Altered metabolic pathways in classic and hypervirulent Klebsiella pneumoniae isolates revealed by proteomics analysis
DOI:
https://doi.org/10.2298/ABS220613022YKeywords:
Klebsiella pneumoniae, Virulence, Proteomics, Metabolic pathwayAbstract
Paper description:
- Differences between the virulence and pathogenic characteristics of hypervirulent Klebsiella pneumoniae and classical pneumoniae are poorly understood.
- Using comparative proteomics we explored the correlation between the expression characteristics of proteins and hypervirulence.
- Of 451 significantly differentially expressed proteins, 185 were significantly upregulated and 266 were significantly downregulated. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the differentially expressed proteins were predominantly metabolism associated.
- Our results contribute to a better understanding of the virulence and pathogenic characteristics of hypervirulent pneumoniae.
Abstract: Klebsiella pneumoniae is an opportunistic pathogen that causes a wide range of infections. The emergence and spread of hypervirulent K. pneumoniae (hvKp), which appears to be different from the classical K. pneumoniae (cKp) in several microbiological aspects, is an urgent global threat. However, the virulence characteristics of hvKp and its differences from cKp are poorly understood. This work aimed to investigate the correlation between the expression characteristics of proteins and hypervirulence, using proteomics. Our results revealed that 185 proteins were upregulated while 266 proteins were downregulated in hvKp isolates when compared with cKp isolates. The differentially expressed proteins were functionally categorized according to the Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. These proteins were predominantly metabolism associated, which indicates that changes in the metabolic pathways in hvKp isolates might in part contribute to hypervirulence.
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Copyright (c) 2022 Hui Yu, Lixia Zhang, Rina Su, Hai Hu, Zhanli Wang
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