# Patent

## Title
Circulating serum microRNA biomarkers and methods for Parkinson's disease prognosis

## Abstract

Biomarkers and methods for identifying, verifying and confirming circulating serum-based microRNAs. The microRNAs (PrognomiRs) can be used to differentiate patient's suffering from rapid progressing Parkinson's disease (PD) from slow progressing PD patients.

## Background
This application is a national phase of PCT Application No. PCT/US2018/036380 filed Jun. 7, 2018, which in turn claims benefit of U.S. Provisional Patent Application No. 62/521,797 filed Jun. 19, 2017, which are hereby incorporated by reference herein in their entirety.


BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to serum-based microRNAs and methods for differentiating patients suffering from Parkinson's disease based on the rate of disease progression, as well as assisting clinicians to determine treatment protocols for such patients.
2. Brief Description of the Background Art
Parkinson's Disease (PD) is a highly specific degeneration of dopamine-containing cells of the substantia nigra of the midbrain, causing a dopamine deficiency in the striatum. PD currently affects about 10 million people world-wide. Effective management of a patient with PD is possible in the first 5-7 years of treatment, after which time a series of often debilitating complications, together referred to as Late Motor Fluctuations (LMF) occur. It is believed that treatment with levodopa ((−)-L-α-amino-beta-(3,4-dihydroxybenzene) propanoic acid), or L-dopa, the most effective antiparkinson drug, may facilitate or even promote the appearance of LMF. Dopamine agonists are employed as a treatment alternative, but they do not offer the same degree of symptomatic relief to patients as L-dopa does.
Symptomatic therapies improve signs and symptoms without affecting the underlying disease state. Levodopa increases dopamine concentration in the striatum, especially when its peripheral metabolism is inhibited by a peripheral decarboxylase inhibitor (PDI). Levodopa/PDI therapy is widely used for symptomatic therapy for Parkinson's disease, such as combinations with levodopa, with carbidopa ((−)-L-α-hydrazino-α-methyl-beta-(3,4-dihydroxybenzene) propanoic acid monohydrate), levodopa and controlled release carbidopa, levodopa and benserazide, levodopa plus controlled release benserazide (2-Amino-3-hydroxy-propionic acid N′-(2,3,4-trihydroxy-benzyl)-hydrazide).
Catechol-O-methyltransferase (COMT) inhibitors enhance levodopa treatment as they inhibit levodopa's metabolism, enhancing its bioavailability and thereby making more of the drug available in the synaptic cleft for a longer period of time. Examples of COMT inhibitors include tolcapone (3,4-dihydroxy-4′-methyl-5-nitrobenzophenone) and entacapone ((E)-2-cyano-3-(3,4-dihydroxy-5-nitrophenyl)-N,N-diethyl-2-propenamide).
Dopamine agonists provide symptomatic benefit by directly stimulating post-synaptic striatal dopamine receptors. Examples include bromocriptine ((5a)-2-Bromo-12′-hydroxy-2′-(1-methylethyl)-5′-(2-methylpropyl)erg-otaman-3′,6′,18-trione), pergolide (8B-[(Methylthio)methyl]-6-propylergoline), ropinirole (4-[2-(Dipropylamino)ethyl]-1,3-dihydro-2H-indol-2-one), pramipexole ((S)-4,5,6,7-Tetrahydro-N6-propyl-2,6-benzothiazolediamine), lisuride (N′-[(8α)-9,10-didehydro-6-methylergolin-8-yl]-N,N-diethyl-urea), cabergoline ((8β)-N-[3-(Dimethylamino)propyl]-N-[(ethylamino)carbonyl]-6-(2-propenyl)ergoline-8-carboxamide), apomorphine ((6aR)-5,6,6a,7-Tetrahydro-6-methyl-4H-dibenzo[de,g]quinoline-10,11-diol), sumanirole (5-(methylamino)-5,6-dihydro-4H-imidazo {4,5,1-ij}quinolin-2(1H)-one), rotigotine ((−)(S)-5,6,7,8-tetrahydro-6-[propyl[2-(2-thienyl)ethyl]amino]-1-naphthol-), talipexole (5,6,7,8-Tetrahydro-6-(2-propenyl)-4H-thiazolo[4,5-d]azepin-2-amine), and dihydroergocriptine (ergotaman-3′,6′,18-trione,9,10-dihydro-12′-hydroxy-2′-methyl-5′-(phenylmethyl) (5′ cc)). Dopamine agonists are effective as monotherapy early in the course of Parkinson's disease and as an adjunct to levodopa in more advanced stages. Unlike levodopa, dopamine agonists directly stimulate post-synaptic dopamine receptors. They do not undergo oxidative metabolism and are not thought to accelerate the disease process.
Amantidine (1-Aminotricyclo (3,3,1,13,7) decane) is an antiviral agent that was discovered by chance to have anti-Parkinsonian activity. Its mechanism of action in PD has not been established, but is believed to work by increasing dopamine release. Patients who receive amantidine either as monotherapy or in combination with levodopa show improvement in akinesia, rigidity and tremor.
