0|, approximately |2.1|, approximately |2.2|, approximately |2.3|, approximately |2.4|, approximately |2.5|, approximately |3.0|, approximately |4.0|, approximately |5.0| or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression range is at least 1, preferably at least 2, 3 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least less than about 1.1, preferably at least less than about 1.2; approximately 1.3; approximately 1.4; approximately 1.5; approximately 1.6; approximately 1.7; approximately 1.8; approximately 1.9; approximately 2.0; approximately 2.1; approximately 2.2; approximately 2.3; approximately 2.4; approximately 2.5; approximately 3.0; approximately 4.0; approximately 5.0 or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the log ₂ of the expression range is at least 1, preferably at least 2 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least less than about |0.1|, preferably at least less than about |0 ,2|, approximately |0.3|, approximately |0.4|, approximately |0.5|, approximately |0.6|, approximately |0.7|, approximately |0.8|, approximately |0, 9|, approximately |1.0|, approximately |1.1|, approximately |1.2|, approximately |1.3|, approximately |1.4|, approximately |1.5|, approximately |1.6 |, approximately |1.7|, approximately |1.8|, approximately |1.9|, approximately |2.0|, approximately |2.1|, approximately |2.2|, approximately |2.3| or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the log ₂ of the expression range is at least 1, preferably at least 2 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least less than about 0.1; preferably at least less than about 0.2; approximately 0.3; approximately 0.4; approximately 0.5; approximately 0.6; approximately 0.7; approximately 0.8; approximately 0.9; approximately 1.0; approximately 1.1; approximately 1.2; approximately 1.3; approximately 1.4; approximately 1.5; approximately 1.6; approximately 1.7; approximately 1.8; approximately 1.9; approximately 2.0; approximately 2.1; approximately 2.2; approximately 2.3 or more.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression level is at least 1, preferably at least 2, 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers as defined herein, at least approximately 0.5; approximately 1; approximately 1.5; at approximately 2; approximately 2.5; at about 3; approximately 3.5; at about 4; at about 5; at about 6; at about 7; at about 8; at about 9; at about 10; at about 15; at about 20; at about 30; at about 40; about 50 or about 100 times lower than the level of expression of the same biomarker determined in an essentially healthy individual, preferably from a control group.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the expression level is at least 1, preferably at least 2, 3, 4, 5, 6, 7, 8 or 9 stroke biomarkers of the present invention are not higher than the level of expression of the same stroke biomarker in an essentially healthy subject, preferably a control group.

In one embodiment, a signature obtained from the analysis of a sample, preferably a body fluid sample, more preferably a blood sample, of an individual is considered different from the reference signature if the summary score calculated for the expression levels of more than one stroke biomarker, preferably at least, 2, 3, 4, 5, 6, 7, 8, or 9 stroke biomarkers of the present invention, not higher than a predetermined reference value, that is, the sum calculated for the expression levels of the same biomarkers in an essentially healthy individual, preferably from the control group.

Thus, the method of determining whether a stroke patient is responding to therapy is particularly suitable for differentiating between responders and non-responders.

As used herein, the term “responder” means an individual or patient who will experience a therapeutic response, that is, an individual who experiences a reduction, amelioration, or cure of stroke symptoms. According to the present invention, respondents have an objective response and, therefore, this term does not include individuals or patients whose stroke is stabilized such that the disease does not progress after therapy.

As used herein, the term “non-responder” (or difficult to treat) includes individuals or patients who do not experience a reduction or improvement in stroke symptoms after treatment. According to the present invention, the term "non-responder" also includes individuals or patients with stable stroke.

Typically, characterization of an individual as a responder or non-respondent can be achieved by reference to a standard or training set. The standard may be a profile of an individual known to be a respondent or non-respondent, or alternatively the standard may be a numerical score. Such predetermined standards may be presented in any suitable form, such as a printed list or chart, a computer program, or other storage medium. In some embodiments, the preset value is the expression level determined before initiation of therapy. When it is concluded that the patient is a non-responder, the physician may decide to discontinue therapy to avoid any further unwanted adverse reactions.

In one embodiment, for monitoring purposes, expression levels may be measured at multiple time points, for example, twice, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times or more. Thus, monitoring expression levels over a period of time after or during therapy may provide an indicator of the degree of success of treatment, since expression levels would be expected to decline more rapidly in individuals with successful treatment compared to an untreated individual whose levels expression may also decrease, but more slowly.

Typically, therapy consists of any method well known to one skilled in the art. Treatment options include, but are not limited to, endovascular procedures and surgery such as thrombectomy.

As used herein, the term "thrombectomy" means any surgical and/or mechanical removal or disruption of a thrombus. In general, there are three classes of devices for performing mechanical thrombectomy: coiled catheters, suction devices, and stent retrievers. Other devices and methods currently under development are also included in the definition of thrombectomy used herein. Typically, a catheter is directed to the area where blood flow is blocked to remove the clot. A combination of thrombectomy and thrombolysis may be used for the patient. In addition, ischemic stroke can be treated by administering a thrombolytic agent to dissolve the clot ("thrombolysis").

Antithrombotics are further classified into the following three subtypes: anticoagulants, antiplatelet drugs, and thrombolytic drugs.

Examples of anticoagulants include, but are not limited to, coumarins, heparin, warfarin, acenocoumarol, phenprocoumon, atromentine, phenindione, fondaparinux, idraparinux, direct factor Xa inhibitors, direct thrombin inhibitors, antithrombin drugs, batroxobin and hementin.

Examples of antiplatelet drugs include, but are not limited to, irreversible cyclooxygenase inhibitors (eg, aspirin or triflusal), adenosine diphosphate receptor inhibitors (eg, clopidogrel, prasugrel, ticagrelor, or ticlopidine), phosphodiesterase inhibitors (eg, cilostazol), glycoprotein IIB/IIIA inhibitors (eg, , abciximab, eptifibatide or tirofiban), adenosine reuptake inhibitors (eg, dipyridamole) and thromboxane inhibitors (eg, thromboxane synthase inhibitors or thromboxane receptor antagonists).

Examples of thrombolytic drugs include, but are not limited to, tissue plasminogen activator t-PA (alteplase and others (desmoteplase, reteplase, tenecteplase, ...), anistreplase, streptokinase and urokinase. t-PA can be administered optionally during endovascular procedures.

In one embodiment, the therapy consists of administering a neuroprotective agent.

Examples of neuroprotective agents generally include, but are not limited to, free radical scavengers, antiglutamate agents, and cyclin-dependent kinase inhibitors.

In some embodiments, the neuroprotective agent is a cyclin-dependent kinase (CDK) inhibitor. Known CDK inhibitors can be classified according to their ability to inhibit cyclin-dependent kinases in general or according to their selectivity for a particular CDK. For example, flavoripidol acts as a “pan” CDK antagonist and does not show selective selectivity for any specific cyclin-dependent kinase (Dai & Grant, 2003. Curr Opin Pharmacol. 3(4):362-70). Purine-based CDK inhibitors, such as olomoucine, roscovitine, purvanolols and CGP74514A, are known to exhibit higher selectivity for cyclin-dependent kinases 1, 2 and 5, but do not have an inhibitory effect on cyclin-dependent kinases 4 and 6 (Dai & Grant, 2003. Curr Opin Pharmacol 3(4):362-70). In addition, it has been shown that inhibitors of purine-based zinc-dependent kinases, such as S-roscovitine, can induce an anti-apoptotic effect in the nervous system (O'Hare et al., 2002. Pharmacol Ther. 93(2-3):135 -43; Timsit & Menn, 2012. Clin Pharmacol Ther. 91(2):327-32; et al., 2017. J Cereb Blood Flow Metab. 37(6):2208-2223) or prevent neuronal death in neurodegenerative diseases such as Alzheimer's disease (Filgueira de Azevedo et al., 2002. Biochem Biophys Res Commun. 297(5):1154-8; Knockaert et al., 2002. Trends Pharmacol Sci 23(9):417-25).

In some embodiments, the therapy consists of hypothermia (eg, Kurisu & Yenari, 2018. Neuropharmacology. 134(Pt B):302-309).

The present invention also relates to a method for determining whether a patient is at risk of stroke, comprising the steps of i) determining the expression level of at least one biomarker selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM, in a sample, preferably a body fluid sample, more preferably a blood sample, taken from an individual ii) comparing the level determined in step i) with a pre-established reference value and iii) concluding that the individual is at risk of developing stroke if the expression level determined in step i) is higher than a pre-established reference value.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the steps of obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of the individual; identifying in said sample the signature of the present invention; and comparing said signature with a reference signature. These stages have been described in detail above in the context of the stroke diagnostic method and should be applied to the present method accordingly.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) identifying in said sample the signature of the present invention;

iii) comparing the signature obtained in stage ii) with the reference signature; And

iv) concluding that the individual is at risk of developing stroke based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual; ii) determining in said sample a signature, preferably by measuring the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of HSPA1B , NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456 , HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

ii) comparing the signature obtained in stage ii) with the reference signature; And

iii) concluding that the individual is at risk of developing stroke based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of PTGS2 , HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the individual is at risk of developing stroke based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression level of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the individual is at risk of developing stroke based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the individual is at risk of developing stroke based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining whether a patient is at risk of stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the individual is at risk of developing stroke based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, the reference signature is determined or derived from data from a control group, preferably comprising at least one substantially healthy individual.

In one embodiment, the individual suffers or has suffered from a cardiovascular disease, or has been diagnosed with a cardiovascular disease (eg, atherosclerosis, hypertension...) that can lead to stroke.

In one embodiment, the individual has experienced a stroke and the method of the present invention is particularly suitable for predicting the likelihood of a recurrent stroke.

Thus, the method of determining whether an individual is at risk of stroke is suitable, in particular, for predicting and, therefore, identifying individuals at risk, followed by the implementation of all possible therapeutic interventions in order to prevent stroke.

As used herein, the term "risk" refers to the likelihood of an event occurring over a specified period of time, and can mean "absolute risk" or "relative risk" to an individual.

"Absolute risk" can be estimated either from actual observational measurements for an appropriate time cohort or from index values derived from statistically valid historical cohorts that have been observed over an appropriate period of time.

“Relative risk” refers to the ratio of an individual's absolute risks to the absolute risks of low-risk or average population-risk cohorts, which may vary depending on how clinical risk factors are assessed. As a rule, they also use the odds ratio, that is, the ratio of positive and negative events for a certain test result (the odds are calculated using the formula p/(1-p), where p is the probability of an event, and (1-p) is the probability of the absence of an event) no conversion.

"Risk assessment" in the context of the present invention includes predicting the likelihood, chances or possibility of the occurrence of an event or pathological condition, the rate of occurrence of the event or the transition from one pathological condition to another (conversion). Risk assessment may also include prediction of future clinical parameters, values of traditional laboratory risk factors, or other indicators of relapse, in absolute or relative terms relative to the group in which previous measurements were taken.

Determining whether an individual is at risk for stroke can be used to make continuous or categorical measures of conversion risk, thereby diagnosing and defining a spectrum of risk for the category of individuals classified as at risk for conversion. In a categorical scenario, the invention can be used to distinguish between normal and other groups of individuals at higher risk. In one embodiment, the present invention can be used to differentiate between normal individuals and at-risk individuals.

The present invention also relates to a method for determining the prognosis of an individual suffering from stroke, comprising i) determining the expression level of at least one biomarker selected from the group consisting of DUSP1, PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, ADM , and TM4SF1 in a sample, preferably a body fluid sample, more preferably a blood sample, obtained from the subject ii) comparing the level determined in step i) with a pre-established reference value and iii) judging that the patient has a good prognosis, if the level determined in step i) is lower than a predetermined reference value, or concluding that the patient has a poor prognosis if the level determined in step i) is higher than a predetermined reference value.

In one embodiment, a method for determining the prognosis of an individual suffering from a stroke includes the steps of obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of the individual, detecting a signature of the present invention in said sample, and comparing said signature to a reference signature. These stages have been described in detail above in the context of the stroke diagnostic method and should be applied to the present method accordingly.

In one embodiment, a method for determining a prognosis for an individual suffering from a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) identifying in said sample the signature of the present invention;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the patient has a good prognosis based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining a prognosis for an individual suffering from a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of HSPA1B, NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456, HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the patient has a good prognosis based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining a prognosis for an individual suffering from a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the patient has a good prognosis based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining a prognosis for an individual suffering from a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression levels of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the patient has a good prognosis based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining a prognosis for an individual suffering from a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the patient has a good prognosis based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, a method for determining a prognosis for an individual suffering from a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) concluding that the patient has a good prognosis based on the correlation of the signature with the reference signature, as detailed above.

In one embodiment, the reference signature is determined or derived from data from a control group, preferably comprising at least one substantially healthy individual.

In one embodiment, a reference signature is determined or derived based on a previous measurement of stroke biomarker expression levels of the present invention in a reference sample obtained from the same individual, e.g., stroke biomarker expression levels measured after the onset of stroke, preferably at least 6 hours later , 12 hours, 18 hours, 24 hours, 36 hours, 48 hours, 60 hours, 72 hours, 84 hours or 96 hours after the onset of stroke.

In the context of this document, the term "prognosis" means the prediction of the likely outcome of a stroke, i.e. course of stroke or patient survival time.

In the context of this document, the term "good prognosis" means A person's chance of surviving a stroke compared to others of the same sex with a similar condition is less than average.

As used herein, the term "poor prognosis" means a stroke patient has a less than average chance of survival compared to others of the same sex suffering from the same condition.

The present invention also relates to a method of treating an individual affected by stroke.

In one embodiment, a method of treating an individual affected by stroke includes the steps of obtaining a sample, preferably a body fluid sample, more preferably a blood sample, of the individual, detecting a signature of the present invention in said sample, and comparing said signature to a reference signature. These stages have been described in detail above in the context of the stroke diagnostic method and should be applied to the present method accordingly.

In one embodiment, a method of treating an individual affected by a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) identifying in said sample the signature of the present invention;

iii) comparing the signature determined in step ii) with the reference signature; And

iv) diagnosing the individual as having a stroke based on the correlation of the obtained signature with a reference signature, as detailed above; And

v) treatment of the individual if, at stage iv), he has been diagnosed with stroke.

In one embodiment, a method of treating an individual affected by a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of HSPA1B , NPAS4, DNAJB1, ATF3, HSPB1, RRAD, NR4A1, CYR61, C-FOS, GADD45G, RGS1, ARC, EGR4, PTGS2, RGS2, CCL3, BAG3, EGR2, HSPA4L, ADM, TM4SF1, EGR1, DUSP1, BTG2, LOC715456 , HMOX1, LDLR, DNAJA4, MCL1, HSPA6, GADD45B, IL6, ADFP, HES4, DUSP5, GEM and G0S2;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature;

iv) diagnosing the individual as having a stroke based on the correlation of the obtained signature with a reference signature, as detailed above; And

v) treatment of the individual if, at stage iv), he has been diagnosed with stroke.

In one embodiment, a method of treating an individual affected by a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or more stroke biomarkers selected from the group consisting of or consisting of PTGS2 , HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

preferably, with the proviso that at least one or two stroke biomarkers mentioned above do not consist of DUSP1 and/or ADM;

iii) comparing the signature determined in step ii) with the reference signature;

iv) diagnosing the individual as having a stroke based on the correlation of the obtained signature with a reference signature, as detailed above; And

v) treating the individual if, at stage iv), he or she has been diagnosed with stroke.

In one embodiment, a method of treating an individual affected by a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining in said sample a signature, preferably by measuring the expression levels of at least three stroke biomarkers selected from the group consisting of or consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature;

iv) diagnosing the individual as having a stroke based on the correlation of the obtained signature with a reference signature, as detailed above; And

v) treatment of the individual if, at stage iv), he has been diagnosed with stroke.

In one embodiment, a method of treating an individual affected by a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3 and TM4SF1;

iii) comparing the signature determined in step ii) with the reference signature;

iv) diagnosing the individual as having a stroke based on the correlation of the obtained signature with a reference signature, as detailed above; And

v) treatment of the individual if, at stage iv), he has been diagnosed with stroke.

In one embodiment, a method of treating an individual affected by a stroke includes the following steps:

i) obtaining a sample, preferably a physiological fluid sample, more preferably a blood sample, of the individual;

ii) determining the signature in said sample, preferably by measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM;

iii) comparing the signature determined in step ii) with the reference signature;

iv) diagnosing the individual as having a stroke based on the correlation of the obtained signature with a reference signature, as detailed above; And

v) treatment of the individual if, at stage iv), he has been diagnosed with stroke.

In one embodiment, a reference signature is determined or obtained using data from a control group, preferably comprising at least one substantially healthy individual.

Examples of suitable treatments and therapies for individuals diagnosed with stroke are well known in the art and are described in detail above herein.

The invention will be further illustrated by the following example and drawings. However, this example should not be interpreted as limiting the scope of the present invention in any way.

Brief description of the figures

Figure 1 is a collection of 3 plots showing the correlation between mRNA levels and microarray signal intensity values for (a) HSPA1B, (b) GADD45G, and (c) CDKN1A by quantitative real-time PCR (qRT-PCR). The x-axis shows the log ₂ ratio of mRNA levels (qRT-PCR) normalized to SMC2, and the y-axis shows the oligonucleotide microarray signal. The P-value is taken from the Mann-Whitney U test.

Figure 2 is a composite of two Venn diagrams for the most significant overlap of two sets of genes differentially expressed in the brain and blood. The top panel shows genes that decreased expression, and the bottom panel shows genes that increased expression.

