CSF assays to detect patients with seeding- competent aggregates of - - PowerPoint PPT Presentation

Claudio Soto, PhD

Mitchell Center for Alzheimer’s disease and Related Brain Disorders, Dept of Neurology McGovern Medical School, University of Texas at Houston and Amprion, Inc

CSF assays to detect patients with seeding- competent aggregates of Abeta, tau and alpha- synuclein

Prion diseases

Protein Misfolding and aggregation

Misfolded Aggregates deposited in the brain

Alzheimer’s disease Parkinson’s disease Huntington’s disease Amyothropic lateral sclerosis

Soto (2003) Nature Rev Neurosci. 4:49-60

Soluble protein Misfolded

• ligomers

Protein misfolding in Neurodegenerative diseases

Amyloid fibrils

Amyloid plaques Cellular dysfunction Tissue damage

Protofibrils

Detection of oligomers: Opportunities and challenges

Formation of misfolded protein oligomers is possibly the earliest pathological event in neurodegenerative diseases and likely begins decades before clinical symptoms. Misfolded oligomers are thought to be the most biologically active structures in neurodegeneration. Soluble oligomers are likely circulating in biological fluids, offering an opportunity for non-invasive detection. Misfolded oligomers are highly heterogeneous in size, structure and biological activity. Some oligomers might be on-pathway and others off-pathway in the amyloid fibrillization process. Misfolded oligomers are likely transient, unstable and exist in a much lower concentration than the respective normal monomeric proteins.

Opportunities Challenges

How to detect small quantities of misfolded proteins in biological fluids of patients affected by neurodegenerative diseases?

Our strategy is to use the ability of misfolded protein aggregates to seed the conversion of the normal protein to enable their high sensitive and specific detection in biological fluids.

Our strategy is to use the ability of misfolded oligomers to seed polymerization

• f monomeric protein to enable their high sensitivity detection.

No seeds Patient’s samples containing seeds

Time Aggregation

No seeds + patient’s samples

Our strategy for sensitive detection

Normal protein Incubation Growing

• f units

Incubation Growing

• f units

+

Protein Misfolding Cyclic Amplification (PMCA)

Seeds Soto et al. (2002) Trends Neurosci. 25:390-394 Fragmentation Multiplication

• f units

Multiplication

• f units

Fragmentation

Applications of PMCA For Prion diseases

Application of PMCA for sensitive detection of prions

Applications of PMCA for Detection of Amyloid-beta Oligomers in Alzheimer’s disease

Alzheimer’s disease neurological alterations

Macroscopic changes Brain atrophy Microscopic changes

0h 5h 10h 24h

200 nm

4KDa 170Kda 0 5

Time (h)

Preparation of Synthetic Ab Oligomers

Salvadores et al. (2014) Cell Reports 7: 261

Current status of Aβ-PMCA

Limit of detection below 10 atto-moles

Alzheimer’s disease (AD) Non-Neurodegenerative Controls (NND) Non-AD Neurodegenerative Controls (NAND)

Detection of Ab Oligomers by Aβ-PMCA in CSF

Salvadores et al. (2014) Cell Reports 7: 261 *** *** *** ***

Sensitivity and Specificity in CSF samples

AD n=50 NND n=37 NAND n=41 (7 PD, 5 ALS, 6 FTD, 5 PSP, 4 HD, 4 DLB, 5 SCA, 5 PPA)

Estimation of sensitivity, specificity and predictive value for Aβ-PMCA using CSF samples

Groups Sensitivity2 Specificity2 Positive Predictive Value2 Negative Predictive Value2

AD vs NAND 100.0% 94.6% 96.2% 100.0% AD vs NND 90.0% 84.2% 88.2% 86.5% AD vs All3 90.0% 92.0% 88.2% 93.2%

Salvadores et al. (2014) Cell Reports 7: 261

FTD: Frontotemporal dementia (Tau aggregates) PD: Parkinson disease and Lewy bodies dementia (α-synuclein aggregates) HD: Huntington’s disease (Huntingtin aggregates) ALS: Amyotrophic lateral sclerosis (SOD and TDP43 aggregates)

*** ***

Specificity against samples that may contain other seeds

Studies of Aβ-PMCA specificity using synthetic aggregates implicated in the two most prevalent protein misfolding diseases besides AD, i.e. Parkinson disease and type 2 diabetes associated to the aggregation of α-synuclein and amylin, respectively.

