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Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing

BACKGROUND: Diabetic foot infections (DFIs) constitute the most common cause for diabetes-related hospitalization and lower extremity amputations. Current diagnostic methods are slow and in some cases do not detect all potential pathogens. Metagenomics sequencing has the potential to merge rapidity...

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Autores principales: Shurko, James, Dallas, Steven, Duhon, Bryson M, Meckel, Jordan, Wang, Chiou-Miin, Lin, Chun-Lin, Lucio, Nicholas, Kirma, Nameer, Lee, Grace C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631935/
http://dx.doi.org/10.1093/ofid/ofx163.125
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author Shurko, James
Dallas, Steven
Duhon, Bryson M
Meckel, Jordan
Wang, Chiou-Miin
Lin, Chun-Lin
Lucio, Nicholas
Kirma, Nameer
Lee, Grace C
author_facet Shurko, James
Dallas, Steven
Duhon, Bryson M
Meckel, Jordan
Wang, Chiou-Miin
Lin, Chun-Lin
Lucio, Nicholas
Kirma, Nameer
Lee, Grace C
author_sort Shurko, James
collection PubMed
description BACKGROUND: Diabetic foot infections (DFIs) constitute the most common cause for diabetes-related hospitalization and lower extremity amputations. Current diagnostic methods are slow and in some cases do not detect all potential pathogens. Metagenomics sequencing has the potential to merge rapidity and comprehensive information about causative pathogens in DFIs. The aim of this study was to evaluate the potential of metagenomics strategies for DFIs. METHODS: Thirty tissue specimens from patients with neuropathic plantar DFIs were analyzed. Specimens were processed using the Molzym Molysis five basic kit to deplete human cells. Microbial DNA was extracted using the Qiagen DNeasy PowerSoil kit. Microbial 16s rRNA was conducted on the Illumina MiSeq instrument. Shotgun metagenomics was conducted using nanopore sequencing for seven samples. Libraries were prepared using the rapid low input PCR library preparation kit (SQK-RI001) and sequenced on a MinION using R9.4 (FLO-MIN 106) flow cells. Real-time identification of pathogens and antimicrobial resistance determinants (ARDs) were conducted using EPI2ME’s WIMP and ARMA applications, respectively. RESULTS: Overall, the cohort characteristics included: 60% male, mean age 49 years, mean HgA1c 10.2%, and median PEDIS score 3. 16s sequencing identified reads belonging to bacteria isolated by culture, but also identified additional anaerobic pathogens in 70% of the specimens. Nanopore sequencing generated an average of 16.4 Mbp and an average read length of 1620–2700 bp. Shotgun metagenomics correctly detected the pathogens found in culture and in 16s rRNA sequencing; the time to accurate classification thresholds was completed in <1 hour. In two samples, several pathogens including anaerobes and fungi were identified that were not isolated by standard culture methods. The resistome included a range of 8–32 ARDs per sample. Furthermore, the resistomes were highly predictive (sensitivity 98% and specificity 88%) for antimicrobial resistance phenotypes detected by standard susceptibility testing. CONCLUSION: Metagenomics-based sequencing has the potential to offer a rapid (<6 hours sample to result time) and accurate strategy for detecting and identifying pathogens and ARDs involved in DFIs. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-56319352017-11-07 Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing Shurko, James Dallas, Steven Duhon, Bryson M Meckel, Jordan Wang, Chiou-Miin Lin, Chun-Lin Lucio, Nicholas Kirma, Nameer Lee, Grace C Open Forum Infect Dis Abstracts BACKGROUND: Diabetic foot infections (DFIs) constitute the most common cause for diabetes-related hospitalization and lower extremity amputations. Current diagnostic methods are slow and in some cases do not detect all potential pathogens. Metagenomics sequencing has the potential to merge rapidity and comprehensive information about causative pathogens in DFIs. The aim of this study was to evaluate the potential of metagenomics strategies for DFIs. METHODS: Thirty tissue specimens from patients with neuropathic plantar DFIs were analyzed. Specimens were processed using the Molzym Molysis five basic kit to deplete human cells. Microbial DNA was extracted using the Qiagen DNeasy PowerSoil kit. Microbial 16s rRNA was conducted on the Illumina MiSeq instrument. Shotgun metagenomics was conducted using nanopore sequencing for seven samples. Libraries were prepared using the rapid low input PCR library preparation kit (SQK-RI001) and sequenced on a MinION using R9.4 (FLO-MIN 106) flow cells. Real-time identification of pathogens and antimicrobial resistance determinants (ARDs) were conducted using EPI2ME’s WIMP and ARMA applications, respectively. RESULTS: Overall, the cohort characteristics included: 60% male, mean age 49 years, mean HgA1c 10.2%, and median PEDIS score 3. 16s sequencing identified reads belonging to bacteria isolated by culture, but also identified additional anaerobic pathogens in 70% of the specimens. Nanopore sequencing generated an average of 16.4 Mbp and an average read length of 1620–2700 bp. Shotgun metagenomics correctly detected the pathogens found in culture and in 16s rRNA sequencing; the time to accurate classification thresholds was completed in <1 hour. In two samples, several pathogens including anaerobes and fungi were identified that were not isolated by standard culture methods. The resistome included a range of 8–32 ARDs per sample. Furthermore, the resistomes were highly predictive (sensitivity 98% and specificity 88%) for antimicrobial resistance phenotypes detected by standard susceptibility testing. CONCLUSION: Metagenomics-based sequencing has the potential to offer a rapid (<6 hours sample to result time) and accurate strategy for detecting and identifying pathogens and ARDs involved in DFIs. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2017-10-04 /pmc/articles/PMC5631935/ http://dx.doi.org/10.1093/ofid/ofx163.125 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Shurko, James
Dallas, Steven
Duhon, Bryson M
Meckel, Jordan
Wang, Chiou-Miin
Lin, Chun-Lin
Lucio, Nicholas
Kirma, Nameer
Lee, Grace C
Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing
title Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing
title_full Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing
title_fullStr Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing
title_full_unstemmed Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing
title_short Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing
title_sort identification of pathogens directly from diabetic foot infections by shotgun metagenomic sequencing
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631935/
http://dx.doi.org/10.1093/ofid/ofx163.125
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