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A method for inferring regional origins of neurodegeneration.

ABSTRACT: Alzheimer's disease, the most common form of dementia, is characterized by the emergence and spread of senile plaques and neurofibrillary tangles, causing widespread neurodegeneration. Though the progression of Alzheimer's disease is considered to be stereotyped, the significant variability within clinical populations obscures this interpretation on the individual level. Of particular clinical importance is understanding where exactly pathology, e.g. tau, emerges in each patient and how the incipient atrophy pattern relates to future spread of disease. Here we demonstrate a newly developed graph theoretical method of inferring prior disease states in patients with Alzheimer's disease and mild cognitive impairment using an established network diffusion model and an L1-penalized optimization algorithm. Although the 'seeds' of origin using our inference method successfully reproduce known trends in Alzheimer's disease staging on a population level, we observed that the high degree of heterogeneity between patients at baseline is also reflected in their seeds. Additionally, the individualized seeds are significantly more predictive of future atrophy than a single seed placed at the hippocampus. Our findings illustrate that understanding where disease originates in individuals is critical to determining how it progresses and that our method allows us to infer early stages of disease from atrophy patterns observed at diagnosis.

SUBMITTER: Torok J

PROVIDER: S-EPMC5837438 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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A method for inferring regional origins of neurodegeneration.

Torok Justin J Maia Pedro D PD Powell Fon F Pandya Sneha S Raj Ashish A

Brain : a journal of neurology 20180301 3

Alzheimer's disease, the most common form of dementia, is characterized by the emergence and spread of senile plaques and neurofibrillary tangles, causing widespread neurodegeneration. Though the progression of Alzheimer's disease is considered to be stereotyped, the significant variability within clinical populations obscures this interpretation on the individual level. Of particular clinical importance is understanding where exactly pathology, e.g. tau, emerges in each patient and how the inci ...[more]

PMID: 29409009

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Project description:In neurodegenerative diseases, it remains unclear why certain brain regions are selectively vulnerable to protein aggregation. In transgenic mice expressing human A53T ?-synuclein, the brainstem and spinal cord develop the most prominent ?-synuclein inclusions which correlate with age-dependent motor dysfunction. Herein we present the novel finding that this selective aggregation is in part dependent on the inability of chaperone-mediated autophagy (CMA) to effectively degrade ?-synuclein in these brain regions. Lysosomal assays revealed that CMA activity was significantly decreased in aggregation-prone regions compared to the remainder of the brain. Previously, CMA activity has been shown to be proportional to levels of the CMA receptor Lamp-2a. Using antibodies, brain tissue from Lamp-2a null mice, enzymatic deglycosylation, and mass spectrometry, we identified Lamp2a as a novel 72kDa glycoprotein in the mouse brain. Examination of Lamp-2a levels revealed differences in expression across brain regions. The brainstem and the spinal cord had a more than three-fold greater levels of Lamp-2a as compared to regions less vulnerable to aggregation and exhibited a selective upregulation of Lamp-2a during development of ?-synuclein inclusions. Despite this dynamic response of Lamp-2a, the levels of substrates bound to the brain lysosomes as well as the rates of substrate uptake and degradation were not proportional to the levels of Lamp-2a. These regional differences in CMA activity and Lamp-2a expression were found in both non-transgenic mice as well as A53T ?-syn mice. Therefore, these are inherent variations and not a transgene-specific effect. However, differences in CMA activity may render select brain regions vulnerable to homeostatic dysfunction in the presence of stressors such as overexpression of human A53T ?-syn. Collectively, the data provide a potential mechanism to explain the dichotomy of vulnerability or resistance that underlies brain regions during aggregate formation in neurodegenerative disease.

Project description:BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

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A method for inferring regional origins of neurodegeneration.

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A method for inferring regional origins of neurodegeneration.

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