Characterization of Pathogens in Clinical Specimens via Suppression of Host Background for Efficient Second Generation Sequencing Analyses
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Bioweapons and emerging infectious diseases pose formidable and growing threats to our national security. Rapid advances in biotechnology and the increasing efficiency of global transportation networks virtually guarantee that the United States will face potentially devastating infectious disease outbreaks caused by novel ('unknown') pathogens either intentionally or accidentally introduced into the population. Unfortunately, our nation's biodefense and public health infrastructure is primarily designed to handle previously characterized ('known') pathogens. While modern DNA assays can identify known pathogens quickly, identifying unknown pathogens currently depends upon slow, classical microbiological methods of isolation and culture that can take weeks to produce actionable information. In many scenarios that delay would be costly, in terms of casualties and economic damage; indeed, it can mean the difference between a manageable public health incident and a full-blown epidemic. To close this gap in our nation's biodefense capability, we will develop, validate, and optimize a system to extract nucleic acids from unknown pathogens present in clinical samples drawn from infected patients. This system will extract nucleic acids from a clinical sample, amplify pathogen and specific host response nucleic acid sequences. These sequences will then be suitable for ultra-high-throughput sequencing (UHTS) carried out by a third party. The data generated from UHTS will then be processed through a new data assimilation and Bioinformatic analysis pipeline that will allow us to characterize an unknown pathogen in hours to days instead of weeks to months. Our methods will require no a priori knowledge of the pathogen, and no isolation or culturing; therefore it will circumvent many of the major roadblocks confronting a clinical microbiologist or virologist when presented with an unknown or engineered pathogen.
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Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), is a growing international health crisis. Mtb is able to persist in host tissues in a nonreplicating persistent (NRP) or latent state. This presents a challenge in the treatment of TB. Latent TB can re-activate in 10% of individuals with normal immune systems, higher for those with compromised immune systems. A quantitative understanding of latency-associated virulence mechanisms may help researchers develop more effective methods to battle the spread and reduce TB associated fatalities. Leveraging BioXyce's ability to simulate whole-cell and multi-cellular systems we are developing a circuit-based framework to investigate the impact of pathogenicity-associated pathways on the latency/reactivation phase of tuberculosis infection. We discuss efforts to simulate metabolic pathways that potentially impact the ability of Mtb to persist within host immune cells. We demonstrate how simulation studies can provide insight regarding the efficacy of potential anti-TB agents on biological networks critical to Mtb pathogenicity using a systems chemical biology approach. © 2008 IEEE.