Figure 3 illustrates the kinetics of gene intensity in the blood for the 9 most differentially expressed genes in a male rhesus macaque S1 at different time intervals: before (T ₀ ) and after (T ₁ , T ₂ , T ₃ ) ischemia.

Figure 4 illustrates the kinetics of gene intensity in the blood for the 9 most differentially expressed genes in male rhesus macaques S2 at different time intervals: before (T ₀ ) and after (T ₁ , T ₂ , T ₃ ) ischemia.

Examples

The present invention is further illustrated by the following examples.

Example 1: Determination of a common signature for ischemic brain and blood in a rhesus monkey model with thrombosis

Methods

Animal experiment

The experiments were carried out on 2 males of the Masasa mulatta species, aged 12-13 years, with a body weight of 16.5 to 17.2 kg. In the following, these two monkeys will be referred to as S1 and S2.

The experimental protocol was submitted to the Regional Committee for the Control of Ethical Standards for Experiments involving Animals (Normandy), and permission to conduct this study was obtained (reference number N/02-03-08/03/02-11). The experiments were carried out by authorized researchers (C.O.) in accordance with the ethical laws and guidelines for the care and use of laboratory animals in force in France and in Europe (Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes). In this study, monkeys were housed at the Cyceron Research Center (Animal Experimental Facility, Agreement No. B14118001) in individual cages maintained at 50% relative humidity for 24 hours, on a 12-hour light/12-hour light cycle, and fed store-bought food with the addition of fresh fruit and water without restrictions. Throughout this study, the condition of the animals was monitored by a veterinarian.

Animal Experiment Model

Model involving rhesus monkeys with thrombosis

Experiments were carried out as described previously (Gauberti et al., 2012. Cerebrovasc Dis. 33(4):329–39).

After general anesthesia, the right middle cerebral artery (MCA) was exposed following enucleation. Two sutures were placed to isolate the M1 branch, into which a micropipette was inserted and human thrombin was injected. Approximately 600 μl of thrombin was injected at a concentration of 1 U/μl. The first injection contained 100 μl, followed by 6 injections of 50 μl at 2-minute intervals. The proximal suture was then removed and the remaining thrombin was injected. The distal suture was then removed after 15 minutes.

The entire thrombin injection procedure took approximately 30 minutes in total.

Anesthesia and monitoring of physiological parameters

The animals were administered ketamine (0.1 mg/kg; IM; Imalgene ^® ) as a tranquilizer. Sevoflurane (2.5%; Sevorane ^® ) in 100% oxygen was used for inhalational anesthesia. Muscle relaxation was achieved by administering atracurium (0.5 mg/kg; IV; Tracrium ^® ) through the saphenous vein.

The monkeys were mechanically ventilated with intermittent positive pressure at a fixed respiratory rate of 22 breaths per minute. Tidal volume ( _VT ) was adjusted to achieve normocapnia ( =38-42 mmHg). Anesthesia was maintained with sevoflurane with 66% nitrous oxide.

During surgery, the sevoflurane concentration was increased to 3% and then decreased to 1.5-2% during the magnetic resonance imaging (MRI) procedure. Intravenous perfusion of atracurium was carried out at 0.75 mg/kg/h. Before surgery, the animals were fixed in a stereotaxic frame.

In vivo MPT study

Monkeys were examined on a clinical 3T MPT scanner (Philips Sense Flex M).

Imaging was performed in the axial and coronal plane and included the following sequences: 3D time-of-flight angiography, T2-weighted free fluid-attenuated imaging (FLAIR), diffusion-weighted imaging (DWI), and pre- and post-contrast T1-weighted and perfusion imaging. weighted image (PWI).

Blood samples

Brain tissue samples

Twelve brain tissue samples were taken from each monkey. For each monkey, 6 samples were taken in the vicinity of the infarct site and 6 samples were taken from a corresponding site in the other hemisphere. Thus, a total of 12 ischemic and 12 non-ischemic samples were analyzed.

The two monkeys in question were operated on and sacrificed approximately 5.5 hours after arterial occlusion (S1: 5 hours 12 minutes; S2: 5 hours 35 minutes). MRI was performed between 3 and 4 hours after arterial occlusion (S1: 3h 10 min; S2: 3h 05 min). The animals were injected intracardially after thorectomy with approximately 8 liters of 4°C saline. solution. The brain was then removed after a craniotomy and placed in a mold specially designed to produce frontal sections of the brain. Each slice was placed on a grid to be able to determine x- and y-axis coordinates in the ischemic and non-ischemic hemisphere. On the grid, in random order, Roman numerals were assigned to the right, ischemic side, and Arabic numerals were assigned to the left, non-ischemic side.

Ischemia was visible to the naked eye (data not shown) and was confirmed by the presence of tetrazolium-treated ischemic tissue with reduced metabolism that appeared white compared with the pinkish staining obtained for the contralateral, nonischemic hemisphere.

Three ischemic cortical samples from each animal were prepared: one potentially corresponding to the core and two from marginal areas, potentially corresponding to the penumbra region. Also, three homologous tissue samples from the opposite hemisphere were prepared for each animal.

TTX staining

To assess metabolic activity and therefore ischemic tissue, tissues adjacent to samples to be analyzed for transcriptomics were stained with 1% tetrazolium chloride (TTC).

Total RNA extraction

To isolate total RNA from the cerebral cortex, the RNeasy Microarray Tissue kit was used according to the manufacturer's instructions (Qiagen). RNA integrity was assessed using an Agilent 2100 BioAnalyzer (Agilent Technologies) by measuring the RNA integrity number (RIN) (Schroeder et al., 2006. BMC Mol Biol. 7:3). The RTN value obtained was between 6.3 and 8.8.

The PAXgene Blood RNA Kit (PreAnalytix) was used to isolate total RNA from blood samples. The GlobinClear kit (Ambion) was used to separate globin mRNA from total RNA. The resulting RIN value was between 7.8 and 10.

Macaque Expression Microarray and Sample Selection

A Low Input Quick Amp WT Labeling kit (Agilent Technologies) was used to label RNA from the cerebral cortex (30 ng) and from blood samples (30 ng). A peak RNA control was used to correct for possible staining effects. RNA was converted into cDNA using reverse transcriptase and WT primers (primer with T7 promoter and random primer with T7 promoter). T7 RNA polymerase was used to synthesize and label cRNA with Cy3. Fluorescently labeled cRNA probes were purified using the RNeasy mini kit (Qiagen).

An equal amount (3.75 μg) of Cy3-labeled cRNA probes was hybridized on a 4x44K Agilent DNA chip (catalog number: G2519F, Masassa mulatto). Hybridization was carried out for 17 hours, rotating at 10 rpm at 65°C. The samples were then washed and dried according to the manufacturer's instructions.

Images after hybridization were obtained using an Agilent DNA microarray scanner, and intensity data were obtained using Feature Extraction software (Agilent Technologies). This matrix contains 43803 rhesus monkey probes. These probes are from RefSeq (Release 37, October 2009), Unigene (Release 13, October 2009), UCSC MRNA (October 2009), Ensembl (Release 56, September 2009), UCSC RheMac2 (January 2006). Many probes are predicted based on orthologous human genes. In addition, some probes are annotated only as Masasa mulatto cDNA and their function can only be speculated based on homology to humans. The analysis was carried out in collaboration with Genosplice (Evry, France), which specializes in transcriptomic analysis.

Microarray analysis

Differential expression studies based on expression microarray data obtained using Agilent resources were performed using the linear model for microarray data (LIMMA) used in the Bioconductor project (Smyth, 2004. Stat Appl Genet Mol Biol. 3: Article3 ). The raw data were normalized, first by background correction and then by inter-matrix normalization for all brain and blood tissue samples, also using LIMMA. Probes were annotated using information for Agilent arrays provided on the Array Express website (http://www.ebi.ac.uk/arrayexpress/arrays/A-GEOD-9861/?page=71&pagesize=100&sortby=organism&sortorder=descending) and information by genes provided by Genosplice.

The LIMMA package performs differential expression analysis by first fitting the expression data of each gene to a linear model. It then uses an empirical Bayes approach to borrow information from all genes, allowing us to perform analyzes on a small number of matrices.

All differential expression analyzes based on microarray data from brain tissue samples were performed using LIMMA using the empirical Bayes method (Smyth, 2005. limma: Linear Models for Microarray Data. In: Gentleman et al. (Eds), Bioinformatics and Computational Biology Solutions Using R and Bioconductor Statistics for Biology and Health. New York, NY: Springer).

Statistics

Comparison of genes with high expression levels in brain samples

The two monkeys in question were analyzed separately. Genes were considered “highly expressed genes” if the range of change was greater than 2 (i.e., the log ₂ of the range was greater than 1) in both monkeys when comparing ischemic and non-ischemic brain tissue samples, and if the range of change was less than 2 when comparing ischemic samples from different monkeys or when comparing non-ischemic samples from different monkeys.

Comparison of genes with high expression levels in blood samples

Blood samples were analyzed by comparing a sample before vessel occlusion with each sample after vessel occlusion. The two monkeys in question were analyzed separately. The time point with the maximum range of change was selected for subsequent analysis and designated as “S1 max, blood” and “S2 max, blood” (Table 1). Genes were considered to be “highly expressed” if the range value was greater than 1.5 in both monkeys when comparing the pre-occlusion sample with the values “S1 max blood” and “S2 max blood.”

To compare and identify common highly differentially expressed genes, only highly differentially expressed genes were used in ischemic brain tissue samples and in blood samples taken after arterial occlusion, as defined above.

Data quality control

Internal Validation

To validate the results of microarray analysis, a probe-specific two-step TaqMan R Gene Expression Assay (Applied Biosystems) was used. Genes for validation were selected based on differential expression ranking and biological annotation relevant to ischemia. We chose SMC2 to normalize all target genes because this gene showed consistent levels of expression across ischemic and nonischemic tissue samples. Probe identifiers for quantitative analysis of gene expression: HSPA1B (A 01 P010726), GADD45G (A 01 P018040), CDKN1A (A 01 P002585), SMC2 (A 01 P019124).

100 ng of total RNA from each sample was used to generate cDNA using the Superscript ^® III First-Strand Synthesis Kit (ThermoFisher scientific) according to the manufacturer's protocol. Real-time polymerase chain reactions were performed on a Roche LightCycler R480 system. Comparison of gene expression in ischemic and non-ischemic tissue samples was performed using the C _T comparison method (ΔΔC _T method) using the Mann-Whitney U test (Wilcoxon) using Prism software v6.0c (GraphPad, LaJolla, CA).

External validation

We attempted to verify our results using data from a study conducted by Cook and others (Cook et al., 2012. Nature. 483(7388):213-7). This study also examined stroke transcriptomics in primates with cerebral convolutions, but the study involved cynomolgus monkeys, a close relative of Masas mulaiia (Street et al. 2007. BMC Genomics. 8:480). In order to validate the results obtained for Masas mulatto, we analyzed transcriptomic data for placebo and non-ischemic samples from the study by Cook et al. Data for placebo ischemic and non-ischemic samples were obtained from the GEO databank (accession number: GSE35589) and analyzed using the methods described above.

Partial agreement between the results obtained for brain tissue and blood

Using the RRHO test, significant overlap was found in differential gene expression analysis between blood and brain samples (Plaisier et al., 2010. Nucleic Acids Res. 38(17):e169). This method applies a hypergeometric test to all possible matches of ranked gene lists to identify the cutoff value at which the overlap between two sets of genes is most significant. Complete gene lists were ranked by the mean of the logarithm of the range of variation for the two monkeys. To examine specific genes that changed expression in the blood and brain, we compiled a list of highly differentially expressed genes in brain tissue samples (see methods described above) and calculated the maximum range of change value for each of these genes in blood samples.

Gene Set Enrichment Analysis

Gene set enrichment analysis was performed using the GSEA command line program (Subramanian et al., 2005. Proc Natl Acad Sci U S A. 102(43): 15545-50). The tool contains a predefined set of genes and determines whether each set is enriched at the top or bottom of the ranked experimental list, which serves as an indicator of phenotypic role. The program calculates an enrichment score (ES) for each gene set examined. This score indicates the degree to which a set is overrepresented at the top or bottom of a ranked experimental gene list. The normalized enrichment score (NES) is normalized by the size of the gene set.

The results of differential expression analysis for brain and blood tissue samples were ranked by the logarithm of the range of change. These lists were entered into the GSEA program. The analysis was performed using 100 permutations, and gene sets containing fewer than 10 genes were excluded. The resulting gene sets were ranked by NES value. GSEA ES shows the degree to which a set of genes is overrepresented at the top or bottom of a ranked list of genes. The enrichment score (ES) is calculated by going through the list from top to bottom and increasing the score if the gene is present or decreasing it if the gene is absent. The magnitude of the increase depends on the correlation of a particular gene and phenotype (a predetermined set of genes). ES represents the maximum deviation from 0 found while walking through the list. A positive ES value indicates enrichment at the top of the list (in genes with increased expression levels), and a negative ES value indicates enrichment at the bottom of the list. The normalized ES value shows the difference in the size of gene sets and the correlation between gene sets and expression data, providing an opportunity to compare gene sets.

The relationships between genes in such sets were visualized using the STRING database (Szklarczyk et al., 2015. Nucleic Acids Res. 43(Database issue):D447-52). Genes were entered into the STRING database if their range of variation was at least 2 for brain tissue and 1.5 for blood. The thickness of the lines indicates the confidence of the connection, and large nodes indicate proteins for which tertiary structure information is available.

results

The animals were sacrificed 5.5 hours after the onset of ischemic injury. Axial diffusion-weighted MRI showed focal middle cerebral artery ischemia in both animals (data not shown). Analysis of ischemic lesion volume in two different animals showed that infarct volume was highly variable, with animal S1 exhibiting a markedly larger lesion volume than animal S2. Both S1 and S2 had superficial and deep infarcts. The lesion was also clearly visible in both animals on free fluid suppressed T2-weighted images. MP angiography of the circle of Willis showed proximal occlusion of the middle cerebral artery in S1, although this was not as obvious in S2. Infarction was also visible on T1 sequence at the level of the basal ganglia in both S1 and S2.

Changes in gene expression: comparison of ischemic and non-ischemic brain tissue

In the present study, changes in gene expression between ischemic and nonischemic brain tissue were measured for individual genes, and the effect of these changes was examined at the gene set level. Differential expression analysis was performed by comparing expression data obtained from ischemic brain tissue with data from the corresponding region of the other hemisphere, which did not have ischemic damage.

This analysis revealed that the expression profiles for ischemic and nonischemic brain tissue were distinctly different (data not shown). Genes were considered highly differentially expressed if their range of change was greater than 2 when comparing ischemic and nonischemic brain tissue samples from each monkey, and the genes were not differentially expressed when comparing ischemic tissue samples from two monkeys and nonischemic tissue samples from two monkeys ( range of change <2). When hierarchical clustering was applied to these most differentially expressed genes (37 genes), ischemic and non-ischemic tissue samples formed clear independent groups.

All highly differentially expressed genes had increased expression levels (Table 1).

The values reported in the "S1 brain" and "S2 brain" columns represent the log ₂ range of expression of these genes in the brains of the two monkeys in question.

The "S1 max blood", "S1 time point", "S2 max blood" and "S2 time point" columns show blood data from the same two monkeys. For each, the time point with the maximum log2 value of the range of change is shown.

Genes that are highly differentially expressed in the blood (range of variation >|1.5|) are highlighted in bold and gray shading.

HSPA1B and LOC720054(HSPA1B) are replicate probes for different regions of the same gene.

Internal Validation

To confirm the reliability of the microarray results, the mRNA levels of three ischemia-responsive genes (HSPA1B, GADD45G, CDKN1A) were measured in samples obtained from the study animals using quantitative real-time PCR. SMC2, which was shown to exhibit a consistent level of expression in all samples tested, was used as an internal standard (housekeeping gene). For each RNA sample, three reverse transcriptions were performed, followed by three independent rounds of qPCR, with repeated measurements on both target and reference genes. For all genes, the direction and magnitude of change were highly consistent with the microarray data (Figure 1 and Table 2).

Statistical significance: *p<0.05; **p<0.01; ***p<0.001. Results were normalized to SMC2.

External validation

Analysis of transcriptomic data reported in the stroke study carried out by Cook et al. (Cook et al., 2012. Nature. 483(7388):213-7) in Macaca fascicularis macaques gave results very different from those described in real work. While the present analysis of the data for Macasa mulatta revealed many genes with increased levels of expression in stroke tissues, the data obtained for Macasa fascicularis showed the presence of mainly genes with reduced levels of expression. The most likely reason for this is the wide variation in the methods used to induce stroke in primates. The study by Cook et al used surgical occlusion of the middle cerebral artery, which the authors knew to cause a stroke more severe than that typically seen in humans. It is likely that this model of more severe stroke caused widespread cell death, resulting in the discovery of mostly downregulated genes.

Enrichment of gene sets

Identification of genes that were enriched in ischemic brain tissue was performed by the Broad Institute's gene set enrichment analysis (GSEA) method (Subramanian et al., 2005. Proc Natl Acad Sci USA. 102(43): 15545-50). The top 5 enriched gene sets from the Hallmark GSEA gene sets were: TNF-alpha/nuclear factor-kappa-B signaling, apoptosis, P53 pathway, hypoxia, and upregulation in response to UV irradiation. All with an increased level of expression (Table 3).

60 of the 108 genes in the TNF-alpha/nuclear factor kappa-B signaling pathway set are core enriched (the most enriched group in the set (NES=2.96).

Almost all of the genes in this set for which we had data were upregulated, and none showed a significant downregulation: 15 genes had only slightly downregulated expression, with a maximum range of change of 1.17.

In the apoptosis gene set, 26 of 98 genes are core enriched (NES=2.57).

