Canonical BAF complex regulates the oncogenic program in human T-cell acute lymphoblastic leukemia.

ABSTRACT: Acute leukemia cells require bone marrow microenvironments, known as niches, which provide leukemic cells with niche factors that are essential for leukemic cell survival and/or proliferation. However, it remains unclear how the dynamics of the leukemic cell-niche interaction are regulated. Using a genome-wide CRISPR screen, we discovered that canonical BRG1/BRM-associated factor (cBAF), a variant of the switch/sucrose nonfermenting chromatin remodeling complex, regulates the migratory response of human T-cell acute lymphoblastic leukemia (T-ALL) cells to a niche factor CXCL12. Mechanistically, cBAF maintains chromatin accessibility and allows RUNX1 to bind to CXCR4 enhancer regions. cBAF inhibition evicts RUNX1 from the genome, resulting in CXCR4 downregulation and impaired migration activity. In addition, cBAF maintains chromatin accessibility preferentially at RUNX1 binding sites, ensuring RUNX1 binding at these sites, and is required for expression of RUNX1-regulated genes, such as CDK6; therefore, cBAF inhibition negatively impacts cell proliferation and profoundly induces apoptosis. This anticancer effect was also confirmed using T-ALL xenograft models, suggesting cBAF as a promising therapeutic target. Thus, we provide novel evidence that cBAF regulates the RUNX1-driven leukemic program and governs migration activity toward CXCL12 and cell-autonomous growth in human T-ALL.

RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations.

Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.