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Hill, A.

Publications and source records attributed to Hill, A..

2 recordsLinked to original sources

PIP4Ks Suppress Insulin Signaling Through a Catalytic-Independent Mechanism

Insulin stimulates conversion of phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2) to phosphatidylinositol-3,4,5-trisphosphate (PI(3,4,5)P3), which mediates downstream cellular responses. PI(4,5)P2 is produced by phosphatidylinositol-4-phosphate 5-kinases (PIP5Ks) and byphosphatidylinositol-5-phosphate 4-kinases (PIP4Ks). Here we show that deletion of the three genes that encode PIP4Ks (PIP4K2A, PIP4K2B and PIP4K2C) in vitro results in a paradoxical increase in PI(4,5)P2 and a subsequent increase in insulin-stimulated production of PI(3,4,5)P3. Surprisingly, reintroduction of either wild-type or kinase-dead forms of the PIP4Ks restored cellular PI(4,5)P2 levels and insulin stimulation of the PI3K pathway. These effects are explained by an increase in PIP5K activity upon deletion of PIP4Ks, which we demonstrate can suppress PIP5K activity in vitro through a direct binding interaction. Collectively, our work reveals an important non-catalytic function of PIP4Ks in suppressing PIP5K-mediated PI(4,5)P2 synthesis and insulin-dependent conversion to PI(3,4,5)P3 by PI3K enzymes and suggests that pharmacological depletion of PIP4K enzymes using emerging degrader technologies could represent a novel strategy for stimulating insulin signaling.

cell biology

crisprQTL mapping as a genome-wide association framework for cellular genetic screens

Expression quantitative trait locus (eQTL) and genome-wide association studies (GWAS) are powerful paradigms for mapping the determinants of gene expression and organismal phenotypes, respectively. However, eQTL mapping and GWAS are limited in scope (to naturally occurring, common genetic variants) and resolution (by linkage disequilibrium). Here, we present crisprQTL mapping, a framework in which large numbers of CRISPR/Cas9 perturbations are introduced to each cell on an isogenic background, followed by single-cell RNA-seq (scRNA-seq). crisprQTL mapping is analogous to conventional human eQTL studies, but with individual humans replaced by individual cells; genetic variants replaced by unique combinations of unlinked guide RNA (gRNA)-programmed perturbations per cell; and tissue-level RNA-seq of many individuals replaced by scRNA-seq of many cells. By randomly introducing gRNAs, a single population of cells can be leveraged to test for association between each perturbation and the expression of any potential target gene, analogous to how eQTL studies leverage populations of humans to test millions of genetic variants for associations with expression in a genome-wide manner. However, crisprQTL mapping is neither limited to naturally occurring, common genetic variants nor by linkage disequilibrium. As a proof-of-concept, we applied crisprQTL mapping to evaluate 1,119 candidate enhancers with no strong a priori hypothesis as to their target gene(s). Perturbations were made by a nuclease-dead Cas9 (dCas9) tethered to KRAB, and introduced at a mean allele frequency of 1.1% into a population of 47,650 profiled human K562 cells (median of 15 gRNAs identified per cell). We tested for differential expression of all genes within 1 megabase (Mb) of each candidate enhancer, effectively evaluating 17,584 potential enhancer-target gene relationships within a single experiment. At an empirical false discovery rate (FDR) of 10%, we identify 128 cis crisprQTLs (11%) whose targeting resulted in downregulation of 105 nearby genes. crisprQTLs were strongly enriched for proximity to their target genes (median 34.3 kilobases (Kb)) and the strength of H3K27ac, p300, and lineage-specific transcription factor (TF) ChIP-seq peaks. Our results establish the power of the eQTL mapping paradigm as applied to programmed variation in populations of cells, rather than natural variation in populations of individuals. We anticipate that crisprQTL mapping will facilitate the comprehensive elucidation of the cis-regulatory architecture of the human genome.

genomics