Zhan Xiao-Yong, Zhu Qing-Yi
Guangzhou KingMed Center for Clinical Laboratory, Guangzhou, China.
KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
PLoS One. 2018 Feb 1;13(2):e0190986. doi: 10.1371/journal.pone.0190986. eCollection 2018.
Inadequate discriminatory power to distinguish between L. pneumophila isolates, especially those belonging to disease-related prevalent sequence types (STs) such as ST1, ST36 and ST47, is an issue of SBT scheme. In this study, we developed a multilocus sequence typing (MLST) scheme based on two non-virulence loci (trpA, cca) and three virulence loci (icmK, lspE, lssD), to genotype 110 L. pneumophila isolates from various natural and artificial water sources in Guangdong province of China, and compared with the SBT. The isolates were assigned to 33 STs of the SBT and 91 new sequence types (nSTs) of the MLST. The indices of discrimination (IODs) of SBT and MLST were 0.920 and 0.985, respectively. Maximum likelihood trees of the concatenated SBT and MLST sequences both showed distinct phylogenetic relationships between the isolates from the two environments. More intragenic recombinations were detected in nSTs than in STs, and they were both more abundant in natural water isolates. We found out the MLST had a high discriminatory ability for the disease-associated ST1 isolates: 22 ST1 isolates were assigned to 19 nSTs. Furthermore, we assayed the discrimination of the MLST for 29 reference strains (19 clinical and 10 environmental). The clinical strains were assigned to eight STs and ten nSTs. The MLST could also subtype the prevalent clinical ST36 or ST47 strains: eight ST36 strains were subtyped into three nSTs and two ST47 strains were subtyped into two nSTs. We found different distribution patterns of nSTs between the environmental and clinical ST36 isolates, and between the outbreak clinical ST36 isolates and the sporadic clinical ST36 isolates. These results together revealed the MLST scheme could be used as part of a typing scheme that increased discrimination when necessary.
嗜肺军团菌分离株之间鉴别能力不足,尤其是那些属于与疾病相关的流行序列类型(STs),如ST1、ST36和ST47,是序列分型技术(SBT)方案存在的一个问题。在本研究中,我们基于两个非毒力基因座(trpA、cca)和三个毒力基因座(icmK、lspE、lssD)开发了一种多位点序列分型(MLST)方案,用于对来自中国广东省各种天然和人工水源的110株嗜肺军团菌分离株进行基因分型,并与SBT进行比较。这些分离株被分为SBT的33个STs和MLST的91个新序列类型(nSTs)。SBT和MLST的鉴别指数(IODs)分别为0.920和0.985。串联的SBT和MLST序列的最大似然树均显示了来自两种环境的分离株之间明显的系统发育关系。在nSTs中检测到的基因内重组比在STs中更多,并且它们在天然水分离株中都更丰富。我们发现MLST对与疾病相关的ST1分离株具有高鉴别能力:22株ST1分离株被分为19个nSTs。此外,我们检测了MLST对29株参考菌株(19株临床菌株和10株环境菌株)的鉴别能力。临床菌株被分为8个STs和10个nSTs。MLST还可以对流行的临床ST36或ST47菌株进行亚型分型:8株ST36菌株被分为3个nSTs,2株ST47菌株被分为2个nSTs。我们发现环境ST36分离株与临床ST36分离株之间,以及暴发临床ST36分离株与散发临床ST36分离株之间nSTs的分布模式不同。这些结果共同表明,MLST方案可作为分型方案的一部分,在必要时提高鉴别能力。