UK MSA Research Day 2014 - Multiple System Atrophy Awareness

Report
Cerebrospinal Fluid Studies
in MSA
Nadia Magdalinou
Clinical Research Fellow
27.02.14
Pathophysiology
Protein misfolding and pathological aggregation are common
threads in neurodegeneration
α-Syn deposition in MSA
Tau deposition in PSP
Courtesy of Dr Janice Holton
Overlapping Pathologies & Phenotypes in
“Proteinopathies”
Adapted from Constantinescu and Mondello 2013
Biomarker
“a characteristic that is objectively measured and
evaluated as an indicator of normal biological
processes, pathogenic processes or pharmacologic
response to a therapeutic intervention” (Biomarkers
Definitions Working 2001)
‘Ideal’ biomarker:
•
•
•
•
sensitive
reproducible
closely associated with the disease process
non-invasive and inexpensive
Cerebrospinal fluid
• proximity to brain structures
undergoing degeneration
• Proteins/peptides directly
reflective of disease pathology
would most likely diffuse into the
CSF than any other fluid
• can be tested serially;
assessing evolving pathology
throughout the disease course
CSF studies in Parkinsonism
Main focus to investigate a priori defined compounds
(hypothesis-driven)
• in patients and in healthy controls
• looking for differences, patterns and associations; α-Syn, tau
Recently, trend towards hypothesis-generating, “omics”
techniques
• unbiased and sensitive approach
• identifying markers unexpectedly involved in
neurodegeneration
Even though several promising candidates exist there
is still no reliable biomarker
Putative pathogenic pathways underlying CSF Biomarkers in PD
Parnetti, L. et al. Nat Rev.Neurol. 9, 131-140 (2013)
Total-α-Syn
(Magdalinou, Lees, Zetterberg, JNPP 2014 in press)
Research Groups
Participants
Technique
Main Findings
Van Dijk
et al 2013
PD n=53, HC n=50
TR-FRET
Decrease in both t-α-Syn + t-α-Syn:t-protein ratio levels in
PD vs HC
Kang
et al 2013
Wennstrom
et al 2013
PD n=39 (drug naïve patients);HC n=63
PPMI cohort
PD n=38, PDD n=22, DLB n=33, AD n=46, HC
n=52
ELISA
Decrease in PD vs HC
ELISA
Decrease in PDD>PD>DLB vs AD + HC
Mollenhauer
et al 2013
Hall
et al 2012
PD n=78 (de novo, drug-naive patients), HC
n=48
PD n=90, PDD n=33, DLB n=70, PSP n=45, CBD
n=12, MSA n=48, AD n=48, Controls n=107
ELISA (3rd generation)
Decrease in de novo PD patients vs HC
Bead-based multi-analyte
assay (Luminex)
Modest decrease in AD>DLB+PDD>PD + MSA vs Controls ,
AD and PSP
Aerts
et al 2012
PD n=58, MSA n=47, DLB n=3, VaP n=22, PSP
n=10, CBD n=2
Tateno
et al 2012
Mollenhauer
et al 2011
Parnetti
et al 2011
Shi
et al 2011
Hong
et al 2010
Nogutsi
et al 2009
Spies
et al 2009
Ohrfelt
et al 2009
Mollenhauer
et al 2008
No difference between groups
• Inconsistent initial ELISA
data
• Consensus emerging:
decreased in •DLB,
PDD, PD
PD n=11, DLB n=6, MSA n=11, AD n=9,
ELISA
t-α-Syn decrease in PD, DLB, MSA vs AD + Controls
Controls n=11 and MSA, but not in PSP and CBD • No difference among PD, DLB, MSA
• Can
differentiate
synucleinopathies
Training cohort:
PD n=51,
DLB n=55, MSA
ELISA (1 and 2 generation) from
• Decrease in PD, DLB, MSA vs AD, NPH, PSP and controls
n=29, AD n=62, Controls n=76 Validation
• High degree of concordance in t-a-Syn levels between PD
tauopathies
cohort: PD n=273,
DLB n=66, PSP n=8,and
MSA controls
+ MSA
n=15, NPH n=22, Controls n=23
• Cannot discriminate
between synucleinopathy
PD n=38, DLB n=32 , AD n=48, FTD n=31
ELISA
• t-α-Syn decrease in all diseased groups (esp DLB/FTD)
