Genomics of the unknown: Translational applications of Hologenome sequencing

posted Jun 8, 2012, 5:42 AM by Vinod Scaria   [ updated Jun 8, 2012, 6:09 AM ]
Leh Symposium 2012 | August 3-7, 2012 | Leh, India

Ashok Patowary, Rajendra K Chauhan, Meghna Singh, Shamsudheen K Vellarikkal, Vinita Periwal, Sourav Ghosh, Kushwaha K P,Gajanan N Sapkal, Vijay P Bondre, Milind M Gore, Shahul Hameed, Urvashi Singh, Sridhar Sivasubbu and Vinod Scaria 

1CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi; 2Open Source Drug Discovery Unit, Anusandhan Bhavan, New Delhi; 3BRD Medical College, Gorakhpur, Uttar Pradesh; 4National Institute of Virology, Pune, Maharashtra; 5Abdul Hakeem College, Vellore, Tamil Nadu; 6All India Institute of Medical Sciences, Ansari Nagar, Delhi

Isolation and Identification of pathogens from clinical samples has been the mainstay of clinical microbiology. However, this process is often tedious, time-consuming and costly, limiting the application in general clinical settings to a select set of pathogens. The availability of high throughput sequencing technologies has made it possible to provide an unbiased view of genetic material in biological samples. The improvements in scale/throughput of DNA sequencing coupled with rapidly diminishing cost now offers a new opportunity to identify pathogens using a sequencing approach. The widespread application of DNA sequencing in such settings had been majorly limited by the fact that traditional sequencing methods often required pure isolates for genome analysis, while clinical samples usually exists as mixed genome samples or hologenomes with 2 or more components. We show that effective modifications of the sample processing and analysis protocols would enable identification and assembly of pathogens from mixed genome samples. We have successfully applied hologenome sequencing on biological samples containing mixed genomes. This approach significantly overcomes the limitations of traditional microbiology applications and can be adapted to clinical settings. We show that this could be extended to special settings including epidemics. Apart from identifying pathogens, the technology would enable to potentially understand the dynamics of host-pathogen interactions.