Systems-level analysis of the T-bet interaction network

Immune cell differentiation is essential for maintaining homeostasis and tissue integrity, while also critical for host response against pathogens. Cellular commitment therefore depends on a precise and tightly regulated series of events that trigger specific genetic programs in response to environmental cues. Especially CD4+ T helper (Th) cells have evolved an unprecedented potential to specialize in order to combat diverse set of pathogens. However, tight regulation of T cell responses is required for effective control of infections whilst avoiding the development of autoimmune and immune pathological diseases. The transition from a nave towards a specialized T cell subset is governed by lineage-defining transcription factors that activate signature-associated gene programs for a given cell fate. The transcription factor T-bet induces the Th1 gene profile and is often referred to as the master regulator of Th1 cell commitment. In addition, T-bet is essential for repressing genetic programs associated with alternative lineage decisions through mechanisms that are not well understood. Here, I propose to study Th1 cell differentiation and aim to gain a quantitative and genome-wide insight into gene activation and gene repression controlled by T-bet. I hypothesize that the opposing regulatory functions of T-bet are mediated by DNA-context-dependent recruitment of specific cofactors. I will establish a T-bet centered protein interaction network and characterize the interplay of various additional regulatory proteins involved in Th1 cell differentiation. This requires a system-wide proteomics coupled with a functional approach to detect the T-bet interaction partners in a systematic unbiased manner. Successful completion of the proposed project will advance the state-of-the-art in T cell biology by addressing fundamental questions about how T-bet establishes stable cell states and how manipulation of the T-bet interaction network could be harnessed to selectively change cellular identity. A detailed understanding of Th1 cell differentiation could rationalize the targeting of T-bet-associated proteins for altering T cell fate in the prevention or treatment of human disease. Overall, the project described here will illustrate the power of a system-scale analysis of transcriptional networks and the methods and concepts developed here could easily be applied to other scientific areas.

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