Controls n=32 groups
• ratio: decrease in PD vs all other diseased groups
st
Discovery cohort: PD n=126, MSA n=32
AD n=50, Controls n=137
Validation Cohort :PD n=83
PD n=117, AD n=50, HC n=132
nd
Bead-based multi-analyte
assay (Luminex)
Decrease in PD vs controls and AD
DLB n=16, AD n=21
Bead-based multi-analyte
assay (Luminex)
ELISA
Decrease in PD vs AD and controls (after omitting samples
with high haemoglobin concentration)
No difference
DLB n=40, AD n=131, VaD n=28, FTD n=39
ELISA
No difference
PD n=15, DLB n=15, AD n=66, Controls n= 55
ELISA
Decrease in AD, no difference in parkinsonian groups
PD n=8, DLB n=38, AD n=13, CJD n=8, Controls
n=13
ELISA (1st and 2nd generation)
Marginal decrease in LBD and PD vs all other groups
Putative pathogenic pathways underlying CSF Biomarkers in PD
Parnetti, L. et al. Nat Rev.Neurol. 9, 131-140 (2013)
Phosphorylated (p-α-Syn ) and Oligomeric αsyn (o-α-Syn )
Research Participants
Groups
Analytes
Technique
Main
Findings
Comments
Wang
et al 2012
Discovery cohort:
PD n=83, MSA n=14, PSP
n=30, AD n=25, HC n=51
Validation cohort:
PD n=109, MSA n=20, PSP
n=22, AD n=50, HC n=71
t-α-Syn
p-α-Syn
p-α-Syn:t- α-Syn
ratio
Bead-based multianalyte assay
(Luminex)
Positive correlation
with UPDRS in PD,
no correlation with
H&Y score
Foulds
et al 2012
PD n=39, DLB n=17, PSP
n=12, MSA n=8, Controls
n=26
t-α-Syn
p-α-Syn
o-α-Syn
o-p-α-Syn
Modified Sandwich
ELISAs
• t-α-Syn decrease
in PD+MSA vs
controls
• Increase α-Syn
ratio in MSA vs
PSP
• Increase α-Syn
ratio in PD vs
controls and PSP
• o-p-α-Syn can
differentiate pts
with MSA from
all other groups
Post-mortem samples
No correlation with
disease
severity/duration
• Wang: p-α-Syn:t-α-Syn ratio could discriminated MSA from PSP
• Foulds: o-p-α-Syn can differentiate MSA from other synucleinopathies and
tauopathies
• No correlation with age/disease duration/cognitive function
Putative pathogenic pathways underlying CSF Biomarkers in PD
Total & Phosphorylated Tau
Research
Groups
Kang
et al 2013
Hall
et al 2012
Bech
et al 2012
Anderson
et al 2011
Shi
et al 2011
Parnetti
et al 2011
Montine
et al 2010
Süssmuth
et al 2010
Alves
et al 2010
Ohrfelt
et al 2009
Compta
et al 2009
Parnetti
et al 2008
Participants
Technique
Main Findings
PD n=39 (drug naïve patients), HC n=63
PPMI cohort
PD n=90, PDD n=33, DLB n=70, PSP
n=45, CBD n=12, MSA n=48, AD n=48,
Controls n=107
PD n=22, PDD n=3, DLB n=11, MSA n=10,
PSP n=20, CBD n=3
DLB n=47, PDD n=17, AD n=150
Bead-based multi-analyte
assay (Luminex)
Bead-based multi-analyte
assay (Luminex)
Decrease in t-tau + p-tau in PD vs controls
ELISA
No difference between parkinsonian groups
Increased t-and p-tau in AD vs DLB +PDD
Increased t-tau in DLB vs PDD
• Inconsistent data ELISA
• cohort:PD
Can discriminate
frommulti-analyte
AD
Discovery
n=126, MSA n=32, PD
Bead-based
• Decrease in PD vs to controls
AD n=50, Controls n=137
assay (Luminex)
• Decrease in PD + MSA vs AD
• No difference in parkinsonian conditions
Validation Cohort: PD n=83
PD n=38,•DLBAge,
n=32 , AD
n=48,
FTD n=31 ELISA
Increase in AD>FTD>DLB
not
diagnosis,
strongest factor• affecting
t-tau vs PD and controls
Controls n=32
• No difference between PD and controls
levels
PD n=41, PDD n=11, AD n=49, HC n=150 Bead-based multi-analyte • t-tau: no difference between parkinsonian groups
assay (Luminex)
ELISA
• p-tau: reduced in PD vs HC
p-tau:t-tau ratio lower in PSP and MSA vs PD
ELISA
No difference between PD and controls
PD n=15, DLB n=15, AD n=66, Controls
