Single-molecule chromatin configurations link transcription factor binding to expression in human cells

The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with experimental perturbation assays, offering a limited perspective of the DNN's capabilities within the studied loci. Moreover, existing model explainability tools mainly focus on motif analysis, which becomes complex to interpret for longer sequences. Here we introduce CREME, an in silico perturbation toolkit that interrogates large-scale DNNs to uncover rules of gene regulation that it has learned. Using CREME, we investigate Enformer, a prominent DNN in gene expression prediction, revealing cis-regulatory elements (CREs) that directly enhance or silence target genes. We explore the relationship between CRE distance from transcription start sites and gene expression, as well as the intricate complexity of higher-order CRE interactions. This work advances the ability to translate the powerful predictions of large-scale DNNs to study open questions in gene regulation.

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High-throughput affinity measurements of direct interactions between activation domains and co-activators

DelRosso,

Suzuki,

Griffith

et al. 2024

Preprint

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Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500Kds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g.enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, andin vitroaffinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

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Single-molecule chromatin configurations link transcription factor binding to expression in human cells

Cited by 5 publications

References 70 publications

Interpreting cis-regulatory interactions from large-scale deep neural networks

Interpreting cis-regulatory interactions from large-scale deep neural networks

InterpretingCis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

High-throughput affinity measurements of direct interactions between activation domains and co-activators

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