A genome-wide CRISPR screen identifies CALCOCO2 as a regulator of beta cell function influencing type 2 diabetes risk.
Antje K GrotzYingying YeElena Navarro-GuerreroVarsha RajeshAlina PollnerRomina J BevacquaJing YangAliya F SpigelmanRoberta BaronioAustin BautistaSoren K ThomsenJames LyonSameena NawazNancy SmithAgata Wesolowska-AndersenJocelyn E Manning FoxHanice SunSeung K KimDaniel EbnerPatrick E MacDonaldAnna L GloynPublished in: Nature genetics (2022)
Identification of the genes and processes mediating genetic association signals for complex diseases represents a major challenge. As many of the genetic signals for type 2 diabetes (T2D) exert their effects through pancreatic islet-cell dysfunction, we performed a genome-wide pooled CRISPR loss-of-function screen in a human pancreatic beta cell line. We assessed the regulation of insulin content as a disease-relevant readout of beta cell function and identified 580 genes influencing this phenotype. Integration with genetic and genomic data provided experimental support for 20 candidate T2D effector transcripts including the autophagy receptor CALCOCO2. Loss of CALCOCO2 was associated with distorted mitochondria, less proinsulin-containing immature granules and accumulation of autophagosomes upon inhibition of late-stage autophagy. Carriers of T2D-associated variants at the CALCOCO2 locus further displayed altered insulin secretion. Our study highlights how cellular screens can augment existing multi-omic efforts to support mechanistic understanding and provide evidence for causal effects at genome-wide association studies loci.
Keyphrases
- genome wide
- type diabetes
- copy number
- dna methylation
- cell death
- glycemic control
- genome wide association
- oxidative stress
- high throughput
- endothelial cells
- endoplasmic reticulum stress
- cardiovascular disease
- single cell
- metabolic syndrome
- gene expression
- randomized controlled trial
- cell therapy
- quality improvement
- adipose tissue
- electronic health record
- induced pluripotent stem cells
- stem cells
- machine learning
- reactive oxygen species
- big data