n= 55
PD n=20, PDD n=20, HC n=15
ELISA
No difference between parkinsonian groups
ELISA
t- and p- tau: increase in PDD vs PD and controls
PD n=20, PDD n=8, DLB n=19, AD n=23,
HC n=20
ELISA
• t-tau: DLB>PDD>controls
• p-tau: no difference between parkinsonian groups
PSP-RS n=20, PSP-P n=7, MSA-P n=11,
MSA-C n=14, PD n=23, Controls n=20
PD n=109, AD n=20, HC n=36
Neurofilament-light chain
Research
Groups
Participants
Technique
Main Findings
Comments
Hall
et al 2012
PD n=90, PDD n=33,
DLB n=70, PSP n=45,
CBD n=12, MSA n=48,
AD n=48, Controls
n=107
PD n=22, PDD n=3,
DLB n=11, MSA n=10,
PSP n=20, CBD n=3
Bead-based
multi-analyte
assay
(Luminex)
NF-L differentiates PD from
atypical parkinsonism
Higher levels of NF-L correlate with
disease severity in PD, AD and PD
ELISA
Higher NF-L levels in atypical
patkinsonian disorders vs PD
PD n=10, MSA n=21,
PSP n=14, CBD n=11,
HC n=59
(x2 consecutive
samples available in
all diseased groups,
other than CBD)
ELISA
• NF-L: normal levels in PD,
elevated in MSA, PSP+CBD
• No statistical significance
overtime
Lower levels NF-L in PD despite
significantly longer disease duration
compared with atypical parkinsonian
disorders
NF-L remain stable despite disease
progression
Bech
et al 2012
Constantinescu
et al 2010
• NF-L normal in PD and increased in MSA, PSP and CBD
vs controls
• No difference in atypical parkinsonism
• Levels remain stable despite disease progression in a
longitudinal study
Putative pathogenic pathways underlying CSF Biomarkers in PD
Parnetti, L. et al. Nat Rev.Neurol. 9, 131-140 (2013)
Oxidative Stress Markers
Research Groups
Participants
Analytes
Technique
Main Findings
Comment
Herbert
et al 2013
PD n=43, MSA n=23,
Controls n=30
DJ-1
ELISA
Diagnostic accuracy for
discriminating MSA from
PD improved by
combining DJ-1 with t-tau
+ p-tau
Salvesen
et al 2012
PD n=30, DLB n=17, MSA
n=14, PSP n=19
DJ-1
ELISA
• Increase in MSA>PD
• Significant difference
in MSA vs PD, MSA vs
Controls and PD vs
Controls
No difference among
groups
Shi
et al 2011
DJ-1
Bead-based
multi-analyte
assay (Luminex)
Decrease in MSA + PD vs
controls + AD
Hong
et al 2010
Discovery cohort
PD n=126, MSA n=32
AD n=50, Controls n=137
Validation Cohort
PD n=83
PD n=117, AD n=50, HC
n=132
DJ-1
Bead-based
multi-analyte
assay (Luminex)
Correlation with age (esp
in HC), but not with
disease severity
Constantinescu
et al 2013
PD n=6, MSA n=13, PSP
n=18, CBD n=6, HC n=18
Urate
Maetzler
Et al 2011
PD n=55, PDD n=20, DLB
n=20, Controls n=76
Uric acid
Increase in PD vs DLB
Positive correlation with
Aβ42 in HC but not in DLB
Gmitterová et al
2009
PD n=27, PSS n=21, LBD
n=18, AD n=18 Controls
n=13
8-OHdgG
Enzymatic
method on a
modular system
ADVIA analyser
+ photometric
methods
ELISA
• Decreased levels in PD
vs Controls and AD
• No difference between
AD + Controls
No difference
Increase in PD and PDD
vs controls, but only
significant difference
between non demented
PD + controls
Increase in 8-OHdG levels
with lower MMSE score in
PDD
• DJ1: inconsistent results; ? Increase in MSA could differentiate MSA from PD
• Urate: inconsistent results
Inflammatory Markers
Research
Groups
Wennstrom
et al 2013
Participants
Analytes
Technique
Main Findings
Comment
PD n=38, PDD n=22,
DLB n=33, AD n=46, HC
n=52
Neurosin
ELISA
• Decreased CSF neurosin levels
significantly associated with
decreased t-α-Syn levels in HC,
PD + PDD, but not in AD + DLB
• Correlation with age, esp in HC
Shi
et al 2011
Discovery cohort
