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Understanding the complexity of transcriptional regulation is a major goal of computational biology. Because experimental linkage of regulatory sites to genes is challenging, computational methods considering epigenomics data have been proposed to create tissue-specific regulatory maps. However, we showed that these approaches are not well suited to account for the variations of the regulatory landscape between cell-types. To overcome these drawbacks, we developed a new method called STITCHIT, that identifies and links putative regulatory sites to genes. Within STITCHIT, we consider the chromatin accessibility signal of all samples jointly to identify regions exhibiting a signal variation related to the expression of a distinct gene. STITCHIT outperforms previous approaches in various validation experiments and was used with a genome-wide CRISPR-Cas9 screen to prioritize novel doxorubicin-resistance genes and their associated non-coding regulatory regions. We believe that our work paves the way for a more refined understanding of transcriptional regulation at the gene-level.
Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our approach on the analysis of a genome-wide CRISPR screen in hTERT-RPE-1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our approach is general and can be applied on any cell type and with different CRISPR enzymes.
Functional genomics studies in model organisms and human cell lines provided important insights into gene functions and their context-dependent role in genetic circuits. However, our functional understanding of many of these genes and how they combinatorically regulate key biological processes, remains limited. To enable the SpCas9-dependent mapping of gene-gene interactions in human cells, we established 3Cs multiplexing for the generation of combinatorial gRNA libraries in a distribution-unbiased manner and demonstrate its robust performance. The optimal number for combinatorial hit calling was 16 gRNA pairs and the skew of a library’s distribution was identified as a critical parameter dictating experimental scale and data quality. Our approach enabled us to investigate 247,032 gRNA-pairs targeting 12,736 gene-interactions in human autophagy. We identified novel genes essential for autophagy and provide experimental evidence that gene-associated categories of phenotypic strengths exist in autophagy. Furthermore, circuits of autophagy gene interactions reveal redundant nodes driven by paralog genes. Our combinatorial 3Cs approach is broadly suitable to investigate unexpected gene-interaction phenotypes in unperturbed and diseased cell contexts.
Targeted protein degradation is a drug modality represented by compounds that recruit a target to an E3 ubiquitin ligase to promote target ubiquitination and proteasomal degradation. Historically, the field distinguishes monovalent degraders from bifunctional degraders (PROTACs) that connect target and ligase via separate binding ligands joined via a linker1–4. Here, we elucidate the mechanism of action of a PROTAC-like degrader of the transcriptional coactivator BRD4, composed of a BRD4 ligand linked to a ligand for the E3 ligase CRL4DCAF15. Using orthogonal CRISPR/Cas9 screens we identify the degrader activity is independent of DCAF15, and relies on a different CRL4 substrate receptor, DCAF16. We demonstrate an intrinsic affinity between BRD4 and DCAF16, which is dependent on the tandem bromodomains of BRD4 and further increased by the degrader without physically engaging DCAF16 in isolation. Structural characterization of the resulting ternary complex reveals both BRD4 bromodomains are bivalently engaged in cis by the degrader and are bound to DCAF16 through several interfacial BRD4-DCAF16 and degrader-DCAF16 contacts. Our findings demonstrate that intramolecularly bridging domains can confer glue-type stabilization of intrinsic target-E3 interactions, and we propose this as a general strategy to modulate the surface topology of target proteins to nucleate co-opting of E3 ligases or other cellular effector proteins for effective proximity-based pharmacology.