Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications
The application of direct metagenomic sequencing from positive blood culture broth may solve the challenges of sequencing from low-bacterial-load blood samples in patients with sepsis. Forty prospectively collected blood culture broth samples growing Gram-negative bacteria were extracted using comme...
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Veröffentlicht in: | Journal of clinical microbiology 2022-11, Vol.60 (11), p.e0101222-e0101222 |
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creator | Bauer, Michelle J Peri, Anna Maria Lüftinger, Lukas Beisken, Stephan Bergh, Haakon Forde, Brian M Buckley, Cameron Cuddihy, Thom Tan, Patrice Paterson, David L Whiley, David M Harris, Patrick N A |
description | The application of direct metagenomic sequencing from positive blood culture broth may solve the challenges of sequencing from low-bacterial-load blood samples in patients with sepsis. Forty prospectively collected blood culture broth samples growing Gram-negative bacteria were extracted using commercially available kits to achieve high-quality DNA. Species identification via metagenomic sequencing and susceptibility prediction via a machine-learning algorithm (AREScloud) were compared to conventional methods and other rapid diagnostic platforms (Accelerate Pheno and blood culture identification [BCID] panel). A two-kit method (using MolYsis Basic and Qiagen DNeasy UltraClean kits) resulted in optimal extractions. Taxonomic profiling by direct metagenomic sequencing matched conventional identification in 38/40 (95%) samples. In two polymicrobial samples, a second organism was missed by sequencing. Prediction models were able to accurately infer susceptibility profiles for 6 common pathogens against 17 antibiotics, with an overall categorical agreement (CA) of 95% (increasing to >95% for 5/6 of the most common pathogens, if Klebsiella oxytoca was excluded). The performance of whole-genome sequencing (WGS)-antimicrobial susceptibility testing (AST) was suboptimal for uncommon pathogens (e.g.,
) and some β-lactamase inhibitor antibiotics (e.g., ticarcillin-clavulanate). The time to pathogen identification was the fastest with BCID (1 h from blood culture positivity). Accelerate Pheno provided a susceptibility result in approximately 8 h. Illumina-based direct sequencing methods provided results in time frames similar to those of conventional culture-based methods. Direct metagenomic sequencing from blood cultures for pathogen detection and susceptibility prediction is feasible. Additional work is required to optimize algorithms for uncommon species and complex resistance genotypes as well as to streamline methods to provide more rapid results. |
doi_str_mv | 10.1128/jcm.01012-22 |
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) and some β-lactamase inhibitor antibiotics (e.g., ticarcillin-clavulanate). The time to pathogen identification was the fastest with BCID (1 h from blood culture positivity). Accelerate Pheno provided a susceptibility result in approximately 8 h. Illumina-based direct sequencing methods provided results in time frames similar to those of conventional culture-based methods. Direct metagenomic sequencing from blood cultures for pathogen detection and susceptibility prediction is feasible. Additional work is required to optimize algorithms for uncommon species and complex resistance genotypes as well as to streamline methods to provide more rapid results.</description><identifier>ISSN: 0095-1137</identifier><identifier>EISSN: 1098-660X</identifier><identifier>DOI: 10.1128/jcm.01012-22</identifier><identifier>PMID: 36314799</identifier><language>eng</language><publisher>United States: American Society for Microbiology</publisher><subject>Anti-Bacterial Agents - pharmacology ; Bacteriology ; Blood Culture - methods ; Clinical Microbiology ; Microbial Sensitivity Tests ; Nucleic Acids ; Phenotype</subject><ispartof>Journal of clinical microbiology, 2022-11, Vol.60 (11), p.e0101222-e0101222</ispartof><rights>Copyright © 2022 American Society for Microbiology.</rights><rights>Copyright © 2022 American Society for Microbiology. 2022 American Society for Microbiology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a418t-e99d1280024505ef9b3440c9e71d678c2a69b3595b3f1edeab5132da55f71d1f3</citedby><cites>FETCH-LOGICAL-a418t-e99d1280024505ef9b3440c9e71d678c2a69b3595b3f1edeab5132da55f71d1f3</cites><orcidid>0000-0003-2079-4437 ; 0000-0002-2895-0345 ; 0000-0002-2264-4785</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.asm.org/doi/pdf/10.1128/jcm.01012-22$$EPDF$$P50$$Gasm2$$H</linktopdf><linktohtml>$$Uhttps://journals.asm.org/doi/full/10.1128/jcm.01012-22$$EHTML$$P50$$Gasm2$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,3188,27924,27925,52751,52752,52753,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36314799$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ledeboer, Nathan A.