Other medications used in the treatment of Parkinson's disease include MAO-B inhibitors. Inhibition of L-dopa metabolism through inactivation of the monoamino oxidase type B (MAO-B) is an effective means of enhancing the efficacy of both endogenous residual dopamine and that exogenously derived from its precursor, L-dopa. Selegiline (methyl-(1-methyl-2-phenyl-ethyl)-prop-2-ynyl-amine) is a MAO-B inhibitor. There is evidence that treatment with selegiline may slow down disease progression in PD by blocking formation of free radicals derived from the oxidative metabolism of dopamine. Other examples of MAO B inhibitors include lazabemide (N-(2-Aminoethyl)-5-chloro-2-pyridinecarboxamide), rasagiline (N-propargyl-1-(R)aminoindan and caroxazone (2-oxo-2H-1,3-benzoxazine-3(4H)-acetamide).
It is imperative to diagnose individuals with PD at an early stage and it is also important to determine the prognosis of the disease to increase the efficacy of therapeutic agents. However, there are neither any objective tests nor established biomarkers for diagnosing PD. Moreover, the heterogeneity, subtypes and progression of the disease make it difficult to develop specific therapeutic candidates.
MicroRNAs (“miRNAs) are a class of non-coding RNAs that play key roles in the regulation of gene expression. miRNAs act at the post-transcriptional level and fine-tune the expression of as much as 30% of all mammalian protein-encoding genes. Mature miRNAs are short, single-stranded RNA molecules approximately 22 nucleotides in length. miRNAs may be encoded by multiple loci, and may be organized in tandemly co-transcribed clusters. miRNA genes are transcribed by RNA polymerase II as large primary transcripts (pri-microRNA) that are processed by a protein complex containing the RNase III enzyme Drosha, DGCR8 and other cofactors, to form an approximately 70 nucleotide precursor microRNA (pre-miRNA). (Cathew R W, Cell, 2009; Kim V N, Nat Rev Mol Cel Biol, 2009; Siomi H, Mol Cel, 2010; Bartel D P, Cell, 2004; Lee Y, Nature 2003; Han J, Genes Dev, 2004.) Pre-miRNA is transported to the cytoplasm by Exportin-5 where it is processed by DICER, a second RNase III enzyme, together with TRBP, PACT and Ago2 in the RNA Induced Silencing Complex resulting in miRNA duplexes (Kim V N, Nat Rev Mol Cel Biol, 2009; Gregory R I, Nature 2004; MAcRae I J, PNAS, 2008). The guide strands of miRNA duplexes separate and associate with Ago 2 for incorporation into a ribonuclear particle to form the RNA-induced silencing complex RISC that mediates gene silencing. The mechanisms of miRNA range from direct degradation or silencing of mRNA and repression of translation to post-transcriptional upregulations. (MacRae I J, PNAS, 2008.)
The presence of miRNAs has been reported in body fluids including blood, cerebrospinal fluid (CSF), plasma, serum and saliva at detectable levels. The tissue-specificity of miRNAs suggests their vital and integral role in various physiological processes. The tissue-enrichment promises a new but less explored role as diagnostic biomarker and potential therapeutic target. Circulating miRNAs are understood to originate from passive leakage from damaged tissue as a result of cell lysis or apoptosis, active transport from cells via microvesicles, such as exosomes, or bound within RISC protein complexes (Etheridge et al, 2011). Exosome and osmotic pump-mediated delivery of small RNA molecules to the brain and CNS, respectively, provides a solution to overcoming the limitations of miRNA-based therapies (Alvarez-Erviti et al., 2011; Koval et al, 2013, Hum. Mol. Gen). miRNA has been demonstrated to be exceptionally stable and thus present as powerful candidates to be potential biomarkers (Chen et al, 2008; Grasso, 2014).
SUMMARY OF THE INVENTION
It is an object of the present invention to identify miRNAs relevant to patients suffering from Parkinson's disease.
It is another object of the present invention to provide methods for determining the rate of disease progression for patients suffering from Parkinson's disease.
It is another object of the present invention to determine the methods for determining treatment option based on the rate of progression of the disease for patients suffering from Parkinson's disease.
These objects and others are achieved by the present invention, which provides miRNA biomarkers that may be used singly, in pairs or in combination to determine patients suffering from Parkinson's disease.



