Precision Metagenomics for Detection of Markers Associated with Fluoroquinolone Resistance in UTIs

1. 22.EP.21.01
John W.A. Rossen1,2, Courtney E. Gonzalez2, Marta Mangifesta2, Rita C. Stinnett2, Kate Broadbent2, Amy Hanson2, Malcolm Boswell2,
Keith Arora-Williams2, Eun A. Kim2, Jamie K. Lemon2, Robert Schlaberg2
1 Department of Medical Microbiology and Infection Prevention , University of Groningen, University Medical Center Groningen, The Netherlands; 2 IDbyDNA, Salt Lake City, UT, USA
Results
Precision Metagenomics for Detection of Markers Associated with
Fluoroquinolone Resistance in UTIs
Acknowledgements: We are grateful to the entire Microbiology Unit at PathGroup Labs for coordination of sample selection, de-identification and shipment, and to Illumina for reagents for library preparation and enrichment in support of this
study. We also thank the following individuals for their key contributions to this work: Anagha Kadam, PhD, Heng Xie, PhD, Lauge Farnaes, MD, PhD, and the IDbyDNA Laboratory, Bioinformatics, and Software teams.
Background
• Urinary tract infections (UTIs) affect ~150 million people
worldwide annually. Uncomplicated UTI may ascend
from the lower urogenital tract in healthy women to
cause pyelonephritis, requiring hospitalization.1,2
• Together with cephalosporins, oral fluoroquinolones
(FQ) are the only antimicrobial agents recommended
for outpatient management of acute pyelonephritis.
Intravenous FQ are important for management of
inpatient pyelonephritis in Europe and the United
States.2,3 Historical overuse of FQ in the outpatient
setting has precipitated an increase in antimicrobial
resistance (AMR); resistance rates greater than 20% in E.
coli have been observed in some nations in Europe.3,4
• Metagenomic next-generation sequencing (NGS) is a
culture-independent approach with promise for the
accurate and semi-quantitative detection of
uropathogens.5 Target-enriched next generation
sequencing offers greater analytical sensitivity than
shotgun sequencing for characterization of pathogens
and associated AMR.6
• In this study, we compared AMR marker detection and
phenotypic antimicrobial susceptibility by the Urinary
Pathogen ID/AMR Panel (UPIP), a target-enriched next-
generation sequencing approach (Precision
Metagenomics, PM). UPIP (IDbyDNA, Salt Lake City, UT,
USA) targets 121 bacterial, 35 viral, 14 fungal and 4
protozoal pathogens plus >3,500 antimicrobial
resistance markers, including known mechanisms of
resistance for trimethoprim and sulfamethoxazole in
gram-negative bacterial uropathogens.
Methods
• Culture was performed per routine protocols and
antimicrobial susceptibility (AST) determined on a BD
Phoenix system at a large commercial clinical laboratory.
• De-identified remnant clinical urine samples with
complete results were shipped to IDbyDNA in a project
classified as exempt human subjects research over a
period of 6 months.
• Urine samples were spiked with an internal control
(Bacteriophage T7, Microbiologics) and DNA extracted
(Quick-DNA Urine Kit, ZymoBiomics). Sequencing
libraries were prepared, target-enriched in three-plex
with the Urinary Pathogen ID/AMR Panel (UPIP,
IDbyDNA), and sequenced on the NextSeq550 platform
(Illumina®) to a target depth of ~1M reads/library.
• Sequencing data were analyzed with the commercially
available, automated Explify® UPIP Data Analysis solution
(research-use only). Sample data were excluded from this
analysis if sequencing yield was below 500,000
reads/library or the reads per kilobase of target per
million (RPKM) for the internal control was below 1,000.
References
1. Simmering JE, Tang F, Cavanaugh JE, Polgreen LA, Polgreen PM. 2017. Open Forum Infect Dis.
4(1):ofw281.
2. Anger J, Lee U, Ackerman AL, et al. 2019. Journal of Urology. 202:282-289.
3. Bonkat G, Bartoletti R, Bruyère F, et al. 2022. EAU Guidelines Office, Arnhem, The Netherlands.
4. Kahlmeter G, Ahman J and Matuschek E. 2015. Infect Dis Ther. 4(4):417-423.
5. Janes VA, Matamoros S, Munk P, et al. 2022. Lancet Microbe. https://doi.org/10.1016/ S2666-
5247(22)00088-X
6. Mangifesta M, Stinnett R, Broadbent K, Hanson A, Rossen J, Lemon J, Schalberg R. Poster
presented by Marta Mangifesta at the 2022 meeting of the American Society of Microbiology
(ASM Microbe 2022; Washington, DC, USA).
P604
Limitations
• Delays in DNA extraction may adversely impact the
analytical sensitivity of UPIP. Remnant urine samples
were received in the PM laboratory no earlier than 24
hours following inoculation for urine culture.
• A definitive link between host organism and a
detected AMR marker cannot be made by
metagenomic analysis.
• Clinical information (diagnosis, symptoms,
antimicrobial management) is not available for the
remnant samples.
Discussion
• A practical UTI solution must be able to identify,
quantify, and characterize the antimicrobial
susceptibility of potential uropathogens.
• In this study, a Precision Metagenomics method
detected AMR markers associated with a commonly
used agent for the management of pyelonephritis and
complicated UTI, directly from urine samples.
• Interpretation of metagenomic results is complicated
by detection of multiple potential host organisms in a
sample by PM and multifactorial mechanisms of
resistance, but may yield important new insights
through characterization of the “resistome”.
• Knowledge of genomic AMR markers quicker than
standard urine culture AST results has the potential to
inform antimicrobial decision making earlier than
current standard of care testing, with refinement and
standardization of reporting and interpretation
criteria.
