A New Framework to Accurately Quantify Soil Bacterial Community Diversity from DGGE
Identifieur interne : 001961 ( Istex/Corpus ); précédent : 001960; suivant : 001962A New Framework to Accurately Quantify Soil Bacterial Community Diversity from DGGE
Auteurs : Jonathan Lalande ; Richard Villemur ; Louise DeschênesSource :
- Microbial Ecology [ 0095-3628 ] ; 2013-10-01.
Abstract
Abstract: Denaturing gradient gel electrophoresis (DGGE) has been and remains extensively used to assess and monitor the effects of various treatments on soil bacterial communities. Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community’s true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson’s 1/D indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson’s 1/D were accurate at ±10 %. Richness estimations were less accurate, ranging from −11 to 31 % of the expected values. The framework showed great potential to study the structure and diversity of soil bacterial communities.
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DOI: 10.1007/s00248-013-0230-3
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Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community’s true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson’s 1/D indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson’s 1/D were accurate at ±10 %. Richness estimations were less accurate, ranging from −11 to 31 % of the expected values. The framework showed great potential to study the structure and diversity of soil bacterial communities.</div></front></TEI><istex><corpusName>springer-journals</corpusName><author><json:item><name>Jonathan Lalande</name><affiliations><json:string>École Polytechnique de Montréal, Chemical Engineering Department, CIRAIG, 2500 chemin de Polytechnique, H3T 1J4, Montréal, QC, Canada</json:string><json:string>E-mail: jonathan.lalande@polymtl.ca</json:string></affiliations></json:item><json:item><name>Richard Villemur</name><affiliations><json:string>Environmental Microbiology Research Group, INRS–Institut Armand-Frappier Research Center, 531 boul. des Prairies, H7V 1B7, Laval, QC, Canada</json:string></affiliations></json:item><json:item><name>Louise Deschênes</name><affiliations><json:string>École Polytechnique de Montréal, Chemical Engineering Department, CIRAIG, 2500 chemin de Polytechnique, H3T 1J4, Montréal, QC, Canada</json:string></affiliations></json:item></author><articleId><json:string>230</json:string><json:string>s00248-013-0230-3</json:string></articleId><arkIstex>ark:/67375/VQC-BJT08FGV-W</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: Denaturing gradient gel electrophoresis (DGGE) has been and remains extensively used to assess and monitor the effects of various treatments on soil bacterial communities. Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community’s true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson’s 1/D indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson’s 1/D were accurate at ±10 %. Richness estimations were less accurate, ranging from −11 to 31 % of the expected values. 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Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community’s true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson’s 1/D indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson’s 1/D were accurate at ±10 %. Richness estimations were less accurate, ranging from −11 to 31 % of the expected values. 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ID="Aff1"><OrgDivision>École Polytechnique de Montréal, Chemical Engineering Department</OrgDivision><OrgName>CIRAIG</OrgName><OrgAddress><Street>2500 chemin de Polytechnique</Street><City>Montréal</City><State>QC</State><Country Code="CA">Canada</Country><Postcode>H3T 1J4</Postcode></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Environmental Microbiology Research Group</OrgDivision><OrgName>INRS–Institut Armand-Frappier Research Center</OrgName><OrgAddress><Street>531 boul. des Prairies</Street><City>Laval</City><State>QC</State><Country Code="CA">Canada</Country><Postcode>H7V 1B7</Postcode></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En" OutputMedium="All"><Heading>Abstract</Heading><Para>Denaturing gradient gel electrophoresis (DGGE) has been and remains extensively used to assess and monitor the effects of various treatments on soil bacterial communities. Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community’s true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson’s 1/<Emphasis Type="Italic">D</Emphasis> indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson’s 1/<Emphasis Type="Italic">D</Emphasis> were accurate at ±10 %. Richness estimations were less accurate, ranging from −11 to 31 % of the expected values. The framework showed great potential to study the structure and diversity of soil bacterial communities.