In the hypoxia gene set, 33 of 118 genes are core enriched (NES=2.38).

In the set of genes upregulated in response to UV irradiation, 31 of 96 genes are core enriched (NES=2.3).

The most significantly enriched set of genes with reduced expression levels were oxidative phosphorylation genes. 84 of the 120 genes in this set are an enriched group of genes (NES = -2.57).

Interaction of gene products

A database of STRING (Szklarczyk et al., 2015. Nucleic Acids Res. 43(Database issue):D447-52) (data not shown).

IL-6 has been noted to have multiple connections within networks formed by apoptosis, hypoxia, and TNF-alpha/nuclear factor-kappa-bi signaling pathway genes. In addition, CDKN1A is present in all four of these networks. The interaction of CDKN1A with HMOX1 is observed in a network involving apoptosis genes, the p53 pathway, and hypoxia. The ATF3 gene is also present in all four of these networks, interacting with IL-6 in three of them. The 5th ranked brain gene set (Table 3) “Increased expression in response to UV irradiation” is not shown because none of the most differentially expressed genes in this set have any known interactions.

Changes in gene expression: comparison of blood samples taken before and after vessel occlusion

Detection of genes in the blood that are differentially expressed during cerebral ischemia was accomplished by comparing all blood samples taken before the artery occlusion with all samples taken after the artery occlusion. When hierarchical clustering was applied to differentially expressed genes, samples taken before and after vessel occlusion formed separate groups (data not shown). There also appear to be differences in the blood expression profiles of the two macaques studied, based on the clustering results. Monkey S1 exhibits a more pronounced increase in the expression levels of these genes compared to monkey S2. These differences between the two monkeys are not observed as clearly in the expression profiles obtained from the brain samples (data not shown).

As with the brain samples, results regarding differential expression in the blood samples were analyzed by GSEA. The 5 most enriched gene sets were: TNF-alpha/nuclear factor-kappa-B signaling, hypoxia, hedgehog signaling, inflammatory response, and angiogenesis (Table 4).

The TNF-alpha/nuclear factor-kappa-bi and hypoxia signaling pathway gene sets were also among the top 5 gene sets for brain samples (Table 4).

37 of 108 genes in the TNF-alpha/nuclear factor-kappa-B signaling pathway set are core enriched (NES=1.92).

In the hypoxia gene set, 42 of 118 genes are core enriched (NES=1.91).

In the Hedgehog signaling pathway gene set, 10 of 24 genes are core enriched (NES=1.77).

In the inflammatory response gene set, 35 of 110 genes are core enriched (NES=1.77).

In the angiogenesis gene set, 9 out of 20 genes are core enriched (NES=1.76).

Differentially expressed genes common to brain and blood

Results obtained for differential gene expression in brain and blood tissue samples were examined for significant overlap after ranking genes by range of variation. We clearly show that the two ranked sets of genes are highly similar, particularly at two extremes: genes with the greatest increase and the greatest decrease in expression during ischemic injury. We also showed the overlapping genes that were most significant, with 2,156 down-regulated genes showing significant similarity between brain and blood tissue samples, and 493 up-regulated genes also showing significant overlap (Figure 2). Most of these genes have a relatively small range of expression changes, but the degree of overlap between differentially expressed genes in brain and blood tissue samples is very high.

Although many of the genes in these overlapping sets were not highly differentially expressed, there were some that were, and we were primarily interested in genes with very high expression changes in the brain tissue samples. Of the genes identified as highly differentially expressed in brain tissue, 9 were also among those highly differentially expressed in blood (range of change ≥1.5; Table 1, in bold). All 9 genes mentioned fell within the region of the most significant hypergeometric overlap (shown in bold). These genes include: PTGS2, G0S2-like, DUSP1, LDLR-like, HMOX1, HSPA1B, BAG3, ADM, and TM4SF1. For most of the genes listed, there was a sharp increase in expression levels in the blood, which decreased over time (Figures 3 and 4).

Discussion

A nonhuman primate model (Gauberti et al., 2012. Cerebrovasc Dis. 33(4):329–39) injecting human thrombin into the middle cerebral artery was used to study changes in gene expression profiles during ischemic cerebral stroke. This is an embolic model of focal ischemia with partial ischemic damage to the middle cerebral artery. A time interval of 6 hours was used because it represents the time window during which therapeutic intervention can be performed in people (thrombolysis up to 4 hours 30 minutes; thrombectomy up to 6 hours after the onset of stroke).

A study of macaque gene expression microarray data from brain and blood tissue samples showed that ischemic and non-ischemic samples can be distinguished based on expression profiles, and that in stroke, most highly differentially expressed genes have increased expression levels 6 hours after the onset of ischemic injury. Many of these upregulated genes are involved in metabolic pathways associated with cell death and repair of damaged DNA. Comparison of genes differentially expressed in the brain and blood revealed significant overlap in gene expression profiles.

The results obtained indicate the potential for identifying ischemic stroke through transcriptomics of brain tissue and blood samples.

Gene expression in stroke-affected brains

The quality of the obtained data was confirmed by principal component analysis (PCA), which showed that discrimination between both monkeys, as well as ischemia and non-ischemia, is possible based on gene expression data. Moreover, the results obtained were further confirmed during internal validation.

Hierarchical clustering of data obtained for gene expression in Masas mulatta brain tissues revealed significant independent clustering of ischemic and non-ischemic brain tissue samples (data not shown). These results suggest the potential to identify the presence of stroke-affected brain tissue based on gene expression profiles. The most significantly differentially expressed genes were upregulated, consistent with many previous ischemia studies in mice (Büttner et al. 2009. Brain Res. 1252:1-14; Hori et al. 2012. Dis Model Mech. 5( 2):270-83). In the present study, highly differentially expressed genes in brain tissue are defined as genes with a range of change of at least 2 in both monkeys, and genes are not differentially expressed when comparing nonischemic samples of the two monkeys or when comparing ischemic samples of the two monkeys (range of change <2). The results of our analysis showed that in brain tissue, 37 genes had an increased level of expression and none had a decreased level of expression. These results are similar to those obtained by Büttner and colleagues, who found 115 genes with increased and 19 genes with decreased expression levels after 6 hours (Büttner et al., 2009. Brain Res. 1252:1-14). The fact that Büttner and colleagues found a larger number of genes may be explained by the use of a larger matrix for expression profiling, as well as differences in expression between animal species.

All highly differentially expressed genes in ischemic brain tissue showed increased expression levels. Many of these genes have previously been identified as genes whose expression levels are increased in ischemic stroke in association with a number of functions, particularly stress response, apoptosis, and signal transduction.

Stress response genes that showed increased levels of expression included 7 heat shock proteins HSPA1B (Hsp70), HSP40, HSPB1, HSPA4L, and DNAJA4 (Hsp40-like) (Table 1). Increased expression of heat shock proteins during ischemia has already been noted in many studies (Schmidt-Kastner et al., 2002. Brain Res Mol Brain Res. 108(l-2):81-93; Büttner et al., 2009. Brain Res 1252:1-14; Kawahara et al 2004. J Cereb Blood Flow Metab 24(2):212-23; Tang et al 2002. Eur J Neurosci 15(12): 1937-52. ). The human orthologue of this gene is described in detail as a stress-induced gene that stabilizes proteins to prevent their aggregation. It is also involved in the ubiquitin-proteasome metabolic pathway (NCBI, Accession: NM 005346.5). Hsp70 is the most commonly reported heat shock protein with increased expression in ischemic stroke (Cox-Limpens et al. 2014. Brain Res. 1564:85-100). In the species Macasa mulatta, protein 1B (HSPA1B), a member of the heat shock protein A (Hsp70) family, is the gene with the most increased expression level in ischemic brain tissue. Studies of HSPA1B in mice using gene knockout technology have shown that cardiomyocytes lacking this gene are more susceptible to the damaging effects of ischemia (Kim et al., 2006. Circulation. 113(22):2589-97). Cerebral ischemia studies found that hsp70 knockout mice had larger infarcts than wild-type mice (Lee et al., 2004. Stroke. 35(9):2195-9), and mice overexpressing Hsp70 had smaller infarct size and improved neurological function (Zheng et al. 2008. J Cereb Blood Flow Metab. 28(1):53-63). The high level of differential expression of HSPA1B in macaques suggests that it may also play a neuroprotective role in the primate brain during ischemia. It is also interesting to note that one of the most differentially expressed genes in these microarray analyzes is DNAJB1, a heat shock protein homolog.

Another highly differentially expressed stress response gene is NPAS4, which is also known to be neuroprotective (Chby et al. 2015. Int J Mol Sci. 16(12):29011-28). This transcription factor is expressed in neurons of the brain and plays a role in early response reactions in excitatory and inhibitory neurons. Expression of this gene is induced by a number of situations in which nerve cells experience stress. In our study, the log ₂ range of change for NPAS4 was 3.57 in S1 and 2.65 in S2 (Table 1). The C-FOS gene, controlled by NPAS4 (Ramamoorthi et al., 2011. Science. 334(6063): 1669-75), is also highly differentially expressed in our study, with a log ₂ range of change of 2.53 in S1 and 3. 77 for S2. With the development of cerebral ischemia, the level of expression of the C-FOS gene usually increases (Cox-Limpens et al., 2014. Brain Res. 1564:85-100).

The next most differentially expressed gene is the activating transcription factor 3 (ATF3) gene. ATF3 encodes one member of the CREB family of transcription factors. It is involved in the cellular response to stress. For ATF3, the following log ₂ range values were obtained: 2.99 for S1 and 3.38 for S2 (Table 1). In addition, this gene forms multiple connections within the GSEA sets of most differentially expressed genes (data not shown). Previous experiments in mice have shown that ATF3 knockouts develop more extensive damage and worse neurological function after cerebral ischemia (Wang et al., 2012. Neuroscience. 220:100-8).

Other important stress response genes that have been shown to be upregulated in ischemic brain tissue are two genes that are upregulated upon injury, GADD45G and GADD45B (growth arrest and DNA damage inducible). GADD45 genes are involved in DNA repair. For GADD45G, log was obtained ₂ ranges of expression changes of 2.25 in S1 and 2.04 in S2, for GADD45B this figure was 1.08 and 2.17, respectively. Similar to heat shock proteins, the Gadd45 gene family was mentioned as increasing its expression level in a number. studies examining cerebral ischemia in rodents (Schmidt-Kastner et al., 2002. Brain Res Mol Brain Res. 108(l-2):81-93; 7-9, 20). ischemic attack showed that the expression of these genes is induced by transient global ischemia (Chen et al., 1998. J Cereb Blood Flow Metab. 18(6):646-57). These researchers concluded that this may indicate a neuroprotective function of these genes. genes. Our results suggest that similar increased expression during ischemia is also present in primates, again linking the role of these genes to the cellular response to ischemic injury.

The apoptosis gene BAG3 also showed a significant increase in expression levels in ischemic brain tissue, with a _log2 range of change of 1.7 in S1 and 2.97 in S2. A study of gene expression during cerebral ischemia in rats also showed a high degree of increase in expression levels for this gene (Schmidt-Kastner et al., 2002. Brain Res Mol Brain Res. 108(l-2):81-93). It is known that after the onset of ischemia, the dissolution of proteins associated with the cytoskeleton occurs (Lipton, 1999. Physiol Rev. 79(4): 1431-568). Interestingly, we observed increased ARC protein gene expression levels in ischemic brain tissue (log ₂ range of change = 1.84 in S1 and 1.9 in S2). Increased ARC expression was also observed in previous studies of cerebral ischemia in rats (Büttner et al., 2009. Brain Res. 1252:1-14).

Another class of genes associated with cerebral ischemia are the dual specificity phosphatase (Dusp) genes (Wang et al. 2011. Brain Res. 1372:13-21). The proteins they produce are capable of inactivating MAPK proteins. In our study, two Dusp genes showed high expression increases: DUSP1 ( _log2 range = 1.43 in S1 and 1.43 in S2) and DUSP5 ( _log2 range = 1.05 in S1 and 1.67 in S2). Interestingly, Dusp5 has previously been suggested to be a target of p53 (Ueda et al., 2003. Oncogene. 22(36):5586-91).

Changes in gene expression in the blood

The expression profiles observed in blood were similar to those in brain tissue, however, there were some limitations regarding blood transcriptomics in this study. Due to the relatively small number of samples (1 sample per control time point for each monkey), time course analysis was not possible. Instead of analyzing the time course, samples taken before the artery occlusion were compared in pairs with each sample taken after the occlusion. For the purpose of gene set analysis, the differential expression results obtained from the comparison that showed the greatest change in expression were used in GSEA. The gene sets involved in this analysis showed a high degree of correlation with the expression profiles obtained from the brain samples.

Hierarchical clustering of expression levels determined in blood samples showed that samples taken before and after vessel occlusion formed independent clusters (data not shown). Although the contrast is not as dramatic as in the brain tissue samples, there are still clear differences in gene expression before and after artery blockage. These results suggest that further experiments could identify a panel of blood biomarkers for use in stroke diagnosis. Due to the small number of samples and participants in the present study, it has been difficult to precisely establish the gene sample that could be used for diagnosis, but the clearly distinguishable profiles suggest that this may be possible with a larger number of samples.

An analysis of differentially expressed genes in blood samples from macaques with cerebral ischemia showed that, similar to brain tissue samples, the majority of transcripts detected in the blood had increased expression levels. When combining data from both monkeys and combining data from all samples taken after arterial occlusion, 651 genes showed high differential expression (range of change greater than 2), of which 513 showed increased expression.

Among the mentioned differentially expressed genes, four S100 genes deserve special mention: S100A8, S100A12, S100P, and S100A9. S100 are calcium-binding proteins of glial cells. The most differentially expressed member of this group is S100A8 (log ₂ range of change = 3.29; p = 0.027; data not shown), which is closely associated with proinflammatory function (Sedaghat & Notopoulos, 2008. Hippokratia. 12(4): 198-204 ).

Gene sets associated with cerebral ischemia

Brain

Gene set enrichment analysis of gene expression data from brain tissue samples identified several sets of genes involved in DNA repair and apoptosis that showed increased levels of expression in ischemic brain tissue samples. The 5 most enriched gene sets included TNF-alpha/nuclear factor-kappa-bi signaling, apoptosis, P53 pathway, hypoxia, and upregulation in response to UV irradiation. Some of these metabolic pathways have already been described in previous studies as being associated with cerebral ischemia.

The highest degree of enrichment was observed for the “TNF-alpha/nuclear factor kappa bi signaling pathway” gene set. The cytokine TNF-alpha activates nuclear factor kappa-bi, which is involved in the inflammatory response. Increased expression of pro-inflammatory cytokines has been reported in a number of earlier studies (Schmidt-Kastner et al., 2002. Brain Res Mol Brain Res. 108(1-2):81-93; Lu et al., 2004. J Neurosci Res. 77(6):843-57; Broughton et al. 2009. Stroke 40(5):e331-9).

In addition, sets of genes involved in apoptosis and the p53 metabolic pathway showed increased expression levels. These pathways are activated in cases of cellular stress or DNA damage, and increased levels of various proapoptotic factors have been noted in previous studies (Büttner et al, 2009. Brain Res. 1252:1-14).

In addition, the nuclear factor kappa-bi pathway and the p53 pathway have been shown to contribute to neuroprotection (Zhang et al., 2005. J Cereb Blood Flow Metab. 25(1):30-40; Chen and al., 2011. Antioxid Redox Signal 14(8):1505–17.

Interestingly, the IL-6 gene is a closely associated member of the apoptosis, hypoxia, and FIO-alpha/nuclear factor kappa-bi pathways (data not shown), with a log ₂ range of expression for this gene in brain tissue samples. brain was 1.08 and 1.53 in S1 and S2, respectively. In addition, for several sets of genes (apoptosis, p53 pathway, and hypoxia), interactions between CDKN1A and HMOX1 were noted, with HMOX1 showing a significant increase in expression levels.

Blood

Gene set enrichment analysis using results regarding differential gene expression in blood samples highlighted metabolic pathways associated with signaling function, hypoxia, and inflammatory response. The 5 most enriched gene sets in blood samples from individuals with cerebral ischemia were: TNF-alpha/nuclear factor-kappa-B signaling, hypoxia, hedgehog signaling, inflammatory response, and angiogenesis.

Interestingly, the two most enriched sets of genes in the brain tissue samples were also enriched in the blood samples: the TNF-alpha/nuclear factor-kappa-bi signaling pathway and the hypoxia response. Both of these metabolic pathways are consistent with the response model to ischemic injury. For blood samples, 37 of 108 genes in the TNF-alpha/nuclear factor kappa-bi signaling pathway were in the enriched gene group (NES = 1.92), and 42 of 118 genes in the hypoxia response gene set were enriched (NES = 1.92). belong to the enriched group of genes (NES=1.91). The TNF-alpha/nuclear factor-kappa-bi signaling pathway gene set appears to be the metabolic pathway most associated with ischemic injury, with over 50% of the genes in this set being key contributors to enrichment rate in the case of brain tissue, and over 30% in the case of blood samples.

Differentially expressed genes common to brain and blood

To compare differential gene expression between brain tissue and blood, all genes were ranked by range of variation, each possible hypergeometric overlap was examined, and significant overlap was found for both upregulated and downregulated genes. included in the ranked list. Our analysis of the most significant hypergeometric overlap of genes with increased and decreased expression showed that the overlapping regions contained 493 and 2156 genes, respectively (Figure 2).

In addition, a more refined analysis of the expression data for the blood samples was performed, in which samples taken from each monkey before and after arterial occlusion were compared separately, and only the control time point with the largest range of change was selected. The results were combined with expression data obtained from brain tissue, and genes were ranked by differential expression score for both brain samples (change range ≥2) and blood samples (change range ≥1.5). Nine genes were identified as highly differentially expressed in both brain and blood (Table 1). All 9 genes belonged to the region of most significant hypergeometric overlap described above.

