Skip to main content
NCBI home page
Springer logoLink to Springer
. 2023 May 13;23(2):159. doi: 10.1007/s10142-023-01094-0

Comparative genomic analysis of five coprinoid mushrooms species

Jingjing Wang 1,2,3, Ran Zhang 1,3, Guoao Ding 1,3, Lingling Wang 1,2, Wei Wang 1,2, Yan Zhang 1,2,3,✉, GuiLan Zhu 1,2,3,✉
PMCID: PMC10182949  PMID: 37178396

Abstract

Although coprinoid mushrooms are widely known for the phenomenon of deliquescence and production of fungal laccases and extracellular peroxygenases, the genome structure and genetic diversity of coprinoid mushroom species have not been extensively studied. To reveal the genomic structure and diversity in coprinoid mushroom species, the genomes of five coprinoid mushroom species were compared and analyzed. A total of 24,303 orthologous gene families, including 89,462 genes, were identified in the five species. The numbers of core, softcore, dispensable, and private genes were 5617 (25.6%), 1628 (7.4%), 2083 (9.5%), and 12,574 (57.4%), respectively. Differentiation time analysis revealed that Coprinellus micaceus and Coprinellus angulatus differentiated approximately 181.0 million years ago. Coprinopsis cinerea and Coprinopsis marcescibilis differentiated approximately 131.0 million years ago, and they were differentiated from Candolleomyces aberdarensis approximately 176.0 million years ago. Gene family contraction and expansion analyses showed that 1465 genes and 532 gene families were expanded, and 95 genes and 134 gene families were contracted. Ninety-five laccase-coding genes were detected in the five species, and the distribution of the laccase-coding genes in the five species was not uniform. These data provide a reference for a deeper understanding of the genetic structure of the genomes of coprinoid mushroom species. Furthermore, this study provides a reference for follow-up studies on the genome structure of coprinoid mushroom species and the diversity of specific functional genes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10142-023-01094-0.

Keywords: Fungal laccase, Coprinus species, Comparative genomic analysis, Gene family, Average nucleotide identity, Positive selection

Background

Coprinus species are cosmopolitan (Padamsee et al. 2008) and widely renowned for a phenomenon called deliquescence (Nagy et al. 2010). During this process, fruiting body tissues become blackish inky fluid by autodigestion of the fruiting body cells upon maturation (Hopple and Vilgalys 1999). Another notable feature of Coprinus is that most species can produce fungal laccases (Hoegger et al. 2004; Kilaru et al. 2006; Lin et al. 2013; Hu et al. 2019) and extracellular peroxygenases (Aranda et al. 2009). Laccases are biologically important enzymes that belong to the oxidase group and are useful as green enzymes for cleaner industrial applications to reduce environmental pollution (Senthivelan et al. 2016). Considering that these enzymes have various substrate catalytic properties and numerous applications in various fields, they have received attention from researchers for their use in further intensive studies worldwide (Senthivelan et al. 2016). Although laccases are produced by plants, bacteria, insects, and fungi (Dwivedi et al. 2011; Senthivelan et al. 2016), fungal laccases have been the most extensively studied (Mougin et al. 2003; Liu et al. 2022). However, the distribution of deliquescence and laccase- and peroxygenase-coding genes in Coprinus species does not exhibit a clear pattern, which may be due to the unclear classification system of Coprinus and the relatively complex genomes of Coprinus species (Nagy et al. 2010; Örstadius et al. 2015).

Traditionally, Psathyrellaceae species have been classified into two large genera, Coprinus and Psathyrella. However, the two genera have many common features, including similar habitat preferences, spore characteristics, degradation of spore pigments in sulfuric acid, and developmental, morphological, and ecological traits that are significantly convergent; several alternative classifications have also been proposed (Nagy et al. 2010). Furthermore, it has long been recognized that deliquescent taxa within Psathyrellaceae do not form a monophyletic group (Walther et al. 2005; Vašutová et al. 2008). The genus Coprinus was proposed to be split into four genera (Redhead et al. 2001). Although some studies have added valuable information to our knowledge of the phylogeny of Psathyrellaceae (Nagy et al. 2010; Örstadius et al. 2015; Wächter and Melzer 2020). However, these studies were all based on the diversity analysis of one or several limited genes. Even though these studies provide substantial guidance for the study of the phylogeny of Psathyrellaceae, they fail to provide effective information to study the origin and maintenance mechanisms of Psathyrellaceae functional genes.

