A comparative genomic analysis of lichen-forming fungi reveals new insights into fungal lifestyles

Song, Hyeunjeong; Kim, Ki-Tae; Park, Sook‐Young; Lee, Gir-Won; Choi, Jaeyoung; Jeon, Jongbum; Cheong, Kwang Ho; Choi, Gobong; Hur, Jae‐Seoun; Lee, Yong-Hwan

doi:10.1038/s41598-022-14340-5

Cited by 10 publications

(7 citation statements)

References 92 publications

(121 reference statements)

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“…PCWDEs play key roles in obtaining nutrients and degrading the main structural components of the plant cell wall, i.e., cellulose, hemicellulose and pectin. Lichenized fungi live as symbionts of green algae or cyanobacteria, obtaining diverse nutrients from their partners; therefore, they have fewer PCWDEs than non-lichenized fungi (Song et al 2022). The reduction of PCWDEs is a prevailing trend in ectomycorrhizal Russulaceae (Looney et al 2022), but they retain a certain degree of diversity in components (Kohler et al 2015).…”

Section: / 39mentioning

confidence: 99%

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Chen

Stadler³

et al. 2023

Preprint

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Economically and agriculturally important fungal species have various lifestyles, and they may shift from mutualistic or saprobic to pathogenic depending on the habitat, host tolerance, and resource availability. Traditionally, the determination of fungal lifestyles has been based on observation at a particular host or habitat. Therefore, potential fungal pathogens have been neglected until they cause devastating impacts on human health, food security, and ecosystem stability. This study focused on the class Sordariomycetes to explore the genomic traits that could be used to determine the lifestyles of fungi and the possibility of predicting fungal lifestyles using machine learning algorithms. A total of 638 representative genomes covering five subclasses, 17 orders and 50 families were selected and annotated. Through an extensive literature survey, the lifestyles of 555 genomes were determined, including plant pathogens, saprotrophs, entomopathogens, mycoparasites, endophytes, human pathogens and nematophagous fungi. We evaluated the influence of sequencing technologies and concluded that second sequencing technologies have no influence on genome completeness but tend to generate a reduced size of transposable elements. We constructed three numerical matrices: a basic genomic feature matrix including 25 features; a functional protein matrix including 24 features; and a combined matrix. The most comprehensively comparative analysis to date across multiple lifestyles was conducted based on these matrices. Results indicate that basic genomic features reflect more on phylogeny rather than lifestyle, but the abundance of functional proteins displays relatively high discrimination not only in differentiating taxonomic groups at the higher levels but also in differentiating lifestyles. Genome size, GC content and gene number showed powerful discrimination for differentiating higher ranks, especially at the subclass level. Plant pathogens have the largest secretome; whereas entomopathogens have the smallest secretome; and the abundance of secretomes is a useful indicator to clearly differentiate plant pathogens from entomopathogens, mycoparasites, saprotrophs and entomopathogens, and as well as differentiate entophytes from entomopathogens. Effectors have long been considered as disease determinants, and we did observe that plant pathogens have more effectors than saprotrophs and entomopathogens. However, we also observed a similar abundance of effectors in endophytes, suggesting that effectors maybe not a reliable indicator for pathogenic fungi. Single functional protein could not differentiate all lifestyles, but combinations of multiple numerical features of functional proteins result in accurate differentiation for most lifestyles. Furthermore, models of six machine learning algorithms were trained, optimized and evaluated, and the best-performance model was used to predict the lifestyle of 83 unlabeled genomes. Although the accuracy of the best machine learning model was limited by the inadequate genome number of several lifestyles and the inaccurate lifestyle assignments for some genomes, the predictive model still obtained a high degree of accuracy in differentiating plant pathogens. The predictive model can be further optimized with more sequenced genomes in the future, and provide a more reliable prediction. This can be used as an early warning system to identify potentially devastating fungi and take appropriate measures to prevent their spread.

