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.

SYSTEMS-LEVEL ANALYSIS OF THE T-BET INTERACTION NETWORK Thomas Krausgruber 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. As part of this project, I have established a systems proteomics and functional assay, which was first used to support benchmarking studies of a new technology developed in the lab. Cell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed "single-cell combinatorial fluidic indexing" (scifi). We could demonstrate the scifi-RNA-seq assay is compatible with human primary material, especially with T cells and differentiated T cell subsets. We expect the scifi-RNA-seq protocol to provide an easy to-use, cost-effective, and broadly useful method for applications in single-cell biology. Furthermore, I employed my established methods to characterize structural cells of the body and their immune function. The human body consists of specialized components: Bones and soft tissue provide structure, organs contribute physiological functions, and immune cells protect against pathogens. In reality, many cell types and organs may play more than one role. We could demonstrate a striking example of multi-tasking for structural cells (e.g. epithelium, endothelium, and fibroblasts) which are the most important building blocks of the body. Unexpectedly, we detected high immune gene activity in structural cells which facilitates the communication and interplay with cells of the immune system. Furthermore, we found that many immune genes showed epigenetic signatures that are normally associated with high gene expression, while the observed expression in structural cells was lower than expected. We were able to show that these immune genes are epigenetically pre-programmed for rapid upregulation, for example in response to a pathogen. These results highlight that structural cells are not only essential building blocks of the body, but also contribute extensively to its defense against pathogens.