Comparative genomics provides an important technical means for studying the origin and maintenance of fungal genetic diversity (Ma et al. 2013; de Vries et al. 2017; Kiss et al. 2019; Zhang et al. 2020). However, the genome structure and genetic diversity of coprinoid mushroom species have not been extensively studied. Therefore, in this study, we aimed to reveal the genomic structure and genetic diversity of coprinoid mushroom species by comparing and analyzing the genomes of five coprinoid mushroom species, i.e., Coprinellus angulatus, Coprinellus micaceus, Coprinopsis cinerea, Coprinopsis marcescibilis, and Candolleomyces aberdarensis.

Materials and methods

Genomic data collection of Coprinus species

The published genomes of Coprinus species were searched for and retrieved from NCBI, and the following five fully annotated fungi of the genus were retrieved: C. aberdarensis (60.61 Mb), C. angulatus (59.3 Mb), C. micaceus (77.39 Mb), C. cinerea (36.19 Mb), and C. marcescibilis bilis (38.91 Mb). T. mesenterica was selected as the outgroup (Table S1).

Gene family clustering and enrichment

Amino acid sequences were aligned using BLASTp version 2.6.0 (parameter -evalue 1e − 5 -outfmt 6) (Camacho et al. 2009). Gene family clustering was performed using OrthoFinder version 2.3.12 (parameter -M msa) (Emms and Kelly 2019) Functional annotation results of GO (Ashburner et al. 2000) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto 2000) were shown using the R clusterProfiler package (Wu et al. 2021). The numbers of non-redundant core, softcore, dispensable, and private gene families in each species and all species were counted.

Analysis core and private gene families

A Venn diagram was drawn using the Perl script. Genes common to all genomes were defined as core genes, those common to 90% or more genomes were defined as softcore genes, those private to each genome were defined as private genes, and the remaining genes were defined as dispensable genes. Core and private gene analyses were performed using the R clusterProfiler package (Wu, Hu, Xu, Chen, Guo, Dai, Feng, Zhou, Tang, Zhan, Fu, Liu, Bo, and Yu 2021) based on the private gene and functional annotation results of GO (Ashburner et al. 2000) and KEGG (Kanehisa and Goto 2000).

Phylogeny analysis and calculation of differentiation time

Multiple sequence alignment of protein sequences was performed using MUSCLE version 3.8.31 (Edgar 2004). The alignment sequences were filtered using trimAI v1.4. rev22 (parameter -gt 0.2) (Capella-Gutiérrez et al. 2009). The filtered alignment sequences were merged into supergenes. Finally, an maximum likelihood (ML) phylogenetic tree was constructed based on the supergenes using RAxML version 8.2.10 (Stamatakis 2014) with the PROTGAMMAWAG model.

The fossil time can make the calculation result of the differentiation time more accurate. Fossil timetables were obtained from TIMETREE (http://www.timetree.org/). Based on the topological structure of the phylogenetic tree and the fossil timetable, the differentiation times of the species were estimated using the mcmtree subprogram (parameters nsample = 3,000,000; burnin = 8,000,000; seqtype = 0; model = 4) of PAML version 4.9 (Yang 2007).

Gene family contraction and expansion

The number of gene family members of the ancestors of each branch was estimated using the birth-mortality model based on the species evolutionary tree and gene family clustering results through café version 3.1 (Han et al. 2013), thereby the contraction and expansion of the gene family of the species relative to the ancestors were predicted.

Analysis of laccase synthesis gene family

Laccase gene family numbers were searched on the InterPro website (https://www.ebi.ac.uk/interpro/result/InterProScan/), and PF numbers PF00394, PF07731, and PF07732 were obtained. Gene IDs were searched according to the PF numbers, and protein sequences were obtained according to the gene IDs. An ML phylogenetic tree based on laccase protein sequences was constructed as described above. Motif locations in laccase protein sequences were identified using MEME version 5.4.1 (Bailey and Elkan 1994).

Genome average nucleotide identity analysis

ANI indicates the similarity of all orthologous protein-coding genes between two genomes and is often used to indicate the evolutionary distance between genomes (Pritchard et al. 2016). ANI was calculated using the pyani ANIm algorithm (https://pureportal.strath.ac.uk/en/publications/pyani-v028-average-nucleotide-identity-ani-and-related-measures-f).

