Genome sequence and genomic analysis of liver abscess caused by hypervirulent Klebsiella pneumoniae .
Na PeiXin LiuZijuan JianQun YanQingxia LiuKarsten KristiansenJunhua LiWenen LiuPublished in: 3 Biotech (2023)
Hypervirulent Klebsiella pneumoniae (hvKp) is an important pathotype with enhanced virulence features compared with classical K. pneumoniae (cKp). hvKp usually causes life-threatening infections in the community, often affecting young and healthy individuals. During the past few decades, hvKp-induced liver abscess has been increasingly reported in Asia and is emerging as a global disease. To better comprehend the molecular characteristics of hvKp-induced liver abscess and recognize the global dissemination of hypervirulent strains with resistance determinants, we sequenced the whole genome of 26 K . pneumoniae strains from patients with liver abscess (KLA) and investigated the clinical factors related to different phenotype groups. The epidemiology, virulence-related factors, and antimicrobial resistance determinants were also discussed. The age, gender, and whether being hospitalized showed no differences among the string-positive and -negative groups were also studied. The assembly and annotation suggested that most of the 26 new liver abscess-causing hvKp strains were ST23-K1 or ST86-K2, and only one of the strains exhibited multidrug resistance. Compared with the existing 36 global liver abscess genome sequences, higher sequence type and virulence gene diversity were found in the new genomes. The clinical characteristics and genomic data of the isolated strains will enrich our knowledge for comparative genomic studies, allowing the better understanding of hvKp characteristics and evolution.
Keyphrases
- klebsiella pneumoniae
- escherichia coli
- antimicrobial resistance
- multidrug resistant
- biofilm formation
- healthcare
- pseudomonas aeruginosa
- staphylococcus aureus
- mental health
- high glucose
- genome wide
- gene expression
- cystic fibrosis
- drug induced
- deep learning
- high resolution
- big data
- atomic force microscopy
- middle aged
- oxidative stress
- single molecule
- data analysis
- candida albicans
- artificial intelligence