Discovery of genes coding for carbohydrate-active enzyme by metagenomic analysis of lignocellulosic biomasses.
Identifieur interne : 001439 ( Main/Exploration ); précédent : 001438; suivant : 001440Discovery of genes coding for carbohydrate-active enzyme by metagenomic analysis of lignocellulosic biomasses.
Auteurs : Salvatore Montella [Italie] ; Valeria Ventorino [Italie] ; Vincent Lombard [France] ; Bernard Henrissat [France, Arabie saoudite] ; Olimpia Pepe [Italie] ; Vincenza Faraco [Italie]Source :
- Scientific reports [ 2045-2322 ] ; 2017.
Descripteurs français
- KwdFr :
- Analyse de profil d'expression de gènes (MeSH), Biologie informatique (méthodes), Biomasse (MeSH), Biotransformation (MeSH), Cadres ouverts de lecture (MeSH), Cellulases (génétique), Cellulases (métabolisme), Dépollution biologique de l'environnement (MeSH), Glycosidases (génétique), Glycosidases (métabolisme), Hydrolyse (MeSH), Lignine (métabolisme), Microbiote (MeSH), Métabolisme glucidique (génétique), Métagénome (MeSH), Métagénomique (méthodes), Polyosides (métabolisme), Produits agricoles (MeSH), Séquençage nucléotidique à haut débit (MeSH).
- MESH :
- génétique : Cellulases, Glycosidases, Métabolisme glucidique.
- métabolisme : Cellulases, Glycosidases, Lignine, Polyosides.
- méthodes : Biologie informatique, Métagénomique.
- Analyse de profil d'expression de gènes, Biomasse, Biotransformation, Cadres ouverts de lecture, Dépollution biologique de l'environnement, Hydrolyse, Microbiote, Métagénome, Produits agricoles, Séquençage nucléotidique à haut débit.
English descriptors
- KwdEn :
- Biodegradation, Environmental (MeSH), Biomass (MeSH), Biotransformation (MeSH), Carbohydrate Metabolism (genetics), Cellulases (genetics), Cellulases (metabolism), Computational Biology (methods), Crops, Agricultural (MeSH), Gene Expression Profiling (MeSH), Glycoside Hydrolases (genetics), Glycoside Hydrolases (metabolism), High-Throughput Nucleotide Sequencing (MeSH), Hydrolysis (MeSH), Lignin (metabolism), Metagenome (MeSH), Metagenomics (methods), Microbiota (MeSH), Open Reading Frames (MeSH), Polysaccharides (metabolism).
- MESH :
- chemical , genetics : Cellulases, Glycoside Hydrolases.
- genetics : Carbohydrate Metabolism.
- chemical , metabolism : Cellulases, Glycoside Hydrolases, Lignin, Polysaccharides.
- methods : Computational Biology, Metagenomics.
- Biodegradation, Environmental, Biomass, Biotransformation, Crops, Agricultural, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Hydrolysis, Metagenome, Microbiota, Open Reading Frames.
Abstract
In this study, a high-throughput sequencing approach was applied to discover novel biocatalysts for lignocellulose hydrolysis from three dedicated energy crops, Arundo donax, Eucalyptus camaldulensis and Populus nigra, after natural biodegradation. The microbiomes of the three lignocellulosic biomasses were dominated by bacterial species (approximately 90%) with the highest representation by the Streptomyces genus both in the total microbial community composition and in the microbial diversity related to GH families of predicted ORFs. Moreover, the functional clustering of the predicted ORFs showed a prevalence of poorly characterized genes, suggesting these lignocellulosic biomasses are potential sources of as yet unknown genes. 1.2%, 0.6% and 3.4% of the total ORFs detected in A. donax, E. camaldulensis and P. nigra, respectively, were putative Carbohydrate-Active Enzymes (CAZymes). Interestingly, the glycoside hydrolases abundance in P. nigra (1.8%) was higher than that detected in the other biomasses investigated in this study. Moreover, a high percentage of (hemi)cellulases with different activities and accessory enzymes (mannanases, polygalacturonases and feruloyl esterases) was detected, confirming that the three analyzed samples were a reservoir of diversified biocatalysts required for an effective lignocellulose saccharification.