Specificity of Aβ-PMCA assay against cross-seeding

Blood represents the most convenient fluid for a biochemical diagnosis of Alzheimer’s disease

Why?

✓ Blood offers the best option for a routine, non-invasive test ✓ It is very well accepted that infectious prions are present in blood of animals and

humans and can be detected by PMCA

✓ Aβ has been shown to be present in blood and contribute to brain pathology ✓ Aβ can cross the blood-brain barrier in both directions ✓ Labeled Aβ injected in blood can be retrieved in brain plaques

But.. It is technically very challenging

✓ Blood is a very complex fluid with many other component that interfere with Aβ

aggregation assay

✓ It is likely that the amount of misfolded Aβ oligomers circulating in blood is very

low and its detection will be confounded by the larger concentration of soluble Aβ

✓ Misfolded Aβ oligomers are presumably bound to other proteins, making difficult

its detection

Towards a blood-based diagnosis of AD

Plasma Aβ-PMCA requires a pre-capture step

Pre clearing the Blood Plasma 3000 rpm X 15 min

1:1 dilution in PBS T(0.1%) + PI 100 µl/well in duplicates

Aβ-PMCA

200 µl BP

ELISA plates coated with sequence or conformational antibodies 1:1 dilution in 2X PBS + PI + 1% NP40 Incubation with antibody (sequence or conformational) coated beads

16 h at 22 °C

500 µl BP

Pre-clearing the Blood Plasma Beads washed re-suspended in 20 ul

• f aggregation buffer

10 ul added Into two wells

Aβ-PMCA Strategy 1: Using antibody-coated plates Strategy 2: Immuno-precipitation and concentration by antibody-coated beads

Minimum detectable amount of Aβ oligomers = 20pg, equivalent to 1.1 x 10-16 moles (assuming an average molecular weight of 170KDa for the oligomers) and extent of seeding is proportional to the quantity of seeds

Sensitivity of Aβ-PMCA in spiked plasma

Aggregation, % Time, h

Alzheimer’s disease patients

93.3% sensitivity; 90% specificity

Detection of Aβ oligomers in AD plasma

***

Applications of PMCA for Detection of Tau Oligomers

T50, hours

Cyclic Amplification of Tau Misfolding (Tau-PMCA)

Initial detection limit 0.125 pg of Tau seeds, which is equivalent to 1 atto- mol (assuming a MW of 135K). Direct relationship between the amount of oligomers and the parameters

• f Tau-PMCA

Specificity of Tau-PMCA assay against cross-seeding

Reproducibility of the Tau-PMCA assay

Experiments were done in triplicate with 2 different Tau seeds, at 4 distinct times, in buffer or CSF, with or without freezing/thawing, and with 5 different concentrations

• f seeds. No significant differences were observed in any condition.

** **

1 2 3

M a x i m u m a g g r e g a t i o n , f l u

• r e

s c e n c e u n i t s

Preliminary results with human CSF samples

(4 PSP, 1 FTD, 5 CBD and 1 CTE)

Applications of PMCA for Detection of α-Synuclein Oligomers

Brain alterations in Parkinson’s disease

α-synuclein aggregates in Lewy bodies

Large

• ligomers

Monomer

17 22 120 135 75

KDa

Preparation of Synthetic α-syn aggregates

O h 96 h 240 h

50 nm 50 nm 50 nm

Shahnawaz et al. (2017) JAMA Neurol 74: 163

αSyn-PMCA in automatic machine

Detection limit below 2 pg (15 atto-mol, assuming a MW of 135K). Direct relationship between the amount of oligomers and the parameters

• f αSyn-PMCA.

This system allow continuous monitoring of the entire plate and using a plate stacker we can run up to 20 plates per machine.

