Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing.
Identifieur interne : 000A70 ( PubMed/Checkpoint ); précédent : 000A69; suivant : 000A71Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing.
Auteurs : Xuejian Xiong [Canada] ; Daniel N. Frank ; Charles E. Robertson ; Stacy S. Hung ; Janet Markle ; Angelo J. Canty ; Kathy D. Mccoy ; Andrew J. Macpherson ; Philippe Poussier ; Jayne S. Danska ; John ParkinsonSource :
- PloS one [ 1932-6203 ] ; 2012.
English descriptors
- KwdEn :
- Animals, Base Sequence, Databases, Genetic, Escherichia coli (genetics), Escherichia coli (metabolism), Escherichia coli Proteins (metabolism), Gene Regulatory Networks (genetics), Genes, Bacterial (genetics), Intestines (metabolism), Metagenomics (methods), Mice, Mice, Inbred NOD, Molecular Sequence Annotation, Multigene Family, Peptides (metabolism), Phylogeny, Protein Interaction Maps, RNA, Messenger (genetics), RNA, Messenger (metabolism), RNA, Ribosomal (genetics), Ribosome Subunits, Small, Eukaryotic (genetics), Sequence Analysis, RNA (methods), Transcriptome (genetics).
- MESH :
- chemical , genetics : RNA, Messenger, RNA, Ribosomal.
- chemical , metabolism : Escherichia coli Proteins, Peptides, RNA, Messenger.
- genetics : Escherichia coli, Gene Regulatory Networks, Genes, Bacterial, Ribosome Subunits, Small, Eukaryotic, Transcriptome.
- metabolism : Escherichia coli, Intestines.
- methods : Metagenomics, Sequence Analysis, RNA.
- Animals, Base Sequence, Databases, Genetic, Mice, Mice, Inbred NOD, Molecular Sequence Annotation, Multigene Family, Phylogeny, Protein Interaction Maps.
Abstract
With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.
DOI: 10.1371/journal.pone.0036009
PubMed: 22558305
Affiliations:
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Robertson</name></author><author><name sortKey="Hung, Stacy S" sort="Hung, Stacy S" uniqKey="Hung S" first="Stacy S" last="Hung">Stacy S. Hung</name></author><author><name sortKey="Markle, Janet" sort="Markle, Janet" uniqKey="Markle J" first="Janet" last="Markle">Janet Markle</name></author><author><name sortKey="Canty, Angelo J" sort="Canty, Angelo J" uniqKey="Canty A" first="Angelo J" last="Canty">Angelo J. Canty</name></author><author><name sortKey="Mccoy, Kathy D" sort="Mccoy, Kathy D" uniqKey="Mccoy K" first="Kathy D" last="Mccoy">Kathy D. Mccoy</name></author><author><name sortKey="Macpherson, Andrew J" sort="Macpherson, Andrew J" uniqKey="Macpherson A" first="Andrew J" last="Macpherson">Andrew J. 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Frank</name></author><author><name sortKey="Robertson, Charles E" sort="Robertson, Charles E" uniqKey="Robertson C" first="Charles E" last="Robertson">Charles E. Robertson</name></author><author><name sortKey="Hung, Stacy S" sort="Hung, Stacy S" uniqKey="Hung S" first="Stacy S" last="Hung">Stacy S. Hung</name></author><author><name sortKey="Markle, Janet" sort="Markle, Janet" uniqKey="Markle J" first="Janet" last="Markle">Janet Markle</name></author><author><name sortKey="Canty, Angelo J" sort="Canty, Angelo J" uniqKey="Canty A" first="Angelo J" last="Canty">Angelo J. Canty</name></author><author><name sortKey="Mccoy, Kathy D" sort="Mccoy, Kathy D" uniqKey="Mccoy K" first="Kathy D" last="Mccoy">Kathy D. Mccoy</name></author><author><name sortKey="Macpherson, Andrew J" sort="Macpherson, Andrew J" uniqKey="Macpherson A" first="Andrew J" last="Macpherson">Andrew J. Macpherson</name></author><author><name sortKey="Poussier, Philippe" sort="Poussier, Philippe" uniqKey="Poussier P" first="Philippe" last="Poussier">Philippe Poussier</name></author><author><name sortKey="Danska, Jayne S" sort="Danska, Jayne S" uniqKey="Danska J" first="Jayne S" last="Danska">Jayne S. Danska</name></author><author><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Base Sequence</term><term>Databases, Genetic</term><term>Escherichia coli (genetics)</term><term>Escherichia coli (metabolism)</term><term>Escherichia coli Proteins (metabolism)</term><term>Gene Regulatory Networks (genetics)</term><term>Genes, Bacterial (genetics)</term><term>Intestines (metabolism)</term><term>Metagenomics (methods)</term><term>Mice</term><term>Mice, Inbred NOD</term><term>Molecular Sequence Annotation</term><term>Multigene Family</term><term>Peptides (metabolism)</term><term>Phylogeny</term><term>Protein Interaction Maps</term><term>RNA, Messenger (genetics)</term><term>RNA, Messenger (metabolism)</term><term>RNA, Ribosomal (genetics)</term><term>Ribosome Subunits, Small, Eukaryotic (genetics)</term><term>Sequence Analysis, RNA (methods)</term><term>Transcriptome (genetics)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Messenger</term><term>RNA, Ribosomal</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Escherichia coli Proteins</term><term>Peptides</term><term>RNA, Messenger</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Escherichia coli</term><term>Gene Regulatory Networks</term><term>Genes, Bacterial</term><term>Ribosome Subunits, Small, Eukaryotic</term><term>Transcriptome</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Escherichia coli</term><term>Intestines</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Metagenomics</term><term>Sequence Analysis, RNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Base Sequence</term><term>Databases, Genetic</term><term>Mice</term><term>Mice, Inbred NOD</term><term>Molecular Sequence Annotation</term><term>Multigene Family</term><term>Phylogeny</term><term>Protein Interaction Maps</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22558305</PMID><DateCreated><Year>2012</Year><Month>05</Month><Day>04</Day></DateCreated><DateCompleted><Year>2012</Year><Month>09</Month><Day>07</Day></DateCompleted><DateRevised><Year>2016</Year><Month>10</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>4</Issue><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing.