Identification of Pathogens Directly From Diabetic Foot Infections by Shotgun Metagenomic Sequencing. (4th October 2017)

• Record Type: Journal Article
• Authors: Shurko, James; Dallas, Steven; Duhon, Bryson M; Meckel, Jordan; Wang, Chiou-Miin; Lin, Chun-Lin; Lucio, Nicholas; Kirma, Nameer; Lee, Grace C
• Is Part Of: Open forum infectious diseases. Volume 4(2017)Supplement 1
• Page Start: S113
• Page End: S113
• Publication Date: 2017-10-04
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofx163.125
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: 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.