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Predicting Reactive Astrogliosis Propagation by Bayesian Computational Modeling: the Repeater Stations Model

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Abstract

Reactive astrogliosis occurs upon focal brain injury and in neurodegenerative diseases. The mechanisms that propagate reactive astrogliosis to distal parts of the brain, in a rapid wave that activates astrocytes and other cell types along the way, are not completely understood. It is proposed that damage-associated molecular patterns (DAMP) released by necrotic cells from the injury core have a major role in the reactive astrogliosis initiation but whether they also participate in reactive astrogliosis propagation remains to be determined. We here developed a Bayesian computational model to define the most probable model for reactive astrogliosis propagation. Starting with experimental data from GFAP-immunostained reactive astrocytes, we defined five types of astrocytes based on morphometrical cues and registered the position of each reactive astrocyte cell type in the hemisphere ipsilateral to the injured site after 3 and 7 days post-ischemia. We developed equations for the changes in DAMP concentration (due to diffusion, binding to receptors or degradation), soluble mediators secretion, and for the evolution reactive astrogliosis. We tested four predefined models based on abovementioned previous hypothesis and modifications to it. Our results showed that DAMP diffusion alone has not justified the reactive astrogliosis propagation as previously assumed. Only two models succeeded in accurately reproducing the experimentally measured data and they highlighted the role of microglia and the glial secretion of soluble mediators to sustain the reactive signal and activating neighboring astrocytes. Thus, our in silico analysis proposes that glial cells behave as repeater stations of the injury signal in order to propagate reactive astrogliosis.

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Acknowledgments

JA, LM, and AJR are researchers from CONICET (Argentina). We thank Biot. Andrea Pecile, and Manuel Ponce for the animal care, and Dr. Carla Bonavita for the proofreading of the manuscript. The authors also would like to thank Centro de Simulación Computacional para Aplicaciones Tecnólogicas (CSC-CONICET) for granting use of computational resources which allowed us to perform the in silico analysis included in this work.

Funding

This study was supported by grants CONICET PIP 0479 (AJR) and PIP 0260 (LM), FONCYT PICT 2015-1451 and PICT 2017-2203 (AJR), and UBACYT (AJR).

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Authors and Affiliations

  1. Laboratorio de Neuropatología Molecular, Instituto de Biología Celular y Neurociencia “Prof. E. De Robertis” UBA-CONICET, Facultad de Medicina, Universidad de Buenos Aires, Paraguay 2155 3er piso (1121), Buenos Aires, Argentina

    Jerónimo Auzmendi & Alberto Javier Ramos

  2. Instituto de Química Física de los Materiales, Medio Ambiente y Energía, Facultad de Ciencias Exactas y Naturales, UBA-CONICET, Universidad de Buenos Aires, Ricardo Guiraldes 2160 (1428), Buenos Aires, Argentina

    Luciano Moffatt

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  1. Jerónimo Auzmendi
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  2. Luciano Moffatt
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Correspondence to Luciano Moffatt or Alberto Javier Ramos.

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Animal care for this experimental protocol was in accordance with the NIH guidelines for the Care and Use of Laboratory Animals, the principles presented in the Guidelines for the Use of Animals in Neuroscience Research by the Society for Neuroscience, the ARRIVE guidelines, and it was approved by the CICUAL committee of the School of Medicine, University of Buenos Aires.

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Figure S1

Predicted pattern of DAMP, SM and receptor concentrations at different distances and time points after ischemia. The graphs show the predicted spatio-temporal pattern of DAMP (ω) concentration bound (B), free (F) and free receptor (R) at different distances from the core expressed in millimeters. Any temporal point can be predicted and this graph shows 0.5 to 7 days post ischemia. To simplify the graphs, only model 2 and model 4 are presented. Red lines indicate free signaling molecules, green lines indicate free receptors and blue lines indicate complexes between the signaling molecules and their receptors. Concentration values (c) are normalized (y) against the correspondent equilibrium constant (Keq) by the equation y = c/(c + Keq). Each line represents a sample from the posterior distribution of a signaling profile. (PNG 2888 kb)

High resolution image (TIF 4741 kb)

Figure S2

BCM predicted spatio-temporal pattern of reactive astrocyte type cell abundance. Graphs show the posterior predicted distribution of each reactive astrocyte type at different days after ischemia with their position in millimeters from the ischemic core. Any temporal point can be predicted and this graph shows 0.5 to 7 days post ischemia. To simplify the graphs, only model 2 and model 4 are presented. Astrocytic cell types are presented as color-coded being type I, II, III, IV and V colored in black, brown, red, green and blue, respectively. Each tracing represents a sample from the posterior distribution of the predictions of the models. (PNG 1800 kb)

High resolution image (TIF 3662 kb)

Figure S3 to S7

Graphical representation of the parameters distribution predicted by the different models. To simplify the graphs, only model 2 and model 4 are presented. Prior distribution appears in pink and posterior distribution appears cyan. Each of the figures has a correlation with each of the supplementary tables that list the values of prior distributions of the parameters. (PNG 91 kb)

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Figure S7

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Auzmendi, J., Moffatt, L. & Ramos, A.J. Predicting Reactive Astrogliosis Propagation by Bayesian Computational Modeling: the Repeater Stations Model. Mol Neurobiol 57, 879–895 (2020). https://doi.org/10.1007/s12035-019-01749-9

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