Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms,which may serve as promising sources of enzymes and natural products for industrial applications.Identifying enzymes...Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms,which may serve as promising sources of enzymes and natural products for industrial applications.Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools.The catalytic properties of all enzymes are primarily dictated by their structures,which are predominantly determined by their amino acid sequences.However,this aspect has not been fully considered in the enzyme bioprospecting processes.With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts,structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties.Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise.Here,we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates,in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.展开更多
High-throughput sequencing studies generate vast amounts of taxonomic data.Evolutionary ecological hypotheses of the recovered taxa and Species Hypotheses are difficult to test due to problems with alignments and the ...High-throughput sequencing studies generate vast amounts of taxonomic data.Evolutionary ecological hypotheses of the recovered taxa and Species Hypotheses are difficult to test due to problems with alignments and the lack of a phylogenetic backbone.We propose an updated phylum-and class-level fungal classification accounting for monophyly and divergence time so that the main taxonomic ranks are more informative.Based on phylogenies and divergence time estimates,we adopt phylum rank to Aphelidiomycota,Basidiobolomycota,Calcarisporiellomycota,Glomeromycota,Entomophthoromycota,Entorrhizomycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota and Olpidiomycota.We accept nine subkingdoms to accommodate these 18 phyla.We consider the kingdom Nucleariae(phyla Nuclearida and Fonticulida)as a sister group to the Fungi.We also introduce a perl script and a newick-formatted classification backbone for assigning Species Hypotheses into a hierarchical taxonomic framework,using this or any other classification system.We provide an example of testing evolutionary ecological hypotheses based on a global soil fungal data set.展开更多
The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved i...The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats.Yet,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging.In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels.Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,respectively.This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental stud-ies.In order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences.On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.展开更多
Molecular identification methods,in particular high-throughput sequencing tools,have greatly improved our knowledge about fungal diversity and biogeography,but many of the recovered taxa from natural environments cann...Molecular identification methods,in particular high-throughput sequencing tools,have greatly improved our knowledge about fungal diversity and biogeography,but many of the recovered taxa from natural environments cannot be identified to species or even higher taxonomic levels.This study addresses the phylogenetic placement of previously unrecognized fungal groups by using two complementary approaches:(i)third-generation amplicon sequencing analysis of DNA from global soil samples,screening out ITS reads of<90%similarity to other available Sanger sequences,and(ii)analysis of common fungal taxa that were previously indicated to be enigmatic in terms of taxonomic placement based on the ITS sequences alone(so-called top50 sequences).For the global soil samples,we chose to amplify the full rRNA gene operon using four partly overlapping amplicons and multiple newly developed primers or primer combinations that cover nearly all fungi and a vast majority of non-fungal eukaryotes.We extracted the rRNA 18S(SSU)and 28S(LSU)genes and performed phylogenetic analyses against carefully selected reference material.Both SSU and LSU analyses placed most soil sequences and top50 sequences to known orders and classes,but tens of monophyletic groups and single sequences remained outside described taxa.Furthermore,the LSU analyses recovered a few small groups of sequences that may potentially represent novel phyla.We conclude that rRNA genes-based phylogenetic analyses are efficient tools for determining phylogenetic relationships of fungal taxa that cannot be placed to any order or class using ITS sequences alone.However,in many instances,longer rRNA gene sequences and availability of both SSU and LSU reads are needed to improve taxonomic resolution.By leveraging third-generation sequencing from global soil samples,we successfully provided phylogenetic placement for many previously unidentified sequences and broadened our view on the fungal tree of life,with 10-20%new order-level taxa.In addition,the PacBio sequence data greatly extends fungal class-level information in reference databases.展开更多