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Section: / 39mentioning

confidence: 99%

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Chen

Stadler³

et al. 2023

Preprint

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Section: Introductionmentioning

confidence: 99%

“…Lichens are composite organisms that exist in a symbiotic association between lichen-forming fungi (LFF, mycobiont) and partner algae or cyanobacteria (photobiont) 1 , 2 . This fungal lifestyle constitutes approximately 20% of all known fungal species 2 . The photobiont provides sustenance to the mycobiont via the fixation of carbohydrates and atmospheric nitrogen while sheltering under the protection of the LFF 2 .…”

Section: Introductionmentioning

confidence: 99%

“…This fungal lifestyle constitutes approximately 20% of all known fungal species 2 . The photobiont provides sustenance to the mycobiont via the fixation of carbohydrates and atmospheric nitrogen while sheltering under the protection of the LFF 2 . The lichen symbiosis is an interesting paradigm for the study of environmental adaptation.…”

Section: Introductionmentioning

confidence: 99%

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An Antarctic lichen isolate (Cladonia borealis) genome reveals potential adaptation to extreme environments

Cho,

Lee,

Choi

et al. 2024

Sci Rep

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Cladonia borealis is a lichen that inhabits Antarctica’s harsh environment. We sequenced the whole genome of a C. borealis culture isolated from a specimen collected in Antarctica using long-read sequencing technology to identify specific genetic elements related to its potential environmental adaptation. The final genome assembly produced 48 scaffolds, the longest being 2.2 Mbp, a 1.6 Mbp N50 contig length, and a 36 Mbp total length. A total of 10,749 protein-coding genes were annotated, containing 33 biosynthetic gene clusters and 102 carbohydrate-active enzymes. A comparative genomics analysis was conducted on six Cladonia species, and the genome of C. borealis exhibited 45 expanded and 50 contracted gene families. We identified that C. borealis has more Copia transposable elements and expanded transporters (ABC transporters and magnesium transporters) compared to other Cladonia species. Our results suggest that these differences contribute to C. borealis’ remarkable adaptability in the Antarctic environment. This study also provides a useful resource for the genomic analysis of lichens and genetic insights into the survival of species isolated from Antarctica.

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“…Lichens are relatively underexplored because of their complex biology, as well as the technical challenges around cultivation in laboratory settings [6]. Thanks to advancements in DNA sequencing technologies and bioinformatics, it has recently become easier to obtain high-quality genome sequences from lichen-forming fungi and thus gauge their biosynthetic capabilities [7][8][9]. A major challenge, however, remains the heterologous expression of genes originating from these organisms, thus hampering the linking of metabolites to biosynthetic genes [10,11].…”

Section: Introductionmentioning

confidence: 99%

Discovery and heterologous expression of functional 4-O-dimethylallyl-L-tyrosine synthases from lichen-forming fungi

Iacovelli,

He,

Sokolova

et al. 2024

Preprint

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Fungal DMATS-type aromatic prenyltransferases are a family of biosynthetic enzymes that catalyze the prenylation of a range of aromatic substrates during the biosynthesis of bioactive indole alkaloids, diketopiperazines, and meroterpenoids. Together with their broad substrate scope and soluble nature, this makes DMATS-type prenyltransferases particularly adept for applications in biocatalysis, for example to derivatize aromatic drug leads and improve their bioactivity. Here, we investigated four putative DMATS-type prenyltransferases from lichen-forming fungi, an underexplored group of organisms that produce more than 1,000 unique metabolites. We were able to successfully express two functional lichen prenyltransferases in the heterologous hostA. oryzae, which allowed us to identify them as 4-O-dimethylallyltyrosine synthases. Our extensive bioinformatic analysis shows that related lichen prenyltransferases are likely not active on indoles but rather on aromatic polyketides and phenylpropanoids, common metabolites in these organisms. Overall, our work not only provides new insights into fungal DMATS-type prenyltransferases at the family level, but it also enables future efforts aimed at identifying new candidates for biocatalytic transformations of aromatic compounds.

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A comparative genomic analysis of lichen-forming fungi reveals new insights into fungal lifestyles

Cited by 10 publications

References 92 publications

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

Beyond observation: genomic traits and machine learning algorithms for predicting fungal lifestyles

An Antarctic lichen isolate (Cladonia borealis) genome reveals potential adaptation to extreme environments

Discovery and heterologous expression of functional 4-O-dimethylallyl-L-tyrosine synthases from lichen-forming fungi

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