presentation on early detection of blood based biomarkers for PD

1. BT-309 SEMINAR, TERM
PAPER WRITING
PRESENTED BY
Archana S. Puranik
ROLL NO :- 21

2. A molecular signature in
blood identifies early
Parkinson’s disease
• Authors:- Leonid Molochnikov,
Jose Rabey, Evgenya
Dobronevsky, Ubaldo Bonuccelli,
Roberto Ceravolo, Dniela Frosini ,
Edna Grunblatt, Peter Riederer,
Christian Jacob, Judith Aharon-
Peretz, Yulia Bashenko, Moussa
BH Youdim and Silvia A Mandel
• Journal:- Molecular
Neurodegenration
• Volume :- 7
• Impact Factor :- 5.29
• Citation index :- 289
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3. Purpose of this paper
1. Parkinson’s disease is very
common to old people across
world.
2. The gradual neuro degeneration
results in shivering of hands
which is also a key symptom of
disease.
3. This makes hurdle in performing
daily activities which might hurt
self respect of individual.
4. With this paper an early
detection may improve
individual’s health and
appearance .
3

4. Abstract
The aim of the experiment is to
assess whether a gene from blood
could support detection of early PD.
The transcriptional expression of
seven genes were examined from
62 early ages PD patients and 64
healthy matched control. Stepwise
regression analysis found that five
genes are optimal those are
SKP1,HIP2,ALDH1A1,PSMC4 and
HSP8. The performance of these
gene on de novo PD individuals
resulted in similar ROC and AUC of
0.95 indicating stability of model.
4

5. Methods
1) Study population.
• 185 individuals were enrolled
for blood sample mRNA
extraction:
• 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 64
healthy matched control.
• Patient data such as name,
age, gender is maintained.
5

6. Methods
• Total white blood count as well as
differential blood cell counts were
examined for any bias in gene
expression changes.
6

7. Methods
2) Isolation of total RNA and quality
control.
• Venous blood samples were
collected using PAXgene Blood
RNA System Tubs at different
centers and sent for RNA
extraction and real time PCR
quantification except 10 AD
samples. The blood samples were
frozen at -80 degrees.
• Both control and cases samples
were processed in parallel. Total
RNA was extracted from whole
blood with PAXgene blood RNA
50 kit.
7

8. Methods
• RNA quality was determined by
nano drop 1000
spectrophotometer and by using
automated electrophoresis
system. And RNA samples were
taken from it.
8

9. Methods
3) Quantitative real-time RT-PCR
(QRT-PCR).
• RNA from each blood sample is
converted to cDNA employing the
High-Capacity cDNA Reverse
Transcription Kit.
• QRT-PCR was performed using SYBR
Green detection.
• Oligonucleotide primers are
constructed accordingly.
• Gene expression were analysed .
9

10. Methods
4) Building a risk marker profile.
• The predictive probability to
establish a molecular marker was
calculated by using regression
analysis.
• The predictive probability values
were used to construct a ROC
curve depicting the relationship
between sensitivity and
specificity of early PD group
versus de novo PD group.
10

11. Methods
5) Statistical analysis .
• Comparison between experimental
groups were carried out using
ANOVA technique.
11

12. Results
1) Identification of a PD risk gene
signature.
Table 1 Variables in the predicted
probability equation
12
B P
value
OR 95%
LOW
C.I.
OR
UP
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_ALDHA
1
-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

13. Results
13

14. Results
2)Validation of specificity and
sensitivity of the gene risk panel.
• To validate the diagnostic value of
the PD gene panel, a separate 30
PD patients at advanced disease
stage and 29 patients with
Alzheimer’s disease (AD) were
tested with the logistic
classification model.
• The gene cluster positively
classified all 30 cases as PD (100%
sensitivity) and discriminated PD
from AD with 100% specificity (all
29 cases were classified as non-
PD), thus supporting the
diagnostic value of the molecular
signature for detecting PD.
14

15. Results
15

16. Results
16

17. Conclusions
• Experimental studies
demonstrated that the blood
gene model has strong predictive
value for PD diagnosis and
possibly may help to identify
individuals at early stages who
are good candidates for
neuroprotective treatment.
17

18. Conclusions
• Large-scale, prospective,
controlled studies, which
combine our methodology with
quantification of CSF
total/oligomers of α-synuclein
or/and DJ-1 and brain imaging
may be useful as a multi-modal
biomarker, not only for early
diagnosis but for evaluation of
disease progression.
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21. THANK YOU
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