Genome Sequence Analysis of  Auricularia heimuer  Combined with Genetic Linkage Map

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Abstract

Auricularia heimuer is one of the most popular edible fungi in China. In this study, the whole genome of A. heimuer was sequenced on the Illumina HiSeq X system and compared with other mushrooms genomes. As a wood-rotting fungus, a total of 509 carbohydrate-active enzymes (CAZymes) were annotated in order to explore its potential capabilities on wood degradation. The glycoside hydrolases (GH) family genes in the A. heimuer genome were more abundant than the genes in the other 11 mushrooms genomes. The A. heimuer genome contained 102 genes encoding class III, IV, and V ethanol dehydrogenases. Evolutionary analysis based on 562 orthologous single-copy genes from 15 mushrooms showed that Auricularia formed an early independent branch of Agaricomycetes. The mating-type locus of A. heimuer was located on linkage group 8 by genetic linkage analysis. By combining the genome sequence analysis with the genetic linkage map, the mating-type locus of A. heimuer was located on scaffold45 and consisted of two subunits, α and β. Each subunit consisted of a pair of homeodomain mating-type protein genes HD1 and HD2. The mapping revealed conserved synteny at the whole mating-type loci and mirror symmetry relations near the β subunit between A. heimuer and Exidia glandulosa. This study proposed the potential for the bioethanol production by consolidated bioprocessing of A. heimuer. It will promote understanding of the lignocellulose degradation system and facilitate more efficient conversion of the agricultural wastes used for mushroom cultivation. It also will advance the research on the fruiting body development and evolution of A. heimuer.

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Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

Vardges Ter-Hovhannisyan, Alexandre Lomsadze, Yury Chernoff … (2008)

We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.

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Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3.

Mira V. Han, Gregg W C Thomas, Jose Lugo-Martinez … (2013)

Current sequencing methods produce large amounts of data, but genome assemblies constructed from these data are often fragmented and incomplete. Incomplete and error-filled assemblies result in many annotation errors, especially in the number of genes present in a genome. This means that methods attempting to estimate rates of gene duplication and loss often will be misled by such errors and that rates of gene family evolution will be consistently overestimated. Here, we present a method that takes these errors into account, allowing one to accurately infer rates of gene gain and loss among genomes even with low assembly and annotation quality. The method is implemented in the newest version of the software package CAFE, along with several other novel features. We demonstrate the accuracy of the method with extensive simulations and reanalyze several previously published data sets. Our results show that errors in genome annotation do lead to higher inferred rates of gene gain and loss but that CAFE 3 sufficiently accounts for these errors to provide accurate estimates of important evolutionary parameters.

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Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes

Anthony Levasseur, Elodie Drula, Vincent Lombard … (2013)

Background Since its inception, the carbohydrate-active enzymes database (CAZy; http://www.cazy.org) has described the families of enzymes that cleave or build complex carbohydrates, namely the glycoside hydrolases (GH), the polysaccharide lyases (PL), the carbohydrate esterases (CE), the glycosyltransferases (GT) and their appended non-catalytic carbohydrate-binding modules (CBM). The recent discovery that members of families CBM33 and family GH61 are in fact lytic polysaccharide monooxygenases (LPMO), demands a reclassification of these families into a suitable category. Results Because lignin is invariably found together with polysaccharides in the plant cell wall and because lignin fragments are likely to act in concert with (LPMO), we have decided to join the families of lignin degradation enzymes to the LPMO families and launch a new CAZy class that we name “Auxiliary Activities” in order to accommodate a range of enzyme mechanisms and substrates related to lignocellulose conversion. Comparative analyses of these auxiliary activities in 41 fungal genomes reveal a pertinent division of several fungal groups and subgroups combining their phylogenetic origin and their nutritional mode (white vs. brown rot). Conclusions The new class introduced in the CAZy database extends the traditional CAZy families, and provides a better coverage of the full extent of the lignocellulose breakdown machinery.

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Author and article information

Journal

Journal ID (nlm-ta): J Fungi (Basel)

Journal ID (iso-abbrev): J Fungi (Basel)

Journal ID (publisher-id): jof

Title: Journal of Fungi

Publisher: MDPI

ISSN (Electronic): 2309-608X

Publication date (Electronic): 16 March 2020

Publication date Collection: March 2020

Volume: 6

Issue: 1

Electronic Location Identifier: 37

Affiliations

[1 ]Lab of genetic breeding of edible mushromm, Horticultural, College of Horticulture, Jilin Agricultural University, Changchun 130118, China; fangming@ 123456jlau.edu.cn

[2 ]Engineering Research Centre of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China

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[* ]Correspondence: yaofj@ 123456aliyun.com (F.Y.); zhangymf@ 123456aliyun.com (Y.Z.)

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Publisher ID: jof-06-00037

DOI: 10.3390/jof6010037

PMC ID: 7151073

PubMed ID: 32188049

SO-VID: 21b901bb-fb18-499d-b4fa-08aa0ecc7111

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

Genome Sequence Analysis of Auricularia heimuer Combined with Genetic Linkage Map

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Most cited references 40

Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3.

Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes

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