skip to main content

Title: Prion-like spreading of Alzheimer’s disease within the brain’s connectome
The prion hypothesis states that misfolded proteins can act as infectious agents that template the misfolding and aggregation of healthy proteins to transmit a disease. Increasing evidence suggests that pathological proteins in neurodegenerative diseases adopt prion-like mechanisms and spread across the brain along anatomically connected networks. Local kinetic models of protein misfolding and global network models of protein spreading provide valuable insight into several aspects of prion-like diseases. Yet, to date, these models have not been combined to simulate how pathological proteins multiply and spread across the human brain. Here, we create an efficient and robust tool to simulate the spreading of misfolded protein using three classes of kinetic models, the Fisher–Kolmogorov model, the Heterodimer model and the Smoluchowski model. We discretize their governing equations using a human brain network model, which we represent as a weighted Laplacian graph generated from 418 brains from the Human Connectome Project. Its nodes represent the anatomic regions of interest and its edges are weighted by the mean fibre number divided by the mean fibre length between any two regions. We demonstrate that our brain network model can predict the histopathological patterns of Alzheimer’s disease and capture the key characteristic features of finite-element brain more » models at a fraction of their computational cost: simulating the spatio-temporal evolution of aggregate size distributions across the human brain throughout a period of 40 years takes less than 7 s on a standard laptop computer. Our model has the potential to predict biomarker curves, aggregate size distributions, infection times, and the effects of therapeutic strategies including reduced production and increased clearance of misfolded protein. « less
Authors:
; ; ; ;
Award ID(s):
1727268
Publication Date:
NSF-PAR ID:
10201409
Journal Name:
Journal of The Royal Society Interface
Volume:
16
Issue:
159
Page Range or eLocation-ID:
20190356
ISSN:
1742-5689
Sponsoring Org:
National Science Foundation
More Like this
  1. Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare theirmore »pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.« less
  2. Amyloids are fibrous cross-β protein aggregates that are capable of proliferation via nucleated polymerization. Amyloid conformation likely represents an ancient protein fold and is linked to various biological or pathological manifestations. Self-perpetuating amyloid-based protein conformers provide a molecular basis for transmissible (infectious or heritable) protein isoforms, termed prions. Amyloids and prions, as well as other types of misfolded aggregated proteins are associated with a variety of devastating mammalian and human diseases, such as Alzheimer's, Parkinson's and Huntington's diseases, transmissible spongiform encephalopathies (TSEs), amyotrophic lateral sclerosis (ALS) and transthyretinopathies. In yeast and fungi, amyloid-based prions control phenotypically detectable heritable traits. Simplicitymore »of cultivation requirements and availability of powerful genetic approaches makes yeast Saccharomyces cerevisiae an excellent model system for studying molecular and cellular mechanisms governing amyloid formation and propagation. Genetic techniques allowing for the expression of mammalian or human amyloidogenic and prionogenic proteins in yeast enable researchers to capitalize on yeast advantages for characterization of the properties of disease-related proteins. Chimeric constructs employing mammalian and human aggregation-prone proteins or domains, fused to fluorophores or to endogenous yeast proteins allow for cytological or phenotypic detection of disease-related protein aggregation in yeast cells. Yeast systems are amenable to high-throughput screening for antagonists of amyloid formation, propagation and/or toxicity. This review summarizes up to date achievements of yeast assays in application to studying mammalian and human disease-related aggregating proteins, and discusses both limitations and further perspectives of yeast-based strategies.« less
  3. Amyloid cross-seeding, as a result of direct interaction and co-aggregation between different disease-causative peptides, is considered as a main mechanism for the spread of the overlapping pathology across different cells and tissues between different protein-misfolding diseases (PMDs). Despite the biomedical significance of amyloid cross-seeding in amyloidogenesis, it remains a great challenge to discover amyloid cross-seeding systems and reveal their cross-seeding structures and mechanisms. Herein, we are the first to report that GNNQQNY – a short fragment from yeast prion protein Sup35 – can cross-seed with both amyloid-β (Aβ, associated with Alzheimer's disease) and human islet amyloid polypeptide (hIAPP, associated withmore »type II diabetes) to form β-structure-rich assemblies and to accelerate amyloid fibrillization. Dry, steric β-zippers, formed by the two β-sheets of different amyloid peptides, provide generally interactive and structural motifs to facilitate amyloid cross-seeding. The presence of different steric β-zippers in a variety of GNNQQNY-Aβ and GNNQQNY-hIAPP assemblies also explains amyloid polymorphism. In addition, alteration of steric zipper formation by single-point mutations of GNNQQNY and interactions of GNNQQNY with different Aβ and hIAPP seeds leads to different amyloid cross-seeding efficiencies, further confirming the existence of cross-seeding barriers. This work offers a better structural-based understanding of amyloid cross-seeding mechanisms linked to different PMDs.« less
  4. Protein aggregation is associated with a growing list of human diseases. A substantial fraction of proteins in eukaryotic proteomes constitutes a proteostasis network—a collection of proteins that work together to maintain properly folded proteins. One of the overarching functions of the proteostasis network is the prevention or reversal of protein aggregation. How proteins aggregate in spite of the anti-aggregation activity of the proteostasis machinery is incompletely understood. Exposed hydrophobic patches can trigger degradation by the ubiquitin-proteasome system, a key branch of the proteostasis network. However, in a recent study, we found that model glycine (G)-rich or glutamine/asparagine (Q/N)-rich prion-like domainsmore »differ in their susceptibility to detection and degradation by this system. Here, we expand upon this work by examining whether the features controlling the degradation of our model prion-like domains generalize broadly to G-rich and Q/N-rich domains. Experimentally, native yeast G-rich domains in isolation are sensitive to the degradation-promoting effects of hydrophobic residues, whereas native Q/N-rich domains completely resist these effects and tend to aggregate instead. Bioinformatic analyses indicate that native G-rich domains from yeast and humans tend to avoid degradation-promoting features, suggesting that the proteostasis network may act as a form of selection at the molecular level that constrains the sequence space accessible to G-rich domains. However, the sensitivity or resistance of G-rich and Q/N-rich domains, respectively, was not always preserved in their native protein contexts, highlighting that proteins can evolve other sequence features to overcome the intrinsic sensitivity of some LCDs to degradation.« less
  5. Althouse, Benjamin Muir (Ed.)
    Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number ( R ). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for whichmore »habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R . We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R , centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R , alter host movements, or both.« less