The suprastructure-function relationship between amyloid assemblies and their toxic and infectious potentials

Award Number
BB/S003657/1
Status / Stage
Completed
Dates
1 January 2019 -
28 February 2023
Duration (calculated)
04 years 01 months
Funder(s)
BBSRC (UKRI)
Funding Amount
£265,359.00
Funder/Grant study page
BBSRC UKRI
Contracted Centre
University of Sussex
Principal Investigator
Professor Louise Serpell
PI Contact
L.C.Serpell@sussex.ac.uk
PI ORCID
0000-0001-9335-7751
WHO Catergories
Understanding Underlying Disease
Disease Type
Dementia (Unspecified)

CPEC Review Info
Reference ID703
ResearcherReside Team
Published07/07/2023

Data

Award NumberBB/S003657/1
Status / StageCompleted
Start Date20190101
End Date20230228
Duration (calculated) 04 years 01 months
Funder/Grant study pageBBSRC UKRI
Contracted CentreUniversity of Sussex
Funding Amount£265,359.00

Abstract

This project will discover and quantify the structure-function relationship of amyloid aggregates in nanometre to micrometre range and address a long-standing question of why some amyloid are disease associated while others are tolerated by cells. We will test our hypothesis that, whether amyloid aggregates elicit toxicity and/or whether they are able to propagate as prions or prion-like particles, is determined by their structures in the mesoscopic length scales. We will systematically visualise suprastructure formation in aggregated samples of short amyloidogenic penta/hexa-peptide sequences, human amyloid-beta peptides, human alpha-synuclein, and yeast Sup35NM prion protein using force-curve based AFM imaging, combined with complementary methods including TEM and dynamic light scattering. From the resulting imaging and biophysical data sets, we will enumerate suprastructural parameters (e.g. distributions of length, width, morphology, twist, clustering, persistence length, deformation, modulus etc.) for each of the amyloid. In parallel, we will also perform a range of cellular assays to measure the effect of the same set of amyloid samples on cell viability (e.g. live-dead, internalisation, intracellular accumulation, transmission etc.). Finally, we will combine the structural and the biological/cellular parameters and perform principle component analysis, partial least squares analyses, and agglomerative hierarchical clustering (unsupervised machine-learning method) to discover hidden patterns and links in, and between, key structural parameters and key biological/cellular effects, and to show which and how much the suprastructural parameters are key biological determinants for amyloid aggregates. To further test the predictive power of our hypothesis, we will also find conditions that systematically trap different suprastructures and test their biological response in comparison to our model.