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Metabolic Discrimination of Unknown Bacterial Pathogens

Metabolic Discrimination of Unknown Bacterial Pathogens
未知细菌病原体的代谢区分
批准号:
6818160
负责人:
JOHN PETER WIKSWO
金额:
$107.72万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-02-01 至 2010-01-31

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DESCRIPTION (provided by applicant): A collaboration between Vanderbilt University and the U.S. Army Edgewood Chemical and Biological Center (ECBC) at the Aberdeen Proving Grounds will develop a wide-spectrum, activity-detection technology that employs multiphasic sensing, in order to provide a biofunctional signature of a CBW agent, unknown drug, or other threat. The signatures will be used with advanced algorithms to discriminate between different agents acting on a set of target cell lines. The proposed approach is extraordinarily versatile and general, because we are measuring the biological impact of the toxins, rather than simply their presence. This will address a critically important and as-yet-unmet need for diagnostic tools capable of identifying the mechanism of action of unknown or reengineered threat agents that defy accurate detection by existing, agent-specific sensors. The diagnostic tools will be created to establish signatures of key changes in metabolic and signaling pathways that occur in cell lines responsive to Anthrax, Ricin, Staphylococcal Enterotoxin B (SEB), and Clostridium Botulinum toxins. The interaction of the toxins with cells leads to a multitude of metabolic and signaling events, as toxins disrupt normal cellular functions in specific and non-specific ways that are not yet fully understood. To provide a means to characterize the effects of unknown toxins, the proposed diagnostic system will monitor key parameters of specific metabolic and signaling pathways over a spectrum of time- and volume-scales. Newly developed well-plate protocols for end-point metabolic rates will be coupled with commercial fluorescence assays for signaling events. Additionally, the same metabolic and signaling events will be captured as dynamic biosignatures using a modified Cytosensor that simultaneously monitors multiple analytes on the time scale of minutes. This approach will be scaled down to achieve dynamic resolution on the order of seconds in a microfabricated NanoPhysiometer. This approach should find wide application in the discovery of new drugs and unexpected and/or undesired physiological activity; environmental and industrial toxicology; metabonomics and signaling; and screening of countermeasures, therapies, and prophalaxis for pathogenic bacteria and toxins.
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  • 负责人:
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