Closing the Gap: Harnessing next generation sequence technology to rapidly elucidate healthcare-associated infection transmission pathways Restricted; Files Only
Gable, Paige (Summer 2019)
Abstract
Each year, at least 2 million people acquire infections due to antibiotic resistant bacteria in the U.S., leading directly to at least 23,000 deaths (CDC, 2013). The spread of emerging antimicrobial resistance (AR) has increased the need to rapidly respond to outbreaks and improve infrastructures for detection and prevention of healthcare-associated infections (HAIs). Among the most common nosocomial infections, carbapenemase-resistant Enterobacteriaceae (CRE) pathogens cause over 9,000 HAIs per year, and are resistant to nearly all antibiotics including carbapenems – an antibiotic of last resort (The White House, 2014). Many carbapenems are harbored on mobile genetic elements (MGEs) and their transmission cannot be easily traced using traditional methods of molecular subtyping (e.g., pulsed-field gel electrophoresis, PFGE). Thus, advanced sequencing methods, such as whole-genome sequencing (WGS), are needed to rapidly identify additional transmission pathways and probable linked cases, beyond what PFGE provides. The purpose of this project was to implement WGS to elucidate the movement of MGEs harboring AR genes between multiple HAI bacterial species among high-risk patients residing in a LTACH facility in Orange County, Florida.
A variety of laboratory methods were performed to test 86 clinical and 91 environmental samples including culture and isolation, species identification, DNA extraction, and bacterial resistance mechanism testing. Combined with existing epidemiological data, PFGE was performed for cluster analyses and to identify possible common links between patients and their environment. WGS methods, by short and long-read sequencing platforms, were applied to provide increased resolution, confirm true relatedness, detect MGEs, and reveal plasmid-mediated gene(s). Bioinformatics tools were then applied to analyze raw data findings and identify specific AR characteristics associated with the targeted resistance gene, Klebsiella pneumoniae carbapenemase (blaKPC).
In both the clinical and environmental isolates, WGS identified multiple variants of KPC-producing genes harbored on multiple plasmids across multiple bacterial species in this single healthcare facility. These granular connections were not identified by PFGE. Plasmid discovery within this outbreak elucidated and confirmed the ability of MGEs to spread intra- and inter-organism, and persist within a healthcare environment, ultimately leading to patient acquisition. These findings support the hypothesis that MGEs, especially those containing carbapenemase genes, can be significant drivers of antibiotic resistance and HAI outbreaks.
Table of Contents
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATIONS
CHAPTER
1 INTRODUCTION......................................................................................................... 1
1.1 BACKGROUND............................................................................................... 1
1.2 CURRENT GAPS............................................................................................. 5
1.3 PURPOSE……….............................................................................................. 5
1.4 SIGNIFCANCE................................................................................................. 6
2 REVIEW OF THE LITERATURE............................................................................... 8
2.1 HEALTHCARE-ASSOCIATED INFECTIONS.............................................. 8
2.2 LONG-TERM ACUTE CARE HOSPITALS................................................... 8
2.3 ANTIBIOTIC RESISTANCE......................................................................... 10
2.4 HEALTHCARE ENVIRONMENT AND TRANSMISSION........................ 11
2.5 MOLECULAR AND GENOMIC TYPING................................................... 12
3 APPROACH AND METHODS.................................................................................. 17
3.1 HYPOTHESIS................................................................................................. 17
3.2 INTRODUCTION........................................................................................... 17
3.3 POPULATION AND SAMPLE COLLECTION........................................... 19
3.4 RESEARCH DESIGN..................................................................................... 22
3.5 MATERIALS AND METHODS …………………………………………..26
4 RESULTS..........................................................................................................36
4.1 OUTBREAK SUMMARY.............................................................................. 36
4.2 PFGE FINDINGS............................................................................................ 37
