Implementing Whole-Genome Sequencing for Ongoing Surveillance of Antimicrobial Resistance: Exemplifying Insights Into Klebsiella pneumoniae

Epidemiology and Resistance Phenotypes of Carbapenem-Resistant Klebsiella pneumoniae in Corfu General Hospital (2019-2022): A Comprehensive Time Series Analysis of Resistance Gene Dynamics

Antimicrobial resistance is a significant global health challenge, with Klebsiella pneumoniae being one of the most common antibiotic-resistant pathogens. This study provides an in-depth analysis of the prevalence and resistance patterns of antibiotic-resistant Klebsiella pneumoniae in the General Hospital of Corfu, Greece, between 2019 and 2022, with the aim of understanding the potential impact of the COVID-19 pandemic on the epidemiology of this bacterium. Utilizing a retrospective epidemiological approach, this study analyzed 212 isolates obtained from the hospital's Microbiology Department. These isolates were subjected to genotypic and phenotypic identification, with resistance genes (bla-KPC, bla-NDM, bla-VIM, bla-OXA-48, and mcr-1) and antibiotic resistance patterns as the primary focus. The results revealed a significant shift in resistance gene prevalence, with a notable increase in bla-KPC from 16.67% in 2021 to 58.46% in 2022, and a decrease in bla-NDM from 81.48% in 2021 to 38.46% in 2022. In terms of antibiotic resistance patterns, there was a consistent increase in resistance to amikacin and a significant decrease in resistance to ceftazidime/avibactam. These findings underscore the dynamic nature of carbapenem-resistant Klebsiella pneumoniae (CRKP) resistance and highlight the need for ongoing surveillance and adaptive therapeutic strategies in the face of evolving resistance patterns.

Maternal Colonization Versus Nosocomial Transmission as the Source of Drug-Resistant Bloodstream Infection in an Indian Neonatal Intensive Care Unit: A Prospective Cohort Study

BACKGROUND

Drug-resistant gram-negative (GN) pathogens are a common cause of neonatal sepsis in low- and middle-income countries. Identifying GN transmission patterns is vital to inform preventive efforts.

METHODS

We conducted a prospective cohort study, 12 October 2018 to 31 October 2019 to describe the association of maternal and environmental GN colonization with bloodstream infection (BSI) among neonates admitted to a neonatal intensive care unit (NICU) in Western India. We assessed rectal and vaginal colonization in pregnant women presenting for delivery and colonization in neonates and the environment using culture-based methods. We also collected data on BSI for all NICU patients, including neonates born to unenrolled mothers. Organism identification, antibiotic susceptibility testing, and next-generation sequencing (NGS) were performed to compare BSI and related colonization isolates.

RESULTS

Among 952 enrolled women who delivered, 257 neonates required NICU admission, and 24 (9.3%) developed BSI. Among mothers of neonates with GN BSI (n = 21), 10 (47.7%) had rectal, 5 (23.8%) had vaginal, and 10 (47.7%) had no colonization with resistant GN organisms. No maternal isolates matched the species and resistance pattern of associated neonatal BSI isolates. Thirty GN BSI were observed among neonates born to unenrolled mothers. Among 37 of 51 BSI with available NGS data, 21 (57%) showed a single nucleotide polymorphism distance of ≤5 to another BSI isolate.

CONCLUSIONS

Prospective assessment of maternal GN colonization did not demonstrate linkage to neonatal BSI. Organism-relatedness among neonates with BSI suggests nosocomial spread, highlighting the importance of NICU infection prevention and control practices to reduce GN BSI.

