Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

Abstract

There is growing attention surrounding hospital acquired infections (HAIs) due to high associated healthcare costs, compounded by the scourge of widespread multi-antibiotic resistance. Although hospital environment disinfection is well acknowledged to be key for infection control, an understanding of colonization patterns and resistome profiles of environment-dwelling microbes is currently lacking. We report the first extensive genomic characterization of microbiomes (355), common HAI-associated microbes (891) and transmissible drug resistance cassettes (1435) in a tertiary hospital environment based on a 2-timepoint sampling of 179 sites from 45 beds. Deep shotgun metagenomic sequencing unveiled two distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human microbiome influenced environments that display corresponding patterns of divergence over space and time. To study common nosocomial pathogens that were typically present at low abundances, a combination of culture enrichment and long-read nanopore sequencing was used to obtain thousands of high contiguity genomes (2347) and closed plasmids (5910), a significant fraction of which (>58%) are not represented in current sequence databases. These high-quality assemblies and metadata enabled a rich characterization of resistance gene combinations, plasmid architectures, and the dynamic nature of hospital environment resistomes and their reservoirs. Phylogenetic analysis identified multidrug resistant clonal strains as being more widely disseminated and stably colonizing across hospital sites. Further genomic comparisons with clinical isolates across multiple species supports the hypothesis that multidrug resistant strains can persist in the hospital environment for extended periods (>8 years) to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in the hospital environment and establishes the feasibility of systematic genomic surveys to help target resources more efficiently for preventing HAIs.

BacAnt: A Combination Annotation Server for Bacterial DNA Sequences to Identify Antibiotic Resistance Genes, Integrons, and Transposable Elements

“…3). The ARG cassettes for Gram-negative bacteria were larger and more stable than the cassettes in Gram-positive bacteria, which confirms a previously published observation [16]. In case of A. baumannii, we identified two ARG cassettes, with one containing the genes tet(B), sul2, aph(3'')-Ib and aph(6)-Id, and the other one encoding the genes mph(E), msr(E), armA, aadA1, sul1, aac(6')-Ib' and catB8.…”

“…In case of A. baumannii, we identified two ARG cassettes, with one containing the genes tet(B), sul2, aph(3'')-Ib and aph(6)-Id, and the other one encoding the genes mph(E), msr(E), armA, aadA1, sul1, aac(6')-Ib' and catB8. The ARG cassette which contained mph(E), msr(E) and armA was described previously [16]. For E. coli, the program identified a stable small cassette that shows overlap with that of A. baumannii, including sul2, aph(3'')-Ib and aph(6)-Id.…”

Forensic genomics of a novel Klebsiella quasipneumoniae type from a neonatal intensive care unit in China reveals patterns of colonization, evolution and epidemiology

“…A suggestion for future analyses would be inclusion of more detailed samplings of the NICU environment, including the built environment itself and systematic sampling of staff members and visitors. Our study also urges the continual monitoring of hospital environments to improve understanding of the dynamics of bacterial communities in hospitals as reservoirs of resistance and virulent genes [56].…”

Silent spread of mobile colistin resistance gene mcr-9.1 on IncHI2 ‘superplasmids’ in clinical carbapenem-resistant Enterobacterales

“…Finally, we used non-alignment-based approaches to cluster plasmid genomes but there are few current data which would help define similarity cutoffs to indicate plasmid transmission. Prior reports have assessed plasmid diversity within single J o u r n a l P r e -p r o o f patients [44] and outbreaks [45] but further data across diverse plasmids and host backgrounds are needed to inform infection prevention decisions [32,33].…”