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Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger
BACKGROUND: Filamentous fungi are used as industrial cell factories to produce a diverse portfolio of proteins, organic acids, and secondary metabolites in submerged fermentation. Generating optimized strains for maximum product titres relies on a complex interplay of molecular, cellular, morphologi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239186/ https://www.ncbi.nlm.nih.gov/pubmed/37268954 http://dx.doi.org/10.1186/s13068-023-02345-9 |
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author | Cairns, Timothy C. de Kanter, Tom Zheng, Xiaomei Z. Zheng, Ping Sun, Jibin Meyer, Vera |
author_facet | Cairns, Timothy C. de Kanter, Tom Zheng, Xiaomei Z. Zheng, Ping Sun, Jibin Meyer, Vera |
author_sort | Cairns, Timothy C. |
collection | PubMed |
description | BACKGROUND: Filamentous fungi are used as industrial cell factories to produce a diverse portfolio of proteins, organic acids, and secondary metabolites in submerged fermentation. Generating optimized strains for maximum product titres relies on a complex interplay of molecular, cellular, morphological, and macromorphological factors that are not yet fully understood. RESULTS: In this study, we generate six conditional expression mutants in the protein producing ascomycete Aspergillus niger and use them as tools to reverse engineer factors which impact total secreted protein during submerged growth. By harnessing gene coexpression network data, we bioinformatically predicted six morphology and productivity associated ‘morphogenes’, and placed them under control of a conditional Tet-on gene switch using CRISPR-Cas genome editing. Strains were phenotypically screened on solid and liquid media following titration of morphogene expression, generating quantitative measurements of growth rate, filamentous morphology, response to various abiotic perturbations, Euclidean parameters of submerged macromorphologies, and total secreted protein. These data were built into a multiple linear regression model, which identified radial growth rate and fitness under heat stress as positively correlated with protein titres. In contrast, diameter of submerged pellets and cell wall integrity were negatively associated with productivity. Remarkably, our model predicts over 60% of variation in A. niger secreted protein titres is dependent on these four variables, suggesting that they play crucial roles in productivity and are high priority processes to be targeted in future engineering programs. Additionally, this study suggests A. niger dlpA and crzA genes are promising new leads for enhancing protein titres during fermentation. CONCLUSIONS: Taken together this study has identified several potential genetic leads for maximizing protein titres, delivered a suite of chassis strains with user controllable macromorphologies during pilot fermentation studies, and has quantified four crucial factors which impact secreted protein titres in A. niger. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13068-023-02345-9. |
format | Online Article Text |
id | pubmed-10239186 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102391862023-06-04 Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger Cairns, Timothy C. de Kanter, Tom Zheng, Xiaomei Z. Zheng, Ping Sun, Jibin Meyer, Vera Biotechnol Biofuels Bioprod Research BACKGROUND: Filamentous fungi are used as industrial cell factories to produce a diverse portfolio of proteins, organic acids, and secondary metabolites in submerged fermentation. Generating optimized strains for maximum product titres relies on a complex interplay of molecular, cellular, morphological, and macromorphological factors that are not yet fully understood. RESULTS: In this study, we generate six conditional expression mutants in the protein producing ascomycete Aspergillus niger and use them as tools to reverse engineer factors which impact total secreted protein during submerged growth. By harnessing gene coexpression network data, we bioinformatically predicted six morphology and productivity associated ‘morphogenes’, and placed them under control of a conditional Tet-on gene switch using CRISPR-Cas genome editing. Strains were phenotypically screened on solid and liquid media following titration of morphogene expression, generating quantitative measurements of growth rate, filamentous morphology, response to various abiotic perturbations, Euclidean parameters of submerged macromorphologies, and total secreted protein. These data were built into a multiple linear regression model, which identified radial growth rate and fitness under heat stress as positively correlated with protein titres. In contrast, diameter of submerged pellets and cell wall integrity were negatively associated with productivity. Remarkably, our model predicts over 60% of variation in A. niger secreted protein titres is dependent on these four variables, suggesting that they play crucial roles in productivity and are high priority processes to be targeted in future engineering programs. Additionally, this study suggests A. niger dlpA and crzA genes are promising new leads for enhancing protein titres during fermentation. CONCLUSIONS: Taken together this study has identified several potential genetic leads for maximizing protein titres, delivered a suite of chassis strains with user controllable macromorphologies during pilot fermentation studies, and has quantified four crucial factors which impact secreted protein titres in A. niger. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13068-023-02345-9. BioMed Central 2023-06-02 /pmc/articles/PMC10239186/ /pubmed/37268954 http://dx.doi.org/10.1186/s13068-023-02345-9 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Cairns, Timothy C. de Kanter, Tom Zheng, Xiaomei Z. Zheng, Ping Sun, Jibin Meyer, Vera Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger |
title | Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger |
title_full | Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger |
title_fullStr | Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger |
title_full_unstemmed | Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger |
title_short | Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger |
title_sort | regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in aspergillus niger |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239186/ https://www.ncbi.nlm.nih.gov/pubmed/37268954 http://dx.doi.org/10.1186/s13068-023-02345-9 |
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