Results

Gene family clustering and enrichment

Five available genomic datasets from five Coprinus species were collected for comparative genome analysis. The genome size of the five species ranged from 36.19 to 77.39 Mb. A total of 21,902 orthologous gene families were identified, including 89,462 genes (Fig. 1A). The number of single-copy gene families was relatively stable in different species, with an average copy number of 3134.8 ± 58.98 (Fig. 1A). In this study, 5668 species-specific gene families were identified. Notably, in C. micaceus, the number of paralogous genes was markedly higher than in other species, reaching 5672 private genes (Fig. 1A and B). The numbers of core, softcore, dispensable, and private genes were 5617 (25.6%), 1628 (7.4%), 2083 (9.5%), and 12,574 (57.4%), respectively (Fig. 1C and Table S2). Kyoto ontology (KO) and gene ontology (GO) enrichment results showed that the core genes were primarily involved in regulating energy metabolism, biomass synthesis, and metabolic processes such as translation, mitochondrion, ribosome, rRNA processing, oxidative phosphorylation, biosynthesis amino acids, and fatty acid metabolism (Fig. 2). However, the private genes were primarily involved in some kinase, superoxide dismutase, monooxygenase, oxidoreductase, and peroxidase activities, structural constituents of the cell wall, and response to oxidative stress, which endowed fungi with the ability to participate in metabolism of substances such as tyrosine, glutathione, sphingolipid, glyoxylate, and dicarboxylate, and adapt to different habitats (Fig. 3).

Fig. 1.

Fig. 1

Open in a new tab

The number of homologous genes (A), core and private genes (B), and the number of genes of each classification (C). Genes common to all genomes were defined as core genes, those common to 90% or more genomes were defined as softcore genes, those private to each genome were defined as private genes, and the remaining genes were defined as dispensable genes

Fig. 2.

Fig. 2

Open in a new tab

Gene ontology (A) and Kyoto ontology (B) enrichment of core genes

Fig. 3.

Fig. 3

Open in a new tab

Gene ontology (A) and Kyoto ontology (B) enrichment of private genes

Phylogenetic tree, differentiation time, and gene family contraction and expansion

The phylogenetic tree constructed using 2353 single-copy genes showed that C. micaceus and C. angulatus were clustered into one branch and then clustered with C. aberdarensis. C. cinerea and C. marcescibilis were clustered into one branch. Tremella mesenterica was furthest from its evolution (Fig. 4A). Differentiation time analysis showed that C. micaceus and C. angulatus differentiated approximately 131.0 (from 87.2 to 189.8) million years ago, and they have differentiated from C. aberdarensis approximately 176.0 (from 118.0 to 251.2) million years ago. C. cinerea and C. marcescibilis differentiated approximately 181.0 (from 120.5 to 258.4) million years ago (Fig. 4B). However, genome average nucleotide identity (ANI) analysis showed that, as an outgroup, the ANI between T. mesenterica and C. marcescibilis was 1, and that between T. mesenterica and C. angulatus was 0.93, which was higher than that between C. marcescibilis and C. cinerea, as well as that between C. micaceus and C. angulatus (0.84; Table S3).

Fig. 4.

Fig. 4

Open in a new tab

Phylogenetic tree (A), differentiation time (B), and gene family contraction and expansion (C). Ccin, Coprinopsis cinerea; Cmar, Coprinopsis marcescibilis; Cabe, Candolleomyces aberdarensis; Cmic, Coprinellus micaceus; Cang, Coprinellus angulatus; Tmes, Tremella mesenterica. Maximum likelihood phylogenetic tree was constructed based on the supergenes using RAxML version 8.2.10 with the PROTGAMMAWAG model. Fossil timetables were obtained from TIMETREE (http://www.timetree.org/). Based on the topological structure of the phylogenetic tree and the fossil timetable, the differentiation times of the species were estimated using the mcmtree subprogram (parameters nsample = 3,000,000; burnin = 8,000,000; seqtype = 0; model = 4) of PAML version 4.9

The results through café version 3.1 (Han et al. 2013) indicated that the expansion and contraction gene families in the C. micaceus genome were 532 and 134, respectively, in which the expansion gene families were more numerous than the contraction gene families. However, the expansion and contraction gene families in C. aberdarensis were 301 and 722, respectively, in which the expansion gene families were fewer than the contraction gene families (Fig. 4C). Compared with Coprinopsis, the genomes of the two Coprinellus species contained more expansion and contraction gene families (Fig. 4C). Furthermore, extraction and enrichment analysis of the contraction and expansion genes and gene families showed that 1465 genes and 532 gene families were expanded, and 95 genes and 59 gene families were contracted within species (Table S4).

The GO and KO enrichment results showed that the expansion genes mainly participated in carbohydrate metabolic processes, catalytic activity, and catabolic processes, which endowed the fungi with the ability to synthesize and metabolize substances and adapt to different habitats (Fig. 5). The contraction genes exhibited oxidoreductase activity and participated in protein processing in the endoplasmic reticulum and export (Fig. 5).