PD n=126, MSA n=32
AD n=50, Controls
n=137
Validation Cohort
PD n=83
PD n=86, MSA n=20, AD
n=38 HC n=91
Fractalkine
Bead-based
multi-analyte
assay (Luminex)
• Lowest levels in DLB, but
no difference between
synucleinopathies
• When pooled,
synucleinopathies
decrease levels vs AD +
HC
Decrease in MSA vs PD, AD +
controls
Complement
C3/ factor H
(FH)
Bead-based
multi-analyte
assay (Luminex)
PD n=38, PDD n=20,
DLB n=21m Controls
n=23
Neprilysin
Fluorometric
assay
Wang
et al 2011
Maetzler
et al 2010
• Fractalkine alone could
differentiate PD from MSA
• Fractalkine: Aβ42 ratio:
positive correlation with
disease severity + progression
in PD
• C3: decrease in MSA vs
• C3: Aβ42 ratio + FH: Aβ42 ratio
PD + HC; increase in AD
correlated with PD severity +
vs all other groups
presence of cognitive
• FH: increase in AD vs PD +
impairment
HC
• C3 + FH levels correlated with
• C3:FH ratio: decreas in
disease severity in AD (MMSE
MSA vs all other groups
scores)
Decrease in DLB + PDD vs
• Negative correlation with
PD + Controls
dementia duration
• Positive correlation with Aβ42
levels
MSA and PD could be differentiated by the CSF Fractalkine and not by αSyn
Summary of CSF Markers in MSA
Biomarker Conclusion
t-α-Syn
• most promising marker
• can differentiate synucleinopathies from tauopathies and
controls, but not between synucleinopathy groups
o-p-α-Syn
• ? can discriminate MSA from other synucleinopathies
• larger cohorts and ante mortem CSF studies required
t-tau/p-tau
• no disease specific pattern in parkinsonian disorders
NF-L
• can discriminate PD from atypical parkinsonian conditions
DJ-1
• ? could help differentiate MSA from PD
Oxidative
stress/Inflamma
tory
• promising results requiring further studies
Challenges/Limitations
1. Most studies are retrospective and do not have pathological
confirmation
2. Lack of standardisation of pre-analytical (sampling collection, handling and storage)
and analytical (analysis execution/sample processing) factors
3. Lack of assay standardisation; different assays can give different absolute
concentrations of the protein, making it almost impossible to use global reference limits and
diagnostic cut-off points
4. Heterogeneous neurodegenerative groups: in terms of age, disease duration and
disease severity
5. Heterogeneous controls groups: including healthy controls, patients with nonneurodegenerative neurological conditions or patients with possible neurodegenerative conditions like
mild cognitive impairment and normal pressure hydrocephalus
6. Lack of combination of different biomarker modalities- imaging and
CSF markers
TARGETED CEREBROSPINAL
FLUID MARKERS IN
PARKINSONISM
Methods
Prospective, cohort study of
patients with parkinsonian
conditions, healthy and dementia
controls recruited from NHNN
Hypothesis
Parkinsonian syndromes can be
differentiated using a combination of
targeted cerebrospinal fluid markers
• Patients monitored periodically for at least
two years to maximise accuracy of clinical
diagnosis
• Dx according to current consensus criteria
• Healthy controls with no history of
neurological/psychiatric disease
Standardised protocol for the collection and
storage of CSF (as recommended by the Alzheimer’s
Association QC Program for AD) and sample
processing
≈50% of participants have signed up for
brain donation and we have already
pathological confirmation in 10 patients
A subgroup of participants underwent
brain imaging to assess whether the
combination of multiple modalities
