The dynamics of granuloma formation in tuberculosis

描述（由申请人提供）

DESCRIPTION (provided by applicant) The goal of this proposal is to explain the formation of granuloma in infection with Mycobacterium tuberculosis. Understanding granuloma formation and function will elucidate the primary immune mechanism for controlling tuberculosis infection. Our goal is to simulate the process of granuloma formation on a spatio-temporal scale and present the results in a time-lapse movie format. This will yield an interactive tool to study the role of specific immune elements in granuloma formation and function. Development of a virtual model of human infection will allow for integration of the plethora of chemokine, cytokine, cellular influx information and other relevant immunological factors, as generated by experimental systems. To this end, powerful techniques (e.g., microarrays) are available for obtaining comprehensive gene expression data. Using these methods to survey expression within the granulomas of non-human primates and mice will enable us to determine which immunological mediators are involved in granuloma formation, what the timing of their expression is in the formation, and their location within the granuloma. Further studies will indicate which cell-types are expressing which mediators. Our specific aims are to: (1) Identify the temporal and spatial expression of host immune elements participating in granuloma formation using gene expression tools in murine models of tuberculosis (2) Identify the temporal and spatial expression of host immune elements participating in granuloma formation using gene expression tools in murine models of tuberculosis (3) Determine the dynamics of granuloma formation and function in humans using mathematical models of the granuloma response in tuberculosis. Through this unique approach, the interaction of multiple factors that control the formation of the granuloma will be defined. Key parameters governing these interactions will be identified. The ability to synthesize the data generated by the experiments in the models allows for an understanding of the dynamics of granuloma formation as more than the sum of its parts.