Patowary Ashok, Chauhan Rajendra Kumar, Singh Meghna, Kv Shamsudheen, Periwal Vinita, Kp Kushwaha, Sapkal Gajanand N, Bondre Vijay P, Gore Milind M, Sivasubbu Sridhar, Scaria Vinod
CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110007, India.
BMC Res Notes. 2012 Jan 6;5:11. doi: 10.1186/1756-0500-5-11.
Fast, specific identification and surveillance of pathogens is the cornerstone of any outbreak response system, especially in the case of emerging infectious diseases and viral epidemics. This process is generally tedious and time-consuming thus making it ineffective in traditional settings. The added complexity in these situations is the non-availability of pure isolates of pathogens as they are present as mixed genomes or hologenomes. Next-generation sequencing approaches offer an attractive solution in this scenario as it provides adequate depth of sequencing at fast and affordable costs, apart from making it possible to decipher complex interactions between genomes at a scale that was not possible before. The widespread application of next-generation sequencing in this field has been limited by the non-availability of an efficient computational pipeline to systematically analyze data to delineate pathogen genomes from mixed population of genomes or hologenomes.
We applied next-generation sequencing on a sample containing mixed population of genomes from an epidemic with appropriate processing and enrichment. The data was analyzed using an extensive computational pipeline involving mapping to reference genome sets and de-novo assembly. In depth analysis of the data generated revealed the presence of sequences corresponding to Japanese encephalitis virus. The genome of the virus was also independently de-novo assembled. The presence of the virus was in addition, verified using standard molecular biology techniques.
Our approach can accurately identify causative pathogens from cell culture hologenome samples containing mixed population of genomes and in principle can be applied to patient hologenome samples without any background information. This methodology could be widely applied to identify and isolate pathogen genomes and understand their genomic variability during outbreaks.
快速、特异性地鉴定和监测病原体是任何疫情应对系统的基石，尤其是在新发传染病和病毒流行的情况下。这一过程通常繁琐且耗时，因此在传统环境中效率低下。这些情况下的额外复杂性在于病原体的纯分离株不可用，因为它们以混合基因组或全基因组的形式存在。在这种情况下，新一代测序方法提供了一个有吸引力的解决方案，因为它能以快速且经济的成本提供足够的测序深度，此外还能以前所未有的规模解读基因组之间的复杂相互作用。新一代测序在该领域的广泛应用受到了限制，因为缺乏一个有效的计算流程来系统地分析数据，以便从混合的基因组或全基因组群体中描绘出病原体基因组。
我们对一个含有来自疫情的混合基因组群体的样本进行了新一代测序，并进行了适当的处理和富集。使用了一个广泛的计算流程来分析数据，该流程包括与参考基因组集进行比对和从头组装。对所产生数据的深入分析揭示了与日本脑炎病毒相对应的序列的存在。该病毒的基因组也被独立地从头组装。此外，还使用标准分子生物学技术验证了病毒的存在。
我们的方法可以从含有混合基因组群体的细胞培养全基因组样本中准确鉴定出致病病原体，原则上可以应用于没有任何背景信息的患者全基因组样本。这种方法可以广泛应用于鉴定和分离病原体基因组，并了解它们在疫情期间的基因组变异性。