DOI: 10.1038/srep42623
PubMed: 28198423
PubMed Central: PMC5309792
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Angelo, via Cintia, 4 80126 Naples, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Department of Chemical Sciences, University of Naples "Federico II", Complesso Universitario Monte S. Angelo, via Cintia, 4 80126 Naples</wicri:regionArea><wicri:noRegion>4 80126 Naples</wicri:noRegion></affiliation></author></analytic><series><title level="j">Scientific reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Biodegradation, Environmental (MeSH)</term><term>Biomass (MeSH)</term><term>Biotransformation (MeSH)</term><term>Carbohydrate Metabolism (genetics)</term><term>Cellulases (genetics)</term><term>Cellulases (metabolism)</term><term>Computational Biology (methods)</term><term>Crops, Agricultural (MeSH)</term><term>Gene Expression Profiling (MeSH)</term><term>Glycoside Hydrolases (genetics)</term><term>Glycoside Hydrolases (metabolism)</term><term>High-Throughput Nucleotide Sequencing (MeSH)</term><term>Hydrolysis (MeSH)</term><term>Lignin (metabolism)</term><term>Metagenome (MeSH)</term><term>Metagenomics (methods)</term><term>Microbiota (MeSH)</term><term>Open Reading Frames (MeSH)</term><term>Polysaccharides (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de profil d'expression de gènes (MeSH)</term><term>Biologie informatique (méthodes)</term><term>Biomasse (MeSH)</term><term>Biotransformation (MeSH)</term><term>Cadres ouverts de lecture (MeSH)</term><term>Cellulases (génétique)</term><term>Cellulases (métabolisme)</term><term>Dépollution biologique de l'environnement (MeSH)</term><term>Glycosidases (génétique)</term><term>Glycosidases (métabolisme)</term><term>Hydrolyse (MeSH)</term><term>Lignine (métabolisme)</term><term>Microbiote (MeSH)</term><term>Métabolisme glucidique (génétique)</term><term>Métagénome (MeSH)</term><term>Métagénomique (méthodes)</term><term>Polyosides (métabolisme)</term><term>Produits agricoles (MeSH)</term><term>Séquençage nucléotidique à haut débit (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Cellulases</term><term>Glycoside Hydrolases</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Carbohydrate Metabolism</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Cellulases</term><term>Glycosidases</term><term>Métabolisme glucidique</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Cellulases</term><term>Glycoside Hydrolases</term><term>Lignin</term><term>Polysaccharides</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Metagenomics</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Cellulases</term><term>Glycosidases</term><term>Lignine</term><term>Polyosides</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Biologie informatique</term><term>Métagénomique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Biodegradation, Environmental</term><term>Biomass</term><term>Biotransformation</term><term>Crops, Agricultural</term><term>Gene Expression Profiling</term><term>High-Throughput Nucleotide Sequencing</term><term>Hydrolysis</term><term>Metagenome</term><term>Microbiota</term><term>Open Reading Frames</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Biomasse</term><term>Biotransformation</term><term>Cadres ouverts de lecture</term><term>Dépollution biologique de l'environnement</term><term>Hydrolyse</term><term>Microbiote</term><term>Métagénome</term><term>Produits agricoles</term><term>Séquençage nucléotidique à haut débit</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this study, a high-throughput sequencing approach was applied to discover novel biocatalysts for lignocellulose hydrolysis from three dedicated energy crops, Arundo donax, Eucalyptus camaldulensis and Populus nigra, after natural biodegradation. The microbiomes of the three lignocellulosic biomasses were dominated by bacterial species (approximately 90%) with the highest representation by the Streptomyces genus both in the total microbial community composition and in the microbial diversity related to GH families of predicted ORFs. Moreover, the functional clustering of the predicted ORFs showed a prevalence of poorly characterized genes, suggesting these lignocellulosic biomasses are potential sources of as yet unknown genes. 1.2%, 0.6% and 3.4% of the total ORFs detected in A. donax, E. camaldulensis and P. nigra, respectively, were putative Carbohydrate-Active Enzymes (CAZymes). Interestingly, the glycoside hydrolases abundance in P. nigra (1.8%) was higher than that detected in the other biomasses investigated in this study. Moreover, a high percentage of (hemi)cellulases with different activities and accessory enzymes (mannanases, polygalacturonases and feruloyl esterases) was detected, confirming that the three analyzed samples were a reservoir of diversified biocatalysts required for an effective lignocellulose saccharification.