Systematically Dissecting the Regulatory Logic of Chromatin Modifications

Introduction

Chromatin modifications are a key mechanism for regulating gene expression patterns in normal and disease settings. Nonetheless, despite the ubiquity of epigenomics data, the causal functions of specific chromatin modifications have proved challenging to unravel.

Knowledge Gap

This knowledge gap reflects limited approaches to dissect how chromatin modifications influence transcription quantitatively and context-dependently, across diverse genomic features or variants in any given cell type.

Proposed Approach

Here, we propose to systematically interrogate the functional impact of specific- and combinatorial-chromatin modifications within tens of thousands of contexts in living cells, using a novel modular epigenome editing platform.

Dataset Production

We will produce the largest precision chromatin-perturbation dataset to date, and exploit it to capture multi-modal functional responses at allelically-resolved single-cell resolution, across developmental lineages.

Expected Outcomes

The unprecedented scale of the work will uncover the regulatory logic by which distinct chromatin states interact with genomic motifs, sequence variants, and cellular identity, to shape quantitative gene expression patterns.

Integration and Identification

By integrating data and deploying genetic screens, we will further identify the trans-acting and cis-structural mechanisms that underpin complex relationships.

Technological Advancements

Our technology will circumvent the key limitations of existing approaches by excluding pleiotropy and redundancy, whilst systematically isolating functional genome x epigenome interactions.

Predictive Model Development

The cumulative insights will be used to build a predictive model based on rational rules for how specific changes in chromatin marks instruct - or reflect - gene expression states within specific contexts.

Applications

This will aid design strategies for precision medicine and create guiding principles to attribute functional significance to epigenome profiles in health and disease.