improves diagnostic accuracy
CSF analysis
NHNN
Clinical Lab
Gothenburg
Lab
NFL
Routine Ix:
Total protein
WCC
RCC
‘Dementia’
markers
t-/p- tau
Aβ42
Gothenburg
Lab
Proteomic
patterns
α-Syn
MCP-1
YKL-40
APPα
APPβ
MCP-1 and YKL-40
•
Monocyte Chemoattractant
Protein-1: a small cytokine
•
YKL-40: a secreted glycoprotein
named after its three terminal
amino acids
• involved in neuroinflammatory
processes associated with
neurodegeneration in AD
• Decreased levels of YKL-40 in
synucleinopathies compared
with tauopathies and healthy
controls (Olsson et al 2013)
sAPPα and sAPPβ
• 2 soluble metabolites resulting
from proteolytic processing of
Amyloid Precursor Protein
(APP)
• sAPPα and sAPPβ unaltered
in AD, but not investigated in
other neurodegenerative
conditions
Final number of subjects included in the
analysis
DISEASED SUBJECTS
Total number
eligible for study
n=221
HEALTHY CONTROLS
Total Number
eligible for study
n= 42
Total recruited
n=177
Total recruited
n=30
Total included
in analysis
n=169
Total included
in analysis
n=30
8 patients with mild
cognitive
impairment,
excluded from
analysis
Demographic and Clinical Characteristics
No (%) of
men
Age (yrs)
DisDur
HC
PSP
CBS
MSA
PD
AD
FTD
“Unclass’
n=30
n=40
n=17
n=31
n=31
n=26
n=16
n=8
15
24
5
16
20
9
11
4
(50)
(60)
(29.4)
(51.6)
(64.5)
(34.6)
(68.8)
(50)
63.5 a
69.5 b
71
64
67
63
63.5
74.5 c
(50-67)
(66-72.5)
(63-75)
(60-67)
(61-74)
(58-68)
(57-71.5)
(69-79.5)
N/A
5
4
4
8d
3
2.5
3
(3-8)
(2-5)
(3-6)
(5-15)
(2-4)
(2-4.5)
(2-6)
4e
3
3
2.5
N/A
N/A
3
(3-5)
(3-4)
(2.5-4)
(2-4)
36
41.5
N/A
28
(28-43)
(35-48)
30
27
26
(30-30)
(25-28.5)
(26-28)
(yrs)
H&Y score
UPDRS
MMSE
•
•
•
•
•
N/A
N/A
(2.5-3)
N/A
N/A
ND
ND
ND
ND
(24-30)
N/A
28 f
(24-30)
no significant age difference in parkinsonian syndromes
significant difference in disease duration between the PD group and the rest
significant difference in H&Y score between PSP and MSA, PD and ‘unclassifiable’
no significant difference in UPDRS
significant difference in MMSE scores between PD and controls
‘Dementia’ Markers
HC
PSP
CBS
MSA
PD
AD
FTD
“Unclass’
n=30
n=40
n=17
n=31
n=31
n=26
n=16
n=8
t-tau
303.5
260.5
275
277
339
806 a
241.5
358.5
(ng/mL)
(189-402)
(234-369)
(217-377)
(210-341)
(226-444)
(469-1140)
(219-361.5)
(234-388.5)
p-tau
38
34
36
34
39
81.5 b
35
36
(ng/mL)
(29-54)
(31-44.5)
(30-43)
(28-38)
(31-54)
(57-94)
(30-44.5)
(33-41)
Aβ42
953
659
716
775
770
363.5 c
878
689
(ng/mL)
(771-1199)
(539-838.5)
(547-975)
(520-911)
(584-1044)
(264-511)
(695-1140.5)
(478.5-858)
t-tau/Aβ42
0.3
0.415
0.35
0.43
0.38
2.17 d
0.335
0.48
(ng/mL)
(0.22-0.36)
(0.3-0.53)
(0.23-0.53)
(0.28-0.53)
(0.3-0.61)
(1.495-3.475)
(0.22-0.465)
(0.37-0.66)
Aβ42, t-tau and p-tau showed a significant difference
between AD and all other groups, but did not discriminate
between parkinsonian syndromes
Targeted Markers
• Significant reduction in MSA compared with healthy controls and AD
• No significant difference in PD
Targeted Markers
• There was a significant increase in all studied groups compared with healthy
controls
• PSP, CBS and MSA pts had higher levels compared with PD, AD, FTD and
Unclassifiable pts
Targeted Markers
• There was a significant difference between healthy controls and all
studied groups
• In PSP there were significant higher levels compared with FTD
Targeted Markers
• Healthy controls had significantly lower levels compared with PSP, MSA
and Unclassifiable pts
• There were higher levels in CBS compared with PD and Unclassifiable pts