BRIEF DESCRIPTION OF THE DRAWINGS
The FIGURE shows the mean fold change of five PrognomiRNAs between rapid and slow progressing PD patients.



DETAILED DESCRIPTION OF THE INVENTION
Methods
Serum Samples Handling and Classification
All patients and controls participated in the Norwegian ParkWest project, which is an ongoing prospective population-based longitudinal cohort study investigating the incidence, neurobiology and prognosis of PD. The Norwegian ParkWest study is a prospective longitudinal multicenter cohort study of patients with incident Parkinson's disease (PD) from Western and Southern Norway. Between Nov. 1, 2004 and 31 of Aug. 2006 it was endeavored to recruit all new cases of Parkinson Disease within the study area. Since the start of the study 212 of 265 (80%) of these patients and their age-/sex-matched control group have been followed. Further information about the study can be found at www.parkvest.no.
All possible efforts were undertaken to establish an unselected and population-representative cohort of patients with PD. Patients were included if they had provided serum at study entry and fulfilled diagnostic criteria for PD of the National Institute of Neurological Disorders and Stroke www.ninds.nih.gov/disorders/parkinsons_disease/parkinsons_disease.htm) and UK Brain Bank (www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdfcgi?id=phd000042) at latest follow-up. Patients with secondary parkinsonism at study entry were excluded from this study. Control subjects were recruited from multiple sources, including friends, spouses, and public organizations for elderly and were included in this study if they had provided serum. All patients and controls were Caucasian. The participants of the study have been followed for over eight years to establish the rate of disease progression.
In this study of possible biomarkers for PD we applied a two-stage procedure. For the first discovery phase serum from 8 patients with rapid and 8 patients with slow progression of PD were selected at random. The remaining 164 patients with PD that were eligible for this study were selected for verification purposes.
Serum samples were collected at the same day as the clinical examinations and then stored frozen at −70 degrees Celsius until transported to the facilities in New York on dry ice.
Example 1: Analyses of Differentially Expressed Human miRNA by qPCR RNA Isolation from Serum Samples and QC
After thawing on ice, twenty-four (sixteen PD samples) serum samples were spun down for 5 mins at 3000xg to remove debris. The supernatant was used to perform small RNA isolation using miRCURY® RNA Isolation Kit—Biofluids (Exiqon, MA). Before RNA Isolation, the lysis buffer was spiked with 0.267 fmol/ul of spike-in control cel-miR-39-3p (Qiagen, CA). The remaining part of the RNA isolation was performed following manufacturer's protocol and the isolated RNA was quantified on a Nanodrop 2000 (Thermo Scientific, MA). The RNA was used for running Affymetrix v4 microRNA microarray chips and for subsequent cDNA synthesis and qPCR. RNA from 180 serum samples (from PD patients from ParkWest project) was isolated as described above, they were not quantified by Nanodrop, but the qPCR data resulting from these samples were normalized by a reference small RNA scaRNA17.
miRNA Microarray and Data Analysis
