Back

Genomic Characterization of Tissue Invasive Klebsiella pneumoniae Complex in a Hospital System with a Focus on Species Distribution and Hypervirulence

Bohrhunter, J. L.; Taffner, S.; Wang, J.; Hardy, D. J.; Pecora, N. D.

2020-11-05 microbiology
10.1101/2020.11.04.369173 bioRxiv
Show abstract

Klebsiella pneumoniae is an opportunistic pathogen known for multidrug resistance. Current research indicates that K. pneumoniae is a complex of closely related organisms (K. pneumoniae sensu stricto, K. quasipneumoniae, K. variicola, K. quasivariicola, and K. africana). Additionally, some strains of K. pneumoniae sensu stricto, known as hypervirulent K. pneumoniae (hvKp), cause severe infections in healthy members of the community. There is a paucity of research into rates of hvKp in the United States, the distribution of K. pneumoniae complex members in clinical specimens, and the pathogenic potential of complex members other than K. pneumoniae sensu stricto. We assessed the rates of hvKp and K. pneumoniae complex members in our 886 bed tertiary care facility in Rochester, New York. We performed whole genome sequencing on all inpatient, sterile-source isolates identified by routine culture work-up as K. pneumoniae from October 2018 - October 2019 (n=35). We additionally sequenced all K. pneumoniae liver isolates available in our collection, spanning 2017-2019 (n=18). In the yearlong collection of K. pneumoniae complex isolates, we found that 92.4% were K. pneumoniae sensu stricto (n=32), 5.7% were K. quasipneumoniae (n=2), and 2.9% were K. variicola (n=1). Five hvKp isolates were found, representing 5.7% (n=2) of K. pneumoniae sensu stricto isolates in the yearlong collection and 27.7% (n=5) of the liver isolate collection. All hvKp isolates were from liver; 60% were not associated with prior international residence.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.