BT-309 SEMINAR, TERM PAPER WRITING

PRESENTED BY

Archana S. Puranik

ROLL NO :- 21

A molecular signature in blood identifies early Parkinson’s disease

• Authors:- Leonid Molochnikov,Jose Rabey, EvgenyaDobronevsky, Ubaldo Bonuccelli,Roberto Ceravolo, Dniela Frosini ,Edna Grunblatt, Peter Riederer,Christian Jacob, Judith Aharon-Peretz, Yulia Bashenko, MoussaBH Youdim and Silvia A Mandel

• Journal:- Molecular Neurodegenration

• Volume :- 7

• Impact Factor :- 5.29

• Citation index :- 2892

Purpose of this paper

1. Parkinson’s disease is verycommon to old people acrossworld.

2. The gradual neuro degenerationresults in shivering of handswhich is also a key symptom ofdisease.

3. This makes hurdle in performingdaily activities which might hurtself respect of individual.

4. With this paper an earlydetection may improveindividual’s health andappearance .

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AbstractThe aim of the experiment is toassess whether a gene from bloodcould support detection of early PD.The transcriptional expression ofseven genes were examined from62 early ages PD patients and 64healthy matched control. Stepwiseregression analysis found that fivegenes are optimal those areSKP1,HIP2,ALDH1A1,PSMC4 andHSP8. The performance of thesegene on de novo PD individualsresulted in similar ROC and AUC of0.95 indicating stability of model.

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Methods

1) Study population.• 185 individuals were enrolled

for blood sample mRNAextraction:

• 62 early/mild PD patients.• Early 24 patients within 1st

year of medication.• 30 PD patients with advanced

disease.• 29 patients with AD are

examined along with 64healthy matched control.

• Patient data such as name,age, gender is maintained.

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Methods

• Total white blood count as well asdifferential blood cell counts wereexamined for any bias in geneexpression changes.

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Methods2) Isolation of total RNA and quality control.

• Venous blood samples werecollected using PAXgene BloodRNA System Tubs at differentcenters and sent for RNAextraction and real time PCRquantification except 10 ADsamples. The blood samples werefrozen at -80 degrees.

• Both control and cases sampleswere processed in parallel. TotalRNA was extracted from wholeblood with PAXgene blood RNA50 kit.

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Methods

• RNA quality was determined bynano drop 1000spectrophotometer and by usingautomated electrophoresissystem. And RNA samples weretaken from it.

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Methods

3) Quantitative real-time RT-PCR(QRT-PCR).

• RNA from each blood sample isconverted to cDNA employing theHigh-Capacity cDNA ReverseTranscription Kit.

• QRT-PCR was performed using SYBRGreen detection.

• Oligonucleotide primers areconstructed accordingly.

• Gene expression were analysed .

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Methods4) Building a risk marker profile.

• The predictive probability toestablish a molecular marker wascalculated by using regressionanalysis.

• The predictive probability valueswere used to construct a ROCcurve depicting the relationshipbetween sensitivity andspecificity of early PD groupversus de novo PD group.

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Methods

5) Statistical analysis .

• Comparison between experimentalgroups were carried out usingANOVA technique.

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Results1) Identification of a PD risk genesignature.

Table 1 Variables in the predictedprobability equation

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B Pvalue

OR 95%

LOW

C.I.ORUP

L_SKP1 -0.313 0.003 0.731 0.595 0.898

L_HIP2 0.274 0.008 1.315 1.076 1.608

L_ALDHA1

-0.148 0.030 0.862 0.754 0.986

L_PSMC4 -0.318 0.002 0.727 0.595 0.889

L_HSPA8 0.330 0.001 1.391 1.139 1.699

Results

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Results2)Validation of specificity andsensitivity of the gene risk panel.

• To validate the diagnostic value ofthe PD gene panel, a separate 30PD patients at advanced diseasestage and 29 patients withAlzheimer’s disease (AD) weretested with the logisticclassification model.

• The gene cluster positivelyclassified all 30 cases as PD (100%sensitivity) and discriminated PDfrom AD with 100% specificity (all29 cases were classified as non-PD), thus supporting thediagnostic value of the molecularsignature for detecting PD.

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Results

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Results

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Conclusions

• Experimental studiesdemonstrated that the bloodgene model has strong predictivevalue for PD diagnosis andpossibly may help to identifyindividuals at early stages whoare good candidates forneuroprotective treatment.

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Conclusions

• Large-scale, prospective,controlled studies, whichcombine our methodology withquantification of CSFtotal/oligomers of α-synucleinor/and DJ-1 and brain imagingmay be useful as a multi-modalbiomarker, not only for earlydiagnosis but for evaluation ofdisease progression.

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THANK YOU

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