Fig. 5.

Fig. 5

Open in a new tab

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes ontology (KO) enrichment of expansion and contraction gene families. A–D The GO and KO enrichment of expansion gene families and GO and KO enrichment of contraction gene families, respectively. The diameters of the circle indicate the number of genes

Laccase gene family

Ninety-five laccase genes were detected in the five species (Fig. 6). These genes were highly diverse (Fig. 6). Most laccase genes contain 10 motifs in a similar order (Fig. 6). However, the distribution of the laccase genes among the five species was not uniform. The C. micaceus genome contained the most multicopper oxidase PF07731 genes, the C. marcescibilis genome contained the most multicopper oxidase PF00394 genes, and the C. aberdarensis genome contained the most multicopper oxidase PF07732 genes (Fig. S1).

Fig. 6.

Fig. 6

Open in a new tab

Phylogenetic tree of laccase genes and motif distribution in the laccases. The motif sites were predicted by MEME plus any additional sites detected using a motif scanning algorithm. The MEME sites are shown in solid color, and additional scanned sites are shown in transparent color. Hovering the cursor over a site will reveal details about the site. Only sequences containing a predicted or scanned motif site are shown. The scanned sites are predicted using a log-odds scoring matrix constructed from the MEME sites. Only scanned sites with position p-values less than 0.0001 are shown

Discussion

With the in-depth development of sequencing technology and bioinformatics analysis tools, comparative genomics has been widely used in studies on species evolution and formation, ecological diffusion, habitat adaptation, and antibiotic resistance gene diffusion (Ni 2016; Lin et al. 2018; Qin et al. 2021; Shah et al. 2022; Wang et al. 2022; Yao et al. 2022). The core genome is generally considered the minimum genome necessary for the survival of free-living organisms (Zafar et al. 2002; Pandaranayaka et al. 2019). Therefore, core genes are closely related to the metabolic processes necessary for survival. Private genes are usually related to the secondary metabolites of fungi and enable different fungi to synthesize different small molecules and adapt to different habitats (Wisecaver et al. 2014; Keller 2019; Pandaranayaka et al. 2019; Slot and Gluck-Thaler 2019). Our results indicate that the core genes were mainly involved in the regulation of energy metabolism, biomass synthesis, and metabolic processes, whereas the private genes mainly involved some kinase, superoxide dismutase, monooxygenase, oxidoreductase, and peroxidase activities, structural constituents of the cell wall, and response to oxidative stress, which endowed fungi with the ability to participate in substance metabolisms, such as tyrosine, glutathione, sphingolipid, glyoxylate, and dicarboxylate, and adapt to different habitats. These results indicate that private genes endow different fungi with different secondary metabolic characteristics and habitat adaptability, which is of substantial value for maintaining fungal biodiversity. Notably, the expansion genes mainly participated in carbohydrate metabolic processes, catalytic activity, and catabolic processes, which endowed the fungi with the ability to synthesize and metabolize secondary metabolites and adapt to different habitats. These results imply that fungal differentiation is primarily due to the differentiation of private genes. Considering that secondary metabolites are mainly produced by private genes, it may be of considerable significance to use secondary metabolites of fungi as a basis for fungal classification.

As sequencing technology has advanced, fungal classification has shifted from morphological characteristics to molecular phylogenetic evidence (Nagy et al. 2010; Örstadius et al. 2015). Recently, Wächter and Melzer (2020) inferred that the family Psathyrellaceae forms distinct phylogenetic clades and is divided into 16 genera based on molecular phylogenetic evidence and morphological characteristics (Wächter and Melzer 2020). Although some morphological features caused by private genes may be used as the basis for fungal classification, the secondary metabolites produced by private genes are difficult to reflect in morphology, so it is difficult to use them for fungal morphological classification. Furthermore, genome ANI analysis showed that, as an outgroup, the ANI between T. mesenterica and C. marcescibilis was 1 and that between T. mesenterica and C. angulatus was 0.93, which was higher than that between C. marcescibilis and C. cinerea, as well as that between C. micaceus and C. angulatus. These results indicate that it is difficult to classify fungi effectively using only a few genes. Therefore, it is necessary to identify secondary metabolites for fungal classification.

