Combining Standard Molecular Typing and Whole Genome Sequencing to Investigate Pseudomonas aeruginosa Epidemiology in Intensive Care Units

Abstract

Pseudomonas aeruginosa is one of the main pathogens responsible for nosocomial infections, particularly in Intensive Care Units (ICUs). Due to the complexity of P. aeruginosa ecology, only powerful typing methods can efficiently allow its surveillance and the detection during expanding outbreaks. An increase in P. aeruginosa incidence was observed in the ICUs of the Lausanne University Hospital between 2010 and 2014. All clinical and environmental isolates retrieved during this period were typed with Double locus sequence typing (DLST), which detected the presence of three major genotypes: DLST 1-18, DLST 1-21, and DLST 6-7. DLST 1-18 (ST1076) isolates were previously associated with an epidemiologically well-described outbreak in the burn unit. Nevertheless, DLST 1-21 (ST253) and DLST 6-7 (ST17) showed sporadic occurrence with only few cases of possible transmission between patients. Whole genome sequencing (WGS) was used to further investigate the epidemiology of these three major P. aeruginosa genotypes in the ICUs. WGS was able to differentiate between outbreak and non-outbreak isolates and confirm suspected epidemiological links. Additionally, whole-genome single nucleotide polymorphisms (SNPs) results considered isolates as closely related for which no epidemiological links were suspected, expanding the epidemiological investigation to unsuspected links. The combination of a first-line molecular typing tool (DLST) with a more discriminatory method (WGS) proved to be an accurate and cost-efficient typing strategy for the investigation of P. aeruginosa epidemiology in the ICUs.

Comparison of Whole Genome (wg-) and Core Genome (cg-) MLST (BioNumericsTM) Versus SNP Variant Calling for Epidemiological Investigation of Pseudomonas aeruginosa

“…The ST1076 dataset was composed of 74 isolates from a well characterized outbreak (Tissot et al, 2016), during which the first patient (two isolates) was excluded based on genotyping results (Tissot et al, 2016;Magalhães et al, 2020). wgMLST identified 6066 loci that were present in at least one of these 74 isolates.…”

“…The first three datasets included 155 isolates from the three predominant P. aeruginosa MLST sequence types (ST) (ST17, 253 and 1076) collected in our ICUs over a 5-year surveillance period (2010–2014) ( Magalhães et al, 2020 ) ( Table 1 ). In summary, during this period, all consecutive patients hospitalized in the ICU with a clinical sample growing P. aeruginosa at any site were considered.…”

“…All datasets were previously analyzed with a SNP variant calling approach with exclusion of putative recombinant segments (Tissot et al, 2016;Magalhães et al, 2020). The read data of the first three datasets have been deposited at the Sequence Read Archive (SRA) 1 under the accession project number PRJNA503802.…”

Antibiotic resistant gene of Pseudomonas aeruginosa from non-clinical environment: public health implications in Mthatha, Eastern Cape Province, South Africa

“…Twenty out of thirty-six (36) P. aeruginosa presented multidrug resistance pro les and were classi ed as MDR (55.6%). [21,22]. It has been linked to outbreaks especially in ICU and onco/hematological units with reported overall incidence of carriage varying from 11.7-37.0% [23][24][25], resulting in signi cant morbidity and mortality rate close to 80% [26].…”

NGS-Based S. aureus Typing and Outbreak Analysis in Clinical Microbiology Laboratories: Lessons Learned From a Swiss-Wide Proficiency Test

“…Hospitals and clinical laboratories are increasingly using next generation sequencing (NGS) technology to address a multitude of questions. Especially in clinical microbiology, whole genome sequencing (WGS) has been used for typing (cgMLST, SNP calling) [e.g., (Mellmann et al, 2017;Abdelbary et al, 2019;Zakham et al, 2019;Magalhães et al, 2020)], and enables addressing strain relatedness using high resolution data, e.g., for outbreaks within hospitals or in the community [e.g., (Deurenberg et al, 2017)], or at a larger geographic scale, e.g., for food-borne pathogens (Hendriksen et al, 2018), or other environmental pathogens [e.g., (Wüthrich et al, 2019)]. In addition, WGS data can provide very interesting information on the presence of specific resistance mutations, the acquisition of resistance genes (Ellington et al, 2017) or virulence factors (Tagini and Greub, 2017;Tagini et al, 2018).…”