Plant pathogenic fungi are a large and diverse assemblage of eukaryotes with substantial impacts on natural ecosystems and human endeavours.These taxa often have complex and poorly understood life cycles,lack observab...Plant pathogenic fungi are a large and diverse assemblage of eukaryotes with substantial impacts on natural ecosystems and human endeavours.These taxa often have complex and poorly understood life cycles,lack observable,discriminatory morphological characters,and may not be amenable to in vitro culturing.As a result,species identification is frequently difficult.Molecular(DNA sequence)data have emerged as crucial information for the taxonomic identification of plant pathogenic fungi,with the nuclear ribosomal internal transcribed spacer(ITS)region being the most popular marker.However,international nucleotide sequence databases are accumulating numerous sequences of compromised or low-resolution taxonomic annotations and substandard technical quality,making their use in the molecular identification of plant pathogenic fungi problematic.Here we report on a concerted effort to identify high-quality reference sequences for various plant pathogenic fungi and to re-annotate incorrectly or insufficiently annotated public ITS sequences from these fungal lineages.A third objective was to enrich the sequences with geographical and ecological metadata.The results-a total of 31,954 changes-are incorporated in and made available through the UNITE database for molecular identification of fungi(http://unite.ut.ee),including standalone FASTA files of sequence data for local BLAST searches,use in the next-generation sequencing analysis platforms QIIME and mothur,and related applications.The present initiative is just a beginning to cover the wide spectrum of plant pathogenic fungi,and we invite all researchers with pertinent expertise to join the annotation effort.展开更多
Correction to:Fungal Diversity(2020)105:116 https://doi.org/10.1007/s13225-020-00466-2 There were errors in the name of author LászlóG.Nagy and in affiliation no.31 in the original publication.The original a...Correction to:Fungal Diversity(2020)105:116 https://doi.org/10.1007/s13225-020-00466-2 There were errors in the name of author LászlóG.Nagy and in affiliation no.31 in the original publication.The original article has been corrected.展开更多
Fungi are highly important biotic components of terrestrial ecosystems,but we still have a very limited understanding about their diversity and distribution.This data article releases a global soil fungal dataset of t...Fungi are highly important biotic components of terrestrial ecosystems,but we still have a very limited understanding about their diversity and distribution.This data article releases a global soil fungal dataset of the Global Soil Mycobiome consortium(GSMc)to boost further research in fungal diversity,biogeography and macroecology.The dataset comprises 722,682 fungal operational taxonomic units(OTUs)derived from PacBio sequencing of full-length ITS and 18S-V9 variable regions from 3200 plots in 108 countries on all continents.The plots are supplied with geographical and edaphic metadata.The OTUs are taxonomically and functionally assigned to guilds and other functional groups.The entire dataset has been corrected by excluding chimeras,index-switch artefacts and potential contamination.The dataset is more inclusive in terms of geographical breadth and phylogenetic diversity of fungi than previously published data.The GSMc dataset is available over the PlutoF repository.展开更多
基金Funding was provided by the Agricultural Biotechnology Research Institute of Iran(ABRII),Swedish Research Council(Vetenskapsrådet grant no.:2017-05019)the BBSRC Institute Strategic Programme Gut Microbes and Health(BB/r012490/1,its constituent project BBS/e/F/000Pr10355).
文摘Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms,which may serve as promising sources of enzymes and natural products for industrial applications.Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools.The catalytic properties of all enzymes are primarily dictated by their structures,which are predominantly determined by their amino acid sequences.However,this aspect has not been fully considered in the enzyme bioprospecting processes.With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts,structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties.Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise.Here,we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates,in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.
基金LT acknowledges funding from the Estonian Science Foundation(1399PUT,IUT20-30),MOBERC and ECOLCHANGE.
文摘High-throughput sequencing studies generate vast amounts of taxonomic data.Evolutionary ecological hypotheses of the recovered taxa and Species Hypotheses are difficult to test due to problems with alignments and the lack of a phylogenetic backbone.We propose an updated phylum-and class-level fungal classification accounting for monophyly and divergence time so that the main taxonomic ranks are more informative.Based on phylogenies and divergence time estimates,we adopt phylum rank to Aphelidiomycota,Basidiobolomycota,Calcarisporiellomycota,Glomeromycota,Entomophthoromycota,Entorrhizomycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota and Olpidiomycota.We accept nine subkingdoms to accommodate these 18 phyla.We consider the kingdom Nucleariae(phyla Nuclearida and Fonticulida)as a sister group to the Fungi.We also introduce a perl script and a newick-formatted classification backbone for assigning Species Hypotheses into a hierarchical taxonomic framework,using this or any other classification system.We provide an example of testing evolutionary ecological hypotheses based on a global soil fungal data set.
基金Estonian Science Foundation grants PSG136,PRG632,PUT1170the University of Tartu(PLTOM20903)the European Regional Development Fund(Centre of Excellence EcolChange).
文摘The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats.Yet,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging.In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels.Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,respectively.This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental stud-ies.In order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences.On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.