Laccases, involved in decomposing various aromatic compounds, such as lignin and humic matter, are ubiquitously distributed in nature, such as plants, fungi, and bacteria (Luo et al. 2015; Moreno et al. 2017; Janusz et al. 2020). Laccase activity revealed the linkage between fungal laccase genes and the fate of soil organic matter in ecosystems, and the diversity and richness of fungal laccase genes decreased with soil organic matter concentration (Kellner et al. 2009; Theuerl and Buscot 2010). Moreover, laccases have often been associated with fungal infection of plant and animal hosts (Moreno et al. 2017). Generally, most fungi possess multiple copies of genes encoding laccases and producing several laccase isoenzymes, which implies that these enzymes perform a variety of physiological functions (Luis et al. 2004; Moreno et al. 2017). Characterization of laccase families in fungi requires attention to elucidate their exact functional relationships (Moreno et al. 2017). Twenty-six genes were extracted in five clinical and environmental Fonsecaea species, and those genes possess features used as evidence of functional laccases (Moreno et al. 2017). Our results indicated that 95 laccase genes were detected in the five Coprinus species, which were evidently higher than the five clinical and environmental Fonsecaea species (Moreno et al. 2017). These results implied that the diversity of laccase genes possibly plays a crucial role in the survival of Coprinus species.

Conclusions

A total of 24,303 orthologous gene families were identified in the five Coprinus species, including 89,462 genes. The core genes were mainly involved in the regulation of energy metabolism, biomass synthesis, and metabolic processes, whereas the private genes endowed the fungi with the ability to participate in substance metabolism and adapt to different habitats. These results indicate that private genes endow different fungi with different secondary metabolic characteristics and habitat adaptability, which is of considerable importance for maintaining fungal biodiversity. These results imply that fungal differentiation is mainly due to the differentiation of private genes. Considering that secondary metabolites are primarily produced by private genes, using secondary metabolites of fungi as a basis for fungal classification may be of great significance.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 34 KB) (33.6KB, docx)
Supplementary file2 (XLSX 48 KB) (48.2KB, xlsx)

Acknowledgements

Thanks, Jiajia Ni, a technician at Guangdong Meilikang Bio-Science Ltd., China, for assistance with data analysis.

Author contribution

Yan Zhang and Jingjing Wang designed the experiments. Ran Zhang, Guoao Ding, Wei Wang, and Lingling Wang performed the experiments. Yan Zhang and GuiLan Zhu collected and analyzed the data. Yan Zhang and GuiLan Zhu wrote the draft of the manuscript. All authors revised and approved the final version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China, grant number 31800049; Natural Science Foundation of Higher Education in Anhui Province, grant number KJ2021ZD0113; the Program for Young Outstanding Talents in Anhui Province, grant number gxyqZD2022071; the Opening Laboratory Programme for the Medicinal and Food Homologous Natural Resources Exploration in Anhui Province, grant numbers YSTY2022028 and YSTY2022030. the Top-Notch Talents in Disciplines (Majors) of Colleges and Universities in Anhui Province, grant number gxbjZD2020082.

Data availability

Not applicable.

Declarations

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yan Zhang, Email: zhangyanwind@163.com.

GuiLan Zhu, Email: zhuguilan13@126.com.