Targeted Markers
• Healthy controls had significantly higher levels compared with PSP, MSA and
CBS
• PSP, CBS and MSA had significantly lower levels compared with PD and AD
Targeted Markers
• Healthy controls had significantly higher levels compared with atypical
parkinsonian groups
• Atypical parkinsonian groups had lower levels compared with PD, but there
were no differences between them
• PSP had lower levels compared with AD
Summary
Biomarker Findings
Summary
t-α-Syn
Most promising marker: can differentiate synucleinopathies from
tauopathies and controls, but not between synucleinopathy groups
• In our cohort, we did not confirm above findings; α-Syn was
significantly decreased in MSA and not in PD
t-tau/p-tau
No disease specific pattern in parkinsonian disorders
• Confirmed findings in our cohort
NF-L
Can discriminate PD from atypical parkinsonian conditions, but no
significant difference between PSP, MSA, CBS
• Confirmed findings in our cohort
YKL-40
Decreased levels in synucleinopathies compared with tauopathies
and controls
• Good sensitivity, but poor specificity to differentiate
neurodegenerative diseases from healthy controls
MCP-1
• Significant difference in MSA and PSP compared with
healthy controls
• Could differentiate CBS from PD
APPα
APPβ
• Could discriminate atypical parkinsonian groups from PD
and healthy controls, but there was no significant difference
between PSP, MSA and CBS
Conclusion
• Unpublished data
• Preliminary analysis only
• Promising early results: reproduced other
published data
• Unlikely that a single biomarker will hold the
answer: combination of markers may be required
CEREBROSPINAL FLUID
PROTEOMICS IN PARKINSONISM
Proteomics
• protein content (proteome) of a
sample is characterised
• proteomes between patients
and controls are compared and
differences are identified
Technology:
1. separation of proteins
2. analysing proteins through
mass spectrometry
3. quantifying and identifying
proteins through advanced
data processing
Proteomics Studies
Research
Groups
Technique
Main Findings
Constantinescu SELDI-TOF MS
et al 2010
• 4 proteins: ubiquitin, β2-microglobulin and
two secretographin 1 fragments
• Differentiated PD + HC from atypical
parkinsonism with an AUC of 0.8
Ishigami
et al 2012
• Using the proteomic pattern (combined
set of many protein peaks)
• Able to differentiate PD from MSA, even
at early stages
MALDI-TOF MS
Limitations:
1. inherently biased towards identification of abundant proteins
2. blood contamination in CSF major effect on protein concentration
3. not standardised sample preparation/implementation and processing
technologies between research groups difficult to validate and
replicate results
Proteomics
Hypothesis
Cerebrospinal fluid proteomic patterns can discriminate
between parkinsonian syndromes
Discovery Cohort n=67
• PSP n=18, CBS n=7, MSA n=14, PD n=13, HC n=15
Proteins identified through
mass spectrometry
• 373
• data filtered by removing proteins not
identified in <50% of subjects
• 173 proteins left
Comparisons between study
groups
• Took first 10 proteins from each comparison
group
• only statistically significant proteins retained
• 76 proteins
Discovery Cohort Results
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Immunoglobulinsuperfamilymember8Fragment
Amyloidlikeprotein1OSHomosapiensGNAPLP1PE4SV
NeurosecretoryproteinVGFOSHomosapiensGNVGFPE1
EndothelinBreceptorlikeprotein2OSHomosapiensGN