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28198423</PMID><DateCompleted><Year>2018</Year><Month>10</Month><Day>30</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2045-2322</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><PubDate><Year>2017</Year><Month>02</Month><Day>15</Day></PubDate></JournalIssue><Title>Scientific reports</Title><ISOAbbreviation>Sci Rep</ISOAbbreviation></Journal><ArticleTitle>Discovery of genes coding for carbohydrate-active enzyme by metagenomic analysis of lignocellulosic biomasses.</ArticleTitle><Pagination><MedlinePgn>42623</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1038/srep42623</ELocationID><Abstract><AbstractText>In this study, a high-throughput sequencing approach was applied to discover novel biocatalysts for lignocellulose hydrolysis from three dedicated energy crops, Arundo donax, Eucalyptus camaldulensis and Populus nigra, after natural biodegradation. The microbiomes of the three lignocellulosic biomasses were dominated by bacterial species (approximately 90%) with the highest representation by the Streptomyces genus both in the total microbial community composition and in the microbial diversity related to GH families of predicted ORFs. Moreover, the functional clustering of the predicted ORFs showed a prevalence of poorly characterized genes, suggesting these lignocellulosic biomasses are potential sources of as yet unknown genes. 1.2%, 0.6% and 3.4% of the total ORFs detected in A. donax, E. camaldulensis and P. nigra, respectively, were putative Carbohydrate-Active Enzymes (CAZymes). Interestingly, the glycoside hydrolases abundance in P. nigra (1.8%) was higher than that detected in the other biomasses investigated in this study. Moreover, a high percentage of (hemi)cellulases with different activities and accessory enzymes (mannanases, polygalacturonases and feruloyl esterases) was detected, confirming that the three analyzed samples were a reservoir of diversified biocatalysts required for an effective lignocellulose saccharification.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Montella</LastName><ForeName>Salvatore</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Chemical Sciences, University of Naples "Federico II", Complesso Universitario Monte S. Angelo, via Cintia, 4 80126 Naples, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ventorino</LastName><ForeName>Valeria</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Agricultural Sciences, University of Naples "Federico II", Portici (Napoli), Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lombard</LastName><ForeName>Vincent</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>CNRS UMR 7257, Aix-Marseille University, 13288 Marseille, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>INRA, USC 1408 AFMB, 13288 Marseille, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Henrissat</LastName><ForeName>Bernard</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>CNRS UMR 7257, Aix-Marseille University, 13288 Marseille, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>INRA, USC 1408 AFMB, 13288 Marseille, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pepe</LastName><ForeName>Olimpia</ForeName><Initials>O</Initials><AffiliationInfo><Affiliation>Department of Agricultural Sciences, University of Naples "Federico II", Portici (Napoli), Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Faraco</LastName><ForeName>Vincenza</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Chemical Sciences, University of Naples "Federico II", Complesso Universitario Monte S. Angelo, via Cintia, 4 80126 Naples, Italy.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>02</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Sci Rep</MedlineTA><NlmUniqueID>101563288</NlmUniqueID><ISSNLinking>2045-2322</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011134">Polysaccharides</NameOfSubstance></Chemical><Chemical><RegistryNumber>11132-73-3</RegistryNumber><NameOfSubstance UI="C036909">lignocellulose</NameOfSubstance></Chemical><Chemical><RegistryNumber>9005-53-2</RegistryNumber><NameOfSubstance UI="D008031">Lignin</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 3.2.1.-</RegistryNumber><NameOfSubstance UI="D044602">Cellulases</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 3.2.1.-</RegistryNumber><NameOfSubstance UI="D006026">Glycoside Hydrolases</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001673" MajorTopicYN="N">Biodegradation, Environmental</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018533" MajorTopicYN="Y">Biomass</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001711" MajorTopicYN="N">Biotransformation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D050260" MajorTopicYN="N">Carbohydrate Metabolism</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D044602" MajorTopicYN="N">Cellulases</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000378" 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