The isolated RNA from twenty-four patient serum samples were quantified and subjected to Affymetrix GeneChip® miRNA 4.0 Array by the Yale Center for Genome Analysis (www.medicine.yale.edu/keck/ycga/index.aspx). The normalized .CEL files obtained from Affymetrix Expression Console software were imported into Partek Genomics Suite version 6.6 Copyright © 2012 (Partek, MO) for analysis. The ‘microRNA Expression Workflow’ was employed to detect differentially expressed miRNAs employing ANOVA resulting in lists of miRNAs significantly (p<0.05) expressed between rapid and slow PD cohorts. The miRNAs detected were used for further qPCR verification.
Quantitative Polymerase Chain Reaction
cDNA for miRNA specific qPCR was synthesized using qScriptTM microRNA cDNA Synthesis kit (Quanta Biosciences, Md.) following manufacturer's protocol and subsequent qPCRs were performed using miRNA specific forward primers (Table #) and PerfeCTa® Universal PCR primer (Quanta Biosciences, Md.). scaRNA17 and U6 were used reference small RNAs for normalizing qPCR Cq values whereas cel-miR-39-3p was used as spike-in control. PerfeCTa® SYBR® GREEN SuperMix for IQTM (Quanta Biosciences, Md.) was used for all qPCRs in a MyiQTM Single color Real-Time PCR Detection System (Bio-Rad, Calif.). Standard curve for cel-miR-39-3p was analyzed in MS Excel with R2=0.97882 and PCR efficiency 92.96%. No Template Control (NTC) was implied wherever needed.
Differentially expressed human miRNAs in Parkinson's disease patients' serum samples from The Norwegian ParkWest study were determined employing miRNA microarray. Provided below are the miRNAs with >1.2 fold differential expression.
52 Differentially Expressed Human Pre- and Mature miRNAs with >1.2 Fold Change
hsa-miR-6865-3p, hsa-miR-663a, hsa-miR-92b-3p, hsa-miR-455-3p, hsa-miR-93′7-5p, hsa-let-7b-3p, hsa-miR-6730-3p, hsa-miR-5010-3p, hsa-miR-1825, hsa-mir-4487, hsa-miR-4783-5p, hsa-miR-2117, hsa-mir-5090, hsa-mir-4484, hsa-miR-5094, hsa-mir-611, hsa-miR-4738-3p, hsa-miR-6894-5p, hsa-mir-8072, hsa-mir-762, hsa-let-7e, hsa-miR-6768-5p, hsa-mir-3917, hsa-mir-3673, hsa-mir-4431, hsa-miR-216a-3p, hsa-miR-635, hsa-miR-490-3p, hsa-mir-601, hsa-miR-636, hsa-miR-466, hsa-miR-1271-5p, hsa-miR-548u, hsa-miR-3606-5p, hsa-miR-510-5p, hsa-miR-4306, hsa-mir-4753, hsa-mir-6128, hsa-mir-4251, hsa-miR-1306-5p, hsa-miR-8052, hsa-mir-4310, hsa-mir-3128, hsa-miR-628-5p, hsa-miR-3660, hsa-miR-3156-3p, hsa-miR-548aj-3p, hsa-miR-4791, hsa-mir-532, hsa-miR-202-5p, hsa-miR-3613-3p, hsa-miR-8075
36 differentially expressed mature miRNAs with >1.2 fold change hsa-miR-6865-3p, hsa-miR-663a, hsa-miR-92b-3p, hsa-miR-455-3p, hsa-miR-937-5p, hsa-let-7b-3p, hsa-miR-6730-3p, hsa-miR-5010-3p, hsa-miR-1825, hsa-miR-4783-5p, hsa-miR-2117, hsa-miR-5094, hsa-miR-4738-3p, hsa-miR-6894-5p, hsa-let-7e, hsa-miR-6768-5p, hsa-miR-216a-3p, hsa-miR-635, hsa-miR-490-3p, hsa-miR-636, hsa-miR-466, hsa-miR-1271-5p, hsa-miR-548u, hsa-miR-3606-5p, hsa-miR-510-5p, hsa-miR-4306, hsa-miR-1306-5p, hsa-miR-8052, hsa-miR-628-5p, hsa-miR-3660, hsa-miR-3156-3p, hsa-miR-548aj-3p, hsa-miR-4791, hsa-miR-202-5p, hsa-miR-3613-3p, hsa-miR-8075
16 differentially expressed premature miRNAs with >1.2 fold change hsa-mir-4487, hsa-mir-5090, hsa-mir-4484, hsa-mir-611, hsa-mir-8072, hsa-mir-762, hsa-mir-3917, hsa-mir-3673, hsa-mir-4431, hsa-mir-601, hsa-mir-4753, hsa-mir-6128, hsa-mir-4251, hsa-mir-4310, hsa-mir-3128, hsa-mir-532, These differentially expressed miRNA sequences are illustrated below in Table 1, along with the reference/house-keeping small RNAs cel-miR-39-3p, U6 and ScaRNA17 used as controls. Cel-miR-39-3p is a spike-in control that demonstrates the stability of the RNA samples. U6 and ScaRNA17 are used as internal controls to normalize the readings of the rest of the miRNAs or candidate miRNAs.
Example 1