Nanopore-only assemblies for genomic surveillance of the global priority drug-resistant pathogen, Klebsiella pneumoniae

Oxford Nanopore Technologies (ONT) sequencing has rich potential for genomic epidemiology and public health investigations of bacterial pathogens, particularly in low-resource settings and at the point of care, due to its portability and affordability. However, low base-call accuracy has limited the reliability of ONT data for critical tasks such as antimicrobial resistance (AMR) and virulence gene detection and typing, serotype prediction, and cluster identification. Thus, Illumina sequencing remains the standard for genomic surveillance despite higher capital and running costs. We tested the accuracy of ONT-only assemblies for common applied bacterial genomics tasks (genotyping and cluster detection, implemented via Kleborate, Kaptive and Pathogenwatch), using data from 54 unique Klebsiella pneumoniae isolates. ONT reads generated via MinION with R9.4.1 flowcells were basecalled using three alternative models [Fast, High-accuracy (HAC) and Super-accuracy (SUP), available within ONT's Guppy software], assembled with Flye and polished using Medaka. Accuracy of typing using ONT-only assemblies was compared with that of Illumina-only and hybrid ONT+Illumina assemblies, constructed from the same isolates as reference standards. The most resource-intensive ONT-assembly approach (SUP basecalling, with or without Medaka polishing) performed best, yielding reliable capsule (K) type calls for all strains (100 % exact or best matching locus), reliable multi-locus sequence type (MLST) assignment (98.3 % exact match or single-locus variants), and good detection of acquired AMR genes and mutations (88-100 % correct identification across the various drug classes). Distance-based trees generated from SUP+Medaka assemblies accurately reflected overall genetic relationships between isolates. The definition of outbreak clusters from ONT-only assemblies was problematic due to inflation of SNP counts by high base-call errors. However, ONT data could be reliably used to 'rule out' isolates of distinct lineages from suspected transmission clusters. HAC basecalling + Medaka polishing performed similarly to SUP basecalling without polishing. Therefore, we recommend investing compute resources into basecalling (SUP model), wherever compute resources and time allow, and note that polishing is also worthwhile for improved performance. Overall, our results show that MLST, K type and AMR determinants can be reliably identified with ONT-only R9.4.1 flowcell data. However, cluster detection remains challenging with this technology.

Klebsiella pneumonia in Sudan: Multidrug Resistance, Polyclonal Dissemination, and Virulence

The emergence and global expansion of hyper-virulent and multidrug resistant (MDR) Klebsiella pneumoniae is an increasing healthcare threat worldwide. The epidemiology of MDR K. pneumoniae is under-characterized in many parts of the world, particularly Africa. In this study, K. pneumoniae isolates from hospitals in Khartoum, Sudan, have been whole-genome sequenced to investigate their molecular epidemiology, virulence, and resistome profiles. Eighty-six K. pneumoniae were recovered from patients in five hospitals in Khartoum between 2016 and 2020. Antimicrobial susceptibility was performed by disk-diffusion and broth microdilution. All isolates underwent whole genome sequencing using Illumina MiSeq; cgMLST was determined using Ridom SeqSphere+, and 7-loci MLST virulence genes and resistomes were identified. MDR was observed at 80%, with 35 isolates (41%) confirmed carbapenem-resistant. Thirty-seven sequence types were identified, and 14 transmission clusters (TC). Five of these TCs involved more than one hospital. Ybt9 was the most common virulence gene detected, in addition to some isolates harbouring iuc and rmp1. There is a diverse population of K. pneumoniae in Khartoum hospitals, harbouring multiple resistance genes, including genes coding for ESBLs, carbapenemases, and aminoglycoside-modifying enzymes, across multiple ST's. The majority of isolates were singletons and transmissions were rare.

Establishing a national reference laboratory for antimicrobial resistance using a whole-genome sequencing framework: Nigeria's experience

Whole-genome sequencing (WGS) is finding important applications in the surveillance of antimicrobial resistance (AMR), providing the most granular data and broadening the scope of niches and locations that can be surveilled. A common but often overlooked application of WGS is to replace or augment reference laboratory services for AMR surveillance. WGS has supplanted traditional strain subtyping in many comprehensive reference laboratories and is now the gold standard for rapidly ruling isolates into or out of suspected outbreak clusters. These and other properties give WGS the potential to serve in AMR reference functioning where a reference laboratory did not hitherto exist. In this perspective, we describe how we have employed a WGS approach, and an academic-public health system collaboration, to provide AMR reference laboratory services in Nigeria, as a model for leapfrogging to national AMR surveillance.