基金funded by the Estonian Science Foundation(Grants PUT1399,PRG632,MOBERC21)。
文摘Molecular identification methods,in particular high-throughput sequencing tools,have greatly improved our knowledge about fungal diversity and biogeography,but many of the recovered taxa from natural environments cannot be identified to species or even higher taxonomic levels.This study addresses the phylogenetic placement of previously unrecognized fungal groups by using two complementary approaches:(i)third-generation amplicon sequencing analysis of DNA from global soil samples,screening out ITS reads of<90%similarity to other available Sanger sequences,and(ii)analysis of common fungal taxa that were previously indicated to be enigmatic in terms of taxonomic placement based on the ITS sequences alone(so-called top50 sequences).For the global soil samples,we chose to amplify the full rRNA gene operon using four partly overlapping amplicons and multiple newly developed primers or primer combinations that cover nearly all fungi and a vast majority of non-fungal eukaryotes.We extracted the rRNA 18S(SSU)and 28S(LSU)genes and performed phylogenetic analyses against carefully selected reference material.Both SSU and LSU analyses placed most soil sequences and top50 sequences to known orders and classes,but tens of monophyletic groups and single sequences remained outside described taxa.Furthermore,the LSU analyses recovered a few small groups of sequences that may potentially represent novel phyla.We conclude that rRNA genes-based phylogenetic analyses are efficient tools for determining phylogenetic relationships of fungal taxa that cannot be placed to any order or class using ITS sequences alone.However,in many instances,longer rRNA gene sequences and availability of both SSU and LSU reads are needed to improve taxonomic resolution.By leveraging third-generation sequencing from global soil samples,we successfully provided phylogenetic placement for many previously unidentified sequences and broadened our view on the fungal tree of life,with 10-20%new order-level taxa.In addition,the PacBio sequence data greatly extends fungal class-level information in reference databases.
基金financial support from European Funds through COMPETENational Funds through the Portuguese Foundation for Science and Technology(FCT)within projects PTDC/AGR-FOR/3807/2012-FCOMP-01-0124-FEDER-027979 and PEst-C/MAR/LA0017/2013+4 种基金supported by National Science Foundation Grant DBI 1046115supported by FFG,BMWFJ,BMVIT,ZIT,Zukunftsstiftung Tirol,and Land Steiermark within the Austrian COMET program FFG Grant 824186Financial support to JP was partially provided by the Polish Ministry of Science and Higher Education(MNiSW),grant no.NN303_548839financial support from FAPEMIG and CNPqfunded by the Government of Canada through Genome Canada and the Ontario Genomics Institute through the Biomonitoring 2.0 project(OGI-050).
文摘Plant pathogenic fungi are a large and diverse assemblage of eukaryotes with substantial impacts on natural ecosystems and human endeavours.These taxa often have complex and poorly understood life cycles,lack observable,discriminatory morphological characters,and may not be amenable to in vitro culturing.As a result,species identification is frequently difficult.Molecular(DNA sequence)data have emerged as crucial information for the taxonomic identification of plant pathogenic fungi,with the nuclear ribosomal internal transcribed spacer(ITS)region being the most popular marker.However,international nucleotide sequence databases are accumulating numerous sequences of compromised or low-resolution taxonomic annotations and substandard technical quality,making their use in the molecular identification of plant pathogenic fungi problematic.Here we report on a concerted effort to identify high-quality reference sequences for various plant pathogenic fungi and to re-annotate incorrectly or insufficiently annotated public ITS sequences from these fungal lineages.A third objective was to enrich the sequences with geographical and ecological metadata.The results-a total of 31,954 changes-are incorporated in and made available through the UNITE database for molecular identification of fungi(http://unite.ut.ee),including standalone FASTA files of sequence data for local BLAST searches,use in the next-generation sequencing analysis platforms QIIME and mothur,and related applications.The present initiative is just a beginning to cover the wide spectrum of plant pathogenic fungi,and we invite all researchers with pertinent expertise to join the annotation effort.
文摘Correction to:Fungal Diversity(2020)105:116 https://doi.org/10.1007/s13225-020-00466-2 There were errors in the name of author LászlóG.Nagy and in affiliation no.31 in the original publication.The original article has been corrected.
基金the Estonian Science Foundation(Grant Nos.PRG632,PSG136,MOBTP198,PUT1170)Norway-Baltic EEA financial mechanism(Grant No.EMP442)RSF19-14-00038,DSFP-2021 and Novo Nordisk Fonden(Silva Nova).
文摘Fungi are highly important biotic components of terrestrial ecosystems,but we still have a very limited understanding about their diversity and distribution.This data article releases a global soil fungal dataset of the Global Soil Mycobiome consortium(GSMc)to boost further research in fungal diversity,biogeography and macroecology.The dataset comprises 722,682 fungal operational taxonomic units(OTUs)derived from PacBio sequencing of full-length ITS and 18S-V9 variable regions from 3200 plots in 108 countries on all continents.The plots are supplied with geographical and edaphic metadata.The OTUs are taxonomically and functionally assigned to guilds and other functional groups.The entire dataset has been corrected by excluding chimeras,index-switch artefacts and potential contamination.The dataset is more inclusive in terms of geographical breadth and phylogenetic diversity of fungi than previously published data.The GSMc dataset is available over the PlutoF repository.