TABLE 1





microRNA/



small RNA name
microRNA Sequence







hsa-let-7b-3p
CUAUACAACCUACUGCCUUCCC (SEQ ID NO:1)


hsa-let-7e
CCCGGGCUGAGGUAGGAGGUUGUAUAGUUG



AGGAGGACACCCAAGGAGAUCACUAUACGG



CCUCCUAGCUUUCCCCAGG (SEQ ID NO:2)


hsa-miR-1271-5p
CUUGGCACCUAGCAAGCACUCA (SEQ ID NO:3)


hsa-miR-1306-5p
CCACCUCCCCUGCAAACGUCCA (SEQ ID NO:4)


hsa-miR-1825
UCCAGUGCCCUCCUCUCC (SEQ ID NO:5)


hsa-miR-202-5p
UUCCUAUGCAUAUACUUCUUUG (SEQ ID NO:6)


hsa-miR-2117
UGUUCUCUUUGCCAAGGACAG (SEQ ID NO:7)


hsa-miR-216a-3p
UCACAGUGGUCUCUGGGAUUAU (SEQ ID NO:8)


hsa-mir-3128
UUCCUCUGGCAAGUAAAAAACUCUCAUUUUC



CUUAAAAAAUGAGAGUUUUUUACUUGCAAU



AGGAA (SEQ ID NO:9)


hsa-miR-3156-3p
CUCCCACUUCCAGAUCUUUCU (SEQ ID NO: 10)


hsa-miR-3606-5p
UUAGUGAAGGCUAUUUUAAUU (SEQ ID NO: 11)


hsa-miR-3613-3p
ACAAAAAAAAAAGCCCAACCCUUC 



(SEQ ID NO: 12)


hsa-miR-3660
ACUGACAGGAGAGCAUUUUGA (SEQ ID NO: 13)


hsa-mir-3673
AUAUAUAUAUAUGGAAUGUAUAUACGGAAUA



UAUAUAUAUAUGGAAUGUAUAUACGGAAUAU



AUAUAUAUAUGGAAUGUAUUACGGAAUAUAU



AUAUAUAU (SEQ ID NO: 14)


hsa-mir-3917
GGCGCUUUUGUGCGCGCCCGGGUCUGUUGGUG



CUCAGAGUGUGGUCAGGCGGCUCGGACUGAGC



AGGUGGGUGCGGGGCUCGGAGGAGGCGGC 



(SEQ ID NO: 15)


hsa-mir-4251
CACGUCCUCCAGCUUUUUUCCUUAGUGGCCAA



UUCCUGAGAAAAGGGCCAACGUGCUUCCA 



(SEQ ID NO: 16)


hsa-miR-4306
UGGAGAGAAAGGCAGUA (SEQ ID NO: 17)


hsa-mir-4310
UGGCGUCUGGGGCCUGAGGCUGCAGAACAUUG



CAGCAUUCAUGUCCCACCCCCACCA 



(SEQ ID NO: 18)


hsa-mir-4431
UGGUUUGCGACUCUGAAAACUAGAAGGUUUA



UGACUGGGCAUUUCUCACCCAAUGCCCAAUA



UUGAACUUUCUAGUUGUCAGAGUCAUUAACCC 



(SEQ ID NO: 19)


hsa-mir-4484
GGGUUUCCUCUGCCUUUUUUUCCAAUGAAAAU



AACGAAACCUGUUAUUUCCCAUUGAGGGGGAA



AAAGGCGGGAGAAGCCCCA (SEQ ID NO:20)


hsa-mir-4487
ACUGUCCUUCAGCCAGAGCUGGCUGAAGGGCA



GAAGGGAACUGUCCUUCAGCCAGAGCUGGCUG



AAGGGCAGA (SEQ ID NO:21)


hsa-miR-455-3p
GCAGUCCAUGGGCAUAUACAC (SEQ ID NO:22)


hsa-miR-466
AUACACAUACACGCAACACACAU 



(SEQ ID NO:23)


hsa-miR-4738-3p
UGAAACUGGAGCGCCUGGAGGA (SEQ ID NO:24)


hsa-mir-4753
AUAUCUACACAAGGCCAAAGGAAGAGAACAGA



UAUAUCCACAGUACACUUGGCUGUUCUCUUUC



UUUAGCCUUGUGUAGAUAU (SEQ ID NO:25)


hsa-miR-4783-5p
GGCGCGCCCAGCUCCCGGGCU (SEQ ID NO:26)


hsa-miR-4791
UGGAUAUGAUGACUGAAA (SEQ ID NO:27)


hsa-miR-490-3p
CAACCUGGAGGACUCCAUGCUG (SEQ ID NO:28)


hsa-miR-5010-3p
UUUUGUGUCUCCCAUUCCCCAG (SEQ ID NO:29)


hsa-mir-5090
UCUGAGGUACCCGGGGCAGAUUGGUGUAGGGU



GCAAAGCCUGCCCGCCCCCUAAGCCUUCUGCCC



CCAACUCCAGCCUGUCAGGA (SEQ ID NO:30)


hsa-miR-5094
AAUCAGUGAAUGCCUUGAACCU (SEQ ID NO:31)


hsa-miR-510-5p
UACUCAGGAGAGUGGCAAUCAC (SEQ ID NO:32)


hsa-mir-532
CGACUUGCUUUCUCUCCUCCAUGCCUUGAGUG



UAGGACCGUUGGCAUCUUAAUUACCCUCCCAC



ACCCAAGGCUUGCAGAAGAGCGAGCCU 



(SEQ ID NO:33)


hsa-miR-548aj-
UAAAAACUGCAAUUACUUUUA (SEQ ID NO:34)


