Ancestral genetic exchange between members of many important bacterial pathogen groups has resulted in phylogenetic relationships better described as networks than as bifurcating trees. In certain cases, these reticulated phylogenies have resulted in phenotypic and molecular overlap that challenges the construction of practical approaches for species identification in the clinical microbiology laboratory. Burkholderia cepacia complex (Bcc), a betaproteobacteria species group responsible for significant morbidity in persons with cystic fibrosis and chronic granulomatous disease, represents one such group where network-structured phylogeny has hampered the development of diagnostic methods for species-level discrimination. Here, we present a phylogeny-informed proteomics approach to facilitate diagnostic classification of pathogen groups with reticulated phylogenies, using Bcc as an example. Starting with a set of more than 800 Bcc and Burkholderia gladioli whole-genome assemblies, we constructed phylogenies with explicit representation of inferred interspecies recombination. Sixteen highly discriminatory peptides were chosen to distinguish B. cepacia, Burkholderia cenocepacia, Burkholderia multivorans, and B. gladioli and multiplexed into a single, rapid liquid chromatography-tandem mass spectrometry multiple reaction monitoring (LC-MS/MS MRM) assay. Testing of a blinded set of isolates containing these four Burkholderia species demonstrated 50/50 correct automatic negative calls (100% accuracy with a 95% confidence interval [CI] of 92.9 to 100%), and 70/70 correct automatic species-level positive identifications (100% accuracy with 95% CI 94.9 to 100%) after accounting for a single initial incorrect identification due to a preanalytic error, correctly identified on retesting. The approach to analysis described here is applicable to other pathogen groups for which development of diagnostic classification methods is complicated by interspecies recombination.

中文翻译：

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用系统发育信息学蛋白质组学方法鉴定洋葱伯克霍尔德菌中的物种。

许多重要细菌病原体组成员之间的祖先遗传交换已导致系统发育关系被更好地描述为网络，而不是分叉的树。在某些情况下，这些网状系统发育已导致表型和分子重叠，这对临床微生物学实验室鉴定物种的实用方法的构建提出了挑战。洋葱伯克霍尔德菌复合体（Bcc）是造成囊性纤维化和慢性肉芽肿性疾病的高发病率的β变形细菌物种组，代表了这样一种组，其中网络结构的系统发育阻碍了物种水平判别诊断方法的发展。在这里，我们以Bcc为例，介绍一种系统发育信息学的蛋白质组学方法，以有助于对具有网状系统发育的病原体组进行诊断分类。从一组800多个Bcc和剑兰伯克霍尔德氏菌全基因组装配体开始，我们构建了系统植物，明确推断出种间重组。选择了16种高度区分性的肽来区分洋葱伯克霍尔德菌，伯克霍尔德菌洋葱，伯克霍尔德氏菌（Burkholderia multivorans）和剑兰芽孢杆菌（B.gladioli）并复用到单个快速液相色谱-串联质谱多反应监测（LC-MS / MS MRM）分析中。测试包含这四种伯克霍尔德氏菌的盲分离株物种显示出50/50正确的自动否定检出率（100％的准确度，95％置信区间[CI]为92.9至100％），以及70/70正确的自动物种水平的阳性识别（100％的准确度，95％CI为94.9至100％） 100％），原因是由于分析前的错误导致了一次初步的错误识别，并且在重新测试时正确识别。此处描述的分析方法适用于其他病原体组，对于这些病原体，诊断分类方法的开发由于种间重组而变得复杂。