Bioprospecting for Genes Encoding Hydrocarbon-Degrading Enzymes from Metagenomic Samples Isolated from Northern Adriatic Sea Sediments

Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome...

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Published inFood technology and biotechnology Vol. 56; no. 2; pp. 270 - 277
Main Authors Gacesa, Ranko, Baranasic, Damir, Starcevic, Antonio, Diminic, Janko, Korlević, Marino, Najdek, Mirjana, Blažina, Marijana, Oršolić, Davor, Kolesarić, Domagoj, Long, Paul F., Cullum, John, Hranueli, Daslav, Orlić, Sandi, Zucko, Jurica
Format Journal Article
LanguageEnglish
Published Croatia Sveuciliste U Zagrebu 01.06.2018
Sveuciliste u Zagrebu, Prehramheno-Biotehnoloski Fakultet
University of Zagreb Faculty of Food Technology and Biotechnology
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Abstract Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic -alkane degradation. The use of this system for bioprospecting was exemplified using potential and genes from this library.
AbstractList Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database ( http://redpet.bioinfo.pbf.hr/REDPET ), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n -alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.
Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.
Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.
Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic -alkane degradation. The use of this system for bioprospecting was exemplified using potential and genes from this library.
Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database ( Key words: oil pollution, n-alkane degradation, database
Audience Academic
Author Kolesarić, Domagoj
Cullum, John
Starcevic, Antonio
Korlević, Marino
Oršolić, Davor
Long, Paul F.
Orlić, Sandi
Blažina, Marijana
Baranasic, Damir
Hranueli, Daslav
Zucko, Jurica
Gacesa, Ranko
Diminic, Janko
Najdek, Mirjana
AuthorAffiliation These authors contributed equally to this work
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Copyright COPYRIGHT 2018 Sveuciliste U Zagrebu
2018. This work is published under https://creativecommons.org/licenses/by-nc/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2018 University of Zagreb Faculty of Food Technology and Biotechnology
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CorporateAuthor Department of Chemistry, King’s College London, Franklin-Wilkins Building, Stamford Street, London SE1 9NH, UK
Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia
Centre for Marine Research, Ruđer Bošković Institute, G. Paliaga 5, 52210 Rovinj, Croatia
Institute of Pharmaceutical Science King’s College London, Franklin-Wilkins Building, Stamford Street, London SE1 9NH, UK
Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany
Centre of Research Excellence for Marine Bioprospecting - BioProCro, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
Center of Excellence for Science and Technology Integrating Mediterranean Region, Microbial Ecology, 10000 Zagreb, Croatia
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– name: Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany
– name: Institute of Pharmaceutical Science King’s College London, Franklin-Wilkins Building, Stamford Street, London SE1 9NH, UK
– name: Centre of Research Excellence for Marine Bioprospecting - BioProCro, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
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SubjectTerms Alkanes
Biodegradation
Biodiversity
Bioinformatics
Bioprospecting
Bioremediation
Crude oil
Degradation
Deoxyribonucleic acid
DNA
Enzymes
Functional analysis
Genes
Genetic code
Genomes
Genomics
Hydrocarbon-degrading enzymes
Hydrocarbons
Markov chains
Markov processes
n-alkane degradation
oil pollution
Oil spills
Petroleum
Proteins
Relational data bases
Scientific Notes
Sediment samplers
Sediments
Taxonomy
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Title Bioprospecting for Genes Encoding Hydrocarbon-Degrading Enzymes from Metagenomic Samples Isolated from Northern Adriatic Sea Sediments
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