Regression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger

dc.contributor.authorCairns, Timothy C.
dc.contributor.authorde Kanter, Tom
dc.contributor.authorZheng, Xiaomei Z.
dc.contributor.authorZheng, Ping
dc.contributor.authorSun, Jibin
dc.contributor.authorMeyer, Vera
dc.contributor.otherSpringer
dc.date.accessioned2023-11-17T13:48:22Z
dc.date.available2023-11-17T13:48:22Z
dc.date.issued2023-06-02
dc.description.abstractBackground: 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.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2023
dc.identifier.eissn2731-3654
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/20688
dc.identifier.urihttps://doi.org/10.14279/depositonce-19486
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.subject.otherAspergillus nigeren
dc.subject.othermacromorphologyen
dc.subject.otherpelleten
dc.subject.othercell wallen
dc.subject.otherchitinen
dc.subject.othergrowth rateen
dc.subject.othertotal proteinen
dc.subject.othertet-onen
dc.subject.othergenome editingen
dc.subject.otherdlpAen
dc.subject.othercrzAen
dc.titleRegression modelling of conditional morphogene expression links and quantifies the impact of growth rate, fitness and macromorphology with protein secretion in Aspergillus niger
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber95
dcterms.bibliographicCitation.doi10.1186/s13068-023-02345-9
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleBiotechnology for Biofuels and Bioproducts
dcterms.bibliographicCitation.originalpublishernameSpringer Nature
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.volume16
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 3 Prozesswissenschaften::Inst. Biotechnologie::FG Angewandte und Molekulare Mikrobiologie
tub.publisher.universityorinstitutionTechnische Universität Berlin

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