3p



hsa-miR-548u
CAAAGACUGCAAUUACUUUUGCG 



(SEQ ID NO:35)


hsa-mir-601
UGCAUGAGUUCGUCUUGGUCUAGGAUUGUUG



GAGGAGUCAGAAAAACUACCCCAGGGAUCCU



GAAGUCCUUUGGGUGGA (SEQ ID NO:36)


hsa-mir-611
AAAAUGGUGAGAGCGUUGAGGGGAGUUCCA



GACGGAGAUGCGAGGACCCCUCGGGGUCUGA



CCCACA (SEQ ID NO:37)


hsa-mir-6128
AAGAAGCUUGUAGAUUUUUCUCCCUUACUAU



CUAGAAUUAUAGGACUUCAGUCCAUGAUUUG



GAAAAAUUACUGGAAUUGGAGUCAAAAAUA



AUUUGAAAAUUAGGAAU (SEQ ID NO:38)


hsa-miR-628-5p
AUGCUGACAUAUUUACUAGAGG (SEQ ID NO:39)


hsa-miR-635
ACUUGGGCACUGAAACAAUGUCC 



(SEQ ID NO:40)


hsa-miR-636
UGUGCUUGCUCGUCCCGCCCGCA 



(SEQ ID NO:41)


hsa-miR-663a
AGGCGGGGCGCCGCGGGACCGC (SEQ ID NO:42)


hsa-miR-6730-3p
CCUGACACCCCAUCUGCCCUCA (SEQ ID NO:43)


hsa-miR-6768-5p
CACACAGGAAAAGCGGGGCCCUG 



(SEQ ID NO:44)


hsa-miR-6865-3p
ACACCCUCUUUCCCUACCGCC (SEQ ID NO:45)


hsa-miR-6894-5p
AGGAGGAUGGAGAGCUGGGCCAGA 



(SEQ ID NO:46)


hsa-mir-762
GGCCCGGCUCCGGGUCUCGGCCCGUACAGUCC



GGCCGGCCAUGCUGGCGGGGCUGGGGCCGGG



GCCGAGCCCGCGGCGGGGCC (SEQ ID NO:47)


hsa-miR-8052
CGGGACUGUAGAGGGCAUGAGC (SEQ ID NO:48)


hsa-mir-8072
GCGUCAAGAUGGCGGCGGGGAGGUAGGCAGA



GCAGGACGCCGCUGCUGCCGCCGCCACCGCCG



CCUCCGCUCCAGUCGCC (SEQ ID NO:49)


hsa-miR-8075
UGCUGAUGGCAGAUGUCGGGUCUG



(SEQ ID NO:50)


hsa-miR-92b-3p
UAUUGCACUCGUCCCGGCCUCC (SEQ ID NO:51)


hsa-miR-937-5p
GUGAGUCAGGGUGGGGCUGG (SEQ ID NO:52)


cel-miR-39-3p
UCACCGGGUGUAAAUCAGCUUG (SEQ ID NO:53)


scaRNA17
AGAGGCUUGGGCCGCCGAGCUGGACCCGGAC



CGGUUUUGGGUACUGUACUGGGGGCAGGGCA



GAGAGGG (SEQ ID NO:54)


U6
GUGCUCGCUUCGGCAGCACAUAUACUAAAAUU



GGAACGAUACAGAGAAGAUUAGCAUGGCCCCU



GCGCAAGGAUGACACGCAAAUUCGUGAAGCGU



UCCAUAUUUU (SEQ ID NO:55)









Example 2: Verification of Human Mature miRNAs by qPCR in Sample Cohort of Eight Rapid and Eight Slow Progressing Patients
The mean fold change for hsa-let7b-3p, hsa-miR-3613-3p and hsa-miR-6865-3p, hsa-pre-miR-532, and hsa-miR-663a, PrognomiRs between rapid and slow progressing PD patients are shown below in Table 2 and illustrated in the FIGURE.











