Genomic diversity of hospital-acquired infections revealed through prospective whole genome sequencing-based surveillance

Abstract

AbstractHealthcare-associated infections (HAIs) cause mortality, morbidity, and waste of healthcare resources. HAIs are also an important driver of antimicrobial resistance, which is increasing around the world. Beginning in November 2016, we instituted an initiative to detect outbreaks of HAI using prospective whole genome sequencing-based surveillance of bacterial pathogens collected from hospitalized patients. Here we describe the biodiversity of bacteria sampled from hospitalized patients at a single center, as revealed through systematic analysis of their genomes. We sequenced the genomes of 3,004 bacterial isolates from hospitalized patients collected over a 25-month period. We identified bacteria belonging to 97 distinct species, which were distributed among 14 species groups. Within these groups, isolates could be distinguished from one another by both average nucleotide identity (ANI) and principal component analysis of accessory genes (PCA-A). Genetic distances between isolates and rates of evolution varied between different species, which has implications for the selection of distance cut-offs for outbreak analysis. Antimicrobial resistance genes and the sharing of mobile genetic elements between different species were frequently observed. Overall, this study describes the population structure of pathogens circulating in a single healthcare setting, and shows how investigating microbial population dynamics can inform genomic epidemiology studies.ImportanceHospitalized patients are at increased risk of becoming infected with antibiotic-resistant organisms. We used whole-genome sequencing to survey and compare over 3,000 bacterial isolates collected from hospitalized patients at a large medical center over a two-year period. We identified nearly 100 different bacterial species, suggesting that patients can be infected with a wide variety of different organisms. When we examined how genetic relatedness differed between species, we found that different species are likely evolving at different rates within our hospital. This is significant because the identification of bacterial outbreaks in the hospital currently relies on genetic similarity cut-offs, which are often applied uniformly across organisms. Finally, we found that antibiotic resistance genes and mobile genetic elements were abundant among the bacterial isolates we sampled. Overall, this study provides an in-depth view of the genomic diversity and evolution of bacteria sampled from hospitalized patients, as well as genetic similarity estimates that can inform hospital outbreak detection and prevention efforts.