Dendritic cells (DC) are essential to the development of protective immunity to a number of infectious pathogens. These cells alert the adaptive immune system to the presence of pathogenic invaders and activate these cells to clear infections. To stimulate such activation, however, they must undergo a process termed maturation that increases their potency. DC maturation is a tightly regulated process involving changes in gene expression, intracellular trafficking, cytoskeletal modifications, and mobilization to lymphoid organs. The gene expression network, the dynamic process of interaction among gene expression, regulatory sequences, and trans-acting factors, underlying this process is extremely important for controlling many of the observed changes. Very few studies have examined this process over a comprehensive time course and none have attempted to derive network models of this process. Our long-term goal is to understand, at a systems level, the biology that underlies DC maturation following stimulation by infectious agents.
We aim to identify novel, previously undefined components of the DC maturation network and to identify cause-and-effect relationships that explain how DC maturation is controlled upon exposure to various infectious stimuli. In this project, we aim to develop methods and obtain a better understanding of the gene expression network underlying DC maturation through three specific aims. First, we will assess the dynamics of DC maturation by identifying and clustering genes that are significantly expressed during DC maturation over a comprehensive time course following treatment of DC with poly I:C as a model of viral infection. Second, we aim to identify relationships between significantly expressed genes, thus beginning to identify networks of interactions. And finally, we will demonstrate that we can identify groups genes involved in sub-networks and model the resulting network neighborhoods, thus beginning to establish cause-and-effect versus correlative relationships within the gene expression network. Because DC maturation is such a pivotal event for protective immunity, a broader understanding of the gene expression program and the comprehensive transcriptional regulatory network underlying their maturation is a key to the identification of new targets for the design and development of vaccines and therapies against infectious agents. Public Health Relevance: Dendritic cells (DC) are essential to the development of protective immunity to a broad range of pathogens and are being targeted in the design of vaccines. Understanding the process through which these cells are activated or """"""""matured"""""""" is of great significance to human health as we endeavor to design more effective vaccines and therapies for infectious diseases. We will use a cross-disciplinary approach with global gene expression data combined with computational modeling to gain a comprehensive network model of the process of DC maturation.
Dendritic cells (DC) are essential to the development of protective immunity to a broad range of pathogens and are being targeted in the design of vaccines. Understanding the process through which these cells are activated or matured is of great significance to human health as we endeavor to design more effective vaccines and therapies for infectious diseases. We will use a cross-disciplinary approach with global gene expression data combined with computational modeling to gain a comprehensive network model of the process of DC maturation.
|Olex, Amy L; Turkett, William H; Brzoza-Lewis, Kristina L et al. (2016) Impact of the Type I Interferon Receptor on the Global Gene Expression Program During the Course of Dendritic Cell Maturation Induced by Polyinosinic Polycytidylic Acid. J Interferon Cytokine Res 36:382-400|
|Olex, Amy L; Fetrow, Jacquelyn S (2011) SC²ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data. Bioinformatics 27:1330-1|
|Olex, Amy L; Hiltbold, Elizabeth M; Leng, Xiaoyan et al. (2010) Dynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicates. BMC Immunol 11:41|