Molecular Typing, Characterization of Antimicrobial Resistance, Virulence Profiling and Analysis of Whole-Genome Sequence of Clinical Klebsiella pneumoniae Isolates.
Andrey A ShelenkovYulia MikhaylovaYuri YanushevichAndrei SamoilovLyudmila PetrovaValeria FominaVitaly GusarovMikhail ZamyatinDmitriy ShaginVasiliy AkimkinPublished in: Antibiotics (Basel, Switzerland) (2020)
Klebsiella pneumoniae is one of the most important pathogens concerned with multidrug resistance in healthcare-associated infections. The treating of infections caused by this bacterium is complicated due to the emergence and rapid spreading of carbapenem-resistant strains, which are associated with high mortality rates. Recently, several hypervirulent and carbapenemase-producing isolates were reported that make the situation even more complicated. In order to better understand the resistance and virulence mechanisms, and, in turn, to develop effective treatment strategies for the infections caused by multidrug-resistant K. pneumoniae, more comprehensive genomic and phenotypic data are required. Here, we present the first detailed molecular epidemiology report based on second and third generation (long-read) sequencing for the clinical isolates of K. pneumoniae in the Russian Federation. The data include three schemes of molecular typing, phenotypic and genotypic antibiotic resistance determination, as well as the virulence and plasmid profiling for 36 K. pneumoniae isolates. We have revealed 2 new multilocus sequence typing (MLST)-based sequence types, 32 multidrug-resistant (MDR) isolates and 5 colistin-resistant isolates in our samples. Three MDR isolates belonged to a very rare ST377 type. The whole genome sequences and additional data obtained will greatly facilitate further investigations in the field of antimicrobial resistance studies.
Keyphrases
- klebsiella pneumoniae
- antimicrobial resistance
- multidrug resistant
- genetic diversity
- escherichia coli
- gram negative
- acinetobacter baumannii
- drug resistant
- healthcare
- single cell
- staphylococcus aureus
- pseudomonas aeruginosa
- big data
- electronic health record
- deep learning
- type diabetes
- biofilm formation
- artificial intelligence
- genome wide
- cystic fibrosis
- sensitive detection
- case control