</contributor><creatorcontrib>Bauer, Michelle J</creatorcontrib><creatorcontrib>Peri, Anna Maria</creatorcontrib><creatorcontrib>Lüftinger, Lukas</creatorcontrib><creatorcontrib>Beisken, Stephan</creatorcontrib><creatorcontrib>Bergh, Haakon</creatorcontrib><creatorcontrib>Forde, Brian M</creatorcontrib><creatorcontrib>Buckley, Cameron</creatorcontrib><creatorcontrib>Cuddihy, Thom</creatorcontrib><creatorcontrib>Tan, Patrice</creatorcontrib><creatorcontrib>Paterson, David L</creatorcontrib><creatorcontrib>Whiley, David M</creatorcontrib><creatorcontrib>Harris, Patrick N A</creatorcontrib><title>Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications</title><title>Journal of clinical microbiology</title><addtitle>J Clin Microbiol</addtitle><addtitle>J Clin Microbiol</addtitle><description>The application of direct metagenomic sequencing from positive blood culture broth may solve the challenges of sequencing from low-bacterial-load blood samples in patients with sepsis. Forty prospectively collected blood culture broth samples growing Gram-negative bacteria were extracted using commercially available kits to achieve high-quality DNA. Species identification via metagenomic sequencing and susceptibility prediction via a machine-learning algorithm (AREScloud) were compared to conventional methods and other rapid diagnostic platforms (Accelerate Pheno and blood culture identification [BCID] panel). A two-kit method (using MolYsis Basic and Qiagen DNeasy UltraClean kits) resulted in optimal extractions. Taxonomic profiling by direct metagenomic sequencing matched conventional identification in 38/40 (95%) samples. In two polymicrobial samples, a second organism was missed by sequencing. Prediction models were able to accurately infer susceptibility profiles for 6 common pathogens against 17 antibiotics, with an overall categorical agreement (CA) of 95% (increasing to >95% for 5/6 of the most common pathogens, if Klebsiella oxytoca was excluded). The performance of whole-genome sequencing (WGS)-antimicrobial susceptibility testing (AST) was suboptimal for uncommon pathogens (e.g.,
) and some β-lactamase inhibitor antibiotics (e.g., ticarcillin-clavulanate). The time to pathogen identification was the fastest with BCID (1 h from blood culture positivity). Accelerate Pheno provided a susceptibility result in approximately 8 h. Illumina-based direct sequencing methods provided results in time frames similar to those of conventional culture-based methods. Direct metagenomic sequencing from blood cultures for pathogen detection and susceptibility prediction is feasible. Additional work is required to optimize algorithms for uncommon species and complex resistance genotypes as well as to streamline methods to provide more rapid results.</description><subject>Anti-Bacterial Agents - pharmacology</subject><subject>Bacteriology</subject><subject>Blood Culture - methods</subject><subject>Clinical Microbiology</subject><subject>Microbial Sensitivity Tests</subject><subject>Nucleic Acids</subject><subject>Phenotype</subject><issn>0095-1137</issn><issn>1098-660X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1ks1vFCEYh4nR2LV682w4arJTgRlmlovJdq3VpLWNH9EbYZl3OmwYGIHph3-bf5x0tzZ68MTH-_AQfi8IPafkgFK2eL3RwwGhhLKCsQdoRolYFHVNvj9EM0IELygtmz30JMYNIbSqOH-M9sq6pFUjxAz9OhuTGcxPaPEppN63uPMBHyqdIBhl8cdJWzAaL7Vp8dF1CrlivMNd8AM-99Ekcwn40Pp8cjXZNIW8Cj71W8-33lsojsH5AfBn-DGB08ZdzPEniCYm5TTg8z6X082YZwFas9XPsXItfuuvXEwB1IBPs0dPVgW8HEdrtLql4lP0qFM2wrO7cR99fXf0ZfW-ODk7_rBanhSqootUgBBtTooQVnHCoRPrsqqIFtDQtm4Wmqk6b3HB12VHoQW15rRkreK8ywTtyn30Zucdp_UArQaXc7ByDGZQ4UZ6ZeS_FWd6eeEvpajrpqmrLHh5Jwg-hxCTHEzUYK1y4KcoWVOSumpqQTI636E6-BgDdPfXUCJvGy5zw-W24ZKxjL_a4SoOTG78FFxO4n_si7-fcS_-8xvK33VCuQE</recordid><startdate>20221116</startdate><enddate>20221116</enddate><creator>Bauer, Michelle J</creator><creator>Peri, Anna Maria</creator><creator>Lüftinger, Lukas</creator><creator>Beisken, Stephan</creator><creator>Bergh, Haakon</creator><creator>Forde, Brian M</creator><creator>Buckley, Cameron</creator><creator>Cuddihy, Thom</creator><creator>Tan, Patrice</creator><creator>Paterson, David L</creator><creator>Whiley, David M</creator><creator>Harris, Patrick N A</creator><general>American Society for Microbiology</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2079-4437</orcidid><orcidid>https://orcid.org/0000-0002-2895-0345</orcidid><orcidid>https://orcid.org/0000-0002-2264-4785</orcidid></search><sort><creationdate>20221116</creationdate><title>Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications</title><author>Bauer, Michelle