References

  1. Aranda E, Kinne M, Kluge M, Ullrich R, Hofrichter M. Conversion of dibenzothiophene by the mushrooms Agrocybe aegerita and Coprinellus radians and their extracellular peroxygenases. Appl Microbiol Biotechnol. 2009;82:1057–1066. doi: 10.1007/s00253-008-1778-6. [DOI] [PubMed] [Google Scholar]
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat Genet. 2000;25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol. 1994;2:28–36. [PubMed] [Google Scholar]
  4. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. doi: 10.1186/1471-2105-10-421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–1973. doi: 10.1093/bioinformatics/btp348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. de Vries RP, Riley R, Wiebenga A, Aguilar-Osorio G, Amillis S, Uchima CA, Anderluh G, Asadollahi M, Askin M, Barry K, Battaglia E, Bayram Ö, Benocci T, Braus-Stromeyer SA, Caldana C, Cánovas D, Cerqueira GC, Chen F, Chen W, Choi C, Clum A, Dos Santos RA, Damásio AR, Diallinas G, Emri T, Fekete E, Flipphi M, Freyberg S, Gallo A, Gournas C, Habgood R, Hainaut M, Harispe ML, Henrissat B, Hildén KS, Hope R, Hossain A, Karabika E, Karaffa L, Karányi Z, Kraševec N, Kuo A, Kusch H, LaButti K, Lagendijk EL, Lapidus A, Levasseur A, Lindquist E, Lipzen A, Logrieco AF, MacCabe A, Mäkelä MR, Malavazi I, Melin P, Meyer V, Mielnichuk N, Miskei M, Molnár ÁP, Mulé G, Ngan CY, Orejas M, Orosz E, Ouedraogo JP, Overkamp KM, Park HS, Perrone G, Piumi F, Punt PJ, Ram AF, Ramón A, Rauscher S, Record E, Riaño-Pachón DM, Robert V, Röhrig J, Ruller R, Salamov A, Salih NS, Samson RA, Sándor E, Sanguinetti M, Schütze T, Sepčić K, Shelest E, Sherlock G, Sophianopoulou V, Squina FM, Sun H, Susca A, Todd RB, Tsang A, Unkles SE, van de Wiele N, van Rossen-Uffink D, Oliveira JV, Vesth TC, Visser J, Yu JH, Zhou M, Andersen MR, Archer DB, Baker SE, Benoit I, Brakhage AA, Braus GH, Fischer R, Frisvad JC, Goldman GH, Houbraken J, Oakley B, Pócsi I, Scazzocchio C, Seiboth B, vanKuyk PA, Wortman J, Dyer PS, Grigoriev IV. Comparative genomics reveals high biological diversity and specific adaptations in the industrially and medically important fungal genus Aspergillus. Genome Biol. 2017;18:28. doi: 10.1186/s13059-017-1151-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dwivedi UN, Singh P, Pandey VP, Kumar A. Structure–function relationship among bacterial, fungal and plant laccases. J Mol Catal B Enzym. 2011;68:117–128. doi: 10.1016/j.molcatb.2010.11.002. [DOI] [Google Scholar]
  8. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–1797. doi: 10.1093/nar/gkh340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238. doi: 10.1186/s13059-019-1832-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Han MV, Thomas GW, Lugo-Martinez J, Hahn MW. Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. Mol Biol Evol. 2013;30:1987–1997. doi: 10.1093/molbev/mst100. [DOI] [PubMed] [Google Scholar]
  11. Hoegger PJ, Navarro-González M, Kilaru S, Hoffmann M, Westbrook ED, Kües U. The laccase gene family in Coprinopsis cinerea (Coprinus cinereus) Curr Genet. 2004;45:9–18. doi: 10.1007/s00294-003-0452-x. [DOI] [PubMed] [Google Scholar]
  12. Hopple JS, Jr, Vilgalys R. Phylogenetic relationships in the mushroom genus Coprinus and dark-spored allies based on sequence data from the nuclear gene coding for the large ribosomal subunit RNA: divergent domains, outgroups, and monophyly. Mol Phylogenet Evol. 1999;13:1–19. doi: 10.1006/mpev.1999.0634. [DOI] [PubMed] [Google Scholar]
  13. Hu J, Zhang Y, Xu Y, Sun Q, Liu J, Fang W, Xiao Y, Kües U, Fang Z. Gongronella sp. w5 elevates Coprinopsis cinerea laccase production by carbon source syntrophism and secondary metabolite induction. Appl Microbiol Biotechnol. 2019;103:411–425. doi: 10.1007/s00253-018-9469-4. [DOI] [PubMed] [Google Scholar]
  14. Janusz G, Pawlik A, Świderska-Burek U, Polak J, Sulej J, Jarosz-Wilkołazka A, Paszczyński A. Laccase properties, physiological functions, and evolution. Int J Mol Sci. 2020;21:966. doi: 10.3390/ijms21030966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Keller NP. Fungal secondary metabolism: regulation, function and drug discovery. Nat Rev Microbiol. 2019;17:167–180. doi: 10.1038/s41579-018-0121-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kellner H, Luis P, Schlitt B, Buscot F. Temporal changes in diversity and expression patterns of fungal laccase genes within the organic horizon of a brown forest soil. Soil Biol Biochem. 2009;41:1380–1389. doi: 10.1016/j.soilbio.2009.03.012. [DOI] [Google Scholar]
