Roe Chandler C, Horn Kimberly S, Driebe Elizabeth M, Bowers Jolene, Terriquez Joel A, Keim Paul, Engelthaler David M
Pathogen Genomics Division, Translational Genomics Research Institute, 3051 W. Shamrell Blvd., Suite 106, Flagstaff, AZ 86001 USA.
Flagstaff Medical Center, Flagstaff, AZ USA.
Hereditas. 2016 Nov 14;153:11. doi: 10.1186/s41065-016-0017-x. eCollection 2016.
Prevention of nosocomial transmission of infections is a central responsibility in the healthcare environment, and accurate identification of transmission events presents the first challenge. Phylogenetic analysis based on whole genome sequencing provides a high-resolution approach for accurately relating isolates to one another, allowing precise identification or exclusion of transmission events and sources for nearly all cases. We sequenced 24 methicillin-resistant (MRSA) genomes to retrospectively investigate a suspected point source of three surgical site infections (SSIs) that occurred over a one-year period. The source of transmission was believed to be a surgical team member colonized with MRSA, involved in all surgeries preceding the SSI cases, who was subsequently decolonized. Genetic relatedness among isolates was determined using whole genome single nucleotide polymorphism (SNP) data.
Whole genome SNP typing (WGST) revealed 283 informative SNPs between the surgical team member's isolate and the closest SSI isolate. The second isolate was 286 and the third was thousands of SNPs different, indicating the nasal carriage strain from the surgical team member was not the source of the SSIs. Given the mutation rates estimated for none of the SSI isolates share a common ancestor within the past 16 years, further discounting any common point source for these infections. The decolonization procedures and resources spent on the point source infection control could have been prevented if WGST was performed at the time of the suspected transmission, instead of retrospectively.
Whole genome sequence analysis is an ideal method to exclude isolates involved in transmission events and nosocomial outbreaks, and coupling this method with epidemiological data can determine if a transmission event occurred. These methods promise to direct infection control resources more appropriately.
预防医院感染传播是医疗环境中的一项核心职责,准确识别传播事件是首要挑战。基于全基因组测序的系统发育分析提供了一种高分辨率方法,可准确地将分离株相互关联,几乎能精确识别或排除所有病例的传播事件及源头。我们对24个耐甲氧西林金黄色葡萄球菌(MRSA)基因组进行了测序,以回顾性调查在一年时间内发生的三起手术部位感染(SSI)的疑似点源。传播源被认为是一名携带MRSA的外科团队成员,他参与了所有先于SSI病例的手术,随后进行了去定植。使用全基因组单核苷酸多态性(SNP)数据确定分离株之间的遗传相关性。
全基因组SNP分型(WGST)显示,外科团队成员的分离株与最接近的SSI分离株之间有283个信息性SNP。第二个分离株相差286个SNP,第三个分离株相差数千个SNP,这表明外科团队成员的鼻腔携带菌株不是SSI的源头。鉴于估计的突变率,在过去16年内,没有一个SSI分离株有共同祖先,这进一步排除了这些感染存在任何共同的点源。如果在疑似传播时而非回顾性地进行WGST,本可避免针对点源感染控制所进行的去定植程序及所花费的资源。
全基因组序列分析是排除参与传播事件和医院感染暴发的分离株的理想方法,将该方法与流行病学数据相结合可确定是否发生了传播事件。这些方法有望更恰当地指导感染控制资源的分配。