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 inPloS one Vol. 8; no. 5; p. e65226
Main Authors Salipante, Stephen J., Sengupta, Dhruba J., Rosenthal, Christopher, Costa, Gina, Spangler, Jessica, Sims, Elizabeth H., Jacobs, Michael A., Miller, Samuel I., Hoogestraat, Daniel R., Cookson, Brad T., McCoy, Connor, Matsen, Frederick A., Shendure, Jay, Lee, Clarence C., Harkins, Timothy T., Hoffman, Noah G.
Format Journal Article
LanguageEnglish
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.
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
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  surname: Salipante
  fullname: Salipante, Stephen J.
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– sequence: 8
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– sequence: 13
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/23734239$$D View this record in MEDLINE/PubMed
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2013 Salipante et al 2013 Salipante et al
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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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Snippet Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology...
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StartPage e65226
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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http://dx.doi.org/10.1371/journal.pone.0065226
Volume 8
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