The GMC superfamily of oxidoreductases revisited: analysis and evolution of fungal GMC oxidoreductases

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Abstract

Background

The glucose–methanol–choline (GMC) superfamily is a large and functionally diverse family of oxidoreductases that share a common structural fold. Fungal members of this superfamily that are characterised and relevant for lignocellulose degradation include aryl-alcohol oxidoreductase, alcohol oxidase, cellobiose dehydrogenase, glucose oxidase, glucose dehydrogenase, pyranose dehydrogenase, and pyranose oxidase, which together form family AA3 of the auxiliary activities in the CAZy database of carbohydrate-active enzymes. Overall, little is known about the extant sequence space of these GMC oxidoreductases and their phylogenetic relations. Although some individual forms are well characterised, it is still unclear how they compare in respect of the complete enzyme class and, therefore, also how generalizable are their characteristics.

Results

To improve the understanding of the GMC superfamily as a whole, we used sequence similarity networks to cluster large numbers of fungal GMC sequences and annotate them according to functionality. Subsequently, different members of the GMC superfamily were analysed in detail with regard to their sequences and phylogeny. This allowed us to define the currently characterised sequence space and show that complete clades of some enzymes have not been studied in any detail to date. Finally, we interpret our results from an evolutionary perspective, where we could show, for example, that pyranose dehydrogenase evolved from aryl-alcohol oxidoreductase after a change in substrate specificity and that the cytochrome domain of cellobiose dehydrogenase was regularly lost during evolution.

Conclusions

This study offers new insights into the sequence variation and phylogenetic relationships of fungal GMC/AA3 sequences. Certain clades of these GMC enzymes identified in our phylogenetic analyses are completely uncharacterised to date, and might include enzyme activities of varying specificities and/or activities that are hitherto unstudied.

Electronic supplementary material

The online version of this article (10.1186/s13068-019-1457-0) contains supplementary material, which is available to authorized users.

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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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Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

Holly J. Atkinson, John Morris, Thomas Ferrin … (2009)

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.

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Genome-scale phylogeny and the detection of systematic biases.

Frédéric Delsuc, Jennifer M. Phillips, David Penny (2004)

Phylogenetic inference from sequences can be misled by both sampling (stochastic) error and systematic error (nonhistorical signals where reality differs from our simplified models). A recent study of eight yeast species using 106 concatenated genes from complete genomes showed that even small internal edges of a tree received 100% bootstrap support. This effective negation of stochastic error from large data sets is important, but longer sequences exacerbate the potential for biases (systematic error) to be positively misleading. Indeed, when we analyzed the same data set using minimum evolution optimality criteria, an alternative tree received 100% bootstrap support. We identified a compositional bias as responsible for this inconsistency and showed that it is reduced effectively by coding the nucleotides as purines and pyrimidines (RY-coding), reinforcing the original tree. Thus, a comprehensive exploration of potential systematic biases is still required, even though genome-scale data sets greatly reduce sampling error.

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Author and article information

Contributors

Leander Sützl: leander.suetzl@boku.ac.at

Gabriel Foley: gabriel.foley@uqconnect.edu.au

Elizabeth M J Gillam: e.gillam@uq.edu.au

Mikael Bodén: m.boden@uq.edu.au

Dietmar Haltrich:

ORCID: http://orcid.org/0000-0002-8722-8176

dietmar.haltrich@boku.ac.at

Journal

Journal ID (nlm-ta): Biotechnol Biofuels

Journal ID (iso-abbrev): Biotechnol Biofuels

Title: Biotechnology for Biofuels

Publisher: BioMed Central (London )

ISSN (Electronic): 1754-6834

Publication date (Electronic): 10 May 2019

Publication date PMC-release: 10 May 2019

Publication date Collection: 2019

Volume: 12

Electronic Location Identifier: 118

Affiliations

[1 ]ISNI 0000 0001 2298 5320, GRID grid.5173.0, Food Biotechnology Laboratory, Department of Food Science and Technology, , BOKU-University of Natural Resources and Life Sciences Vienna, ; Vienna, Austria

[2 ]ISNI 0000 0001 2298 5320, GRID grid.5173.0, Doctoral Programme BioToP-Biomolecular Technology of Proteins, , BOKU-University of Natural Resources and Life Sciences Vienna, ; Vienna, Austria

[3 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, School of Chemistry & Molecular Biosciences, , The University of Queensland, ; Brisbane, Australia

Author information

Dietmar Haltrich http://orcid.org/0000-0002-8722-8176

Article

Publisher ID: 1457

DOI: 10.1186/s13068-019-1457-0

PMC ID: 6509819

PubMed ID: 31168323

SO-VID: f703a3f8-0fae-4eef-a228-8a0e802b11a2

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 12 March 2019

Date accepted : 2 May 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;

Award ID: W1224

Award Recipient : Dietmar Haltrich

Custom metadata

ScienceOpen disciplines: Biotechnology

Keywords: gmc oxidoreductase,cazy family aa3,sequence similarity networks,phylogeny,evolution of oxidoreductases

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ScienceOpen disciplines: Biotechnology

Keywords: gmc oxidoreductase, cazy family aa3, sequence similarity networks, phylogeny, evolution of oxidoreductases

The GMC superfamily of oxidoreductases revisited: analysis and evolution of fungal GMC oxidoreductases

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Genome Engineering using CRISPR

Most cited references 84

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

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

Genome-scale phylogeny and the detection of systematic biases.

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