Biology subjects

Kuhl, E.

Publications and source records attributed to Kuhl, E..

40 records · Page 3Linked to original sources

bioRxiv2020

Protein-protein interactions in neurodegenerative diseases: a conspiracy theory.

Neurodegenerative diseases such as Alzheimers or Parkinsons are associated with the prion-like propagation and aggregation of toxic proteins. A long standing hypothesis that amyloid-beta drives Alzheimers disease has proven the subject of contemporary controversy; leading to new research in both the role of tau protein and its interaction with amyloid-beta. Conversely, recent work in mathematical modeling has demonstrated the relevance of nonlinear reaction-diffusion type equations to capture essential features of the disease. Such approaches have been further simplified, to network-based models, and offer researchers a powerful set of computationally tractable tools with which to investigate neurodegenerative disease dynamics. Here, we propose a novel, coupled network-based model for a two-protein system that includes an enzymatic interaction term alongside a simple model of aggregate transneuronal damage. We apply this theoretical model to test the possible interactions between tau proteins and amyloid-beta and study the resulting coupled behavior between toxic protein clearance and proteopathic phenomenology. Our analysis reveals ways in which amyloid-beta and tau proteins may conspire with each other to enhance the nucleation and propagation of different diseases, thus shedding new light on the importance of protein clearance and protein interaction mechanisms in prion-like models of neurodegenerative disease. Author SummaryIn 1906 Dr. Alois Alzheimer delivered a lecture to the Society of Southwest German Psychiatrists. Dr. Alzheimer presented the case of Ms. Auguste Deter; her symptoms would help to define Alzheimers disease (AD). Over a century later, with an aging world population, AD is at the fore of global neurodegenerative disease research. Previously, toxic amyloid-beta protein (A{beta}) was thought to be the primary driver of AD development. Recent research suggests that another protein, tau, plays a fundamental role. Toxic tau protein contributes to cognitive decline and appears to interact with toxic A{beta}; research suggests that toxic A{beta} may further increase the effects of toxic tau. Theoretical mathematical models are an important part of neurodegenerative disease research. Such models: enable extensible computational exploration; illuminate emergent behavior; and reduce research costs. We have developed a novel, theoretical mathematical model of two interacting species of proteins within the brain. We analyze the mathematical model and demonstrate a computational implementation in the context of A{beta}-tau interaction in the brain. Our model clearly suggests that: the removal rate of toxic protein plays a critical role in AD; and the A{beta}-tau conspiracy theory is a nuanced, and exciting path forward for Alzheimers disease research.

neuroscience↗

bioRxiv2019

Spatially-extended nucleation-aggregation-fragmentation models for the dynamics of prion-like neurodegenerative protein-spreading in the brain and its connectome

The prion-like hypothesis of neurodegenerative diseases states that the accumulation of misfolded proteins in the form of aggregates is responsible for tissue death and its associated neurodegenerative pathology and cognitive decline. Some disease-specific misfolded proteins can interact with healthy proteins to form long chains that are transported through the brain along axonal pathways. Since aggregates of different sizes have different transport properties and toxicity, it is important to follow independently their evolution in space and time. Here, we model the spreading and propagation of aggregates of misfolded proteins in the brain using the general Smoluchowski theory of nucleation, aggregation, and fragmentation. The transport processes considered here are either anisotropic diffusion along axonal bundles or discrete Laplacian transport along a network. In particular, we model the spreading and aggregation of both amyloid-{beta} and{tau} molecules in the brain connectome. We show that these two models lead to different size distributions and different propagation along the network. A detailed analysis of these two models reveals the existence of four different stages with different dynamics and invasive properties.

neuroscience↗

bioRxiv2019

Classifying drugs by their arrhythmogenic risk using machine learning

Abstract.An undesirable side effect of drugs are cardiac arrhythmias, in particular a condition called torsades de pointes. Current paradigms for drug safety evaluation are costly, lengthy, and conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create an easy-to-use risk assessment diagram to quickly and reliable stratify the pro-arrhythmic potential of new and existing drugs. We capitalize on recent developments in machine learning and integrate information across ten orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay of two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 23 common drugs, exclusively on the basis of their concentrations at 50% current block. Our new risk assessment diagram explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the pro-arrhythmic potential of new drugs. Our study shapes the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

biophysics↗

bioRxiv2019

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 trigger the misfolding and aggregation of healthy proteins to transmit a variety of neurodegenerative diseases. Increasing evidence suggests that pathogenic proteins in Alzheimers disease adapt prion-like mechanisms and spread across the brain along an anatomically connected network. Local kinetics models of protein misfolding and global network models of protein diffusion provide valuable insight into the dynamics 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 model the prion-like spreading of Alzheimers disease by combining misfolding kinetics and network diffusion through a connectivity-weighted Laplacian graph created from 418 brains of the Human Connectome Project. The nodes of the graph represent anatomic regions of interest and the edges represent their con-nectivity, weighted by the mean fiber number divided by the mean fiber length. We show that our brain network model correctly predicts the neuropathological pattern of Alzheimers disease and captures the key characteristic features of whole brain models at a fraction of their computational cost. To illustrate the potential of brain network modeling in neurodegeneration, we simulate biomarker curves, infection times, and two promising therapeutic strategies to delay the onset of neurodegeneration: reduced production and increased clearance of misfolded protein.

neuroscience↗