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

被引:1
|
作者
Hasegawa, Natsuki [1 ]
Sugiyama, Masashi [2 ,3 ]
Igarashi, Kiyohiko [1 ,3 ]
机构
[1] Univ Tokyo, Grad Sch Agr & Life Sci, Dept Biomat Sci, 1-1-1 Yayoi,Bunkyo Ku, Tokyo 1138657, Japan
[2] RIKEN, Ctr Adv Intelligence Project, Chuo Ku, Tokyo, Japan
[3] Univ Tokyo, Next Life Res Grp UT7, Bunkyo Ku, Tokyo, Japan
基金
日本学术振兴会;
关键词
Acetylxylan esterase; Wood-decay fungi; Carbohydrate-Active enZymes; Machine learning; Random forest algorithm; GENOME; MECHANISMS; DATABASE; INSIGHT; ORIGIN; RISK; ROT;
D O I
10.1186/s10086-024-02159-9
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
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.
引用
收藏
页数:10
相关论文
共 9 条
  • [1] Evolutionary dynamics of host specialization in wood-decay fungi
    Franz-Sebastian Krah
    Claus Bässler
    Christoph Heibl
    John Soghigian
    Hanno Schaefer
    David S. Hibbett
    BMC Evolutionary Biology, 18
  • [2] Evolutionary dynamics of host specialization in wood-decay fungi
    Krah, Franz-Sebastian
    Baessler, Claus
    Heibl, Christoph
    Soghigian, John
    Schaefer, Hanno
    Hibbett, David S.
    BMC EVOLUTIONARY BIOLOGY, 2018, 18
  • [3] Host specialization among wood-decay polypore fungi in a Caribbean mangrove forest
    Gilbert, GS
    Sousa, WP
    BIOTROPICA, 2002, 34 (03) : 396 - 404
  • [4] Random forest machine-learning algorithm classifies white- and brown-rot fungi according to the number of the genes encoding Carbohydrate-Active enZyme families
    Hasegawa, Natsuki
    Sugiyama, Masashi
    Igarashi, Kiyohiko
    APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 2024, 90 (07)
  • [5] Identifying the key factors influencing Chinese carbon intensity using machine learning, the random forest algorithm, and evolutionary analysis
    Liu W.
    Tang Z.
    Xia Y.
    Han M.
    Jiang W.
    Dili Xuebao/Acta Geographica Sinica, 2019, 74 (12): : 2592 - 2603
  • [6] Prediction of species richness and diversity in sub-alpine grasslands using satellite remote sensing and random forest machine-learning algorithm
    Mashiane, Katlego
    Ramoelo, Abel
    Adelabu, Samuel
    APPLIED VEGETATION SCIENCE, 2024, 27 (02)
  • [7] Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery
    Gao, Junfeng
    Nuyttens, David
    Lootens, Peter
    He, Yong
    Pieters, Jan G.
    BIOSYSTEMS ENGINEERING, 2018, 170 : 39 - 50
  • [8] Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multisensor core logging data
    Schnitzler, N.
    Ross, P-S
    Gloaguen, E.
    JOURNAL OF GEOCHEMICAL EXPLORATION, 2019, 205
  • [9] Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method
    Huang, Jiandong
    Sabri, Mohanad Muayad Sabri
    Ulrikh, Dmitrii Vladimirovich
    Ahmad, Mahmood
    Alsaffar, Kifayah Abood Mohammed
    MATERIALS, 2022, 15 (12)