  18. Kilaru S, Hoegger PJ, Kües U. The laccase multi-gene family in Coprinopsis cinerea has seventeen different members that divide into two distinct subfamilies. Curr Genet. 2006;50:45–60. doi: 10.1007/s00294-006-0074-1. [DOI] [PubMed] [Google Scholar]
  19. Kiss E, Hegedüs B, Virágh M, Varga T, Merényi Z, Kószó T, Bálint B, Prasanna AN, Krizsán K, Kocsubé S, Riquelme M, Takeshita N, Nagy LG. Comparative genomics reveals the origin of fungal hyphae and multicellularity. Nat Commun. 2019;10:4080. doi: 10.1038/s41467-019-12085-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lin Y, Zhang Z, Tian Y, Zhao W, Zhu B, Xu Z, Peng R, Yao Q. Purification and characterization of a novel laccase from Coprinus cinereus and decolorization of different chemically dyes. Mol Biol Rep. 2013;40:1487–1494. doi: 10.1007/s11033-012-2191-x. [DOI] [PubMed] [Google Scholar]
  21. Lin H, Yu M, Wang X, Zhang X-H. Comparative genomic analysis reveals the evolution and environmental adaptation strategies of vibrios. BMC Genomics. 2018;19:135. doi: 10.1186/s12864-018-4531-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Liu J, Peng C, Han Q, Wang M, Zhou G, Ye B, Xiao Y, Fang Z, Kües U. Coprinopsis cinerea uses Laccase Lcc9 as a defense strategy to eliminate oxidative stress during fungal-fungal interactions. Appl Environ Microbiol. 2022;88:e0176021. doi: 10.1128/aem.01760-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Luis P, Walther G, Kellner H, Martin F, Buscot F. Diversity of laccase genes from basidiomycetes in a forest soil. Soil Biol Biochem. 2004;36:1025–1036. doi: 10.1016/j.soilbio.2004.02.017. [DOI] [Google Scholar]
  24. Luo L, Zhou Z-C, Gu J-D. Distribution, diversity and abundance of bacterial laccase-like genes in different particle size fractions of sediments in a subtropical mangrove ecosystem. Ecotoxicology. 2015;24:1508–1516. doi: 10.1007/s10646-015-1452-6. [DOI] [PubMed] [Google Scholar]
  25. Ma L-J, Geiser DM, Proctor RH, Rooney AP, O'Donnell K, Trail F, Gardiner DM, Manners JM, Kazan K. Fusarium pathogenomics. Annu Rev Microbiol. 2013;67:399–416. doi: 10.1146/annurev-micro-092412-155650. [DOI] [PubMed] [Google Scholar]
  26. Moreno LF, Feng P, Weiss VA, Vicente VA, Stielow JB, de Hoog S. Phylogenomic analyses reveal the diversity of laccase-coding genes in Fonsecaea genomes. PLoS ONE. 2017;12:e0171291. doi: 10.1371/journal.pone.0171291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Mougin C, Jolivalt C, Briozzo P, Madzak C. Fungal laccases: from structure-activity studies to environmental applications. Environ Chem Lett. 2003;1:145–148. doi: 10.1007/s10311-003-0024-9. [DOI] [Google Scholar]
  28. Nagy LG, Urban A, Örstadius L, Papp T, Larsson E, Vágvölgyi C. The evolution of autodigestion in the mushroom family Psathyrellaceae (Agaricales) inferred from maximum likelihood and Bayesian methods. Mol Phylogenet Evol. 2010;57:1037–1048. doi: 10.1016/j.ympev.2010.08.022. [DOI] [PubMed] [Google Scholar]
  29. Ni J. Novel insight into evolutionary process from average genome size in marine bacterioplanktonic biota. Appl Ecol Environmental Res. 2016;14:65–75. doi: 10.15666/aeer/1402_065075. [DOI] [Google Scholar]
  30. Örstadius L, Ryberg M, Larsson E. Molecular phylogenetics and taxonomy in Psathyrellaceae (Agaricales) with focus on psathyrelloid species: introduction of three new genera and 18 new species. Mycol Prog. 2015;14:25. doi: 10.1007/s11557-015-1047-x. [DOI] [Google Scholar]
  31. Padamsee M, Matheny PB, Dentinger BTM, McLaughlin DJ. The mushroom family Psathyrellaceae: evidence for large-scale polyphyly of the genus Psathyrella. Mol Phylogenet Evol. 2008;46:415–429. doi: 10.1016/j.ympev.2007.11.004. [DOI] [PubMed] [Google Scholar]
  32. Pandaranayaka EP, Frenkel O, Elad Y, Prusky D, Harel A. Network analysis exposes core functions in major lifestyles of fungal and oomycete plant pathogens. BMC Genomics. 2019;20:1020. doi: 10.1186/s12864-019-6409-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Pritchard L, Glover R, Humphris S, Elphinstone J, Toth I (2016) Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal Methods 8:12–24. 10.1039/C5AY02550H
