152. rapid Ultra-high Enrichment of Bacterial Pathogens at Low Concentration from Blood for Species ID and AMR Prediction Using Nanopore Sequencing. (31st December 2020)
- Record Type:
- Journal Article
- Title:
- 152. rapid Ultra-high Enrichment of Bacterial Pathogens at Low Concentration from Blood for Species ID and AMR Prediction Using Nanopore Sequencing. (31st December 2020)
- Main Title:
- 152. rapid Ultra-high Enrichment of Bacterial Pathogens at Low Concentration from Blood for Species ID and AMR Prediction Using Nanopore Sequencing
- Authors:
- Tsui, Chiahao
Cunden, Lisa S
Billings, Nicole
Nanayakkara, Imaly A
Herriott, Ian
Kumcu, Michael E
Martin, Rachel R
Chen, Michelle
Pangestu, Febriana
Knysh, Paul
Maddux, Cabell
Munro, Zachary
Huntley, Miriam - Abstract:
- Abstract: Background: Each year in the United States there are over 1.7 million cases of sepsis that account for a third of hospital deaths. A key to reducing morbidity and mortality rates is early, appropriate antibiotic therapy. Most new diagnostic approaches still suffer from insufficient sensitivity to low bacterial loads in blood and limited sets of detection targets for bacterial species identification (ID) and antimicrobial resistance (AMR) determination. As such, blood culture remains the gold standard for diagnosing bacteremia despite limitations such as > 2-day turnaround time (TAT), incompatibility with fastidious organisms, and frequent inability to recover causative pathogens. Methods: 31 clinically relevant bacterial pathogens, made up of 17 gram-positive and 14 gram-negative bacterial species, were spiked into 2 to 4 healthy donor blood samples at 1 to 5 CFU/mL. The samples were run through our proprietary Blood2Bac™ pipeline, sequenced on a nanopore platform, and data were passed through Keynome®, our proprietary machine learning algorithm to determine species ID and AMR. Results: By assessing the efficiency of pathogen DNA enrichment and genome coverage post sequencing, we report high performance of 3 CFU/mL for 3 bacterial species and ≤ 2 CFU/mL for the 28 remaining species, which includes S. aureus, E. coli, and Streptococcus spp., three of the leading causes of sepsis. For all 31 bacterial species tested, Keynome called species ID with 100% accuracy. InAbstract: Background: Each year in the United States there are over 1.7 million cases of sepsis that account for a third of hospital deaths. A key to reducing morbidity and mortality rates is early, appropriate antibiotic therapy. Most new diagnostic approaches still suffer from insufficient sensitivity to low bacterial loads in blood and limited sets of detection targets for bacterial species identification (ID) and antimicrobial resistance (AMR) determination. As such, blood culture remains the gold standard for diagnosing bacteremia despite limitations such as > 2-day turnaround time (TAT), incompatibility with fastidious organisms, and frequent inability to recover causative pathogens. Methods: 31 clinically relevant bacterial pathogens, made up of 17 gram-positive and 14 gram-negative bacterial species, were spiked into 2 to 4 healthy donor blood samples at 1 to 5 CFU/mL. The samples were run through our proprietary Blood2Bac™ pipeline, sequenced on a nanopore platform, and data were passed through Keynome®, our proprietary machine learning algorithm to determine species ID and AMR. Results: By assessing the efficiency of pathogen DNA enrichment and genome coverage post sequencing, we report high performance of 3 CFU/mL for 3 bacterial species and ≤ 2 CFU/mL for the 28 remaining species, which includes S. aureus, E. coli, and Streptococcus spp., three of the leading causes of sepsis. For all 31 bacterial species tested, Keynome called species ID with 100% accuracy. In addition, Keynome also predicted the AMR profile of pathogens with 100% accuracy for 19 drug/species AMR combinations, including ciprofloxacin for E. coli, clindamycin for S. aureus, and aztreonam for K. pneumoniae. Conclusion: Blood2Bac is able to enrich a wide range of bacterial pathogens directly from blood and enable bacterial whole genome sequencing with an estimated TAT of 12 hours. When coupled with Keynome, our process provides accurate species ID and AMR calls for key BSI pathogens even at single-digit CFU/mL concentrations. Our species-agnostic and culture-free process enables detection of a diverse range of bacterial species with high sensitivity, providing a robust and comprehensive diagnostic. Disclosures: Chiahao Tsui, n/a, Day Zero Diagnostics (Employee, Shareholder) Lisa S. Cunden, PhD, Day Zero Diagnostics (Shareholder) Nicole Billings, PhD, Day Zero Diagnostics (Employee) Imaly A. Nanayakkara, PhD, Day Zero Diagnostics (Employee, Shareholder) Ian Herriott, BS, Day Zero Diagnostics (Employee, Shareholder) Rachel R. Martin, n/a, Day Zero DIagnostics (Employee) Michelle Chen, MS, Day Zero Diagnostics (Employee, Shareholder) Febriana Pangestu, n/a, Day Zero Diagnostics (Employee, Shareholder) Paul Knysh, PhD, Day Zero Diagnostics (Employee) Cabell Maddux, n/a, Day Zero Diagnostics (Employee, Shareholder) Zachary Munro, n/a, Day Zero Diagnostics Inc. (Employee, Shareholder) Miriam Huntley, PhD, Day Zero Diagnostics (Employee, Shareholder) … (more)
- Is Part Of:
- Open forum infectious diseases. Volume 7:Number 1(2020) Supplement
- Journal:
- Open forum infectious diseases
- Issue:
- Volume 7:Number 1(2020) Supplement
- Issue Display:
- Volume 7, Issue 1 (2020)
- Year:
- 2020
- Volume:
- 7
- Issue:
- 1
- Issue Sort Value:
- 2020-0007-0001-0000
- Page Start:
- S206
- Page End:
- S206
- Publication Date:
- 2020-12-31
- Subjects:
- Communicable diseases -- Periodicals
Medical microbiology -- Periodicals
Infection -- Periodicals
616.9 - Journal URLs:
- http://ofid.oxfordjournals.org/ ↗
http://www.oxfordjournals.org/en/ ↗ - DOI:
- 10.1093/ofid/ofaa439.462 ↗
- Languages:
- English
- ISSNs:
- 2328-8957
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26916.xml