</ArticleTitle><Pagination><MedlinePgn>e36009</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0036009</ELocationID><Abstract><AbstractText>With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Xiong</LastName><ForeName>Xuejian</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Program in Molecular Structure and Function, The Hospital for Sick Children, Toronto, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Frank</LastName><ForeName>Daniel N</ForeName><Initials>DN</Initials></Author><Author ValidYN="Y"><LastName>Robertson</LastName><ForeName>Charles E</ForeName><Initials>CE</Initials></Author><Author ValidYN="Y"><LastName>Hung</LastName><ForeName>Stacy S</ForeName><Initials>SS</Initials></Author><Author ValidYN="Y"><LastName>Markle</LastName><ForeName>Janet</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Canty</LastName><ForeName>Angelo J</ForeName><Initials>AJ</Initials></Author><Author ValidYN="Y"><LastName>McCoy</LastName><ForeName>Kathy D</ForeName><Initials>KD</Initials></Author><Author ValidYN="Y"><LastName>Macpherson</LastName><ForeName>Andrew J</ForeName><Initials>AJ</Initials></Author><Author ValidYN="Y"><LastName>Poussier</LastName><ForeName>Philippe</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Danska</LastName><ForeName>Jayne S</ForeName><Initials>JS</Initials></Author><Author ValidYN="Y"><LastName>Parkinson</LastName><ForeName>John</ForeName><Initials>J</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R21 HG005964</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>CTP-82940</GrantID><Agency>Canadian Institutes of Health Research</Agency><Country>Canada</Country></Grant><Grant><GrantID>R21HG005964</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>04</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D029968">Escherichia coli Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D010455">Peptides</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012333">RNA, Messenger</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012335">RNA, Ribosomal</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>ISME J. 2011 Mar;5(3):461-72</RefSource><PMID Version="1">20844569</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2000 Jan 1;28(1):45-8</RefSource><PMID Version="1">10592178</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Science. 1997 Oct 24;278(5338):631-7</RefSource><PMID Version="1">9381173</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Mol Biol. 2001 Dec 14;314(5):1041-52</RefSource><PMID Version="1">11743721</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Curr Opin Biotechnol. 2003 Jun;14(3):303-10</RefSource><PMID 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MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D051379" MajorTopicYN="N">Mice</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016688" MajorTopicYN="N">Mice, Inbred NOD</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058977" MajorTopicYN="N">Molecular Sequence Annotation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005810" MajorTopicYN="N">Multigene Family</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010455" MajorTopicYN="N">Peptides</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010802" MajorTopicYN="N">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D060066" MajorTopicYN="N">Protein Interaction Maps</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012333" MajorTopicYN="N">RNA, Messenger</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012335" MajorTopicYN="N">RNA, Ribosomal</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D054682" MajorTopicYN="N">Ribosome Subunits, Small, Eukaryotic</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017423" MajorTopicYN="N">Sequence Analysis, RNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D059467" MajorTopicYN="N">Transcriptome</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC3338770</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2011</Year><Month>12</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2012</Year><Month>03</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>5</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>5</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>9</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22558305</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0036009</ArticleId><ArticleId IdType="pii">PONE-D-12-00232</ArticleId><ArticleId IdType="pmc">PMC3338770</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Canada</li></country></list><tree><noCountry><name sortKey="Canty, Angelo J" sort="Canty, Angelo J" uniqKey="Canty A" first="Angelo J" last="Canty">Angelo J. Canty</name><name sortKey="Danska, Jayne S" sort="Danska, Jayne S" uniqKey="Danska J" first="Jayne S" last="Danska">Jayne S. Danska</name><name sortKey="Frank, Daniel N" sort="Frank, Daniel N" uniqKey="Frank D" first="Daniel N" last="Frank">Daniel N. Frank</name><name sortKey="Hung, Stacy S" sort="Hung, Stacy S" uniqKey="Hung S" first="Stacy S" last="Hung">Stacy S. Hung</name><name sortKey="Macpherson, Andrew J" sort="Macpherson, Andrew J" uniqKey="Macpherson A" first="Andrew J" last="Macpherson">Andrew J. Macpherson</name><name sortKey="Markle, Janet" sort="Markle, Janet" uniqKey="Markle J" first="Janet" last="Markle">Janet Markle</name><name sortKey="Mccoy, Kathy D" sort="Mccoy, Kathy D" uniqKey="Mccoy K" first="Kathy D" last="Mccoy">Kathy D. Mccoy</name><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name><name sortKey="Poussier, Philippe" sort="Poussier, Philippe" uniqKey="Poussier P" first="Philippe" last="Poussier">Philippe Poussier</name><name sortKey="Robertson, Charles E" sort="Robertson, Charles E" uniqKey="Robertson C" first="Charles E" last="Robertson">Charles E. Robertson</name></noCountry><country name="Canada"><noRegion><name sortKey="Xiong, Xuejian" sort="Xiong, Xuejian" uniqKey="Xiong X" first="Xuejian" last="Xiong">Xuejian Xiong</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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