  34. Qin X, Wang H, Miao C, Yang X, Zhang Y, Feng J, Forsythe SJ, Man C, Jiang Y. Comparative genomics reveals environmental adaptation differences between Cronobacter species. Food Res Int. 2021;147:110541. doi: 10.1016/j.foodres.2021.110541. [DOI] [PubMed] [Google Scholar]
  35. Redhead S, Vilgalys R, Moncalvo J-M, Johnson J, Hopple Jr. Coprinus Pers and the disposition of Coprinus species sensu lato. Taxon. 2001;50:203. doi: 10.2307/1224525. [DOI] [Google Scholar]
  36. Senthivelan T, Kanagaraj J, Panda RC. Recent trends in fungal laccase for various industrial applications: an eco-friendly approach - a review. Biotechnol Bioprocess Eng. 2016;21:19–38. doi: 10.1007/s12257-015-0278-7. [DOI] [Google Scholar]
  37. Shah IH, Manzoor MA, Sabir IA, Ashraf M, Haq F, Arif S, Abdullah M, Niu Q, Zhang Y. Genome-wide identification and comparative analysis of MATE gene family in Cucurbitaceae species and their regulatory role in melon (Cucumis melo) under salt stress. Hortic Environ Biotechnol. 2022;63:595–612. doi: 10.1007/s13580-021-00413-3. [DOI] [Google Scholar]
  38. Slot JC, Gluck-Thaler E. Metabolic gene clusters, fungal diversity, and the generation of accessory functions. Curr Opin Genet Dev. 2019;58–59:17–24. doi: 10.1016/j.gde.2019.07.006. [DOI] [PubMed] [Google Scholar]
  39. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–1313. doi: 10.1093/bioinformatics/btu033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Theuerl S, Buscot F. Laccases: toward disentangling their diversity and functions in relation to soil organic matter cycling. Biol Fertil Soils. 2010;46:215–225. doi: 10.1007/s00374-010-0440-5. [DOI] [Google Scholar]
  41. Vašutová M, Antonín V, Urban A. Phylogenetic studies in Psathyrella focusing on sections Pennatae and Spadiceae — new evidence for the paraphyly of the genus. Mycol Res. 2008;112:1153–1164. doi: 10.1016/j.mycres.2008.04.005. [DOI] [PubMed] [Google Scholar]
  42. Wächter D, Melzer A. Proposal for a subdivision of the family Psathyrellaceae based on a taxon-rich phylogenetic analysis with iterative multigene guide tree. Mycol Prog. 2020;19:1151–1265. doi: 10.1007/s11557-020-01606-3. [DOI] [Google Scholar]
  43. Walther G, Garnica S, Weiß M. The systematic relevance of conidiogenesis modes in the gilled Agaricales1 1Dedicated to John Webster on the occasion of his 80th birthday. Mycol Res. 2005;109:525–544. doi: 10.1017/S0953756205002868. [DOI] [PubMed] [Google Scholar]
  44. Wang J, Qian J, Jiang Y, Chen X, Zheng B, Chen S, Yang F, Xu Z, Duan B. Comparative analysis of chloroplast genome and new insights into phylogenetic relationships of polygonatum and tribe polygonateae. Front Plant Sci. 2022;13:882189. doi: 10.3389/fpls.2022.882189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wisecaver JH, Slot JC, Rokas A. The evolution of fungal metabolic pathways. PLoS Genet. 2014;10:1004816. doi: 10.1371/journal.pgen.1004816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation Camb. 2021;2:100141. doi: 10.1016/j.xinn.2021.100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–1591. doi: 10.1093/molbev/msm088. [DOI] [PubMed] [Google Scholar]
  48. Yao X, Wang S, Wang Z, Li D, Jiang Q, Zhang Q, Gao L, Zhong C, Huang H, Liu Y. The genome sequencing and comparative analysis of a wild kiwifruit Actinidia eriantha. Molecular Horticulture. 2022;2:13. doi: 10.1186/s43897-022-00034-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Zafar N, Mazumder R, Seto D. CoreGenes: a computational tool for identifying and cataloging "core" genes in a set of small genomes. BMC Bioinformatics. 2002;3:12. doi: 10.1186/1471-2105-3-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Zhang Y, Wang J, Yajun C, Zhou M, Wang W, Geng M, Xu D, Xu Z. Comparative genomics uncovers the genetic diversity and synthetic biology of secondary metabolite production of trametes. Mycobiology. 2020;48:104–114. doi: 10.1080/12298093.2020.1725361. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 34 KB) (33.6KB, docx)
Supplementary file2 (XLSX 48 KB) (48.2KB, xlsx)

Data Availability Statement

Not applicable.

Articles from Functional & Integrative Genomics are provided here courtesy of Springer

RESOURCES