State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, PR China.
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.
Infect Genet Evol. 2022 Jun;100:105275. doi: 10.1016/j.meegid.2022.105275. Epub 2022 Mar 23.
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.
公共数据库中储存的基因组数量呈指数级增长，这有助于我们更全面地了解肺炎克雷伯氏菌的系统发育和流行病学。然而，基于现有方法，推断肺炎克雷伯氏菌的进化关系具有挑战性。在本研究中，我们确定了肺炎克雷伯氏菌的核心基因，并从 GC 含量、突变率、大小和潜在功能等方面对其进行了分析。然后，我们开发了一种基于稳定基因的大数据分析方法，并与现有方法进行了比较。我们的新方法实现了对肺炎克雷伯氏菌更精确的系统发育分析。使用这种基于基因的方法，我们基于近 953 个基因组的公共数据库探索了全球的系统发育关系。研究结果为肺炎克雷伯氏菌的系统发育和流行病学分析提供了有用的信息，对安全应用也具有重要意义。
