{"doi":"10.1115/1.4056915","title":"Dynein Dysfunction Prevents Maintenance of High Concentrations of Slow Axonal Transport Cargos at the Axon Terminal: A Computational Study","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Here, we report computational studies of bidirectional transport in an axon, specifically focusing on predictions when the retrograde motor becomes dysfunctional. We are motivated by reports that mutations in dynein-encoding genes can cause diseases associated with peripheral motor and sensory neurons, such as type 2O Charcot-Marie-Tooth disease. We use two different models to simulate bidirectional transport in an axon: an anterograde-retrograde model, which neglects passive transport by diffusion in the cytosol, and a full slow transport model, which includes passive transport by diffusion in the cytosol. As dynein is a retrograde motor, its dysfunction should not directly influence anterograde transport. However, our modeling results unexpectedly predict that slow axonal transport fails to transport cargos against their concentration gradient without dynein. The reason is the lack of a physical mechanism for the reverse information flow from the axon terminal, which is required so that the cargo concentration at the terminal could influence the cargo concentration distribution in the axon. Mathematically speaking, to achieve a prescribed concentration at the terminal, equations governing cargo transport must allow for the imposition of a boundary condition postulating the cargo concentration at the terminal. Perturbation analysis for the case when the retrograde motor velocity becomes close to zero predicts uniform cargo distributions along the axon. The obtained results explain why slow axonal transport must be bidirectional to allow for the maintenance of concentration gradients along the axon length. Our result is limited to small cargo diffusivity, which is a reasonable assumption for many slow axonal transport cargos (such as cytosolic and cytoskeletal proteins, neurofilaments, actin, and microtubules) which are transported as large multiprotein complexes or polymers.</jats:p>","journal":"Journal of Biomechanical Engineering","year":2023,"id":33121,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":1,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":173988,"name":"Andrey V. Kuznetsov","orcid":"0000-0002-2692-6907","position":1,"is_corresponding":false},{"id":173987,"name":"Ivan A. Kuznetsov","orcid":"0000-0003-0023-3405","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36795013","pmcid":null,"openalex_id":"https://openalex.org/W4321002291","authors":[],"funders":[{"funder_name":"Division of Chemical, Bioengineering, Environmental, and Transport Systems","grant_id":"CBET-2042834","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"F30 MH122076-01","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"F30 MH122076","title":null},{"funder_name":"National Institutes of Health","grant_id":"5F30MH122076-04","title":"Computationally Driven Design of De Novo Genetically Encoded Voltage Indicators"},{"funder_name":"National Science Foundation","grant_id":"2042834","title":"A microscale study of turbulent flow in the porous medium and at the porous/fluid interface: combining LES, DNS, and Neural Network approaches"}],"total_grants":5,"fwci":0.3925,"citation_percentile":0.60794508,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2026,"count":1}],"oa_status":"bronze","license":"ASME Site License Agreemen","oa_locations":[{"url":"https://asmedigitalcollection.asme.org/biomechanical/article-pdf/145/7/071001/6997588/bio_145_07_071001.pdf","host_type":"journal"},{"url":"https://asmedigitalcollection.asme.org/biomechanical/article-pdf/145/7/071001/6997588/bio_145_07_071001.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1115/1.4056915","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/36795013","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10158974","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10158974/pdf/bio-22-1199_071001.pdf","host_type":"repository"},{"url":"https://doi.org/10.1101/2022.06.19.496644","host_type":""}],"fields_of_study":["Microtubule and mitosis dynamics","Cellular transport and secretion","Advanced Neuroimaging Techniques and Applications","0301 basic medicine","0303 health sciences","03 medical and health sciences","Dyneins","Axonal Transport","Presynaptic Terminals","Axons","Microtubules"],"mesh_terms":["Axons","Axonal Transport","Dyneins","Microtubules","Presynaptic Terminals"],"keywords":["Axoplasmic transport","Axon","Axon terminal","Dynein","Terminal (telecommunication)","Neuroscience","Molecular motor","Biophysics","Biology","Cell biology","Computer science","Microtubule","Neuron","α-synuclein","Molecular Motors","Mathematical Modeling","Slow And Fast Axonal Transport","Presynaptic Terminals","Dyneins","Axonal Transport","Microtubules","Axons"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-09T15:42:02.499171Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}