TABLE 2







PrognomiR
Fold change
Significance




















hsa-let7b-3p
0.17
0.02



hsa-miR-3613-3p
0.53
0.01



hsa-miR-6865-3p
0.44
0.002



hsa-pre-miR-532
0.43
0.0005



hsa-miR-663a
0.46
0.01










The median values for relative expression of let7b-3p, miR-3613-3p, miR-6865-3p, pre-mir-532 and miR-663a are plotted as lines with each box displaying inner quartiles as a measure of distribution with whiskers. Results shown are derived qTR-PCR verification performed in initial eight rapid and eight slow progressing PD patients. Means for each group are denoted as triangles on the plot. Significance is denoted as *, p<0.05; **, p<0.01; ***, p<0.001.
Example 3
Measurement of levels of a combination of two or more miRNAs in serum from patients can assist in distinctly differentiating between a potential rapid as opposed to slow progressing PD patient. A serum sample is obtained from blood withdrawn from patients suspected of PD. The serum is used for total microRNA isolation and enrichment. This RNA would then be tested using qPCR to measure the levels of any two or more of the 52 miRNAs mentioned in Example 1, or any one of five miRNAs mentioned in Example 2. Detectable levels of one or more of the aforementioned 52 miRNAs with >1.2 fold change, or any one or more of these five miRNAs confirms the rate of progression for patients with PD. If desired, other sample fluids may be utilized, including plasma, venous or arterial blood, or CSF samples withdrawn by lumbar puncture. Such plasma, blood or CSF samples are processed as discussed above regarding serum, e.g., so as to provide a sample for processing and evaluation outside the human or animal body. It will be understood that measurement of more than two miRNAs in combination or a set of combinations used in a test matrix may desirably increase the accuracy of predicting PD progression. Following diagnosis, the result is then communicated to the patient.
As discussed and illustrated above, detecting the aforementioned miRNAs with >1.2 fold change may be used to differentiate potential rapid progressing PD from potential slow progressing PD in afflicted patients. However, as readily understood by those of ordinary skill herein, other thresholds may be suitably employed as desired with possible concomitant variation in accuracy, including >1.0, 1.1, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0 fold changes. Moreover, it will be similarly understood that these varying fold change thresholds may be more suitably used with particular miRNAs without affecting accuracy.
Example 4
Since a combination of miRNA can be used for predicting prognosis it may be advisable to test all the candidates to eliminate any cohort-based variation. It is understood that any detectable amounts of relevant miRNA will indicate PD pathology. However, those of ordinary skill in the art recognize it may be clinically helpful to use values of slow (8) v rapid (8) samples to set an artificial threshold for determination of rate of disease progression. Differential miRNA levels can be used to develop prognostic biomarker kits that can be used by clinicians for estimating prognosis of the disease as well as enriching patient cohorts in clinical trials. In this study the presence and quantification of miRNA from serum was determined by qRT-PCR which amplifies and quantifies the RNA is question. Other suitable techniques known to those of ordinary skill herein may be alternatively utilized, including use of labeled antisense sequences and labeled antibodies. Suitable antibodies are preferentially selective, referring to a binding reaction between two molecules that is typically more than 10 to 100 times background molecular associations under measurement conditions. Thus, under designated immunoassay conditions, the specified antibodies bind to a particular miRNA sequence, thereby identifying its presence. Specific binding to an antibody under such conditions requires an antibody that is selected for its specificity for a particular miRNA. For example, antibodies raised against a particular miRNA can be selected by subtracting out antibodies that cross-react with other molecules. A variety of immunoassay formats may be used to select antibodies specifically immunoreactive with a particular miRNA including solid-phase ELISA immunoassays (see, e.g., Harlow & Lane, Antibodies, A Laboratory Manual (1988) for a description of immunoassay formats and conditions that can be used to determine specific immunoreactivity). Methods for determining whether two molecules specifically interact are disclosed therein, and methods of determining binding affinity and specificity are well known in the art (see, for example, Harlow and Lane, Antibodies: A laboratory manual (Cold Spring Harbor Laboratory Press, 1988); Friefelder, “Physical Biochemistry: Applications to biochemistry and molecular biology” (W.H. Freeman and Co. 1976)). The term “antibody” as used herein encompasses naturally occurring antibodies as well as non-naturally occurring antibodies, including, for example, single chain antibodies, chimeric, bifunctional and humanized antibodies, as well as antigen-binding fragments thereof, (e.g., Fab′, F(ab′)2, Fab, Fv and rIgG). See also, Pierce Catalog and Handbook, 1994-1995 (Pierce Chemical Co., Rockford, Ill.). See also, e.g., Kuby, J., Immunology, 3rd Ed., W.H. Freeman & Co., New York (1998). Such non-naturally occurring antibodies can be constructed using solid phase peptide synthesis, can be produced recombinantly or can be obtained, for example, by screening combinatorial libraries consisting of variable heavy chains and variable light chains as described by Huse et al., Science, Vol. 246 (1989) 1275-81. These and other methods of making, for example, chimeric, humanized, CDR-grafted, single chain, and bifunctional antibodies are well known to those skilled in the art (Winter and Harris, Immunol. Today, Vol. 14 (1993) 243-46; Ward et al., Nature, Vol. 341 (1989) 544-46; Harlow and Lane, supra, 1988; Hilyard et al., Protein Engineering: A practical approach (IRL Press 1992); Borrabeck, Antibody Engineering, 2d ed. (Oxford University Press 1995). Methods for producing both monoclonal and polyclonal antibodies from identified RNA sequences are well known in the art.
Example 5
Many neurodegenerative diseases are closely related to each other when it comes to symptoms as well as pathological markers. The circulating prognostic markers for one neurodegenerative disease can be useful for diagnosis/prognosis of other disease. A method to diagnose/prognoses other neurodegenerative diseases like Dementia with Lewy body (DLB), Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Multiple system atrophy (MSA), CorticoBasal Degeneration (CBD), Progressive Supranuclear Palsy (PSP) can also be developed using similar miRNA measurements of candidates mentioned above. Disease specific kits can be developed similar to one mentioned in [0024] with various combinations of miRNAs listed in [0018].
Example 6
The miRNAs detected in one or more combinations can regulate several proteins in the cells. Novel protein targets for PD can be discovered using these microRNAs and their combinations. The involvement of these proteins in PD etiology can be further established to target them for therapy.
Example 7
Small nucleic acid molecules derived from miRNAs mentioned in [0018] will be designed to therapeutically intervene by specifically targeting genes in PD brains to achieve complete or partial remedy.

