Integrative framework for mapping the cell-type specific effects of mutations
Rationale and Objectives
Mutations in transcription factors can have pleiotropic and unpredictable effects since they may impact many genes in many cell types, yet their direct target genes may differ in each cell type. The goal of this project is to generate a general framework for understanding the cell-type specific effect of any given patient-specific mutation in transcription factors studied by the consortium and elsewhere, with a focus on NFkB. The Zaugg group has recently developed a tool to generate cell-type specific gene regulatory networks11, which are based on connecting transcription factors to enhancers to their target genes. In previous work, we have found that many TFs, including NFkB, regulate a very cell-type specific set of genes.
To understand the impact of NFkB mutations in different immune cell types. For this,
1) to perform single cell RNA and ATAC-seq profiling in peripheral blood of 30 patients that harbour a mutation in NFkB along with 20 healthy donors. Using these data, we will devise a framework for generating gene regulatory networks based on inter-individual co-variation in RNA expression, transcription factor activity and enhancer accessibility, based on our previous work in bulk data11.
2) Using this, to compare the regulon of NFkB across the different cell types and obtain a detailed map of the cell-type specific direct effects of NFkB mutations.
3) using cell-type specific differential expression between the patients and healthy donors, our networks will identify transcription factors that are cooperating with NFkB to drive its cell-type specific effects.
4) The framework developed in this project will be used to integrate the multiomics data generated within and outside the consortium, to derive similar hypotheses for other mutations and to test the effect of corrections, and to interpret common genetic variants associated with immune disorders.
Overall, this project will provide an integrative framework that predicts the regulatory pathways that are potentially mis-regulated and will pinpoint the cell types in which a given network is disrupted. The findings from this project will be validated with the experiments performed by other groups in the consortium where some of the mutations will be corrected.
UKLFR (Warnatz) to receive training on patient sample processing, m10-12 (2 month); UKLFR (Grimbacher) to learn about NFkB signaling, m13-14 (1 month); GRL (Vento-Tormo) to establish a joint analysis framework, m15-18 (3months). Finally, the DC will do a secondment at qGenomics to learn how to process and analyse samples for cfATAC-seq, m25-27 (2 months).
PhD in Bioinformatics, EMBL, Heidelberg, Germany