FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in...

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Autores principales:	Gabriel A Vignolle, Denise Schaffer, Leopold Zehetner, Robert L Mach, Astrid R Mach-Aigner, Christian Derntl
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/090bfdcb82d64fa987e5133b784830b7
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spelling	oai:doaj.org-article:090bfdcb82d64fa987e5133b784830b72021-12-02T19:58:14ZFunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.1553-734X1553-735810.1371/journal.pcbi.1009372https://doaj.org/article/090bfdcb82d64fa987e5133b784830b72021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009372https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.Gabriel A VignolleDenise SchafferLeopold ZehetnerRobert L MachAstrid R Mach-AignerChristian DerntlPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009372 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Gabriel A Vignolle Denise Schaffer Leopold Zehetner Robert L Mach Astrid R Mach-Aigner Christian Derntl FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
description	Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.
format	article
author	Gabriel A Vignolle Denise Schaffer Leopold Zehetner Robert L Mach Astrid R Mach-Aigner Christian Derntl
author_facet	Gabriel A Vignolle Denise Schaffer Leopold Zehetner Robert L Mach Astrid R Mach-Aigner Christian Derntl
author_sort	Gabriel A Vignolle
title	FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
title_short	FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
title_full	FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
title_fullStr	FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
title_full_unstemmed	FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
title_sort	funorder: a robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/090bfdcb82d64fa987e5133b784830b7
work_keys_str_mv	AT gabrielavignolle funorderarobustandsemiautomatedmethodfortheidentificationofessentialbiosyntheticgenesthroughcomputationalmolecularcoevolution AT deniseschaffer funorderarobustandsemiautomatedmethodfortheidentificationofessentialbiosyntheticgenesthroughcomputationalmolecularcoevolution AT leopoldzehetner funorderarobustandsemiautomatedmethodfortheidentificationofessentialbiosyntheticgenesthroughcomputationalmolecularcoevolution AT robertlmach funorderarobustandsemiautomatedmethodfortheidentificationofessentialbiosyntheticgenesthroughcomputationalmolecularcoevolution AT astridrmachaigner funorderarobustandsemiautomatedmethodfortheidentificationofessentialbiosyntheticgenesthroughcomputationalmolecularcoevolution AT christianderntl funorderarobustandsemiautomatedmethodfortheidentificationofessentialbiosyntheticgenesthroughcomputationalmolecularcoevolution
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FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.

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