J ; Peri, Anna Maria ; Lüftinger, Lukas ; Beisken, Stephan ; Bergh, Haakon ; Forde, Brian M ; Buckley, Cameron ; Cuddihy, Thom ; Tan, Patrice ; Paterson, David L ; Whiley, David M ; Harris, Patrick N A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a418t-e99d1280024505ef9b3440c9e71d678c2a69b3595b3f1edeab5132da55f71d1f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Anti-Bacterial Agents - pharmacology</topic><topic>Bacteriology</topic><topic>Blood Culture - methods</topic><topic>Clinical Microbiology</topic><topic>Microbial Sensitivity Tests</topic><topic>Nucleic Acids</topic><topic>Phenotype</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bauer, Michelle J</creatorcontrib><creatorcontrib>Peri, Anna Maria</creatorcontrib><creatorcontrib>Lüftinger, Lukas</creatorcontrib><creatorcontrib>Beisken, Stephan</creatorcontrib><creatorcontrib>Bergh, Haakon</creatorcontrib><creatorcontrib>Forde, Brian M</creatorcontrib><creatorcontrib>Buckley, Cameron</creatorcontrib><creatorcontrib>Cuddihy, Thom</creatorcontrib><creatorcontrib>Tan, Patrice</creatorcontrib><creatorcontrib>Paterson, David L</creatorcontrib><creatorcontrib>Whiley, David M</creatorcontrib><creatorcontrib>Harris, Patrick N A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of clinical microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bauer, Michelle J</au><au>Peri, Anna Maria</au><au>Lüftinger, Lukas</au><au>Beisken, Stephan</au><au>Bergh, Haakon</au><au>Forde, Brian M</au><au>Buckley, Cameron</au><au>Cuddihy, Thom</au><au>Tan, Patrice</au><au>Paterson, David L</au><au>Whiley, David M</au><au>Harris, Patrick N A</au><au>Ledeboer, Nathan A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications</atitle><jtitle>Journal of clinical microbiology</jtitle><stitle>J Clin Microbiol</stitle><addtitle>J Clin Microbiol</addtitle><date>2022-11-16</date><risdate>2022</risdate><volume>60</volume><issue>11</issue><spage>e0101222</spage><epage>e0101222</epage><pages>e0101222-e0101222</pages><issn>0095-1137</issn><eissn>1098-660X</eissn><abstract>The application of direct metagenomic sequencing from positive blood culture broth may solve the challenges of sequencing from low-bacterial-load blood samples in patients with sepsis. Forty prospectively collected blood culture broth samples growing Gram-negative bacteria were extracted using commercially available kits to achieve high-quality DNA. Species identification via metagenomic sequencing and susceptibility prediction via a machine-learning algorithm (AREScloud) were compared to conventional methods and other rapid diagnostic platforms (Accelerate Pheno and blood culture identification [BCID] panel). A two-kit method (using MolYsis Basic and Qiagen DNeasy UltraClean kits) resulted in optimal extractions. Taxonomic profiling by direct metagenomic sequencing matched conventional identification in 38/40 (95%) samples. In two polymicrobial samples, a second organism was missed by sequencing. Prediction models were able to accurately infer susceptibility profiles for 6 common pathogens against 17 antibiotics, with an overall categorical agreement (CA) of 95% (increasing to >95% for 5/6 of the most common pathogens, if Klebsiella oxytoca was excluded). The performance of whole-genome sequencing (WGS)-antimicrobial susceptibility testing (AST) was suboptimal for uncommon pathogens (e.g.,
) and some β-lactamase inhibitor antibiotics (e.g., ticarcillin-clavulanate). The time to pathogen identification was the fastest with BCID (1 h from blood culture positivity). Accelerate Pheno provided a susceptibility result in approximately 8 h. Illumina-based direct sequencing methods provided results in time frames similar to those of conventional culture-based methods. Direct metagenomic sequencing from blood cultures for pathogen detection and susceptibility prediction is feasible. Additional work is required to optimize algorithms for uncommon species and complex resistance genotypes as well as to streamline methods to provide more rapid results.</abstract><cop>United States</cop><pub>American Society for Microbiology</pub><pmid>36314799</pmid><doi>10.1128/jcm.01012-22</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-2079-4437</orcidid><orcidid>https://orcid.org/0000-0002-2895-0345</orcidid><orcidid>https://orcid.org/0000-0002-2264-4785</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Anti-Bacterial Agents - pharmacology Bacteriology Blood Culture - methods Clinical Microbiology Microbial Sensitivity Tests Nucleic Acids Phenotype |
title | Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications |
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