Specificity of αSyn-PMCA

No signal was detectable when the reaction was incubated with Aβ or Tau oligomers. The concentration of seeds added was very high (higher than the highest amount shown in the previous graph). These seeds produce a very large signal in the respective Aβ-PMCA and Tau-PMCA assays. The results indicate that αSyn-PMCA is very specific to detect αSyn

• ligomers.

Shahnawaz et al. (2017) JAMA Neurol 74: 163

αSyn-PMCA in CSF

Using optimized conditions, we can detect as little as 0.02 pg of αSyn

• ligomers in CSF, which translate to around 0.15 atto-mols (assuming a

MW of 135 KDa). Clear signal was observed in the samples from patients affected by Parkinson’s disease and no signal in controls. αSyn-PMCA signal can be reduced by immuno-depletion of oligomers. Aggregation, % Time, h Aggregation, % Time, h

Shahnawaz et al. (2017) JAMA Neurol 74: 163

86% sensitivity Results of a blinded study in CSF

Parkinson’s Disease Disease Controls

# #

***

# #

# These two patients developed symptoms of PD 1 and 4 years after sample was collected, one of them was confirmed by autopsy.

Fluorescence

Shahnawaz et al. (2017) JAMA Neurol 74: 163

Sensitivity, specificity and predictive values

Parameter Value 95% confidence intervals Sensitivity for PD 88.5% 79.2 – 94.6% Sensitivity for DLB 100.0% 94.9-100.0% Sensitivity for MSA 80% 79.5-94.6% Specificity against disease controls 96.9% 89.3-99.6% Specificity against controls and neurodegenerative diseases 94.0% 86.5-98.0% Positive predictive value 94.7% 88.0-98.3% Negative predictive value 87.6% 78.7-93.7% Sensitivity, Specificity and predictive value for αSyn-PMCA in CSF samples

Data was analyzed by ROC curves using results from 76 samples from PD patients, 10 DLB, 10 MSA and 65 control patients affected by unrelated diseases and 18 from other neurodegenerative diseases (except AD). Two samples originally provided as controls were later confirmed to be taken at the pre-clinical stage of PD or DLB. These samples were included in the disease group for the purpose of the analysis. Predictive positive and negative values were determined considering all synucleinopathies samples and controls affected by other neurological and neurodegenerative diseases (except AD).

Shahnawaz et al. (2017) JAMA Neurol 74: 163

aSyn-PMCA correlate with disease progression

Hoehn &Yahr scale

rs = -0.5354 P= 0.0058

T50, h

rs = -0.3608 P=0.0189

Japanese Cohort German Cohort Data suggest a relationship between disease severity at the moment of CSF collection and the time to reach 50% aggregation in the αSyn-PMCA assay. Further studies need to be done to confirm this result, hopefully with longitudinal samples.

Shahnawaz et al. (2017) JAMA Neurol 74: 163

PD=109 Controls= 82

Sensitivity=95.41% Specificity= 90.24% Positive Predictive Value=92.85% Negative Predictive Value= 93.67% False positives False negatives

Parkinson’s disease Control samples

Maximum fluorescence Maximum fluorescence

Validation of aSyn PMCA with MJF Samples

Aggregation, ThT Fluorescence Time

T50 : time required to reach 50% of maximum aggregation. Provides information about the amount of seeds present in the mixture Maximum fluorescence : Signal at plateau level. Provides information about the presence or absence of seeds (positive/negatives). It is also dependent on the structure of the aggregates in terms of their accessibility for ThT binding.

Explanation of PMCA analysis

Interpretation of αSyn-PMCA results

Aggregation, % Time, h

. 0 1 . 1 1 1 1 1 4 6 8

α-synuclein oligomers, pg T50, h

Quantification of αSyn oligomers

Aggregation, %

Differentiation between Parkinson’s disease and Multiple system atrophy by the characterization of α-Synuclein conformational strains

Parkinson’s disease Controls Multiple System Atrophy Maximum fluorescence

Distinguishing PD and MSA by αSyn-PMCA

50 100 150 200 250 300 350 400 1000 2000 3000 4000 5000 6000 7000

MSA PD

Time (h) Fluorescence (AU)

