A new and effective genes-based method for phylogenetic analysis of Klebsiella pneumoniae.

Core genes Klebsiella pneumoniae Nitrogen-fixing bacteria Phylogenetic analysis Virulence genes

Journal

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
ISSN: 1567-7257
Titre abrégé: Infect Genet Evol
Pays: Netherlands
ID NLM: 101084138

Informations de publication

Date de publication:
06 2022
Historique:
received: 02 06 2021
revised: 16 03 2022
accepted: 21 03 2022
pubmed: 28 3 2022
medline: 20 4 2022
entrez: 27 3 2022
Statut: ppublish

Résumé

The exponential increase in the number of genomes deposited in public databases can help us gain a more holistic understanding of the phylogeny and epidemiology of Klebsiella pneumoniae. However, inferring the evolutionary relationships of K. pneumoniae based on big genomic data is challenging for existing methods. In this study, core genes of K. pneumoniae were determined and analysed in terms of differences in GC content, mutation rate, size, and potential functions. We then developed a stable genes-based method for big data analysis and compared it with existing methods. Our new method achieved a higher resolution phylogenetic analysis of K. pneumoniae. Using this genes-based method, we explored global phylogenetic relationships based on a public database of nearly 953 genomes. The results provide useful information to facilitate the phylogenetic and epidemiological analysis of K. pneumoniae, and the findings are relevant for security applications.

Identifiants

pubmed: 35339697
pii: S1567-1348(22)00072-7
doi: 10.1016/j.meegid.2022.105275
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

105275

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Xiaoqin Zhou (X)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.

Qiyu Chu (Q)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China; College of Life and Environment Sciences, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China.

Shengming Li (S)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.

Menglei Yang (M)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.

Yangyang Bao (Y)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.

Yang Zhang (Y)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.

Shuilin Fu (S)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.

Heng Gong (H)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China. Electronic address: gongheng@ecust.edu.cn.

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Classifications MeSH