Scrapieresponsiveprotein1OSHomosapiensGNSCRG1P
Lymphocyteantigen6HOSHomosapiensGNLY6HPE2SV1
Isoform2ofCalsyntenin1OSHomosapiensGNCLSTN1
HaptoglobinOSHomosapiensGNHPPE1SV1HPT_HUMA
Alpha1antichymotrypsinOSHomosapiensGNSERPINA3PE
ComplementC3OSHomosapiensGNC3PE1SV2CO3_HU
Iggamma4chainCregionOSHomosapiensGNIGHG4PE1
Collagenalpha1IchainOSHomosapiensGNCOL1A1PE
ApolipoproteinEOSHomosapiensGNAPOEPE1SV1A
Isoform2ofFibrinogenalphachainOSHomosapiensG
IsoformGammaAofFibrinogengammachainOSHomosap
ProteinAMBPOSHomosapiensGNAMBPPE1SV1AMBP
Isoform2ofMajorprionproteinOSHomosapiensGNP
Alpha1BglycoproteinOSHomosapiensGNA1BGPE1SV4
IgkappachainVIIIregionVGFragmentOSHomosapie
Secretogranin1OSHomosapiensGNCHGBPE1SV2S
Heparincofactor2OSHomosapiensGNSERPIND1PE1SV
Isoform2ofGelsolinOSHomosapiensGNGSNGELS
MonocytedifferentiationantigenCD14OSHomosapiensG
ComplementcomponentC7OSHomosapiensGNC7PE1SV2
ChromograninAOSHomosapiensGNCHGAPE1SV7CMG
Secretogranin2OSHomosapiensGNSCG2PE1SV2SC
Insulinlikegrowthfactorbindingprotein2OSHomosa
Fibulin1OSHomosapiensGNFBLN1PE1SV4FBLN1_H
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IsoformCofFibulin1OSHomosapiensGNFBLN1FB
PeptidylprolylcistransisomeraseBOSHomosapiensG
Insulinlikegrowthfactorbindingprotein6OSHomosa
Zincalpha2glycoproteinOSHomosapiensGNAZGP1PE1
Phosphatidylethanolaminebindingprotein1OSHomosapi
SerumamyloidA4proteinOSHomosapiensGNSAA4PE1
Chitinase3likeprotein1OSHomosapiensGNCHI3L1PE
ProstaglandinH2DisomeraseOSHomosapiensGNPTGDSP
CellsurfaceglycoproteinMUC18OSHomosapiensGNMCAM
LumicanOSHomosapiensGNLUMPE1SV2LUM_HUMAN
LysozymeCOSHomosapiensGNLYZPE1SV1LYSC_HUM
Isoform2ofEGFcontainingfibulinlikeextracellula
ProcollagenCendopeptidaseenhancer1OSHomosapiens
Extracellularmatrixprotein1OSHomosapiensGNT
Metalloproteinaseinhibitor1OSHomosapiensGNT
InteralphaGlobulininhibitorH2OSHomosapiensGN
Vsetandtransmembranedomaincontainingprotein2AOS
Secretogranin3OSHomosapiensGNSCG3PE1SV3SC
ProteinFAM3COSHomosapiensGNFAM3CPE1SV1FAM
Neuralproliferationdifferentiationandcontrolprotei
Second Stage of Proteomics Project
Validation Cohort n=67
• PSP n=18, CBS n=7, MSA n=14, PD n=13, HC n=15
Proteins identified
• data filtered by removing proteins not identified in <50% of
subjects/not statistically significant
Match statistically significant proteins found in
both Discovery & Validation cohorts
Study proteins identified using immunoassays or
targeted spectrometry assays
Conclusion
• Early and accurate diagnosis in MSA is very important,
esp with emergence of disease modifying drugs
• Clinical diagnosis is inaccurate, particularly in the early
stages
• ‘Holy grail’- accurate diagnostic test
• Remains elusive; on-going work with established,
hypothesis testing biomarkers and hypothesis generating
markers from proteomics studies
• Combination of markers may be required
THANK YOU
Acknowledgements
RLWI
DRC
Gothenburg NHNN
Andrew Lees
Tom Warner
John Hardy
Rohan de Silva
Janice Holton
Helen Ling
Atbin Djamshidian
Alastair Noyce
Karen Doherty
Geshanti Honhamuni
Connie Luk
Iliyana Komsiyska
Karen Shaw
Jason Warren Henrik Zetterberg
Nick Fox
Johan Gobom
Cath Mummery Max Petzold
Jon Schott
Martin Rossor
Ross Paterson
Jamie Toombs
Katie Judd
Funding: PSP association, RLWI, Wolfson foundation award
Henry Houlden
Nick Wood
Kailash Bhatia
Patricia Limousin
Tom Foltynie
Simon Farmer
Paul Jarman
Paola Giunti
Chris Mathias
Gordon Ingle
Lucia Schottlaender
Mike Lunn
Miles Chapman

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