Rapid 16S rRNA Next-Generation Sequencing of Polymicrobial Clinical Samples for Diagnosis of Complex Bacterial Infections
Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of c...
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Published in | PloS one Vol. 8; no. 5; p. e65226 |
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Main Authors | , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
29.05.2013
Public Library of Science (PLoS) |
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Abstract | Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (∼360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use. |
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AbstractList | Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (∼360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use. Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (∼360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use.Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (∼360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use. |
Author | Miller, Samuel I. Matsen, Frederick A. Shendure, Jay Rosenthal, Christopher Lee, Clarence C. Harkins, Timothy T. Sims, Elizabeth H. Sengupta, Dhruba J. Jacobs, Michael A. Hoffman, Noah G. Costa, Gina Hoogestraat, Daniel R. Spangler, Jessica Salipante, Stephen J. McCoy, Connor Cookson, Brad T. |
AuthorAffiliation | 1 Department of Laboratory Medicine, University of Washington, Seattle, Washington, United States of America University of Aberdeen, United Kingdom 3 Department of Microbiology, University of Washington, Seattle, Washington, United States of America 5 Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America 2 Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America 4 Life Technologies, Beverly, Massachusetts, United States of America |
AuthorAffiliation_xml | – name: 5 Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America – name: 3 Department of Microbiology, University of Washington, Seattle, Washington, United States of America – name: 1 Department of Laboratory Medicine, University of Washington, Seattle, Washington, United States of America – name: 4 Life Technologies, Beverly, Massachusetts, United States of America – name: University of Aberdeen, United Kingdom – name: 2 Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America |
Author_xml | – sequence: 1 givenname: Stephen J. surname: Salipante fullname: Salipante, Stephen J. – sequence: 2 givenname: Dhruba J. surname: Sengupta fullname: Sengupta, Dhruba J. – sequence: 3 givenname: Christopher surname: Rosenthal fullname: Rosenthal, Christopher – sequence: 4 givenname: Gina surname: Costa fullname: Costa, Gina – sequence: 5 givenname: Jessica surname: Spangler fullname: Spangler, Jessica – sequence: 6 givenname: Elizabeth H. surname: Sims fullname: Sims, Elizabeth H. – sequence: 7 givenname: Michael A. surname: Jacobs fullname: Jacobs, Michael A. – sequence: 8 givenname: Samuel I. surname: Miller fullname: Miller, Samuel I. – sequence: 9 givenname: Daniel R. surname: Hoogestraat fullname: Hoogestraat, Daniel R. – sequence: 10 givenname: Brad T. surname: Cookson fullname: Cookson, Brad T. – sequence: 11 givenname: Connor surname: McCoy fullname: McCoy, Connor – sequence: 12 givenname: Frederick A. surname: Matsen fullname: Matsen, Frederick A. – sequence: 13 givenname: Jay surname: Shendure fullname: Shendure, Jay – sequence: 14 givenname: Clarence C. surname: Lee fullname: Lee, Clarence C. – sequence: 15 givenname: Timothy T. surname: Harkins fullname: Harkins, Timothy T. – sequence: 16 givenname: Noah G. surname: Hoffman fullname: Hoffman, Noah G. |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23734239$$D View this record in MEDLINE/PubMed |
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Copyright | 2013 Salipante et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2013 Salipante et al 2013 Salipante et al |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have the following interests: co-authors Gina Costa, Jessica Spangler, Clarence Lee, and Timothy Harkins are employees of Life Technologies (parent company of Ion Torrent), and that this work was financially supported in part by Life Technologies Corporation. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. Conceived and designed the experiments: SJS DJS BTC JS NGH. Performed the experiments: SJS DRH JS EHS MAJ GC CL. Analyzed the data: SJS CR CM FAM NGH TH. Contributed reagents/materials/analysis tools: NGH FAM CM SIM CL TH. Wrote the paper: SJS NGH. |
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SubjectTerms | Bacteria Bacteria - classification Bacteria - genetics Bacterial diseases Bacterial infections Bacterial Infections - microbiology Bacterial Typing Techniques - methods Biology Culture Cystic fibrosis Cystic Fibrosis - genetics Cystic Fibrosis - microbiology Diagnostic systems DNA, Bacterial - chemistry DNA, Bacterial - genetics Gene expression Gene sequencing Genomes High-Throughput Nucleotide Sequencing - methods Human subjects Humans Infections Laboratories Medical diagnosis Medical research Medicine Metabolism Metagenome - genetics Microbiology Microbiota Microbiota - genetics Microorganisms Multilocus sequence typing Patients Phylogeny Polymerase Chain Reaction Public health Reproducibility of Results Respiratory tract Ribonucleic acid RNA RNA, Ribosomal, 16S - classification RNA, Ribosomal, 16S - genetics rRNA 16S Species Species classification Species Specificity Sputum Sputum - microbiology |
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Title | Rapid 16S rRNA Next-Generation Sequencing of Polymicrobial Clinical Samples for Diagnosis of Complex Bacterial Infections |
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