Whole genome sequencing of Streptococcus pneumoniae: development, evaluation and verification of targets for serogroup and serotype prediction using an automated pipeline.
Whole genome sequencing of Streptococcus pneumoniae: development, evaluation and verification of targets for serogroup and serotype prediction using an automated pipeline.
复制标题
作者：
Kapatai G;Sheppard CL;Al-Shahib A;Litt DJ;Underwood AP;Harrison TG;Fry NK
肺炎链球菌通常表达92种血清学上不同的荚膜多糖（cps）类型（血清型）之一。这些血清型中的一些彼此密切相关;使用市售的分型抗血清，这些血清型被分配到包含显示交叉反应性的类型的常见血清群。在该血清分型方案中，因子抗血清用于基于反应模式在血清组内分配血清型。这种血清分型方法在技术上要求很高，需要相当多的经验，并且结果的阅读可能是主观的。本研究描述了S.本发明涉及肺炎球菌荚膜操纵子基因序列，以确定血清型区别特征，以及基于全基因组序列（WGS）的自动血清分型生物信息学工具PneumoCaT（肺炎球菌荚膜分型）的开发、评估和验证。最初，WGS数据来自871 S。将肺炎分离株定位到92种血清型的参考CPS基因座序列。根据cps操纵子内的序列相似性，92种血清型中有32种可以明确鉴定。剩下的60只被分配到20个“基因组”中的一个，这些基因组大致对应于免疫学定义的血清组。通过比较每个基因组的cps参考序列，确定了20个基因组中18个血清型的独特分子差异，并使用871株分离株进行了验证。该信息用于在PneumoCaT生物信息学工具内设计决策树型算法，以预测89/94的血清型水平来自WGS数据的（92 + 2个分子类型/亚型）以及血清群24和32的血清群水平，目前占提交给国家参考实验室（NRL）的英国转介侵袭性分离株的2.1%，英国公共卫生（2014年6月至2015年7月）。使用涵盖72/92种血清型的2065株英国分离株（包括19株不可分型分离株）的内部验证集和来自泰国（n = 2，531）、美国（n = 181）和冰岛（n = 252）的2964株分离株的外部验证集评价PneumoCaT。PneumoCaT能够预测99.1%的可分型UK分离株和99.0%的非UK分离株的血清型。在可能进行进一步调查的英国分离株中评价了一致性;在91.5%的病例中，预测的荚膜类型与血清学来源的血清型一致。复检后，一致性增加到99.3%，在大多数解决的病例中（97.8%; 135/138），不一致性是由原始血清分型错误引起的。重复检测证明，PneumoCaT的预测血清型结果具有100%的重现性。总之，我们开发了一种基于WGS的血清分型方法，该方法可以预测89/94种血清型的血清型水平和其余4种血清型的血清组水平。这种方法可以整合到参考实验室的常规分型工作流程中，减少对表型免疫学检测的需求。
Streptococcus pneumoniae typically express one of 92 serologically distinct capsule polysaccharide (cps) types (serotypes). Some of these serotypes are closely related to each other; using the commercially available typing antisera, these are assigned to common serogroups containing types that show cross-reactivity. In this serotyping scheme, factor antisera are used to allocate serotypes within a serogroup, based on patterns of reactions. This serotyping method is technically demanding, requires considerable experience and the reading of the results can be subjective. This study describes the analysis of the S. pneumoniae capsular operon genetic sequence to determine serotype distinguishing features and the development, evaluation and verification of an automated whole genome sequence (WGS)-based serotyping bioinformatics tool, PneumoCaT (Pneumococcal Capsule Typing). Initially, WGS data from 871 S. pneumoniae isolates were mapped to reference cps locus sequences for the 92 serotypes. Thirty-two of 92 serotypes could be unambiguously identified based on sequence similarities within the cps operon. The remaining 60 were allocated to one of 20 ‘genogroups’ that broadly correspond to the immunologically defined serogroups. By comparing the cps reference sequences for each genogroup, unique molecular differences were determined for serotypes within 18 of the 20 genogroups and verified using the set of 871 isolates. This information was used to design a decision-tree style algorithm within the PneumoCaT bioinformatics tool to predict to serotype level for 89/94 (92 + 2 molecular types/subtypes) from WGS data and to serogroup level for serogroups 24 and 32, which currently comprise 2.1% of UK referred, invasive isolates submitted to the National Reference Laboratory (NRL), Public Health England (June 2014–July 2015). PneumoCaT was evaluated with an internal validation set of 2065 UK isolates covering 72/92 serotypes, including 19 non-typeable isolates and an external validation set of 2964 isolates from Thailand (n = 2,531), USA (n = 181) and Iceland (n = 252). PneumoCaT was able to predict serotype in 99.1% of the typeable UK isolates and in 99.0% of the non-UK isolates. Concordance was evaluated in UK isolates where further investigation was possible; in 91.5% of the cases the predicted capsular type was concordant with the serologically derived serotype. Following retesting, concordance increased to 99.3% and in most resolved cases (97.8%; 135/138) discordance was shown to be caused by errors in original serotyping. Replicate testing demonstrated that PneumoCaT gave 100% reproducibility of the predicted serotype result. In summary, we have developed a WGS-based serotyping method that can predict capsular type to serotype level for 89/94 serotypes and to serogroup level for the remaining four. This approach could be integrated into routine typing workflows in reference laboratories, reducing the need for phenotypic immunological testing.
登录
查看更多内容
影响因子：
3.2
作者：
Aanensen, David M.;Mavroldi, Angeliki;Spratt, Brian G.
通讯作者：
Spratt, Brian G.
影响因子：
3.7
作者：
Lambertsen L;Kerrn MB
通讯作者：
Kerrn MB
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
9.4
作者：
Burton, Robert L.;Geno, K. Aaron;Nahm, Moon H.
通讯作者：
Nahm, Moon H.
影响因子：
9.4
作者：
Baek, Jin Yang;Park, In Ho;Ko, Kwan Soo
通讯作者：
Ko, Kwan Soo