Biology subjects

Kuhl, E.

Publications and source records attributed to Kuhl, E..

3 recordsLinked to original sources

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↗