Axonal transport during injury on a theoretical axon.

TASEP-LK axonopathy kinesins microtubules neurotransmission traumatic brain injury

Journal

Frontiers in cellular neuroscience
ISSN: 1662-5102
Titre abrégé: Front Cell Neurosci
Pays: Switzerland
ID NLM: 101477935

Informations de publication

Date de publication:
2023
Historique:
received: 02 05 2023
accepted: 12 07 2023
medline: 28 8 2023
pubmed: 28 8 2023
entrez: 28 8 2023
Statut: epublish

Résumé

Neurodevelopment, plasticity, and cognition are integral with functional directional transport in neuronal axons that occurs along a unique network of discontinuous polar microtubule (MT) bundles. Axonopathies are caused by brain trauma and genetic diseases that perturb or disrupt the axon MT infrastructure and, with it, the dynamic interplay of motor proteins and cargo essential for axonal maintenance and neuronal signaling. The inability to visualize and quantify normal and altered nanoscale spatio-temporal dynamic transport events prevents a full mechanistic understanding of injury, disease progression, and recovery. To address this gap, we generated DyNAMO, a Dynamic Nanoscale Axonal MT Organization model, which is a biologically realistic theoretical axon framework. We use DyNAMO to experimentally simulate multi-kinesin traffic response to focused or distributed tractable injury parameters, which are MT network perturbations affecting MT lengths and multi-MT staggering. We track kinesins with different motility and processivity, as well as their influx rates, in-transit dissociation and reassociation from inter-MT reservoirs, progression, and quantify and spatially represent motor output ratios. DyNAMO demonstrates, in detail, the complex interplay of mixed motor types, crowding, kinesin off/on dissociation and reassociation, and injury consequences of forced intermingling. Stalled forward progression with different injury states is seen as persistent dynamicity of kinesins transiting between MTs and inter-MT reservoirs. DyNAMO analysis provides novel insights and quantification of axonal injury scenarios, including local injury-affected ATP levels, as well as relates these to influences on signaling outputs, including patterns of gating, waves, and pattern switching. The DyNAMO model significantly expands the network of heuristic and mathematical analysis of neuronal functions relevant to axonopathies, diagnostics, and treatment strategies.

Identifiants

pubmed: 37636588
doi: 10.3389/fncel.2023.1215945
pmc: PMC10450981
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1215945

Informations de copyright

Copyright © 2023 Chandra, Chatterjee, Olmsted, Mukherjee and Paluh.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Soumyadeep Chandra (S)

Electrical and Computer Science Engineering, Purdue University, West Lafayette, IN, United States.

Rounak Chatterjee (R)

Department of Electronics, Electrical and Systems Engineering, University of Birmingham, Birmingham, United Kingdom.

Zachary T Olmsted (ZT)

Nanobioscience, College of Nanoscale Science and Engineering, State University of New York Polytechnic Institute, Albany, NY, United States.
Department of Neurosurgery, Ronald Reagan UCLA Medical Center, University of California, Los Angeles, Los Angeles, CA, United States.

Amitava Mukherjee (A)

Nanobioscience, College of Nanoscale Science and Engineering, State University of New York Polytechnic Institute, Albany, NY, United States.
School of Computing, Amrita Vishwa Vidyapeetham (University), Kollam, Kerala, India.

Janet L Paluh (JL)

Nanobioscience, College of Nanoscale Science and Engineering, State University of New York Polytechnic Institute, Albany, NY, United States.

Classifications MeSH