A quantitative image analysis pipeline for the characterization of filamentous fungal morphologies as a tool to uncover targets for morphology engineering: a case study using aplD in Aspergillus niger

dc.contributor.authorCairns, Timothy C.
dc.contributor.authorFeurstein, Claudia
dc.contributor.authorZheng, Xiaomei
dc.contributor.authorZheng, Ping
dc.contributor.authorSun, Jibin
dc.contributor.authorMeyer, Vera
dc.date.accessioned2020-01-16T09:18:11Z
dc.date.available2020-01-16T09:18:11Z
dc.date.issued2019-06-15
dc.description.abstractBackground Fungal fermentation is used to produce a diverse repertoire of enzymes, chemicals, and drugs for various industries. During submerged cultivation, filamentous fungi form a range of macromorphologies, including dispersed mycelia, clumped aggregates, or pellets, which have critical implications for rheological aspects during fermentation, gas/nutrient transfer, and, thus, product titres. An important component of strain engineering efforts is the ability to quantitatively assess fungal growth phenotypes, which will drive novel leads for morphologically optimized production strains. Results In this study, we developed an automated image analysis pipeline to quantify the morphology of pelleted and dispersed growth (MPD) which rapidly and reproducibly measures dispersed and pelleted macromorphologies from any submerged fungal culture. It (i) enables capture and analysis of several hundred images per user/day, (ii) is designed to quantitatively assess heterogeneous cultures consisting of dispersed and pelleted forms, (iii) gives a quantitative measurement of culture heterogeneity, (iv) automatically generates key Euclidian parameters for individual fungal structures including particle diameter, aspect ratio, area, and solidity, which are also assembled into a previously described dimensionless morphology number MN, (v) has an in-built quality control check which enables end-users to easily confirm the accuracy of the automated calls, and (vi) is easily adaptable to user-specified magnifications and macromorphological definitions. To concomitantly provide proof of principle for the utility of this image analysis pipeline, and provide new leads for morphologically optimized fungal strains, we generated a morphological mutant in the cell factory Aspergillus niger based on CRISPR-Cas technology. First, we interrogated a previously published co-expression networks for A. niger to identify a putative gamma-adaptin encoding gene (aplD) that was predicted to play a role in endosome cargo trafficking. Gene editing was used to generate a conditional aplD expression mutant under control of the titratable Tet-on system. Reduced aplD expression caused a hyperbranched growth phenotype and diverse defects in pellet formation with a putative increase in protein secretion. This possible protein hypersecretion phenotype could be correlated with increased dispersed mycelia, and both decreased pellet diameter and MN. Conclusion The MPD image analysis pipeline is a simple, rapid, and flexible approach to quantify diverse fungal morphologies. As an exemplar, we have demonstrated that the putative endosomal transport gene aplD plays a crucial role in A. niger filamentous growth and pellet formation during submerged culture. This suggests that endocytic components are underexplored targets for engineering fungal cell factories.en
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlinen
dc.identifier.eissn1754-6834
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10606
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9532
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc570 Biowissenschaften; Biologiede
dc.subject.ddc660 Chemische Verfahrenstechnikde
dc.subject.othermorphology engineeringmen
dc.subject.otherAspergillus nigeren
dc.subject.otherimage analysisen
dc.subject.otherpelleten
dc.subject.otherdispersed myceliaen
dc.subject.otherpolar growthen
dc.subject.otherendocytosisen
dc.subject.othertet-onen
dc.subject.otherCRISPRen
dc.subject.otherprotein secretionen
dc.titleA quantitative image analysis pipeline for the characterization of filamentous fungal morphologies as a tool to uncover targets for morphology engineering: a case study using aplD in Aspergillus nigeren
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber149en
dcterms.bibliographicCitation.doi10.1186/s13068-019-1473-0en
dcterms.bibliographicCitation.issue12en
dcterms.bibliographicCitation.journaltitleBiotechnology for Biofuelsen
dcterms.bibliographicCitation.originalpublishernameBioMed Centralen
dcterms.bibliographicCitation.originalpublisherplaceLondonen
tub.accessrights.dnbfreeen
tub.affiliationFak. 3 Prozesswissenschaften::Inst. Biotechnologie::FG Angewandte und Molekulare Mikrobiologiede
tub.affiliation.facultyFak. 3 Prozesswissenschaftende
tub.affiliation.groupFG Angewandte und Molekulare Mikrobiologiede
tub.affiliation.instituteInst. Biotechnologiede
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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