Goyal Manisha, Hauben Lysiane, Pouseele Hannes, Jaillard Magali, De Bruyne Katrien, van Belkum Alex, Goering Richard
BioMérieux, Open Innovation and Partnerships, 3 Route du Port Michaud, 38390 La Balme Les Grottes, France.
BioMérieux, Applied Maths NV, 9830 Sint-Martens-Latem, Belgium.
Diagnostics (Basel). 2020 Dec 12;10(12):1078. doi: 10.3390/diagnostics10121078.
is a cause of health care-associated infections. The epidemiological study of infection (CDI) traditionally involves PCR ribotyping. However, ribotyping will be increasingly replaced by whole genome sequencing (WGS). This implies that WGS types need correlation with classical ribotypes (RTs) in order to perform retrospective clinical studies. Here, we selected genomes of hyper-virulent strains of RT001, RT017, RT027, RT078, and RT106 to try and identify new discriminatory markers using in silico ribotyping PCR and De Bruijn graph-based Genome Wide Association Studies (DBGWAS). First, in silico ribotyping PCR was performed using reference primer sequences and 30 genomes of the five different RTs identified above. Second, discriminatory genomic markers were sought with DBGWAS using a set of 160 independent genomes (14 ribotypes). RT-specific genetic polymorphisms were annotated and validated for their specificity and sensitivity against a larger dataset of 2425 genomes covering 132 different RTs. In silico PCR ribotyping was unsuccessful due to non-specific or missing theoretical RT PCR fragments. More successfully, DBGWAS discovered a total of 47 new markers (13 in RT017, 12 in RT078, 9 in RT106, 7 in RT027, and 6 in RT001) with minimum q-values of 0 to 7.40 × 10, indicating excellent marker selectivity. The specificity and sensitivity of individual markers ranged between 0.92 and 1.0 but increased to 1 by combining two markers, hence providing undisputed RT identification based on a single genome sequence. Markers were scattered throughout the genome in intra- and intergenic regions. We propose here a set of new genomic polymorphisms that efficiently identify five hyper-virulent RTs utilizing WGS data only. Further studies need to show whether this initial proof-of-principle observation can be extended to all 600 existing RTs.
是医疗保健相关感染的一个原因。艰难梭菌感染（CDI）的流行病学研究传统上涉及PCR核糖体分型。然而，核糖体分型将越来越多地被全基因组测序（WGS）所取代。这意味着为了进行回顾性临床研究，WGS类型需要与经典核糖体分型（RTs）相关联。在此，我们选择了RT001、RT017、RT027、RT078和RT106的高毒力菌株的基因组，试图使用计算机核糖体分型PCR和基于德布鲁因图的全基因组关联研究（DBGWAS）来识别新的鉴别标记。首先，使用参考引物序列对上述五种不同RTs的30个基因组进行计算机核糖体分型PCR。其次，使用一组160个独立基因组（14种核糖体分型）通过DBGWAS寻找鉴别性基因组标记。对RT特异性基因多态性进行注释，并针对涵盖132种不同RTs的2425个基因组的更大数据集验证其特异性和敏感性。由于非特异性或缺失理论RT PCR片段，计算机PCR核糖体分型未成功。更成功的是，DBGWAS总共发现了47个新标记（RT0|7中有13个，RT078中有12个，RT106中有9个，RT027中有7个，RT001中有6个），最小q值为0至7.40×10，表明标记具有出色的选择性。单个标记的特异性和敏感性在0.92至1.0之间，但通过组合两个标记可提高到1，因此基于单个基因组序列提供了无可争议的RT鉴定。标记分散在基因组的基因内和基因间区域。我们在此提出一组新的基因组多态性，仅利用WGS数据就能有效识别五种高毒力RTs。进一步的研究需要表明这一初步的原理验证观察结果是否可以扩展到所有600种现有的RTs。
