Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning

Abstract Managing the worldwide steady increase in the production of plastic while mitigating the Earth’s global pollution is one of the greatest challenges nowadays. Fungi are often involved in biodegradation processes thanks to their ability to penetrate into substrates and release powerful catabo...

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Main Authors: Flavio Cognigni, Marta Elisabetta Eleonora Temporiti, Lidia Nicola, Nicolas Gueninchault, Solveig Tosi, Marco Rossi
Format: Article
Language:English
Published: Nature Portfolio 2023-12-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-50199-w
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author Flavio Cognigni
Marta Elisabetta Eleonora Temporiti
Lidia Nicola
Nicolas Gueninchault
Solveig Tosi
Marco Rossi
author_facet Flavio Cognigni
Marta Elisabetta Eleonora Temporiti
Lidia Nicola
Nicolas Gueninchault
Solveig Tosi
Marco Rossi
author_sort Flavio Cognigni
collection DOAJ
description Abstract Managing the worldwide steady increase in the production of plastic while mitigating the Earth’s global pollution is one of the greatest challenges nowadays. Fungi are often involved in biodegradation processes thanks to their ability to penetrate into substrates and release powerful catabolic exoenzymes. However, studying the interaction between fungi and plastic substrates is challenging due to the deep hyphal penetration, which hinders visualisation and evaluation of fungal activity. In this study, a multiscale and multimodal correlative microscopy workflow was employed to investigate the infiltrative and degradative ability of Fusarium oxysporum fungal strain on polyethylene terephthalate (PET) fragments. The use of non-destructive high-resolution 3D X-ray microscopy (XRM) coupled with a state-of-art Deep Learning (DL) reconstruction algorithm allowed optimal visualisation of the distribution of the fungus on the PET fragment. The fungus preferentially developed on the edges and corners of the fragment, where it was able to penetrate into the material through fractures. Additional analyses with scanning electron microscopy (SEM), Raman and energy dispersive X-ray spectroscopy (EDX) allowed the identification of the different phases detected by XRM. The correlative microscopy approach unlocked a more comprehensive understanding of the fungus-plastic interaction, including elemental information and polymeric composition.
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spelling doaj.art-4b5367636f004036bebcdca191be5d172023-12-31T12:09:44ZengNature PortfolioScientific Reports2045-23222023-12-0113111310.1038/s41598-023-50199-wExploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learningFlavio Cognigni0Marta Elisabetta Eleonora Temporiti1Lidia Nicola2Nicolas Gueninchault3Solveig Tosi4Marco Rossi5Department of Basic and Applied Sciences for Engineering (SBAI), University of Rome LA SAPIENZALaboratory of Mycology, Department of Earth and Environmental Sciences, University of PaviaLaboratory of Mycology, Department of Earth and Environmental Sciences, University of PaviaCarl Zeiss X-ray Microscopy, IncLaboratory of Mycology, Department of Earth and Environmental Sciences, University of PaviaDepartment of Basic and Applied Sciences for Engineering (SBAI), University of Rome LA SAPIENZAAbstract Managing the worldwide steady increase in the production of plastic while mitigating the Earth’s global pollution is one of the greatest challenges nowadays. Fungi are often involved in biodegradation processes thanks to their ability to penetrate into substrates and release powerful catabolic exoenzymes. However, studying the interaction between fungi and plastic substrates is challenging due to the deep hyphal penetration, which hinders visualisation and evaluation of fungal activity. In this study, a multiscale and multimodal correlative microscopy workflow was employed to investigate the infiltrative and degradative ability of Fusarium oxysporum fungal strain on polyethylene terephthalate (PET) fragments. The use of non-destructive high-resolution 3D X-ray microscopy (XRM) coupled with a state-of-art Deep Learning (DL) reconstruction algorithm allowed optimal visualisation of the distribution of the fungus on the PET fragment. The fungus preferentially developed on the edges and corners of the fragment, where it was able to penetrate into the material through fractures. Additional analyses with scanning electron microscopy (SEM), Raman and energy dispersive X-ray spectroscopy (EDX) allowed the identification of the different phases detected by XRM. The correlative microscopy approach unlocked a more comprehensive understanding of the fungus-plastic interaction, including elemental information and polymeric composition.https://doi.org/10.1038/s41598-023-50199-w
spellingShingle Flavio Cognigni
Marta Elisabetta Eleonora Temporiti
Lidia Nicola
Nicolas Gueninchault
Solveig Tosi
Marco Rossi
Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning
Scientific Reports
title Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning
title_full Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning
title_fullStr Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning
title_full_unstemmed Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning
title_short Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning
title_sort exploring the infiltrative and degradative ability of fusarium oxysporum on polyethylene terephthalate pet using correlative microscopy and deep learning
url https://doi.org/10.1038/s41598-023-50199-w
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