## Claims

1. A method for treating a human patient with rapid progressing Parkinson's disease, comprising the steps of, in this order:
obtaining a first sample comprising serum, plasma or blood from said patient;
determining a level of expression of at least one miRNA selected from the group consisting of SEQ ID NOS: 1, 12, 42 and 45, and optionally further determining a level of expression of at least one miRNA selected from the group consisting of SEQ ID NOS: 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 46, 47, 48, 49, 50, 51 and 52 within said first sample;
comparing the level of expression of the determined SEQ ID NOS to a level of expression of the determined SEQ ID NOS in a second sample from a cohort with slow progressing Parkinson's disease and confirming the expression level of the determined SEQ ID NOS in said first sample is greater than 1.2-fold over that of said second sample, thereby indicating the patient has rapid progressing Parkinson's disease; and
treating said rapid progressing Parkinson's disease with at least one of L-dopa, a dopamine agonist, a catechol-O-methyltransferase (COMT) inhibitor, amantidine (1-Aminotricyclo (3,3,1,13,7) decane or a monoamino oxidase type B inhibitor.

2. The method according to claim 1, wherein expression of miRNAs according to at least two of said SEQ ID NOS: 1, 12, 42 and 45 is compared relative to the cohort.

3. The method according to claim 2, wherein expression of miRNAs according to at least SEQ ID NOS: 1 and 12 is compared relative to the cohort.

4. The method according to claim 2, wherein expression of miRNAs according to at least SEQ ID NOS: 1 and 42 is compared relative to the cohort.

5. The method according to claim 2, wherein expression of miRNAs according to at least SEQ ID NOS: 1 and 45 is compared relative to the cohort.

6. The method according to claim 2, wherein expression of miRNAs according to at least SEQ ID NOS: 12 and 42 is compared relative to the cohort.

7. The method according to claim 2, wherein expression of miRNAs according to at least SEQ ID NOS: 12 and 45 is compared relative to the cohort.

8. The method according to claim 2, wherein expression of miRNAs according to at least SEQ ID NOS: 42 and 45 is compared relative to the cohort.

9. The method according to claim 2, wherein expression of miRNAs according to SEQ ID NO: 33 is also compared relative to the cohort.

10. The method according to claim 2, wherein expression of miRNAs according to at least three of said SEQ ID NOS: 1, 12, 42 and 45 is compared relative to the cohort.

11. The method according to claim 10, wherein expression of miRNAs according to at least SEQ ID NOS: 1, 12 and 42 is compared relative to the cohort.

12. The method according to claim 10, wherein expression of miRNAs according to at least SEQ ID NOS: 1, 12 and 45 is compared relative to the cohort.

13. The method according to claim 10, wherein expression of miRNAs according to at least SEQ ID NOS: 1, 42 and 45 is compared relative to the cohort.

14. The method according to claim 10, wherein expression of miRNAs according to at least SEQ ID NOS: 12, 42 and 45 is compared relative to the cohort.

15. The method according to claim 10, wherein expression of miRNAs according to SEQ ID NO: 33 is also compared relative to the cohort.

16. The method according to claim 10, wherein expression of miRNAs according to each of said SEQ ID NOS: 1, 12, 42 and 45 is compared relative to the cohort.

17. The method according to claim 16, wherein expression of miRNAs according to SEQ ID NO: 33 is also compared relative to the cohort.

18. The method according to claim 1, wherein expression of miRNAs according to SEQ ID NO: 33 is also compared relative to the cohort.

19. The method according to claim 1, wherein said first sample comprises serum.