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Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

Optimizing the production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, for many proteins, this is also the limiting production step. Current soluti...

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Autores principales: Sosa-Carrillo, Sebastián, Galez, Henri, Napolitano, Sara, Bertaux, François, Batt, Gregory
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212943/
https://www.ncbi.nlm.nih.gov/pubmed/37231013
http://dx.doi.org/10.1038/s41467-023-38807-9
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author Sosa-Carrillo, Sebastián
Galez, Henri
Napolitano, Sara
Bertaux, François
Batt, Gregory
author_facet Sosa-Carrillo, Sebastián
Galez, Henri
Napolitano, Sara
Bertaux, François
Batt, Gregory
author_sort Sosa-Carrillo, Sebastián
collection PubMed
description Optimizing the production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, for many proteins, this is also the limiting production step. Current solutions involve extensive engineering of the chassis cell to facilitate protein trafficking and limit protein degradation triggered by excessive secretion-associated stress. Here, we propose instead a regulation-based strategy in which induction is dynamically adjusted to an optimal strength based on the current stress level of the cells. Using a small collection of hard-to-secrete proteins, a bioreactor-based platform with automated cytometry measurements, and a systematic assay to quantify secreted protein levels, we demonstrate that the secretion sweet spot is indicated by the appearance of a subpopulation of cells that accumulate high amounts of proteins, decrease growth, and face significant stress, that is, experience a secretion burnout. In these cells, adaptations capabilities are overwhelmed by a too strong production. Using these notions, we show for a single-chain antibody variable fragment that secretion levels can be improved by 70% by dynamically keeping the cell population at optimal stress levels using real-time closed-loop control.
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spelling pubmed-102129432023-05-27 Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches Sosa-Carrillo, Sebastián Galez, Henri Napolitano, Sara Bertaux, François Batt, Gregory Nat Commun Article Optimizing the production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, for many proteins, this is also the limiting production step. Current solutions involve extensive engineering of the chassis cell to facilitate protein trafficking and limit protein degradation triggered by excessive secretion-associated stress. Here, we propose instead a regulation-based strategy in which induction is dynamically adjusted to an optimal strength based on the current stress level of the cells. Using a small collection of hard-to-secrete proteins, a bioreactor-based platform with automated cytometry measurements, and a systematic assay to quantify secreted protein levels, we demonstrate that the secretion sweet spot is indicated by the appearance of a subpopulation of cells that accumulate high amounts of proteins, decrease growth, and face significant stress, that is, experience a secretion burnout. In these cells, adaptations capabilities are overwhelmed by a too strong production. Using these notions, we show for a single-chain antibody variable fragment that secretion levels can be improved by 70% by dynamically keeping the cell population at optimal stress levels using real-time closed-loop control. Nature Publishing Group UK 2023-05-25 /pmc/articles/PMC10212943/ /pubmed/37231013 http://dx.doi.org/10.1038/s41467-023-38807-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sosa-Carrillo, Sebastián
Galez, Henri
Napolitano, Sara
Bertaux, François
Batt, Gregory
Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
title Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
title_full Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
title_fullStr Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
title_full_unstemmed Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
title_short Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
title_sort maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212943/
https://www.ncbi.nlm.nih.gov/pubmed/37231013
http://dx.doi.org/10.1038/s41467-023-38807-9
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