Cellular Injury and Adaptation

Washington University, Saint Louis MO

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): The initial cycle of this grant coupled with an NIGMS Microarray Supplement provided the first evidence that leukocyte transcriptional profiles (transcriptomes) of spleen and blood could be used to classify septic phenotypes. However, these whole organ transcriptome profiles cannot verify the mportance of any particular gene product or its cell of origin. The goal of the second cycle of this grant is to define the regulatory pathways responsible for the sepsis-induced differentiation of spleen CD4+ T-cells. The hypothesis of this application is that the septic state induces a perturbation in the differentiatiori of CD4+ T cells that leads to a predominantly Th2 phenotype. We will study this maturation process as the septic state evolves by interrogating the terminal differentiation of splenic CD4+ T-cells following cecal ligation and puncture (CLP). Unbiased, genome-wide transcriptional and proteomic profiling data will be used to develop a dynamic model of this phenotypic commitment, thereby identifying those gene products that regulate this crucial decision. Aim 1: Determine alterations in spleen CD4+ T-cell phenotype at 0, 6, 12, and 24 hours after CLP. A complement of tools is used to measure changes over time in CD4+ T-cell subpopulaticns, including FACS for both cellular markers and cytokine production (Th1 and Th2 effector phenotypes). Aim 2: Model changes in regulatory pathways of spleen effector CD4+ T-cells at 6, 12, and 24 hours after CLP. Transcriptome profiling is performed using whole-genome GeneChips. Proteomic studies employ contemporary technologies, including multiplex fluorescent 2-D gel electrophoresis These data will be used to construct static and dynamical models of the CD4+ T-cell response. A well-characterized therapy that improves Th1 responsiveness and survival after CLP, systemic IL-12, will be used to perturb the system, testing the robustness of the models.