The 9 genes mentioned include: HSPA1B, PTGS2, BAG3, ADM, TM4SF1, DUSP1, HMOX1, IDL-like, and G0S2. HSPA1B, BAG3, and DUSP1 were discussed in the previous section. Most of these genes show a sharp increase in expression levels, which decrease over time (Figures 3 and 4).

Prostaglandin endoperoxide synthase 2 (PTGS2/COX2) is an enzyme that plays a role in the biosynthesis of prostaglandins. The expression level of this gene is known to increase during inflammation, and it is a target of aspirin. Aspirin suppresses the release of prostaglandins, and in certain doses reduces the risk of stroke (Tohgi et al., 1992. Stroke. 23(10): 1400-3; Eikelboom et al., 2002. Circulation. 105(14): 1650-5) .

Blood adrenomedullin (ADM) gene expression levels have been shown to be associated with ischemic stroke severity (Liu et al. 2014. Int J Neurosci. 124(4):271–80). Liu and other researchers have suggested that the level of ADM expression in peripheral blood leukocytes may indicate the severity of tissue damage. This hypothesis is consistent with our results, as monkey S1 exhibited more extensive lesions, as supported by MRI findings (data not shown), and exhibited higher levels of ADM expression in blood samples compared to S2. Although this expression profile is reversed in brain tissue.

Heme oxygenase 1 (HMOX1) is a member of the heat shock protein family that we looked at earlier. It is believed to be part of a system that protects cells from damage caused by oxidative stress, such as stroke (Chen & Maines, 2000. Cell Mol Biol (Noisy-le-grand). 46(3):609-17). In addition, previous studies have found an increase in HMOX1 expression in the brain after cerebral ischemia (Zhao et al. 2017. J Stroke Cerebrovasc Dis. 26(7):1622–1634).

Conclusion

Our findings demonstrate a common signature between ischemic brain and blood and support the development of blood transcriptomics as a tool for biopsy transcriptomic expression profiling to characterize patients with ischemic stroke and develop associated biomarkers needed to assess drug efficacy. neuroprotectors in patients.

Although plasma extracellular noncoding RNAs (ex-RNAs) are known to be a class of circulating RNA molecules that directly drive gene expression networks in target tissues (Mick et al. 2017. Stroke. 48(4):828-834), the discovery of a common RNA coding sequence profile in the brain and blood was quite unexpected. This ischemia-related gene expression profile observed in the blood of patients with cerebral ischemia has already been mentioned in a study conducted by Moore and others (2005. Circulation. 111(2):212-21). They found genes associated with hypoxia in PBMCs, even though hypoxia was not observed directly in PBMCs, although the brain suffered from hypoxia. The findings support the conclusion of Moore and others that blood can be used as a diagnostic tool to confirm the presence of ischemic stroke.

Example 2

Clinical trial protocol

A non-human primate model was used (Gauberti et al., 2012. Cerebrovasc Dis. 33(4): 329-39), with human thrombin injected into the middle cerebral artery. This study showed that ischemic and non-ischemic samples can be distinguished based on expression profiles 6 hours after the onset of ischemia. The identified genes with increased expression belong to metabolic pathways associated with cell death and DNA repair. Comparison of genes differentially expressed in the brain and blood revealed significant overlap in gene expression profiles.

Taken together, these results highlighted the potential for identifying ischemic stroke using transcriptomic profiling performed on brain tissue and blood samples.

The nine most significantly overlapping genes with elevated expression levels in the brain and blood were identified and further used in human studies.

It is proposed to extrapolate the data obtained for monkeys to humans. Upregulated genes identified in both the ischemic brain and peripheral blood in a primate model of ischemic stroke are examined in samples from 20 ischemic stroke patients, 20 hemorrhagic stroke patients, and matched controls. We then look at the corresponding proteins to be able to potentially develop a rapid test similar to those that already exist for other cardiovascular diseases.

Pilot case-control study

A case-control study of first-time ischemic stroke, paired based on patients' age, sex, and CVD risk (European Heart Score) before the index stroke.

Cases of ischemic stroke were taken from the Brest stroke registry. The registry includes 900 patients per year. The control group is recruited from one center (Brest University Hospital).

Primary goal:

To develop a test for determining an RNA biomarker in the blood based on identified genes whose expression is significantly increased.

Study plan:

A case-control study of first-time ischemic stroke, paired based on patients' age, sex, and CVD risk (European Heart Score) before the index stroke.

Primary endpoint:

Expression (expressed as log2) of each gene measured 6 hours after onset of ischemia.

Primary statistical analysis:

The expression of each candidate gene is compared with case and control using paired Student's t test.

Secondary statistical analysis:

The expression of each gene is compared for cases between the acute phase of ischemic stroke and 3 months after.

Sample size estimate for the full study:

The difference between cases and controls is set to 1.5 times for cases compared to controls (Range of change CI=1.5 and log2CI=0.585), for a log2 standard deviation of expression severity less than 1 (personal data).

Under the stated hypotheses (difference = 0.585 and standard deviation = 1), 90% power and Bonferroni correction for multiple testing, the estimated number of individuals required is: 110 cases and 110 controls. This calculation does not take into account the effect of the possible increase in power provided by the pairwise distribution, since the level of response correlation is unknown.

Inclusion criteria

For patients:

Age >18 years; onset of carotid ischemic stroke less than 6 hours ago, the ability to collect a blood sample at T1 up to 6 hours after the onset of stroke; suitable candidate for thrombolysis or thrombectomy.

Initial NIHSS score >0; patients with CT or MRI results consistent with ischemic stroke; patients with multimodal imaging that allows analysis of the penumbra using MRI perfusion or CT perfusion of the supra-aortic and intracerebral vessels (circle of Willis).

Exclusion criteria include patients with intracerebral hemorrhage.

For control:

Age >18 years; standardized questionnaire without previous stroke; high-risk individuals without cardiovascular disease or previous stroke.

Individual Registration Form (IRF):

Using the standardized Brest Stroke registry enrollment form, demographic and clinical characteristics, cardiovascular risk factors, and treatments received were recorded for each case and control.

Brain Imaging:

In the acute phase - perfusion MRI or CT and MP angiography or vascular scanning

Clinical observation and monitoring by imaging methods:

For patients, Rankin score at 3 months ± 15 days and brain MRI at 3 months ± 15 days.

Number of samples per patient:

42 mL of blood is collected at 6 different time points: <6 hours, 12 hours ± 2 hours, 24 hours ± 4 hours, 48 hours ± 4 hours, 7 days ± 2 days, and 3 months ± 15 days after stroke.

Number of samples per 1 participant in the control group: 42 ml of blood is collected once for each participant in the control group.

Number of patients and control groups:

20 patients with ischemic stroke; 20 control group participants matched to patients with ischemic stroke by age, gender and European Heart score.

Patients with hemorrhagic stroke

Hemorrhagic stroke cases are selected from the Brest stroke registry as a separate group with the same number of samples per patient as the ischemic stroke group.

Inclusion criteria

For patients:

Age > 18 years; intracerebral hemorrhage that occurred less than 6 hours ago; Possibility of collecting a blood sample at T1 up to 6 hours after stroke.

Initial NIHSS score >0; patients with CT or MRI compatible with hemorrhagic stroke.

Exclusion criteria include patients with traumatic intracerebral hemorrhage and patients with ischemic stroke.

Number of patients

20 patients with hemorrhagic stroke. Ethical considerations:

The project is carried out in accordance with the principles of the Declaration of Helsinki and the requirements of Good Clinical Practice (GCP - ICHE6). It is submitted for approval to the Human Research Ethics Committee in accordance with the current French clinical trial regulations. Each participant provides informed consent in writing.

Data management and biobanking:

Data entry into the database managed by Clinsight software (Capture system) is carried out by a data management specialist. The biobank is stored in the center of biological resources (CRB - Center de Resources Biologiques) at the University Hospital of Brest, certified NFS 96-900.

Selection of patients and controls

Selection centers:

Selection of patients and controls and collection of blood samples are carried out at the University Hospital (CHRU) of Brest.

Image characterization and centralized post-processing

The purpose of initial functional imaging (perfusion MRI or CT) is to map the infarct core and penumbra, and changes in both areas are monitored after 3 months using MRI. Initial imaging takes into account vessel patency. The software allows for standardized perfusion analysis of images obtained by MRI and CT methods. Other software can estimate the final volume from MRI and compare it with the penumbra determined on first imaging.

Imaging Platforms:

For primary imaging, contrast-enhanced 64-row (or more) multidetector CT or 1.5 Tesla or 3.0 Tesla MRI scanners are used. Data acquisition methods:

Any of the following:

- non-contrast CT scan of the head; CT brain perfusion; CT angiography of the head and neck; or

- MRI of the brain, including diffusion-weighted imaging (DWI), free fluid-attenuated image (FLAIR) and gradient-recovered echo (GRE), magnetic resonance time-of-flight angiography (MRA) of the head, dynamic brain perfusion MRI susceptibility contrast (DSC), MP angiography with gadolinium for the aortic arch and cervical arteries.

In the CT group, the ischemic core is assessed by either relative cerebral blood flow or cerebral blood volume (CBV). In the MRI patient population, the ischemic core (tissue considered irreversibly damaged) is assessed based on the threshold considered irreversibly damaged) is assessed based on the external diffusion coefficient (ADC) threshold.

Critically hypoperfused tissue is assessed for the MRI and CT groups based on time to maximum signal intensity threshold (Tmax) > 6 seconds. For MRI or CT, lesion volume according to DWI or ASPECTS (Alberta Stroke Program Early CT Score) are recorded, respectively. The location of the occlusion is also recorded (internal carotid artery (ICA), M1, M2, basilar artery, or no occlusion).

After 3 months, MRI of the brain, including diffusion-weighted imaging (DWI), 3D T2-weighted free fluid-attenuated imaging (3D T2-FLAIR), and 3D T1 gradient-recovered echo (GRE), T2-gradient-recovered echo, magnetic resonance time-of-flight angiography of the head.

Post-processing of images

Post-processing of raw perfusion imaging data (CT and MRI) in DICOM format is done using perfusion imbalance analysis software. The final infarct volume is determined by T2-weighted free fluid-attenuated imaging (T2 FLAIR) according to segmentation techniques. Engineers from the Laboratory for Medical Information Processing (LaTIM - INSERM UMR 1101, Brest, France) are taking part in the study.

--->

List of sequences

<110> CENTER HOSPITALIE REGIONAL E UNIVERSITER DE BREST [FR]

ESTABLISMON FRANCE DU SANE [FR]

INSTITUTE NATIONAL DE LA SANTE ET DE LA RESULTS MEDICAL (INSERM) [FR]

UNIVERSITY DE BRETAGNE OXIDANTAL [FR]

<120> Methods for diagnosing stroke, the effectiveness of therapy, and determining risk