4.3 ISOLATE RELATEDNESS........................................................................... 37
4.4 GENOTYPIC ANTIBIOTIC RESISTANCE................................................. 40
4.5 MOBILE GENETIC ELEMENTS.................................................................. 40
4.6 TREE COMPARISONS.................................................................................. 42
5 SUMMARY AND CONCLUSION............................................................................44
5.1 Summary of findings....................................................................................... 44
5.2 Conclusion....................................................................................................... 46
5.3 Lessons Learned.............................................................................................. 47
5.4 Strengths and Weaknesses............................................................................... 47
5.5 Next Steps for the Future................................................................................. 48
REFERENCES.................................................................................................................. 60
List of Tables
Table 1: Bacterial species isolates for each patient sample........................................................... 20
Table 2: Overview of environmental sample collections.............................................................. 21
Table 3: Bacterial species isolates from environmental samples.................................................. 21
Table 4: The subset of blaKPC isolates selected for WGS testing.................................................. 24
Table 5: Isolate Relatedness - hqSNP matrix: Enterobacter (all isolates).................................... 53
Table 6: Isolate Relatedness - hqSNP matrix: Enterobacter roggenkampii................................. 53
Table 7: Isolate Relatedness - hqSNP matrix: Enterobacter hormaechei (ST 597)..................... 53
Table 8: Isolate Relatedness - hqSNP matrix: Klebsiella pneumoniae (all isolates).................... 54
Table 9: Isolate Relatedness - hqSNP matrix: Klebsiella pneumoniae (ST 14)............................ 55
Table 10: Isolate Relatedness - hqSNP matrix: Citrobacter isolates............................................ 55
Table 11: Isolate Relatedness - hqSNP matrix: Serratia isolates.................................................. 55
Table 12: Overview of MLST schemes and key AMR genes....................................................... 56
Table 13: PacBio genome hybrid assembly of the ~50Kb blaKPC-3 plasmid................................ 58
Table 14: PacBio genome hybrid assembly of the ~180Kb blaKPC-2 plasmid.............................. 59
Table 15: Phylogenetic tree landscapes – Enterobacter cloacae complex isolates...................... 43
Table 16: Phylogenetic tree landscapes – Klebsiella pneumoniae complex isolates.................... 43
List of Figures
Figure 1: Rationale of the statistical approach used in treespace.................................................. 25
Figure 2: Workflow of the QuAISAR-H pipeline......................................................................... 34
Figure 3: PacBio genomic comparison of the blaKPC-3 plasmid................................................... 41
Figure 4: PacBio genomic comparison of the blaKPC-2 plasmid................................................... 42
Figure 5: Enterobacter PFGE similarity tree structure................................................................. 50
Figure 6: Enterobacter hqSNP similarity tree structure................................................................ 50
Figure 7: Klebsiella PFGE similarity tree structure...................................................................... 51
Figure 8: Klebsiella hqSNP similarity tree structure..................................................................... 51
Figure 9: PFGE Dendrogram - Enterobacter cloacae complex isolates....................................... 52
Figure 10: PFGE Dendrogram - Klebsiella pneumoniae isolates................................................. 52
List of Abbreviations
AMR Antimicrobial resistance
AR Antibiotic resistance
CPO Carbapenemase-producing organisms
CRE Carbapenemase-resistant Enterobacteriaceae
DNA Deoxyribonucleic acid
GN Gram-negative bacteria
HAI Healthcare-associated infections
ICU Intensive care unit
PPS Point-prevalence surveys
PFGE Pulsed-field gel electrophoresis
LTACH Long-term acute care hospitals
KPC Klebsiella pneumoniae carbapenemase
MALDI-TOF Matrix-Assisted Laser Desorption Ionization Time-of-Flight
MGE Mobile genetic elements
MLST Multilocus sequence typing
NGS Next Generation Sequencing
RT-PCR Real-Time Polymerase Chain Reaction
SNP Single nucleotide polymorphisms
SNV Single nucleotide variants
WGS Whole Genome Sequencing
VIM Verona Integron-Encoded Metallo-β-Lactamase
CRPA Carbapenem-Resistant Pseudomonas aeruginosa
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