BackgroundKlebsiella pneumoniae ST101 is an emerging high-risk clone which exhibits extensive drug resistance. Bacterial strains residing in multiple hosts show unique signatures related to host adaptation. In this study, we assess the genetic relationship of K. pneumoniae ST101 isolated from hospital samples, the environment, community, and livestock using whole genome sequencing (WGS).Materials and MethodsWe selected ten K. pneumoniae ST101 strains from hospitalized patients in Italy (n = 3) (2014) and Spain (n = 5) (2015–2016) as well as Belgian livestock animals (n = 2) (2017–2018). WGS was performed with 2 × 250 bp paired-end sequencing (Nextera XT) sample preparation kit and MiSeq (Illumina Inc.). Long-read sequencing (Pacbio Sequel I) was used to sequence the two livestock strains and three Italian hospital-associated strains. Furthermore, a public ST101 sequence collection of 586 strains (566 hospital-associated strains, 12 environmental strains, six strains from healthy individuals, one food-associated strain and one pig strain) was obtained. BacPipe and Kleborate were used to conduct genome analysis. ISFinder was used to find IS elements, and PHASTER was utilized to identify prophages. A phylogenetic tree was constructed to illustrate genetic relatedness.ResultsHospital-associated K. pneumoniae ST101 showed higher resistance scores than non-clinical isolates from healthy individuals, the environment, food and livestock (1.85 ± 0.72 in hospital-associated isolates vs. 1.14 ± 1.13 in non-clinical isolates, p < 0.01). Importantly, the lack of integrative conjugative elements ICEKp bearing iron-scavenging yersiniabactin siderophores (ybt) in livestock-associated strains suggests a lower pathogenicity potential than hospital-associated strains. Mobile genetic elements (MGE) appear to be an important source of diversity in K. pneumoniae ST101 strains from different origins, with a highly stable genome and few recombination events outside the prophage-containing regions. Core genome MLST based analysis revealed a distinct genetic clustering between human and livestock-associated isolates.ConclusionThe study of K. pneumoniae ST101 hospital-associated and strains from healthy individuals and animals revealed a genetic diversity between these two groups, allowing us to identify the presence of yersiniabactin siderophores in hospital-associated isolates. Resistance and virulence levels in livestock-associated strains were considerably lower than hospital-associated strains, implying that the public health risk remains low. The introduction of an ICEKp into animal strains, on the other hand, might pose a public threat over time.

We identified a novel van gene cluster in a clinical Enterococcus faecium isolate with vancomycin minimum inhibitory concentration (MIC) of 4 µg/mL. The ligase gene, vanP, was part of a van operon cluster of 4,589 bp on a putative novel integrative conjugative element located in a ca 98 kb genomic region presumed to be acquired by horizontal gene transfer from Clostridium scidens and Roseburia sp. 499. Screening for van genes in E. faecium strains with borderline susceptibility to vancomycin is important.

Understanding the myriad pathways by which antimicrobial-resistance genes (ARGs) spread across biomes is necessary to counteract the global menace of antimicrobial resistance. We screened 17939 assembled metagenomic samples covering 21 biomes, differing in sequencing quality and depth, unevenly across 46 countries, 6 continents, and 14 years (2005-2019) for clinically crucial ARGs, mobile colistin resistance (mcr), carbapenem resistance (CR), and (extended-spectrum) beta-lactamase (ESBL and BL) genes. These ARGs were most frequent in human gut, oral and skin biomes, followed by anthropogenic (wastewater, bioreactor, compost, food), and natural biomes (freshwater, marine, sediment). Mcr-9 was the most prevalent mcr gene, spatially and temporally; blaOXA-233 and blaTEM-1 were the most prevalent CR and BL/ESBL genes, but blaGES-2 and blaTEM-116 showed the widest distribution. Redundancy analysis and Bayesian analysis showed ARG distribution was non-random and best-explained by potential host genera and biomes, followed by collection year, anthropogenic factors and collection countries. Preferential ARG occurrence, and potential transmission, between characteristically similar biomes indicate strong ecological boundaries. Our results provide a high-resolution global map of ARG distribution and importantly, identify checkpoint biomes wherein interventions aimed at disrupting ARGs dissemination are likely to be most effective in reducing dissemination and in the long term, the ARG global burden.