Acetylxylan esterase is the key to the host specialization of wood-decay fungi predicted by random forest machine-learning algorithm

Abstract Wood-decay fungi produce extracellular enzymes that metabolize wood components such as cellulose, hemicellulose and lignin. Each fungus has a preference of wood species as the host, but identification of these preferences requires a huge amount of cultivation data. Here, we developed a method of predicting the wood species preference, Angiosperm specialist or Gymnosperm specialist or generalist, of wood-decay fungi using the random forest machine-learning algorithm, trained on the numbers of families associated with host specialization in the Carbohydrate-Active enZymes database. The accuracy of the prediction was about 80%, which is lower than that of the classification of white- and brown-rot fungi (more than 98%) by the same method, but the reason for this may be the ambiguity of the definition of “preference” and “generalists”. Carbohydrate esterase (CE) family 1 acetylxylan esterase was the most significant contributor to the prediction of host specialization, followed by family 1 carbohydrate-binding module and CE family 15, mainly containing glucuronoyl esterases. These results suggest that the ability to degrade glucuronoacetylxylan, a major hemicellulose of Angiosperm, is the key factor determining the host specialization of wood-decay fungi.

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Publication Details

Journal
Journal of Wood Science
Published
2024-10-03
DOI
https://doi.org/10.1186/s10086-024-02159-9
Citations
4
Primary Topic
Forest Ecology and Biodiversity Studies
Type
article
Field-Weighted Citation Impact
2.16

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article

Acetylxylan esterase is the key to the host specialization of wood-decay fungi predicted by random forest machine-learning algorithm

Kiyohiko Igarashi, Natsuki Hasegawa, Masashi Sugiyama
4 citations
Journal of Wood Science
Forest Ecology and Biodiversity Studies
2.16
article

Acetylxylan esterase is the key to the host specialization of wood-decay fungi predicted by random forest machine-learning algorithm

Kiyohiko Igarashi, Natsuki Hasegawa, Masashi Sugiyama
article en
4 citations

Abstract

Abstract Wood-decay fungi produce extracellular enzymes that metabolize wood components such as cellulose, hemicellulose and lignin. Each fungus has a preference of wood species as the host, but identification of these preferences requires a huge amount of cultivation data. Here, we developed a method of predicting the wood species preference, Angiosperm specialist or Gymnosperm specialist or generalist, of wood-decay fungi using the random forest machine-learning algorithm, trained on the numbers of families associated with host specialization in the Carbohydrate-Active enZymes database. The accuracy of the prediction was about 80%, which is lower than that of the classification of white- and brown-rot fungi (more than 98%) by the same method, but the reason for this may be the ambiguity of the definition of “preference” and “generalists”. Carbohydrate esterase (CE) family 1 acetylxylan esterase was the most significant contributor to the prediction of host specialization, followed by family 1 carbohydrate-binding module and CE family 15, mainly containing glucuronoyl esterases. These results suggest that the ability to degrade glucuronoacetylxylan, a major hemicellulose of Angiosperm, is the key factor determining the host specialization of wood-decay fungi.

Journal of Wood ScienceVol. 70(1)
RIKEN Center for Advanced Intelligence Project (JP), The University of Tokyo (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 13%
Forest Ecology and Biodiversity Studies
2.16
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Acetylxylan esterase is the key to the host specialization of wood-decay fungi predicted by random forest machine-learning algorithm — Kiyohiko Igarashi, Natsuki Hasegawa, et al. · Journal of Wood Science (2024) | TGRS Research Map | TGRS