stroke

<150> RU 2020140532

<160> 18

<170> BiSSAP 1.3.6

<210> 1

<211> 604

<212>PRT

<213> Homo sapiens

<223> PTGS2 – Accession number NP_000954.1

<400> 1

Met Leu Ala Arg Ala Leu Leu Leu Cys Ala Val Leu Ala Leu Ser His

1 5 10 15

Thr Ala Asn Pro Cys Cys Ser His Pro Cys Gln Asn Arg Gly Val Cys

20 25 30

Met Ser Val Gly Phe Asp Gln Tyr Lys Cys Asp Cys Thr Arg Thr Gly

35 40 45

Phe Tyr Gly Glu Asn Cys Ser Thr Pro Glu Phe Leu Thr Arg Ile Lys

50 55 60

Leu Phe Leu Lys Pro Thr Pro Asn Thr Val His Tyr Ile Leu Thr His

65 70 75 80

Phe Lys Gly Phe Trp Asn Val Val Asn Asn Ile Pro Phe Leu Arg Asn

85 90 95

Ala Ile Met Ser Tyr Val Leu Thr Ser Arg Ser His Leu Ile Asp Ser

100 105 110

Pro Pro Thr Tyr Asn Ala Asp Tyr Gly Tyr Lys Ser Trp Glu Ala Phe

115 120 125

Ser Asn Leu Ser Tyr Tyr Thr Arg Ala Leu Pro Pro Val Pro Asp Asp

130 135 140

Cys Pro Thr Pro Leu Gly Val Lys Gly Lys Lys Gln Leu Pro Asp Ser

145 150 155 160

Asn Glu Ile Val Glu Lys Leu Leu Leu Arg Arg Lys Phe Ile Pro Asp

165 170 175

Pro Gln Gly Ser Asn Met Met Phe Ala Phe Phe Ala Gln His Phe Thr

180 185 190

His Gln Phe Phe Lys Thr Asp His Lys Arg Gly Pro Ala Phe Thr Asn

195 200 205

Gly Leu Gly His Gly Val Asp Leu Asn His Ile Tyr Gly Glu Thr Leu

210 215 220

Ala Arg Gln Arg Lys Leu Arg Leu Phe Lys Asp Gly Lys Met Lys Tyr

225 230 235 240

Gln Ile Ile Asp Gly Glu Met Tyr Pro Pro Thr Val Lys Asp Thr Gln

245 250 255

Ala Glu Met Ile Tyr Pro Pro Gln Val Pro Glu His Leu Arg Phe Ala

260 265 270

Val Gly Gln Glu Val Phe Gly Leu Val Pro Gly Leu Met Met Tyr Ala

275 280 285

Thr Ile Trp Leu Arg Glu His Asn Arg Val Cys Asp Val Leu Lys Gln

290 295 300

Glu His Pro Glu Trp Gly Asp Glu Gln Leu Phe Gln Thr Ser Arg Leu

305 310 315 320

Ile Leu Ile Gly Glu Thr Ile Lys Ile Val Ile Glu Asp Tyr Val Gln

325 330 335

His Leu Ser Gly Tyr His Phe Lys Leu Lys Phe Asp Pro Glu Leu Leu

340 345 350

Phe Asn Lys Gln Phe Gln Tyr Gln Asn Arg Ile Ala Ala Glu Phe Asn

355 360 365

Thr Leu Tyr His Trp His Pro Leu Leu Pro Asp Thr Phe Gln Ile His

370 375 380

Asp Gln Lys Tyr Asn Tyr Gln Gln Phe Ile Tyr Asn Asn Ser Ile Leu

385 390 395 400

Leu Glu His Gly Ile Thr Gln Phe Val Glu Ser Phe Thr Arg Gln Ile

405 410 415

Ala Gly Arg Val Ala Gly Gly Arg Asn Val Pro Pro Ala Val Gln Lys

420 425 430

Val Ser Gln Ala Ser Ile Asp Gln Ser Arg Gln Met Lys Tyr Gln Ser

435 440 445

Phe Asn Glu Tyr Arg Lys Arg Phe Met Leu Lys Pro Tyr Glu Ser Phe

450 455 460

Glu Glu Leu Thr Gly Glu Lys Glu Met Ser Ala Glu Leu Glu Ala Leu

465 470 475 480

Tyr Gly Asp Ile Asp Ala Val Glu Leu Tyr Pro Ala Leu Leu Val Glu

485 490 495

Lys Pro Arg Pro Asp Ala Ile Phe Gly Glu Thr Met Val Glu Val Gly

500 505 510

Ala Pro Phe Ser Leu Lys Gly Leu Met Gly Asn Val Ile Cys Ser Pro

515 520 525

Ala Tyr Trp Lys Pro Ser Thr Phe Gly Gly Glu Val Gly Phe Gln Ile

530 535 540

Ile Asn Thr Ala Ser Ile Gln Ser Leu Ile Cys Asn Asn Val Lys Gly

545 550 555 560

Cys Pro Phe Thr Ser Phe Ser Val Pro Asp Pro Glu Leu Ile Lys Thr

565 570 575

Val Thr Ile Asn Ala Ser Ser Ser Arg Ser Gly Leu Asp Asp Ile Asn

580 585 590

Pro Thr Val Leu Leu Lys Glu Arg Ser Thr Glu Leu

595 600

<210> 2

<211> 4510

<212> DNA

<213> Homo sapiens

<223> PTGS2 – Accession number NM_000963.4

<400> 2

aattgtcata cgacttgcag tgagcgtcag gagcacgtcc aggaactcct cagcagcgcc 60

tccttcagct ccacagccag acgccctcag acagcaaagc ctacccccgc gccgcgccct 120

gcccgccgct gcgatgctcg cccgcgccct gctgctgtgc gcggtcctgg cgctcagcca 180

tacagcaaat ccttgctgtt cccacccatg tcaaaaccga ggtgtatgta tgagtgtggg 240

atttgaccag tataagtgcg attgtacccg gacaggattc tatggagaaa actgctcaac 300

accggaattt ttgacaagaa taaaattatt tctgaaaccc actccaaaca cagtgcacta 360

catacttacc cacttcaagg gattttggaa cgttgtgaat aacattccct tccttcgaaa 420

tgcaattatg agttatgtgt tgacatccag atcacatttg attgacagtc caccaactta 480

caatgctgac tatggctaca aaagctggga agccttctct aacctctcct attatactag 540

agcccttcct cctgtgcctg atgattgccc gactcccttg ggtgtcaaag gtaaaaagca 600

gcttcctgat tcaaatgaga ttgtggaaaa attgcttcta agaagaaagt tcatccctga 660

tccccagggc tcaaacatga tgtttgcatt ctttgcccag cacttcacgc atcagttttt 720

caagacagat cataagcgag ggccagcttt caccaacggg ctgggccatg gggtggactt 780

aaatcatatt tacggtgaaa ctctggctag acagcgtaaa ctgcgccttt tcaaggatgg 840

aaaaatgaaa tatcagataa ttgatggaga gatgtatcct cccacagtca aagatactca 900

ggcagagatg atctaccctc ctcaagtccc tgagcatcta cggtttgctg tggggcagga 960

ggtctttggt ctggtgcctg gtctgatgat gtatgccaca atctggctgc gggaacacaa 1020

cagagtatgc gatgtgctta aacaggagca tcctgaatgg ggtgatgagc agttgttcca 1080

gacaagcagg ctaatactga taggagagac tattaagatt gtgattgaag attatgtgca 1140

acacttgagt ggctatcact tcaaactgaa atttgaccca gaactacttt tcaacaaaca 1200

attccagtac caaaatcgta ttgctgctga atttaacacc ctctatcact ggcatcccct 1260

tctgcctgac acctttcaaa ttcatgacca gaaatacaac tatcaacagt ttatctacaa 1320

caactctata ttgctggaac atggaattac ccagtttgtt gaatcattca ccaggcaaat 1380

tgctggcagg gttgctggtg gtaggaatgt tccacccgca gtacagaaag tatcacaggc 1440

ttccattgac cagagcaggc agatgaaata ccagtctttt aatgagtacc gcaaacgctt 1500

tatgctgaag ccctatgaat catttgaaga acttacagga gaaaaggaaa tgtctgcaga 1560

gttggaagca ctctatggtg acatcgatgc tgtggagctg tatcctgccc ttctggtaga 1620

aaagcctcgg ccagatgcca tctttggtga aaccatggta gaagttggag caccattctc 1680

cttgaaagga cttatgggta atgttatatg ttctcctgcc tactggaagc caagcacttt 1740

tggtggagaa gtgggttttc aaatcatcaa cactgcctca attcagtctc tcatctgcaa 1800

taacgtgaag ggctgtccct ttacttcatt cagtgttcca gatccagagc tcattaaaac 1860

agtcaccatc aatgcaagtt cttcccgctc cggactagat gatatcaatc ccacagtact 1920

actaaaagaa cgttcgactg aactgtagaa gtctaatgat catatttatt tatttatatg 1980

aaccatgtct attaatttaa ttatttaata atatttatat taaactcctt atgttactta 2040

acatcttctg taacagaagt cagtactcct gttgcggaga aaggagtcat acttgtgaag 2100

acttttatgt cactactcta aagattttgc tgttgctgtt aagtttggaa aacagttttt 2160

attctgtttt ataaaccaga gagaaatgag ttttgacgtc tttttacttg aatttcaact 2220

tatattataa gaacgaaagt aaagatgttt gaatacttaa acactgtcac aagatggcaa 2280

aatgctgaaa gtttttacac tgtcgatgtt tccaatgcat cttccatgat gcattagaag 2340

taactaatgt ttgaaatttt aaagtacttt tggttatttt tctgtcatca aacaaaaaca 2400

ggtatcagtg cattattaaa tgaatattta aattagacat taccagtaat ttcatgtcta 2460

ctttttaaaa tcagcaatga aacaataatt tgaaatttct aaattcatag ggtagaatca 2520

cctgtaaaag cttgtttgat ttcttaaagt tattaaactt gtacatatac caaaaagaag 2580

ctgtcttgga tttaaatctg taaaatcagt agaaatttta ctacaattgc ttgttaaaat 2640

attttataag tgatgttcct ttttcaccaa gagtataaac ctttttagtg tgactgttaa 2700

aacttccttt taaatcaaaa tgccaaattt attaaggtgg tggagccact gcagtgttat 2760

cttaaaataa gaatattttg ttgagatatt ccagaatttg tttatatggc tggtaacatg 2820

taaaatctat atcagcaaaa gggtctacct ttaaaataag caataacaaa gaagaaaacc 2880

aaattattgt tcaaatttag gtttaaactt ttgaagcaaa ctttttttta tccttgtgca 2940

ctgcaggcct ggtactcaga ttttgctatg aggttaatga agtaccaagc tgtgcttgaa 3000

taatgatatg ttttctcaga ttttctgttg tacagtttaa tttagcagtc catatcacat 3060

tgcaaaagta gcaatgacct cataaaatac ctcttcaaaa tgcttaaatt catttcacac 3120

attaatttta tctcagtctt gaagccaatt cagtaggtgc attggaatca agcctggcta 3180

cctgcatgct gttccttttc ttttcttctt ttagccattt tgctaagaga cacagtcttc 3240

tcatcacttc gtttctccta ttttgtttta ctagttttaa gatcagagtt cactttcttt 3300

ggactctgcc tatattttct tacctgaact tttgcaagtt ttcaggtaaa cctcagctca 3360

ggactgctat ttagctcctc ttaagaagat taaaagagaa aaaaaaaggc ccttttaaaa 3420

atagtataca cttattttaa gtgaaaagca gagaatttta tttatagcta attttagcta 3480

tctgtaacca agatggatgc aaagaggcta gtgcctcaga gagaactgta cggggtttgt 3540

gactggaaaa agttacgttc ccattctaat taatgccctt tcttatttaa aaacaaaacc 3600

aaatgatatc taagtagttc tcagcaataa taataatgac gataatactt cttttccaca 3660

tctcattgtc actgacattt aatggtactg tatattactt aatttattga agattattat 3720

ttatgtctta ttaggacact atggttataa actgtgttta agcctacaat cattgatttt 3780

tttttgttat gtcacaatca gtatattttc tttggggtta cctctctgaa tattatgtaa 3840

acaatccaaa gaaatgattg tattaagatt tgtgaataaa tttttagaaa tctgattggc 3900

atattgagat atttaaggtt gaatgtttgt ccttaggata ggcctatgtg ctagcccaca 3960

aagaatattg tctcattagc ctgaatgtgc cataagactg accttttaaa atgttttgag 4020

ggatctgtgg atgcttcgtt aatttgttca gccacaattt attgagaaaa tattctgtgt 4080

caagcactgt gggttttaat atttttaaat caaacgctga ttacagataa tagtatttat 4140

ataaataatt gaaaaaaatt ttcttttggg aagagggaga aaatgaaata aatatcatta 4200

aagataactc aggagaatct tctttacaat tttacgttta gaatgtttaa ggttaagaaa 4260

gaaatagtca atatgcttgt ataaaacact gttcactgtt ttttttaaaa aaaaaacttg 4320

atttgttatt aacattgatc tgctgacaaa acctgggaat ttgggttgtg tatgcgaatg 4380

tttcagtgcc tcagacaaat gtgtatttaa cttatgtaaa agataagtct ggaaataaat 4440

gtctgtttat ttttgtacta tttaaaaatt gacagatctt ttctgaagat aaactttgat 4500

tgtttctata 4510

<210> 3

<211> 288

<212>PRT

<213> Homo sapiens

<223> HMOX1 – Access number NP_002124.1

<400> 3

Met Glu Arg Pro Gln Pro Asp Ser Met Pro Gln Asp Leu Ser Glu Ala

1 5 10 15

Leu Lys Glu Ala Thr Lys Glu Val His Thr Gln Ala Glu Asn Ala Glu

20 25 30

Phe Met Arg Asn Phe Gln Lys Gly Gln Val Thr Arg Asp Gly Phe Lys

35 40 45

Leu Val Met Ala Ser Leu Tyr His Ile Tyr Val Ala Leu Glu Glu Glu

50 55 60

Ile Glu Arg Asn Lys Glu Ser Pro Val Phe Ala Pro Val Tyr Phe Pro

65 70 75 80

Glu Glu Leu His Arg Lys Ala Ala Leu Glu Gln Asp Leu Ala Phe Trp

85 90 95

Tyr Gly Pro Arg Trp Gln Glu Val Ile Pro Tyr Thr Pro Ala Met Gln

100 105 110

Arg Tyr Val Lys Arg Leu His Glu Val Gly Arg Thr Glu Pro Glu Leu

115 120 125

Leu Val Ala His Ala Tyr Thr Arg Tyr Leu Gly Asp Leu Ser Gly Gly

130 135 140

Gln Val Leu Lys Lys Ile Ala Gln Lys Ala Leu Asp Leu Pro Ser Ser

145 150 155 160

Gly Glu Gly Leu Ala Phe Phe Thr Phe Pro Asn Ile Ala Ser Ala Thr

165 170 175

Lys Phe Lys Gln Leu Tyr Arg Ser Arg Met Asn Ser Leu Glu Met Thr

180 185 190

Pro Ala Val Arg Gln Arg Val Ile Glu Glu Ala Lys Thr Ala Phe Leu

195 200 205

Leu Asn Ile Gln Leu Phe Glu Glu Leu Gln Glu Leu Leu Thr His Asp

210 215 220

Thr Lys Asp Gln Ser Pro Ser Arg Ala Pro Gly Leu Arg Gln Arg Ala

225 230 235 240

Ser Asn Lys Val Gln Asp Ser Ala Pro Val Glu Thr Pro Arg Gly Lys

245 250 255

Pro Pro Leu Asn Thr Arg Ser Gln Ala Pro Leu Leu Arg Trp Val Leu

260 265 270

Thr Leu Ser Phe Leu Val Ala Thr Val Ala Val Gly Leu Tyr Ala Met

275 280 285

<210> 4

<211> 1554

<212> DNA

<213> Homo sapiens

<223> HMOX1 – Accession number NM_002133.3

<400> 4

aacgcctgcc tcctctcgag cgtcctcagc gcagccgccg cccgcggagc cagcacgaac 60

gagcccagca ccggccggat ggagcgtccg caacccgaca gcatgcccca ggatttgtca 120

gaggccctga aggaggccac caaggaggtg cacacccagg cagagaatgc tgagttcatg 180

aggaactttc agaagggcca ggtgacccga gacggcttca agctggtgat ggcctccctg 240

taccacatct atgtggccct ggaggaggag attgagcgca acaagggagag cccagtcttc 300

gcccctgtct acttcccaga agagctgcac cgcaaggctg ccctggagca ggacctggcc 360

ttctggtacg ggccccgctg gcaggaggtc atcccctaca caccagccat gcagcgctat 420

gtgaagcggc tccacgaggt ggggcgcaca gagcccgagc tgctggtggc ccacgcctac 480

acccgctacc tgggtgacct gtctggggggc caggtgctca aaaagattgc ccagaaagcc 540

ctggacctgc ccagctctgg cgagggcctg gccttcttca ccttccccaa cattgccagt 600

gccaccaagt tcaagcagct ctaccgctcc cgcatgaact ccctggagat gactcccgca 660

gtcaggcaga gggtgataga agaggccaag actgcgttcc tgctcaacat ccagctcttt 720

gaggagttgc aggagctgct gacccatgac accaaggacc agagcccctc acgggcacca 780

gggcttcgcc agcggggccag caacaaagtg caagattctg cccccgtgga gactcccaga 840

gggaagcccc cactcaacac ccgctcccag gctccgcttc tccgatgggt ccttacactc 900

agctttctgg tggcgacagt tgctgtaggg ctttatgcca tgtgaatgca ggcatgctgg 960

ctcccagggc catgaacttt gtccggtgga aggccttctt tctagagagg gaattctctt 1020

ggctggcttc cttaccgtgg gcactgaagg ctttcagggc ctccagccct ctcactgtgt 1080

ccctctctct ggaaaggagg aaggagccta tggcatcttc cccaacgaaa agcacatcca 1140

ggcaatggcc taaacttcag agggggcgaa gggatcagcc ctgcccttca gcatcctcag 1200

ttcctgcagc agagcctgga agacacccta atgtggcagc tgtctcaaac ctccaaaagc 1260

cctgagtttc aagtatcctt gttgacacgg ccatgaccac tttccccgtg ggccatggca 1320

atttttacac aaacctgaaa agatgttgtg tcttgtgttt ttgtcttatt tttgttggag 1380

ccactctgtt cctggctcag cctcaaatgc agtatttttg ttgtgttctg ttgttttttat 1440

agcagggttg gggtggtttt tgagccatgc gtgggtgggg agggaggtgt ttaacggcac 1500

tgtggccttg gtctaacttt tgtgtgaaat aataaacaac attgtctgat agta 1554

<210> 5

<211> 860

<212>PRT

<213> Homo sapiens

<223> LDLR – Access number NP_000518.1

<400> 5

Met Gly Pro Trp Gly Trp Lys Leu Arg Trp Thr Val Ala Leu Leu Leu

1 5 10 15

Ala Ala Ala Gly Thr Ala Val Gly Asp Arg Cys Glu Arg Asn Glu Phe

20 25 30

Gln Cys Gln Asp Gly Lys Cys Ile Ser Tyr Lys Trp Val Cys Asp Gly

35 40 45

Ser Ala Glu Cys Gln Asp Gly Ser Asp Glu Ser Gln Glu Thr Cys Leu

50 55 60

Ser Val Thr Cys Lys Ser Gly Asp Phe Ser Cys Gly Gly Arg Val Asn

65 70 75 80

Arg Cys Ile Pro Gln Phe Trp Arg Cys Asp Gly Gln Val Asp Cys Asp

85 90 95

Asn Gly Ser Asp Glu Gln Gly Cys Pro Pro Lys Thr Cys Ser Gln Asp

100 105 110

Glu Phe Arg Cys His Asp Gly Lys Cys Ile Ser Arg Gln Phe Val Cys

115 120 125

Asp Ser Asp Arg Asp Cys Leu Asp Gly Ser Asp Glu Ala Ser Cys Pro

