The 3D chromatin conformation has a strong impact on the regulation of gene expression. Chromatin compartments or topological associateded domains (TADs) form transcriptional units. Modifications of the 3d structure in disease or during development impacts gene expression. 3D structure is defined using chromatin-interaction maps obtained through Hi-C methods. However, this data is not always available. Hence, we would like to build a model allowing to predict the chromatin organization from other omics datatypes, such as ATAC-seq and other sequencing data. A similar model has been developed (preciseTAD), however based on a large number of data types. We want to focus on chromatin accessibility data, using both the signal strength and the patterns of transcription factor footprints. We want to apply this to an in-house dataset of chromatin accessibility data obtained in microglia infected with HIV, to determine the changes in the chromatin conformation between uninfected, actively infected and uninfected status.
When confronted with a chronic infection or continuous stimulation by tumor cells, CD8 T-cells can undergo an exhaustion phenomenon and loose their effector potential, hence diminishing the adaptative immune response. Understanding the molecular mechanisms leading to this exhaustion phenotype might allow to design strategies to revert the exhaustion state back to normal effector functions. In collaboration with TU Munich and University Hospital Freiburg, we are studying CD8 exhaustion in the context of HCV infection. Using single-cell expression data from patients, we are reconstructing gene regulatory networks (GRN) describing how cell-type specific transcription factors regulate target genes. We would like to understand the plasticity of these regulatory networks, and how they are rewired between exhausted, cured and spontaneously resolved HCV infection. The goal of the project is to (1) validate the gene regulatory networks using external data, and (2) apply a boolean modelling strategy to determine the dynamical behavior of the networks and identify attractor states.
Single-cell experiments allow to observe and measure the expression variability between cells. While cells from different cell types show different transcriptional programs, such a stochastic bahavior is also observed even between cells from isogenic pools. This transcriptional noise is believed to contribute to fast adaptability to changing environmental conditions. Using single-cell chromatin accessibility datasets, we can now also measure the chromatin variability. In previous studies, we have observed a high epigenetic variability (Liu et al., 2019). Using a new method, we can integrated the scRNA and scATAC-seq data to reconstruct gene regulatory networks, and study the level of regulatory variability across different cells. In this project, we want to understand (1) which genes are subject to a high transcriptional and epigenetic variability, and (2) reconstruct gene regulatory networks and connect these two levels of variability using a simple dynamical model. This will be done using published data with both data modalities (scRNA/scATAC), and in data that we will obtain from CD8 T-cells in the course of the project.