• r e

s c e n c e ( A U ) L C O 5

• P

D L C O 5

• M

S A 5 5 6 6 5 7 7 5 8 5 1 W a v e l e n g h t , n m F l u

• r e

s c e n c e ( A U ) L C O 7

• P

D L C O 7

• M

S A

Differentiating PD and MSA strains: amyloid binding dyes

PD MSA 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 PD MSA 1 2 3 4 5 1 2 3 4 5 PD MSA KDa KDa KDa

10 15 25 35 40 10 15 25 35 40 10 15 25 35 40

3 6 14

1 2 3 1 2 3 PD MSA KDa

Differentiating PD and MSA strains: proteolytic resistance

Differentiating PD and MSA strains: structural features

PD MSA

1600 1620 1640 1660 1680 1700 .0 .5 .0 .5 .0 .5

Wavenumber (cm-1) 1700 1680 1660 1640 1620 1600 2.5 2.0 1.5 1.0 0.5 Absorbance PD MSA

2 1 2 2 5 2 4

• 5
• 2

5 2 5 5 P D M S A M

• l a

r e l l i p t i c i t y

✓ Cyclic amplification of protein misfolding (PMCA) is a platform technology that

can be adapted to detect disease-relevant aggregates implicated in various protein misfolding disorders.

✓ PMCA has been optimized for detection of PrPSc, Aβ, Tau and α-synuclein in

biological fluids of patients affected by diverse neurodegenerative diseases.

✓ PrP-PMCA enables highly sensitive and specific detection of infectious prions in

human samples of blood and urine, as well as during all the pre-symptomatic phase

• f the disease in a primate model of human vCJD.

✓ Optimized Aβ-PMCA enables detection of Aβ misfolded oligomers in CSF and

plasma of patients affected by AD with high sensitivity and specificity.

✓ Optimized αSyn-PMCA permit high sensitive and specific detection of α-

Synuclein misfolded oligomers in CSF of PD patients. Signal correlates with disease progression.

✓ PMCA may be useful to detect the presence of misfolded oligomers in biological

fluids, determine their quantity, and identify the type of conformational strains. This might be useful for disease diagnosis, monitor disease progression, pre-clinical diagnosis, evaluate efficacy of treatments, target engagement in clinical trials and personalized medicine.

Conclusions

Acknowledgments

Rodrigo Morales, PhD Ines Moreno-Gonzalez, PhD Sandra Pritzkow, PhD Mohammad Shahnawaz, PhD Abhisek Mukherjee, PhD Enrique Armijo, PhD Luis Concha, PhD Karina Cuanalo, PhD Fei Wang, PhD Marcelo Chacon, PhD Thomas Eckland, PhD George Edwards Carlos Kramm Nicolas Mendez Jonathan Schulz Adam Lyon Ruben Gomez Gutierrez Nazaret Gamez Ruiz Katerine Do Paulina Soto Damian Gorski Michelle Pinho Prakruti Rabadia Gloria Galvan Jennifer Bales

Former lab members Funding

Joaquin Castilla, PhD Gabriela Saborio, MD Claudio Hetz, PhD Lisbell Estrada, PhD Fabio Moda, PhD Claudia Duran-Aniotz, PhD Paula Saa, PhD Celine Adessi, PhD Bruno Permanne, PhD Kinsey Maundrell, PhD Sylvain Bieler, PhD Leoncio Vergara, MD Manuel Camacho, PhD Kristi Green, PhD Veer Gupta, PhD Raphaele Buser, PhD Milene Russelaskis, PhD Macarena Lolas, MD Veronica Garcia, PhD Dennisse Gonzalez Marcelo Barria, PhD Natalia Salvadores, PhD Baian Chen, PhD Zane Martin, PhD Rodrigo Diaz, PhD Kyung-Won Park, PhD Ping Ping Hu, PhD Diego Morales, PhD Javiera Bravo, PhD Charles Mays, PhD Abha Sood, PhD Andrea Flores Uffaf Khan Jorge De Castro Laurence Anderes NIH (NINDS, NIA, NIAID, NIGMS), US Department of Defense, Mitchell Foundation, CART Foundation, Alzheimer’s Association, PrioNet Canada/Merck Serono, Michael J. Fox Foundation, ALS Association, Huffington Foundation