130 135 140

Val Leu Thr Cys Gly Pro Ala Ser Phe Gln Cys Asn Ser Ser Thr Cys

145 150 155 160

Ile Pro Gln Leu Trp Ala Cys Asp Asn Asp Pro Asp Cys Glu Asp Gly

165 170 175

Ser Asp Glu Trp Pro Gln Arg Cys Arg Gly Leu Tyr Val Phe Gln Gly

180 185 190

Asp Ser Ser Pro Cys Ser Ala Phe Glu Phe His Cys Leu Ser Gly Glu

195 200 205

Cys Ile His Ser Ser Trp Arg Cys Asp Gly Gly Pro Asp Cys Lys Asp

210 215 220

Lys Ser Asp Glu Glu Asn Cys Ala Val Ala Thr Cys Arg Pro Asp Glu

225 230 235 240

Phe Gln Cys Ser Asp Gly Asn Cys Ile His Gly Ser Arg Gln Cys Asp

245 250 255

Arg Glu Tyr Asp Cys Lys Asp Met Ser Asp Glu Val Gly Cys Val Asn

260 265 270

Val Thr Leu Cys Glu Gly Pro Asn Lys Phe Lys Cys His Ser Gly Glu

275 280 285

Cys Ile Thr Leu Asp Lys Val Cys Asn Met Ala Arg Asp Cys Arg Asp

290 295 300

Trp Ser Asp Glu Pro Ile Lys Glu Cys Gly Thr Asn Glu Cys Leu Asp

305 310 315 320

Asn Asn Gly Gly Cys Ser His Val Cys Asn Asp Leu Lys Ile Gly Tyr

325 330 335

Glu Cys Leu Cys Pro Asp Gly Phe Gln Leu Val Ala Gln Arg Arg Cys

340 345 350

Glu Asp Ile Asp Glu Cys Gln Asp Pro Asp Thr Cys Ser Gln Leu Cys

355 360 365

Val Asn Leu Glu Gly Gly Tyr Lys Cys Gln Cys Glu Glu Gly Phe Gln

370 375 380

Leu Asp Pro His Thr Lys Ala Cys Lys Ala Val Gly Ser Ile Ala Tyr

385 390 395 400

Leu Phe Phe Thr Asn Arg His Glu Val Arg Lys Met Thr Leu Asp Arg

405 410 415

Ser Glu Tyr Thr Ser Leu Ile Pro Asn Leu Arg Asn Val Val Ala Leu

420 425 430

Asp Thr Glu Val Ala Ser Asn Arg Ile Tyr Trp Ser Asp Leu Ser Gln

435 440 445

Arg Met Ile Cys Ser Thr Gln Leu Asp Arg Ala His Gly Val Ser Ser

450 455 460

Tyr Asp Thr Val Ile Ser Arg Asp Ile Gln Ala Pro Asp Gly Leu Ala

465 470 475 480

Val Asp Trp Ile His Ser Asn Ile Tyr Trp Thr Asp Ser Val Leu Gly

485 490 495

Thr Val Ser Val Ala Asp Thr Lys Gly Val Lys Arg Lys Thr Leu Phe

500 505 510

Arg Glu Asn Gly Ser Lys Pro Arg Ala Ile Val Val Asp Pro Val His

515 520 525

Gly Phe Met Tyr Trp Thr Asp Trp Gly Thr Pro Ala Lys Ile Lys Lys

530 535 540

Gly Gly Leu Asn Gly Val Asp Ile Tyr Ser Leu Val Thr Glu Asn Ile

545 550 555 560

Gln Trp Pro Asn Gly Ile Thr Leu Asp Leu Leu Ser Gly Arg Leu Tyr

565 570 575

Trp Val Asp Ser Lys Leu His Ser Ile Ser Ser Ile Asp Val Asn Gly

580 585 590

Gly Asn Arg Lys Thr Ile Leu Glu Asp Glu Lys Arg Leu Ala His Pro

595 600 605

Phe Ser Leu Ala Val Phe Glu Asp Lys Val Phe Trp Thr Asp Ile Ile

610 615 620

Asn Glu Ala Ile Phe Ser Ala Asn Arg Leu Thr Gly Ser Asp Val Asn

625 630 635 640

Leu Leu Ala Glu Asn Leu Leu Ser Pro Glu Asp Met Val Leu Phe His

645 650 655

Asn Leu Thr Gln Pro Arg Gly Val Asn Trp Cys Glu Arg Thr Thr Leu

660 665 670

Ser Asn Gly Gly Cys Gln Tyr Leu Cys Leu Pro Ala Pro Gln Ile Asn

675 680 685

Pro His Ser Pro Lys Phe Thr Cys Ala Cys Pro Asp Gly Met Leu Leu

690 695 700

Ala Arg Asp Met Arg Ser Cys Leu Thr Glu Ala Glu Ala Ala Val Ala

705 710 715 720

Thr Gln Glu Thr Ser Thr Val Arg Leu Lys Val Ser Ser Thr Ala Val

725 730 735

Arg Thr Gln His Thr Thr Thr Arg Pro Val Pro Asp Thr Ser Arg Leu

740 745 750

Pro Gly Ala Thr Pro Gly Leu Thr Thr Val Glu Ile Val Thr Met Ser

755 760 765

His Gln Ala Leu Gly Asp Val Ala Gly Arg Gly Asn Glu Lys Lys Pro

770 775 780

Ser Ser Val Arg Ala Leu Ser Ile Val Leu Pro Ile Val Leu Leu Val

785 790 795 800

Phe Leu Cys Leu Gly Val Phe Leu Leu Trp Lys Asn Trp Arg Leu Lys

805 810 815

Asn Ile Asn Ser Ile Asn Phe Asp Asn Pro Val Tyr Gln Lys Thr Thr

820 825 830

Glu Asp Glu Val His Ile Cys His Asn Gln Asp Gly Tyr Ser Tyr Pro

835 840 845

Ser Arg Gln Met Val Ser Leu Glu Asp Asp Val Ala

850 855 860

<210> 6

<211> 5292

<212> DNA

<213> Homo sapiens

<223> LDLR – Accession number NM_000527.4

<400> 6

ctcttgcagt gaggtgaaga catttgaaaa tcaccccact gcaaactcct ccccctgcta 60

gaaacctcac attgaaatgc tgtaaatgac gtgggccccg agtgcaatcg cgggaagcca 120

gggtttccag ctaggacaca gcaggtcgtg atccgggtcg ggacactgcc tggcagaggc 180

tgcgagcatg gggccctggg gctggaaatt gcgctggacc gtcgccttgc tcctcgccgc 240

ggcggggact gcagtgggcg acagatgcga aagaaacgag ttccagtgcc aagacgggaa 300

atgcatctcc tacaagtggg tctgcgatgg cagcgctgag tgccaggatg gctctgatga 360

gtcccaggag acgtgcttgt ctgtcacctg caaatccggg gacttcagct gtgggggccg 420

tgtcaaccgc tgcattcctc agttctggag gtgcgatggc caagtggact gcgacaacgg 480

ctcagacgag caaggctgtc cccccaagac gtgctcccag gacgagtttc gctgccacga 540

tgggaagtgc atctctcggc agttcgtctg tgactcagac cgggactgct tggacggctc 600

agacgaggcc tcctgcccgg tgctcacctg tggtcccgcc agcttccagt gcaacagctc 660

cacctgcatc ccccagctgt gggcctgcga caacgacccc gactgcgaag atggctcgga 720

tgagtggccg cagcgctgta ggggtcttta cgtgttccaa ggggacagta gcccctgctc 780

ggccttcgag ttccactgcc taagtggcga gtgcatccac tccagctggc gctgtgatgg 840

tggccccgac tgcaaggaca aatctgacga ggaaaactgc gctgtggcca cctgtcgccc 900

tgacgaattc cagtgctctg atggaaactg catccatggc agccggcagt gtgaccggga 960

atatgactgc aaggacatga gcgatgaagt tggctgcgtt aatgtgacac tctgcgagg 1020

acccaacaag ttcaagtgtc acagcggcga atgcatcacc ctggacaaag tctgcaacat 1080

ggctagac tgccgggact ggtcagatga acccatcaaa gagtgcggga ccaacgaatg 1140

cttggacaac aacggcggct gttcccacgt ctgcaatgac cttaagatcg gctacgagtg 1200

cctgtgcccc gacggcttcc agctggtggc ccagcgaaga tgcgaagata tcgatgagtg 1260

tcaggatccc gacacctgca gccagctctg cgtgaacctg gagggtggct acaagtgcca 1320

gtgtgaggaa ggcttccagc tggaccccca cacgaaggcc tgcaaggctg tgggctccat 1380

cgcctacctc ttcttcacca accggcacga ggtcaggaag atgacgctgg accggagcga 1440

gtacaccagc ctcatcccca acctgaggaa cgtggtcgct ctggacacgg aggtggccag 1500

caatagaatc tactggtctg acctgtccca gagaatgatc tgcagcaccc agcttgacag 1560

agcccacggc gtctcttcct atgacaccgt catcagcaga gacatccagg cccccgacgg 1620

gctggctgtg gactggatcc acagcaacat ctactggacc gactctgtcc tgggcactgt 1680

ctctgttgcg gataccaagg gcgtgaagag gaaaacgtta ttcagggaga acggctccaa 1740

gccaagggcc atcgtggtgg atcctgttca tggcttcatg tactggactg actggggaac 1800

tcccgccaag atcaagaaag ggggcctgaa tggtgtggac atctactcgc tggtgactga 1860

aaacattcag tggcccaatg gcatcaccct agatctcctc agtggccgcc tctactgggt 1920

tgactccaaa cttcactcca tctcaagcat cgatgtcaac gggggcaacc ggaagaccat 1980

cttggaggat gaaaagaggc tggcccaccc cttctccttg gccgtctttg aggacaaagt 2040

attttggaca gatatcatca acgaagccat tttcagtgcc aaccgcctca caggttccga 2100

tgtcaacttg ttggctgaaa acctactgtc cccagaggat atggttctct tccacaacct 2160

cacccagcca agaggagtga actggtgtga gaggaccacc ctgagcaatg gcggctgcca 2220

gtatctgtgc ctccctgccc cgcagatcaa cccccactcg cccaagttta cctgcgcctg 2280

cccggacggc atgctgctgg ccagggacat gaggagctgc ctcacagagg ctgaggctgc 2340

agtggccacc caggagacat ccaccgtcag gctaaaggtc agctccacag ccgtaaggac 2400

acagcacaca accacccgac ctgttcccga cacctcccgg ctgcctgggg ccacccctgg 2460

gctcaccacg gtggagatag tgacaatgtc tcaccaagct ctgggcgacg ttgctggcag 2520

aggaaatgag aagaagccca gtagcgtgag ggctctgtcc attgtcctcc ccatcgtgct 2580

cctcgtcttc ctttgcctgg gggtcttcct tctatggaag aactggcggc ttaagaacat 2640

caacagcatc aactttgaca accccgtcta tcagaagacc acagaggatg aggtccacat 2700

ttgccacaac caggacggct acagctaccc ctcgagacag atggtcagtc tggaggatga 2760

cgtggcgtga acatctgcct ggagtcccgt ccctgcccag aacccttcct gagacctcgc 2820

cggccttgtt ttattcaaag acagagaaga ccaaagcatt gcctgccaga gctttgtttt 2880

atatatttat tcatctggga ggcagaacag gcttcggaca gtgcccatgc aatggcttgg 2940

gttgggattt tggtttcttc ctttcctcgt gaaggataag agaaacaggc ccggggggac 3000

caggatgaca cctccattc tctccaggaa gttttgagtt tctctccacc gtgacacaat 3060

cctcaaacat ggaagatgaa aggggagggg atgtcaggcc cagagaagca agtggctttc 3120

aacacacaac agcagatggc accaacggga ccccctggcc ctgcctcatc caccaatctc 3180

taagccaaac ccctaaactc aggagtcaac gtgtttacct cttctatgca agccttgcta 3240

gacagccagg ttagcctttg ccctgtcacc cccgaatcat gacccaccca gtgtctttcg 3300

aggtgggttt gtaccttcct taagccagga aagggattca tggcgtcgga aatgatctgg 3360

ctgaatccgt ggtggcaccg agaccaaact cattcaccaa atgatgccac ttcccagagg 3420

cagagcctga gtcactggtc acccttaata tttattaagt gcctgagaca cccggttacc 3480

ttggccgtga ggacacgtgg cctgcaccca ggtgtggctg tcaggacacc agcctggtgc 3540

ccatcctccc gacccctacc cacttccatt cccgtggtct ccttgcactt tctcagttca 3600

gagttgtaca ctgtgtacat ttggcatttg tgttattatt ttgcactgtt ttctgtcgtg 3660

tgtgttggga tgggatccca ggccagggaa agcccgtgtc aatgaatgcc ggggacagag 3720

aggggcaggt tgaccgggac ttcaaagccg tgatcgtgaa tatcgagaac tgccattgtc 3780

gtctttatgt ccgcccacct agtgcttcca cttctatgca aatgcctcca agccattcac 3840

ttccccaatc ttgtcgttga tgggtatgtg tttaaaacat gcacggtgag gccgggcgca 3900

gtggctcacg cctgtaatcc cagcactttg ggaggccgag gcgggtggat catgaggtca 3960

ggagatcgag accatcctgg ctaacacgtg aaaccccgtc tctactaaaa atacaaaaaa 4020

ttagccgggc gtggtggcgg gcacctgtag tcccagctac tcgggaggct gaggcaggag 4080

aatggtgtga acccgggaag cggagcttgc agtgagccga gattgcgcca ctgcagtccg 4140

cagtctggcc tgggcgacag agcgagactc cgtctcaaaa aaaaaaaaca aaaaaaaacc 4200

atgcatggtg catcagcagc ccatggcctc tggccaggca tggcgaggct gaggtgggag 4260

gatggtttga gctcaggcat ttgaggctgt cgtgagctat gattatgcca ctgctttcca 4320

gcctgggcaa catagtaaga ccccatctct taaaaaatga atttggccag acacaggtgc 4380

ctcacgcctg taatcccagc actttgggag gctgagctgg atcacttgag ttcaggagtt 4440

ggagaccagg cctgagcaac aaagcgagat cccatctcta caaaaaccaa aaagttaaaa 4500

atcagctggg tacggtggca cgtgcctgtg atcccagcta cttgggaggc tgaggcagga 4560

ggatcgcctg agcccaggag gtggaggttg cagtgagcca tgatcgagcc actgcactcc 4620

agcctgggca acagatgaag accctatttc agaaatacaa ctataaaaaa ataaataaat 4680

cctccagtct ggatcgtttg acgggacttc aggttctttc tgaaatcgcc gtgttactgt 4740

tgcactgatg tccggagaga cagtgacagc ctccgtcaga ctcccgcgtg aagatgtcac 4800

aagggattgg caattgtccc cagggacaaa acactgtgtc ccccccagtg cagggaaccg 4860

tgataagcct ttctggtttc ggagcacgta aatgcgtccc tgtacagata gtggggattt 4920

tttgttatgt ttgcactttg tatattggtt gaaactgtta tcacttatat atatatatat 4980

acacacatat atataaaatc tatttatttt tgcaaaccct ggttgctgta tttgttcagt 5040

gactattctc ggggccctgt gtagggggtt attgcctctg aaatgcctct tctttatgta 5100

caaagattat ttgcacgaac tggactgtgt gcaacgcttt ttgggagaat gatgtccccg 5160

ttgtatgtat gagtggcttc tgggagatgg gtgtcacttt ttaaaccact gtatagaagg 5220

tttttgtagc ctgaatgtct tactgtgatc aattaaattt cttaaatgaa ccaatttgtc 5280

taaaaaaaaa aa 5292

<210> 7

<211> 641

<212>PRT

<213> Homo sapiens

<223> HSPA1B – Accession number NP_005337.2

<400> 7

Met Ala Lys Ala Ala Ala Ile Gly Ile Asp Leu Gly Thr Thr Tyr Ser

1 5 10 15

Cys Val Gly Val Phe Gln His Gly Lys Val Glu Ile Ile Ala Asn Asp

20 25 30

Gln Gly Asn Arg Thr Thr Pro Ser Tyr Val Ala Phe Thr Asp Thr Glu

35 40 45

Arg Leu Ile Gly Asp Ala Ala Lys Asn Gln Val Ala Leu Asn Pro Gln

50 55 60

Asn Thr Val Phe Asp Ala Lys Arg Leu Ile Gly Arg Lys Phe Gly Asp

65 70 75 80

Pro Val Val Gln Ser Asp Met Lys His Trp Pro Phe Gln Val Ile Asn

85 90 95

Asp Gly Asp Lys Pro Lys Val Gln Val Ser Tyr Lys Gly Glu Thr Lys

100 105 110

Ala Phe Tyr Pro Glu Glu Ile Ser Ser Met Val Leu Thr Lys Met Lys

115 120 125

Glu Ile Ala Glu Ala Tyr Leu Gly Tyr Pro Val Thr Asn Ala Val Ile

130 135 140

Thr Val Pro Ala Tyr Phe Asn Asp Ser Gln Arg Gln Ala Thr Lys Asp

145 150 155 160

Ala Gly Val Ile Ala Gly Leu Asn Val Leu Arg Ile Ile Asn Glu Pro

165 170 175

Thr Ala Ala Ala Ile Ala Tyr Gly Leu Asp Arg Thr Gly Lys Gly Glu

180 185 190

Arg Asn Val Leu Ile Phe Asp Leu Gly Gly Gly Thr Phe Asp Val Ser

195 200 205

Ile Leu Thr Ile Asp Asp Gly Ile Phe Glu Val Lys Ala Thr Ala Gly

210 215 220

Asp Thr His Leu Gly Gly Glu Asp Phe Asp Asn Arg Leu Val Asn His

225 230 235 240

Phe Val Glu Glu Phe Lys Arg Lys His Lys Lys Asp Ile Ser Gln Asn

245 250 255

Lys Arg Ala Val Arg Arg Leu Arg Thr Ala Cys Glu Arg Ala Lys Arg

260 265 270

Thr Leu Ser Ser Ser Thr Gln Ala Ser Leu Glu Ile Asp Ser Leu Phe

275 280 285

Glu Gly Ile Asp Phe Tyr Thr Ser Ile Thr Arg Ala Arg Phe Glu Glu

290 295 300

Leu Cys Ser Asp Leu Phe Arg Ser Thr Leu Glu Pro Val Glu Lys Ala

305 310 315 320

Leu Arg Asp Ala Lys Leu Asp Lys Ala Gln Ile His Asp Leu Val Leu

325 330 335

Val Gly Gly Ser Thr Arg Ile Pro Lys Val Gln Lys Leu Leu Gln Asp

340 345 350

Phe Phe Asn Gly Arg Asp Leu Asn Lys Ser Ile Asn Pro Asp Glu Ala

355 360 365

Val Ala Tyr Gly Ala Ala Val Gln Ala Ala Ile Leu Met Gly Asp Lys

370 375 380

Ser Glu Asn Val Gln Asp Leu Leu Leu Leu Asp Val Ala Pro Leu Ser

385 390 395 400

Leu Gly Leu Glu Thr Ala Gly Gly Val Met Thr Ala Leu Ile Lys Arg

405 410 415

Asn Ser Thr Ile Pro Thr Lys Gln Thr Gln Ile Phe Thr Thr Tyr Ser

420 425 430

Asp Asn Gln Pro Gly Val Leu Ile Gln Val Tyr Glu Gly Glu Arg Ala

435 440 445

Met Thr Lys Asp Asn Asn Leu Leu Gly Arg Phe Glu Leu Ser Gly Ile

450 455 460

Pro Pro Ala Pro Arg Gly Val Pro Gln Ile Glu Val Thr Phe Asp Ile

465 470 475 480

Asp Ala Asn Gly Ile Leu Asn Val Thr Ala Thr Asp Lys Ser Thr Gly

485 490 495

Lys Ala Asn Lys Ile Thr Ile Thr Asn Asp Lys Gly Arg Leu Ser Lys

500 505 510

Glu Glu Ile Glu Arg Met Val Gln Glu Ala Glu Lys Tyr Lys Ala Glu

515 520 525

Asp Glu Val Gln Arg Glu Arg Val Ser Ala Lys Asn Ala Leu Glu Ser

530 535 540

Tyr Ala Phe Asn Met Lys Ser Ala Val Glu Asp Glu Gly Leu Lys Gly

545 550 555 560