</Para></Abstract><ArticleNote Type="ESMHint"><Heading>Electronic supplementary material</Heading><SimplePara>The online version of this article (doi:<ExternalRef><RefSource>10.1007/s00248-013-0230-3</RefSource><RefTarget Address="10.1007/s00248-013-0230-3" TargetType="DOI"></RefTarget></ExternalRef>) contains supplementary material, which is available to authorized users.</SimplePara></ArticleNote></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A New Framework to Accurately Quantify Soil Bacterial Community Diversity from DGGE</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A New Framework to Accurately Quantify Soil Bacterial Community Diversity from DGGE</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">Jonathan</namePart><namePart type="family">Lalande</namePart><affiliation>École Polytechnique de Montréal, Chemical Engineering Department, CIRAIG, 2500 chemin de Polytechnique, H3T 1J4, Montréal, QC, Canada</affiliation><affiliation>E-mail: jonathan.lalande@polymtl.ca</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Richard</namePart><namePart type="family">Villemur</namePart><affiliation>Environmental Microbiology Research Group, INRS–Institut Armand-Frappier Research Center, 531 boul. des Prairies, H7V 1B7, Laval, QC, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Louise</namePart><namePart type="family">Deschênes</namePart><affiliation>École Polytechnique de Montréal, Chemical Engineering Department, CIRAIG, 2500 chemin de Polytechnique, H3T 1J4, Montréal, QC, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer US; http://www.springer-ny.com</publisher><place><placeTerm type="text">New York</placeTerm></place><dateCreated encoding="w3cdtf">2012-12-19</dateCreated><dateIssued encoding="w3cdtf">2013-10-01</dateIssued><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: Denaturing gradient gel electrophoresis (DGGE) has been and remains extensively used to assess and monitor the effects of various treatments on soil bacterial communities. Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community’s true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson’s 1/D indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson’s 1/D were accurate at ±10 %. Richness estimations were less accurate, ranging from −11 to 31 % of the expected values. The framework showed great potential to study the structure and diversity of soil bacterial communities.</abstract><note>Methods</note><relatedItem type="host"><titleInfo><title>Microbial Ecology</title></titleInfo><titleInfo type="abbreviated"><title>Microb Ecol</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><originInfo><publisher>Springer</publisher><dateIssued encoding="w3cdtf">2013-09-18</dateIssued><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><subject><genre>Journal-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SC3">Biomedical and Life Sciences</topic></subject><subject><genre>Journal-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="SCL">Life Sciences</topic><topic authority="SpringerSubjectCodes" authorityURI="SCL23004">Microbiology</topic><topic authority="SpringerSubjectCodes" authorityURI="SCL19007">Ecology</topic><topic authority="SpringerSubjectCodes" authorityURI="SCL19082">Microbial Ecology</topic><topic authority="SpringerSubjectCodes" authorityURI="SCU21006">Geoecology/Natural Processes</topic><topic authority="SpringerSubjectCodes" authorityURI="SCU26008">Nature Conservation</topic><topic authority="SpringerSubjectCodes" authorityURI="SC212000">Water Quality/Water Pollution</topic></subject><identifier type="ISSN">0095-3628</identifier><identifier type="eISSN">1432-184X</identifier><identifier type="JournalID">248</identifier><identifier type="IssueArticleCount">23</identifier><identifier type="VolumeIssueCount">4</identifier><part><date>2013</date><detail type="volume"><number>66</number><caption>vol.</caption></detail><detail type="issue"><number>3</number><caption>no.</caption></detail><extent unit="pages"><start>647</start><end>658</end></extent></part><recordInfo><recordOrigin>Springer Science+Business Media New York, 2013</recordOrigin></recordInfo></relatedItem><identifier type="istex">FDE1E3D19BE7B1CCF3197B6A24B752CB80E8AA6A</identifier><identifier type="ark">ark:/67375/VQC-BJT08FGV-W</identifier><identifier type="DOI">10.1007/s00248-013-0230-3</identifier><identifier type="ArticleID">230</identifier><identifier type="ArticleID">s00248-013-0230-3</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer Science+Business Media New York, 2013</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</recordContentSource><recordOrigin>Springer Science+Business Media New York, 2013</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-BJT08FGV-W/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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