Conclusion
• Precision Metagenomics provides a culture-
independent alternative to urine culture for detection
of a broad range of uropathogens and AMR markers.
Future Directions
• Antimicrobial resistance/susceptibility phenotype
prediction from genotype is a growing field, with the
ultimate goal being a practical alternative to
traditional culture-dependent antimicrobial
susceptibility testing.
• Thresholds for reporting and interpretation criteria
should be refined through interrogation of larger
datasets of well-characterized clinical samples to
demonstrate successful discrimination of UTI from
asymptomatic bacteriuria.
• The clinical utility of Precision Metagenomics testing
to improve clinical outcomes and to inform new
antimicrobial stewardship strategies, e.g. protocols
that “reflex” to urine culture, merits future evaluation.
UPIP Detected Relevant Gram-Negative Uropathogens and AMR
Markers in Clinical Urine Samples
AMR Marker Co-Detection Profiles May Provide Insight to
Better Predict Susceptibility
• UPIP predicts resistance of 69 uropathogens to 46 relevant antimicrobials based on the
detection of 3,728 associated antimicrobial resistance markers.
• In Gram-negative bacteria, mechanisms of fluoroquinolone resistance are associated with
mutations in the bacterial DNA gyrase and topoisomerase enzymes and through the action of
multidrug efflux pump systems, including the oqxAB and Resistance-nodulation-cell division
(RND) efflux systems or other susceptibility modifying genes, such as the plasmid-encoded Qnr.
• AMR markers are only reported by UPIP if a relevant associated microorganism is also reported.
• Targeted enrichment of AMR genes results in high coverage and read depth, allowing for allele
identification and variant calling in contrast to approaches such as shotgun metagenomics.
Figure 3. Upset plot of co-detected AMR markers in
samples flagged as non-susceptible to ciprofloxacin
and/or levofloxacin by standard culture and AST
(n=68 samples total)
• Overall, detection of 1 or more relevant marker by UPIP was in greater agreement
with AST results for Ciprofloxacin (82% agreement) than for Levofloxacin (65%) in 36
samples for which results were available for both methods.
• Negative agreement was higher for Levofloxacin (86%) than for Ciprofloxacin (44%).
Detection of AMR Markers by UPIP is Concordant with
Observed Phenotypic Resistance to Ciprofloxacin
Figure 1. Coverage profile of sequencing reads enriched by UPIP
Table 1 (left). Reported susceptibility rates of Gram–
negative uropathogens for fluoroquinolones
UPIP-Detected Organism
Samples
with gyrA
variant
detection
Samples
with parC
variant
detection
Samples
with Qnr
detection
Escherichia coli (n=131) 45 30 11
Klebsiella pneumoniae (n=85) 0 0 12
Proteus mirabilis (n=39) 0 0 2
C. freundii complex (n=25) 0 0 12
Pseudomonas aeruginosa (n=29) 4 0 0
E. cloacae complex (n=24) 2 0 2
Morganella morganii (n=18) 0 0 0
Providencia rettgeri (n=11) 0 0 2
Providencia stuartii (n=2) 0 0 0
Table 2 (right). Overall detection frequency by UPIP of AMR
markers associated with FQ resistance
• Overall, UPIP detected 1 or more
markers associated with FQ
resistance in 30 of 68 samples
that grew a Gram-negative
uropathogen characterized as
phenotypically non-susceptible.
The most common gyrA marker was an epistatic group, D87N + S83L
The most common parC marker was S80I
gyrA and parC markers were co-detected in 14/30 (46.7%) samples
•Positive Agreement •Negative Agreement
82%
65%
0
10
20
30
40
Ciprofloxacin Levofloxacin
Total
AST
Resistant
Samples
VME (False Susceptible Result)
Positive Agreement
44%
86%
0
50
100
150
200
Ciprofloxacin Levofloxacin
Total
AST
Susceptible
Samples
ME (False Resistant Result)
Negative Agreement
Figure 4. Agreement between UPIP phenotype prediction based on AMR marker
detection and AST in gram negative bacteria for fluoroquinolone antibiotics
Total
culture
positive
samples
Ciprofloxain
L
evofloxacin
Escherichia coli 62 72% 74%
Klebsiella pneumoniae 41 85% 95%
Klebsiella aerogenes 16 100% 100%
Proteus mirabilis 16 88% 88%
C. freundii complex 15 93% 93%
Pseudomonas aeruginosa 14 64% 71%
E. cloacae complex 13 92% 92%
Klebsiella oxytoca 12 100% 100%
Serratia marcescens 9 100% 100%
Morganella morganii 8 75% 88%
Citrobacter koseri 7 100% 100%
Providencia rettgeri 6 83% 83%
Klebsiella variicola 4 100% 100%
Pantoea agglomerans 3 100% 100%
Citrobacter amalonaticus 2 100% 100%
Providencia stuartii 1 0% 0%
AMR Marker Detection
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please contact:
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IDbyDNA
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Urinary Pathogen ID/AMR Panel (UPIP, IDbyDNA) Enables Detection
of Uropathogens and Relevant AMR Markers Directly from Urine
Uropathogen Detection
Figure 2. Gram–negative uropathogens detected most frequently by urine culture and UPIP
• Culture identified a gram-negative
bacterial uropathogen in 221/311
samples (71%) in this study.
• UPIP identified the same organism
in 217/221 (98.2%).
• Overall, 68 (30.8%) of urine
samples that grew a gram-
negative uropathogen were
characterized as non-susceptible
(reported MIC interpretation of
intermediate or resistant) to
Ciprofloxacin and/or Levofloxacin.