Lys Ile Ser Glu Ala Asp Lys Lys Lys Val Leu Asp Lys Cys Gln Glu

565 570 575

Val Ile Ser Trp Leu Asp Ala Asn Thr Leu Ala Glu Lys Asp Glu Phe

580 585 590

Glu His Lys Arg Lys Glu Leu Glu Gln Val Cys Asn Pro Ile Ile Ser

595 600 605

Gly Leu Tyr Gln Gly Ala Gly Gly Pro Gly Pro Gly Gly Phe Gly Ala

610 615 620

Gln Gly Pro Lys Gly Gly Ser Gly Ser Gly Pro Thr Ile Glu Glu Val

625 630 635 640

Asp

<210> 8

<211> 2517

<212> DNA

<213> Homo sapiens

<223> HSPA1B – Accession number NM_005346.5

<400> 8

aaacggccag cctgaggagc tgctgcgagg gtccgcttcg tctttcgaga gtgactcccg 60

cggtcccaag gctttccaga gcgaacctgt gcggctgcag gcaccggcgt gttgagtttc 120

cggcgttccg aaggactgag ctcttgtcgc ggatcccgtc cgccgtttcc agcccccagt 180

ctcagagcgg agcccacaga gcagggcacc ggcatggcca aagccgcggc gatcggcatc 240

gacctgggca ccacctactc ctgcgtgggg gtgttccaac acggcaaggt ggagatcatc 300

gccaacgacc agggcaaccg caccaccccc agctacgtgg ccttcacgga caccgagcgg 360

ctcatcgggg atgcggccaa gaaccaggtg gcgctgaacc cgcagaacac cgtgtttgac 420

gcgaagcggc tgatcggccg caagttcggc gacccggtgg tgcagtcgga catgaagcac 480

tggcctttcc aggtgatcaa cgacggagac aagcccaagg tgcaggtgag ctacaagggg 540

gagaccaagg cattctaccc cgaggagatc tcgtccatgg tgctgaccaa gatgaaggag 600

atcgccgagg cgtacctggg ctacccggtg accaacgcgg tgatcaccgt gccggcctac 660

ttcaacgact cgcagcgcca ggccaccaag gatgcgggtg tgatcgcggg gctcaacgtg 720

ctgcggatca tcaacgagcc cacggccgcc gccatcgcct acggcctgga cagaacgggc 780

aagggggagc gcaacgtgct catctttgac ctgggcgggg gcaccttcga cgtgtccatc 840

ctgacgatcg acgacggcat cttcgaggtg aaggccacgg ccggggacac ccacctgggt 900

ggggaggact ttgacaacag gctggtgaac cacttcgtgg aggagttcaa gagaaaacac 960

aagaaggaca tcagccagaa caagcgagcc gtgaggcggc tgcgcaccgc ctgcgagagg 1020

gccaagagga ccctgtcgtc cagcacccag gccagcctgg agatcgactc cctgtttgag 1080

ggcatcgact tctacacgtc catcaccagg gcgaggttcg aggagctgtg ctccgacctg 1140

ttccgaagca ccctggagcc cgtggagaag gctctgcgcg acgccaagct ggacaaggcc 1200

cagattcacg acctggtcct ggtcggggggc tccacccgca tccccaaggt gcagaagctg 1260

ctgcaggact tcttcaacgg gcgcgacctg aacaagagca tcaaccccga cgaggctgtg 1320

gcctacgggg cggcggtgca ggcggccatc ctgatggggg acaagtccga gaacgtgcag 1380

gacctgctgc tgctggacgt ggctcccctg tcgctggggc tggagacggc cggaggcgtg 1440

atgactgccc tgatcaagcg caactccacc atccccacca agcagacgca gatcttcacc 1500

acctactccg acaaccaacc cggggtgctg atccaggtgt acgagggcga gagggccatg 1560

acgaaagaca acaatctgtt ggggcgcttc gagctgagcg gcatccctcc ggcccccagg 1620

ggcgtgcccc agatcgaggt gaccttcgac atcgatgcca acggcatcct gaacgtcacg 1680

gccacggaca agagcaccgg caaggccaac aagatcacca tcaccaacga caagggccgc 1740

ctgagcaagg aggagatcga gcgcatggtg caggaggcgg agaagtacaa agcggaggac 1800

gaggtgcagc gcgagagggt gtcagccaag aacgccctgg agtcctacgc cttcaacatg 1860

aagagcgccg tggaggatga ggggctcaag ggcaagatca gcgaggcgga caagaagaag 1920

gttctggaca agtgtcaaga ggtcatctcg tggctggacg ccaacacctt ggccgagaag 1980

gacgagtttg agcacaagag gaaggagctg gagcaggtgt gtaaccccat catcagcgga 2040

ctgtaccagg gtgccggtgg tcccggggcct ggcggcttcg gggctcaggg tcccaaggga 2100

gggtctgggt caggccctac cattgaggag gtggattagg ggcctttgtt ctttagtatg 2160

tttgtctttg aggtggactg ttgggactca aggactttgc tgctgttttc ctatgtcatt 2220

tctgcttcag ctctttgctg cttcacttct ttgtaaagtt gtaacctgat ggtaattagc 2280

tggcttcatt atttttgtag tacaaccgat atgttcatta gaattctttg catttaatgt 2340

tgatactgta agggtgtttc gttcccttta aatgaatcaa cactgccacc ttctgtacga 2400

gtttgtttgt tttttttttt tttttttttt tttgcttggc gaaaacacta caaaggctgg 2460

gaatgtatgt ttttataatt tgtttattta aatatgaaaa ataaaatgtt aaacttt 2517

<210> 9

<211> 103

<212>PRT

<213> Homo sapiens

<223> G0S2 – Access number NP_056529.1

<400> 9

Met Glu Thr Val Gln Glu Leu Ile Pro Leu Ala Lys Glu Met Met Ala

1 5 10 15

Gln Lys Arg Lys Gly Lys Met Val Lys Leu Tyr Val Leu Gly Ser Val

20 25 30

Leu Ala Leu Phe Gly Val Val Leu Gly Leu Met Glu Thr Val Cys Ser

35 40 45

Pro Phe Thr Ala Ala Arg Arg Leu Arg Asp Gln Glu Ala Ala Val Ala

50 55 60

Glu Leu Gln Ala Ala Leu Glu Arg Gln Ala Leu Gln Lys Gln Ala Leu

65 70 75 80

Gln Glu Lys Gly Lys Gln Gln Asp Thr Val Leu Gly Gly Arg Ala Leu

85 90 95

Ser Asn Arg Gln His Ala Ser

100

<210> 10

<211> 876

<212> DNA

<213> Homo sapiens

<223> G0S2 – Accession number NM_015714.4

<400> 10

tgccagcggc ggagtctcca actgggagag ctgcagctgc cgagaggagg agaacgctga 60

ggtcggtcgg accaacggac gcgctgaccg ctgccaactg cagctcgcgc tgcctcctgc 120

tcgcgccgtg ccactaaggt cattcccgcc tccgagagcc cagagccgag atggaaacgg 180

tccaggagct gatccccctg gccaaggaga tgatggccca gaagcgcaag gggaagatgg 240

tgaagctgta cgtgctgggc agcgtgctgg ccctcttcgg cgtggtgctc ggcctgatgg 300

agactgtgtg cagccccttc acggccgcca gacgtctgcg ggacccaggag gcagccgtgg 360

cggagctgca ggccgccctg gagcgacagg ctctccagaa gcaagccctg caggagaaag 420

gcaagcagca ggacacggtc ctcggcggcc gggccctgtc caaccggcag cacgcctcct 480

aggaactgtg ggagaccagc ggagtgggag ggagacgcag tagacagaga cagaccgaga 540

gaggaatgga gagacagagg gggcgcgcgc acaggagcct gactccgctg ggagagtgca 600

ggagcacgtg ctgtttttta tttggactta acttcagaga aaccgctgac atctagaact 660

gacctaccac aagcatccac caaaggagtt tgggattgag ttttgctgct gtgcagcact 720

gcattgtcat gacatttcca acactgtgtg aattatctaa atgcgtctac cattttgcac 780

tagggaggaa ggataaatgc tttttatgtt attattatta attattacaa tgaccaccat 840

tttgcatttt gaaataaaaa aactttttat accata 876

<210> 11

<211> 575

<212>PRT

<213> Homo sapiens

<223> BAG3 – Accession number NP_004272.2

<400> 11

Met Ser Ala Ala Thr His Ser Pro Met Gln Val Ala Ser Gly Asn

1 5 10 15

Gly Asp Arg Asp Pro Leu Pro Pro Gly Trp Glu Ile Lys Ile Asp Pro

20 25 30

Gln Thr Gly Trp Pro Phe Phe Val Asp His Asn Ser Arg Thr Thr Thr

35 40 45

Trp Asn Asp Pro Arg Val Pro Ser Glu Gly Pro Lys Glu Thr Pro Ser

50 55 60

Ser Ala Asn Gly Pro Ser Arg Glu Gly Ser Arg Leu Pro Pro Ala Arg

65 70 75 80

Glu Gly His Pro Val Tyr Pro Gln Leu Arg Pro Gly Tyr Ile Pro Ile

85 90 95

Pro Val Leu His Glu Gly Ala Glu Asn Arg Gln Val His Pro Phe His

100 105 110

Val Tyr Pro Gln Pro Gly Met Gln Arg Phe Arg Thr Glu Ala Ala Ala

115 120 125

Ala Ala Pro Gln Arg Ser Gln Ser Pro Leu Arg Gly Met Pro Glu Thr

130 135 140

Thr Gln Pro Asp Lys Gln Cys Gly Gln Val Ala Ala Ala Ala Ala Ala

145 150 155 160

Gln Pro Pro Ala Ser His Gly Pro Glu Arg Ser Gln Ser Pro Ala Ala

165 170 175

Ser Asp Cys Ser Ser Ser Ser Ser Ser Ala Ser Leu Pro Ser Ser Gly

180 185 190

Arg Ser Ser Leu Gly Ser His Gln Leu Pro Arg Gly Tyr Ile Ser Ile

195 200 205

Pro Val Ile His Glu Gln Asn Val Thr Arg Pro Ala Ala Gln Pro Ser

210 215 220

Phe His Gln Ala Gln Lys Thr His Tyr Pro Ala Gln Gln Gly Glu Tyr

225 230 235 240

Gln Thr His Gln Pro Val Tyr His Lys Ile Gln Gly Asp Asp Trp Glu

245 250 255

Pro Arg Pro Leu Arg Ala Ala Ser Pro Phe Arg Ser Ser Val Gln Gly

260 265 270

Ala Ser Ser Arg Glu Gly Ser Pro Ala Arg Ser Ser Thr Pro Leu His

275 280 285

Ser Pro Ser Pro Ile Arg Val His Thr Val Val Asp Arg Pro Gln Gln

290 295 300

Pro Met Thr His Arg Glu Thr Ala Pro Val Ser Gln Pro Glu Asn Lys

305 310 315 320

Pro Glu Ser Lys Pro Gly Pro Val Gly Pro Glu Leu Pro Pro Gly His

325 330 335

Ile Pro Ile Gln Val Ile Arg Lys Glu Val Asp Ser Lys Pro Val Ser

340 345 350

Gln Lys Pro Pro Pro Pro Ser Glu Lys Val Glu Val Lys Val Pro Pro

355 360 365

Ala Pro Val Pro Cys Pro Pro Pro Ser Pro Gly Pro Ser Ala Val Pro

370 375 380

Ser Ser Pro Lys Ser Val Ala Thr Glu Glu Arg Ala Ala Pro Ser Thr

385 390 395 400

Ala Pro Ala Glu Ala Thr Pro Pro Lys Pro Gly Glu Ala Glu Ala Pro

405 410 415

Pro Lys His Pro Gly Val Leu Lys Val Glu Ala Ile Leu Glu Lys Val

420 425 430

Gln Gly Leu Glu Gln Ala Val Asp Asn Phe Glu Gly Lys Lys Thr Asp

435 440 445

Lys Lys Tyr Leu Met Ile Glu Glu Tyr Leu Thr Lys Glu Leu Leu Ala

450 455 460

Leu Asp Ser Val Asp Pro Glu Gly Arg Ala Asp Val Arg Gln Ala Arg

465 470 475 480

Arg Asp Gly Val Arg Lys Val Gln Thr Ile Leu Glu Lys Leu Glu Gln

485 490 495

Lys Ala Ile Asp Val Pro Gly Gln Val Gln Val Tyr Glu Leu Gln Pro

500 505 510

Ser Asn Leu Glu Ala Asp Gln Pro Leu Gln Ala Ile Met Glu Met Gly

515 520 525

Ala Val Ala Ala Asp Lys Gly Lys Lys Asn Ala Gly Asn Ala Glu Asp

530 535 540

Pro His Thr Glu Thr Gln Gln Pro Glu Ala Thr Ala Ala Ala Thr Ser

545 550 555 560

Asn Pro Ser Ser Met Thr Asp Thr Pro Gly Asn Pro Ala Ala Pro

565 570 575

<210> 12

<211> 2608

<212> DNA

<213> Homo sapiens

<223> BAG3 – Accession number NM_004281.3

<400> 12

gcggagctcc gcatccaacc ccgggccgcg gccaacttct ctggactgga ccagaagttt 60

ctagccggcc agttgctacc tccctttatc tcctccttcc cctctggcag cgaggaggct 120

atttccagac acttccaccc ctctctggcc acgtcacccc cgcctttaat tcataaaggt 180

gcccggcgcc ggcttcccgg acacgtcggc ggcggagagg ggcccacggc ggcggcccgg 240

ccagagactc ggcgcccgga gccagcgccc cgcacccgcg ccccagcggg cagaccccaa 300

cccagcatga gcgccgccac ccactcgccc atgatgcagg tggcgtccgg caacggtgac 360

cgcgaccctt tgccccccgg atgggagatc aagatcgacc cgcagaccgg ctggcccttc 420

ttcgtggacc acaacagccg caccactacg tggaacgacc cgcgcgtgcc ctctgagggc 480

cccaaggaga ctccatcctc tgccaatggc ccttcccggg agggctctag gctgccgcct 540

gctagggaag gccaccctgt gtacccccag ctccgaccag gctacattcc cattcctgtg 600

ctccatgaag gcgctgagaa ccggcaggtg caccctttcc atgtctatcc ccagcctggg 660

atgcagcgat tccgaactga ggcggcagca gcggctcctc agaggtccca gtcacctctg 720

cggggcatgc cagaaaccac tcagccagat aaacagtgtg gacaggtggc agcggcggcg 780

gcagcccagc ccccagcctc ccacggacct gagcggtccc agtctccagc tgcctctgac 840

tgctcatcct catcctcctc ggccagcctg ccttcctccg gcaggagcag cctgggcagt 900

caccagctcc cgcgggggta catctccatt ccggtgatac acgagcagaa cgttacccgg 960

ccagcagccc agccctcctt ccaccaagcc cagaagacgc actacccagc gcagcagggg 1020

gagtaccaga cccaccagcc tgtgtaccac aagatccagg gggatgactg ggagccccgg 1080

cccctgcggg cggcatcccc gttcaggtca tctgtccagg gtgcatcgag ccgggagggc 1140

tcaccagcca ggagcagcac gccactccac tccccctcgc ccatccgtgt gcacaccgtg 1200

gtcgacaggc ctcagcagcc catgacccat cgagaaactg cacctgtttc ccagcctgaa 1260

aacaaaccag aaagtaagcc aggcccagtt ggaccagaac tccctcctgg acacatccca 1320

attcaagtga tccgcaaaga ggtggattct aaacctgttt cccagaagcc cccacctccc 1380

tctgagaagg tagaggtgaa agttccccct gctccagttc cttgtcctcc tcccagccct 1440

ggcccttctg ctgtcccctc ttcccccaag agtgtggcta cagaagagag ggcagccccc 1500

agcactgccc ctgcagaagc tacacctcca aaaccaggag aagccgaggc tcccccaaaa 1560

catccaggag tgctgaaagt ggaagccatc ctggagaagg tacaggggct ggagcaggct 1620

gtagacaact ttgaaggcaa gaagactgac aaaaagtacc tgatgatcga agagtatttg 1680

accaaagagc tgctggccct ggattcagtg gaccccgagg gacgagccga tgtgcgtcag 1740

gccaggagag acggtgtcag gaaggttcag accatcttgg aaaaacttga acagaaagcc 1800

attgatgtcc caggtcaagt ccaggtctat gaactccagc ccagcaacct tgaagcagat 1860

cagccactgc aggcaatcat ggagatgggt gccgtggcag cagacaaggg caagaaaaat 1920

gctggaaatg cagaagatcc ccacacagaa acccagcagc cagaagccac agcagcagcg 1980

acttcaaacc ccagcagcat gacagacacc cctggtaacc cagcagcacc gtagcctctg 2040

ccctgtaaaa atcagactcg gaaccgatgt gtgctttagg gaattttaag ttgcatgcat 2100

ttcagagact ttaagtcagt tggtttttat tagctgcttg gtatgcagta acttgggtgg 2160

aggcaaaaca ctaataaaag ggctaaaaag gaaaatgatg cttttcttct atattcttac 2220

tctgtacaaa taaagaagtt gcttgttgtt tgagaagttt aaccccgttg cttgttgttc 2280

tgcagccctg tctacttggg cacccccacc acctgttagc tgtggttgtg cactgtcttt 2340

tgtagctctg gactggaggg gtagatgggg agtcaattac ccatcacata aatatgaaac 2400

atttatcaga aatgttgcca ttttaatgag atgattttct tcatctcata attaaaatac 2460

ctgactttag agagagtaaa atgtgccagg agccatagga atatctgtat gttggatgac 2520

tttaatgcta cattttaaaa aaagaaaata aagtaataat ataactcaaa aaaaaaaaaa 2580

aaaaaaaaaa aaaaaaaaaa aaaaaaaa 2608

<210> 13

<211> 239

<212>PRT

<213> Homo sapiens

<223> TM4SF1 – Accession number XP_016861874.1

<400> 13

Met Cys Tyr Gly Lys Cys Ala Arg Cys Ile Gly His Ser Leu Val Gly

1 5 10 15

Leu Ala Leu Leu Cys Ile Ala Ala Asn Ile Leu Leu Tyr Phe Pro Asn

20 25 30

Gly Glu Thr Lys Tyr Ala Ser Glu Asn His Leu Ser Arg Phe Val Trp

35 40 45

Phe Phe Ser Gly Ile Val Gly Gly Gly Leu Leu Met Leu Leu Pro Ala

50 55 60

Phe Val Phe Ile Gly Leu Glu Gln Asp Asp Cys Cys Gly Cys Cys Gly

65 70 75 80

His Glu Asn Cys Gly Lys Arg Cys Ala Met Leu Ser Ser Val Leu Ala

85 90 95

Ala Leu Ile Gly Ile Ala Gly Ser Gly Tyr Cys Val Ile Val Ala Ala

100 105 110

Leu Gly Leu Ala Glu Gly Pro Leu Cys Leu Asp Ser Leu Gly Gln Trp

115 120 125

Asn Tyr Thr Phe Ala Ser Thr Glu Gly Gln Tyr Leu Leu Asp Thr Ser

130 135 140

Thr Trp Ser Glu Cys Thr Glu Pro Lys His Ile Val Glu Trp Asn Val

145 150 155 160

Ser Leu Phe Ser Ile Leu Leu Ala Leu Gly Gly Ile Glu Phe Ile Leu

165 170 175

Cys Leu Ile Gln Val Ile Asn Gly Val Leu Gly Gly Ile Cys Gly Phe

180 185 190

Cys Cys Ser His Gln Gln Val Arg Thr Cys Met Lys Ile Asn Met Thr

195 200 205

Ala Lys Arg Thr Asn Pro Gly Gln Ser His Asn Leu Pro Leu Phe His

210 215 220

Cys Asn Leu Tyr Ile Ser Leu Val Phe Ile Cys Lys Thr Leu Tyr

225 230 235

<210> 14

<211> 972

<212> DNA

<213> Homo sapiens

<220>

<223> TM4SF1 – Accession number XM_017006385.2

<400> 14

aagggcggga cattccccct gcctcttcgc accacagcca gagcctgcca ttaggaccaa 60

tgaaagcaaa gtacctcatc ccctcagtga ctaagaatcg cagtatttaa gaggtagcag 120

gaatgggctg agagtggtgt ttgctttctc caccagaagg gcacactttc atctaatttg 180

gggtatcact gagctgaaga caaagagaag ggggagaaaa cctagcagac caccatgtgc 240

tatgggaagt gtgcacgatg catcggacat tctctggtgg ggctcgccct cctgtgcatc 300

gcggctaata ttttgcttta ctttcccaat ggggaaacaa agtatgcctc cgaaaaccac 360

ctcagccgct tcgtgtggtt cttttctggc atcgtaggag gtggcctgct gatgctcctg 420

ccagcatttg tcttcattgg gctggaacag gatgactgct gtggctgctg tggccatgaa 480

aactgtggca aacgatgtgc gatgctttct tctgtattgg ctgctctcat tggaattgca 540

ggatctggct actgtgtcat tgtggcagcc cttggcttag cagaaggacc actatgtctt 600

gattccctcg gccagtggaa ctacaccttt gccagcactg agggccagta ccttctggat 660

acctccacat ggtccgagtg cactgaaccc aagcacattg tggaatggaa tgtatctctg 720

ttttctatcc tcttggctct tggtggaatt gaattcatct tgtgtcttat tcaagtaata 780

aatggagtgc ttggaggcat atgtggcttt tgctgctctc accaacaggt aagaacctgc 840

atgaaaatca atatgactgc taaaagaacc aacccaggac agagccacaa tcttcctcta 900

tttcattgta atttatatat ttcacttgta ttcatttgta aaactttgta ttagtgtaac 960

atactcccca ca 972

<210> 15

<211> 367

<212>PRT

<213> Homo sapiens

<223> DUSP1 – Access number NP_004408.1

<400> 15

Met Val Met Glu Val Gly Thr Leu Asp Ala Gly Gly Leu Arg Ala Leu

1 5 10 15

Leu Gly Glu Arg Ala Ala Gln Cys Leu Leu Leu Asp Cys Arg Ser Phe

20 25 30

Phe Ala Phe Asn Ala Gly His Ile Ala Gly Ser Val Asn Val Arg Phe

35 40 45

Ser Thr Ile Val Arg Arg Arg Ala Lys Gly Ala Met Gly Leu Glu His

50 55 60

Ile Val Pro Asn Ala Glu Leu Arg Gly Arg Leu Leu Ala Gly Ala Tyr

65 70 75 80

His Ala Val Val Leu Leu Asp Glu Arg Ser Ala Ala Leu Asp Gly Ala

85 90 95

Lys Arg Asp Gly Thr Leu Ala Leu Ala Ala Gly Ala Leu Cys Arg Glu

100 105 110

Ala Arg Ala Ala Gln Val Phe Phe Leu Lys Gly Gly Tyr Glu Ala Phe

115 120 125

Ser Ala Ser Cys Pro Glu Leu Cys Ser Lys Gln Ser Thr Pro Met Gly

130 135 140

Leu Ser Leu Pro Leu Ser Thr Ser Val Pro Asp Ser Ala Glu Ser Gly

145 150 155 160

Cys Ser Ser Cys Ser Thr Pro Leu Tyr Asp Gln Gly Gly Pro Val Glu

165 170 175

Ile Leu Pro Phe Leu Tyr Leu Gly Ser Ala Tyr His Ala Ser Arg Lys

180 185 190

Asp Met Leu Asp Ala Leu Gly Ile Thr Ala Leu Ile Asn Val Ser Ala

195 200 205

Asn Cys Pro Asn His Phe Glu Gly His Tyr Gln Tyr Lys Ser Ile Pro

210 215 220

Val Glu Asp Asn His Lys Ala Asp Ile Ser Ser Trp Phe Asn Glu Ala

225 230 235 240

Ile Asp Phe Ile Asp Ser Ile Lys Asn Ala Gly Gly Arg Val Phe Val

245 250 255

His Cys Gln Ala Gly Ile Ser Arg Ser Ala Thr Ile Cys Leu Ala Tyr

260 265 270

Leu Met Arg Thr Asn Arg Val Lys Leu Asp Glu Ala Phe Glu Phe Val

275 280 285

Lys Gln Arg Arg Ser Ile Ile Ser Pro Asn Phe Ser Phe Met Gly Gln

290 295 300

Leu Leu Gln Phe Glu Ser Gln Val Leu Ala Pro His Cys Ser Ala Glu

305 310 315 320

Ala Gly Ser Pro Ala Met Ala Val Leu Asp Arg Gly Thr Ser Thr Thr

325 330 335

Thr Val Phe Asn Phe Pro Val Ser Ile Pro Val His Ser Thr Asn Ser

340 345 350

Ala Leu Ser Tyr Leu Gln Ser Pro Ile Thr Thr Ser Pro Ser Cys

355 360 365

<210> 16

<211> 2013

<212> DNA

<213> Homo sapiens

<223> DUSP1 – Access number NM_004417.4

<400> 16

gcgaaggaca tttgggctgt gtgtgcgacg cgggtcggag gggcagtcgg gggaaccgcg 60

aagaagccga ggagcccgga gccccgcgtg acgctcctct ctcagtccaa aagcggcttt 120

tggttcggcg cagagagacc cggggggtcta gcttttcctc gaaaagcgcc gccctgccct 180

tggccccgag aacagacaaa gagcaccgca gggccgatca cgctgggggc gctgaggccg 240

gccatggtca tggaagtggg caccctggac gctggaggcc tgcgggcgct gctgggggag 300

cgagcggcgc aatgcctgct gctggactgc cgctccttct tcgctttcaa cgccggccac 360

atcgccggct ctgtcaacgt gcgcttcagc accatcgtgc ggcgccgggc caagggcgcc 420

atgggcctgg agcacatcgt gcccaacgcc gagctccgcg gccgcctgct ggccggcgcc 480

taccacgccg tggtgttgct ggacgagcgc agcgccgccc tggacggcgc caagcgcgac 540

ggcaccctgg ccctggcggc cggcgcgctc tgccgcgagg cgcgcgccgc gcaagtcttc 600

ttcctcaaag gaggatacga agcgttttcg gcttcctgcc cggagctgtg cagcaaacag 660

tcgaccccca tggggctcag ccttcccctg agtactagcg tccctgacag cgcggaatct 720

gggtgcagtt cctgcagtac cccactctac gatcagggtg gcccggtgga aatcctgccc 780

tttctgtacc tgggcagtgc gtatcacgct tcccgcaagg acatgctgga tgccttgggc 840

atcactgcct tgatcaacgt ctcagccaat tgtcccaacc attttgaggg tcactaccag 900

tacaagagca tccctgtgga ggacaaccac aaggcagaca tcagctcctg gttcaacgag 960

gccattgact tcatagactc catcaagaat gctggaggaa gggtgtttgt ccactgccag 1020

gcaggcattt cccggtcagc caccatctgc cttgcttacc ttatgaggac taatcgagtc 1080

aagctggacg aggcctttga gtttgtgaag cagaggcgaa gcatcatctc tcccaacttc 1140

agcttcatgg gccagctgct gcagtttgag tcccaggtgc tggctccgca ctgttcggca 1200

gaggctggga gccccgccat ggctgtgctc gaccgaggca cctccaccac caccgtgttc 1260

aacttccccg tctccatccc tgtccactcc acgaacagtg cgctgagcta ccttcagagc 1320

cccattacga cctctcccag ctgctgaaag gccacgggag gtgaggctct tcacatccca 1380

ttgggactcc atgctccttg agaggagaaa tgcaataact ctgggagggg ctcgagagg 1440

ctggtcctta tttatttaac ttcacccgag ttcctctggg tttctaagca gttatggtga 1500

tgacttagcg tcaagacatt tgctgaactc agcacattcg ggaccaatat atagtgggta 1560

catcaagtcc atctgacaaa atggggcaga agagaaagga ctcagtgtgt gatccggttt 1620

ctttttgctc gcccctgttt tttgtagaat ctcttcatgc ttgacatacc taccagtatt 1680

attcccgacg acacatatac atatgagaat ataccttatt tatttttgtg taggtgtctg 1740

ccttcacaaa tgtcattgtc tactcctaga agaaccaaat acctcaattt ttgtttttga 1800

gtactgtact atcctgtaaa tatatcttaa gcaggtttgt tttcagcact gatggaaaat 1860

accagtgttg ggtttttttt tagttgccaa cagttgtatg tttgctgatt atttatgacc 1920

tgaaataata tatttcttct tctaagaaga cattttgtta cataaggatg acttttttat 1980

acaatggaat aaattatggc atttctattg aaa 2013

<210> 17

<211> 185

<212>PRT

<213> Homo sapiens

<223> ADM – Access number NP_001115.1

<400> 17

Met Lys Leu Val Ser Val Ala Leu Met Tyr Leu Gly Ser Leu Ala Phe

1 5 10 15

Leu Gly Ala Asp Thr Ala Arg Leu Asp Val Ala Ser Glu Phe Arg Lys

20 25 30

Lys Trp Asn Lys Trp Ala Leu Ser Arg Gly Lys Arg Glu Leu Arg Met

35 40 45

Ser Ser Ser Tyr Pro Thr Gly Leu Ala Asp Val Lys Ala Gly Pro Ala

50 55 60

Gln Thr Leu Ile Arg Pro Gln Asp Met Lys Gly Ala Ser Arg Ser Pro

65 70 75 80

Glu Asp Ser Ser Pro Asp Ala Ala Arg Ile Arg Val Lys Arg Tyr Arg

85 90 95

Gln Ser Met Asn Asn Phe Gln Gly Leu Arg Ser Phe Gly Cys Arg Phe

100 105 110

Gly Thr Cys Thr Val Gln Lys Leu Ala His Gln Ile Tyr Gln Phe Thr

115 120 125

Asp Lys Asp Lys Asp Asn Val Ala Pro Arg Ser Lys Ile Ser Pro Gln

130 135 140

Gly Tyr Gly Arg Arg Arg Arg Arg Ser Leu Pro Glu Ala Gly Pro Gly

145 150 155 160

Arg Thr Leu Val Ser Ser Lys Pro Gln Ala His Gly Ala Pro Ala Pro

165 170 175

Pro Ser Gly Ser Ala Pro His Phe Leu

180 185

<210> 18

<211> 1492

<212> DNA

<213> Homo sapiens

<223> ADM – Access number NM_001124.3

<400> 18

actcagtggt ttcttggtga cactggatag aacagctcaa gccttgccac ttcgggcttc 60

tcactgcagc tgggcttgga cttcggagtt ttgccattgc cagtgggacg tctgagactt 120

tctccttcaa gtacttggca gatcactctc ttagcaggt ctgcgcttcg cagccgggat 180

gaagctggtt tccgtcgccc tgatgtacct gggttcgctc gccttcctag gcgctgacac 240

cgctcggttg gatgtcgcgt cggagtttcg aaagaagtgg aataagtggg ctctgagtcg 300

tgggaagagg gaactgcgga tgtccagcag ctaccccacc gggctcgctg acgtgaaggc 360

cgggcctgcc cagaccctta ttcggcccca ggacatgaag ggtgcctctc gaagccccga 420

agacagcagt ccggatgccg cccgcatccg agtcaagcgc taccgccaga gcatgaacaa 480

cttccagggc ctccggagct ttggctgccg cttcgggacg tgcacggtgc agaagctggc 540

acaccagatc taccagttca cagataagga caaggacaac gtcgccccca ggagcaagat 600

cagcccccag ggctacggcc gccggcgccg gcgctccctg cccgaggccg gcccgggtcg 660

gactctggtg tcttctaagc cacaagcaca cggggctcca gcccccccga gtggaagtgc 720

tccccacttt ctttaggatt taggcgccca tggtacaagg aatagtcgcg caagcatccc 780

gctggtgcct cccgggacga aggacttccc gagcggtgtg gggaccgggc tctgacagcc 840

ctgcggagac cctgagtccg ggaggcaccg tccggcggcg agctctggct ttgcaagggc 900

ccctccttct gggggcttcg cttccttagc cttgctcagg tgcaagtgcc ccaggggggcg 960

gggtgcagaa gaatccgagt gtttgccagg cttaaggaga ggagaaactg agaaatgaat 1020

gctgagaccc ccggagcagg ggtctgagcc acagccgtgc tcgcccacaa actgatttct 1080

cacggcgtgt caccccacca gggcgcaagc ctcactatta cttgaacttt ccaaaaccta 1140

aagaggaaaa gtgcaatgcg tgttgtacat acagaggtaa ctatcaatat ttaagtttgt 1200

tgctgtcaag attttttttg taacttcaaa tatagagata tttttgtacg ttatatattg 1260

tattaagggc attttaaaag caattatatt gtcctcccct attttaagac gtgaatgtct 1320

cagcgaggtg taaagttgtt cgccgcgtgg aatgtgagtg tgtttgtgtg catgaaagag 1380

aaagactgat tacctcctgt gtggaagaag gaaacaccga gtctctgtat aatctattta 1440

cataaaatgg gtgatatgcg aacagcaaac caataaactg tctcaatgct ga 1492

<---

Claims (30)

1. A method for diagnosing stroke in an individual, including: i) determining a signature in a sample taken from an individual by measuring the expression levels of at least three biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM; ii) comparing the signature obtained in step i) with the reference signature; And iii) diagnosing an individual as having suffered a stroke if the expression levels of at least three biomarkers in the signature are higher than the expression levels of the same at least three biomarkers in the reference signature. 2. The method according to claim 1, characterized in that step i) includes determining the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM. 3. The method of claim 1, wherein the reference signature is obtained by measuring biomarker expression levels in a control group consisting of substantially healthy individuals to differentiate between stroke and stroke mimics, wherein the substantially healthy subjects are not previously diagnosed or identified as having or suffering from a stroke. 4. The method according to claim 1 for differentiating a stroke and imitating a stroke. 5. The method according to claim 1, where the stroke is an ischemic stroke, transient ischemic attack, or hemorrhagic stroke. 6. The method according to claim 1, where the sample is a sample of blood, plasma or serum. 7. The method according to claim 1, where the sample is not a sample of brain tissue. 8. A method for determining whether an individual who has suffered a stroke has responded to therapy, comprising: i) determining a signature in a sample taken from an individual by measuring the expression levels of at least two biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM; ii) comparing the signature obtained in step i) with the reference signature; And iii) concluding that an individual has responded if the expression levels of the previously mentioned at least two biomarkers in the signature are lower than the expression levels of the same at least two biomarkers in the reference signature. 9. The method according to claim 8, wherein at least two biomarkers do not consist of DUSP1 and ADM. 10. The method according to claim 8, characterized in that step i) includes measuring the expression levels of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM. 11. The method of claim 8, wherein the reference signature is obtained by measuring biomarker expression levels in a sample taken from the same individual before the start of said therapy. 12. The method of claim 8, wherein the stroke is an ischemic stroke, transient ischemic attack, or hemorrhagic stroke. 13. The method according to claim 8, where the sample is a sample of blood, plasma or serum. 14. The method according to claim 8, where the sample is not a sample of brain tissue. 15. A method for determining an individual’s risk of developing a stroke, including: i) determining a signature in a sample taken from an individual by measuring the expression levels of at least three biomarkers selected from the group consisting of PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM; ii) comparing the signature obtained in step i) with the reference signature; And iii) a determination that an individual is at risk for stroke if the expression levels of at least three biomarkers in the signature are higher than the expression levels of the same at least three biomarkers in the reference signature. 16. The method according to claim 15, characterized in that step i) includes measuring the expression levels of nine biomarkers selected from PTGS2, HMOX1, LDLR, HSPA1B, G0S2, BAG3, TM4SF1, DUSP1 and ADM. 17. The method of claim 15, wherein the reference signature is obtained by measuring biomarker expression levels in a control group of substantially healthy individuals, wherein the substantially healthy subjects are not previously diagnosed or identified as having or suffering from stroke. 18. The method according to claim 15, characterized in that the individual has previously had a stroke, and this method is intended to determine the risk of a recurrent stroke in this individual. 19. The method according to claim 15, characterized in that the stroke is an ischemic stroke, transient ischemic attack or hemorrhagic stroke. 20. The method according to claim 15, characterized in that the sample is a blood sample, a plasma sample or a serum sample. 21. The method according to claim 15, characterized in that the sample is not a sample of brain tissue.