bioRxivGenomicsPreprint
Natural variation in gene expression and Zika virus susceptibility revealed by villages of neural progenitor cells
Michael F. WellsiD, James C. Nemesh, Sulagna Dia GhoshiD, Jana M. MitchelliD, Curtis J. MelloiD, Daniel MeyeriD, Kavya Raghunathan, Matthew TegtmeyeriD, Derek Hawes, Anna NeumanniD, Kathleen A. WorringeriD, Joseph J. RaymondiD, Sravya Kommineni, Karrie Chan, Daniel J. Ho, Brant K. PetersoniD, Federica PiccioniiD, Ralda NehmeiD, Kevin C. EgganiD, Steven A. McCarrolliD
Abstract
Variation in the human genome contributes to abundant diversity in human traits and vulnerabilities, but the underlying molecular and cellular mechanisms are not yet known, and will need scalable approaches to accelerate their recognition. Here, we advanced and applied an experimental platform that analyzes genetic, molecular, and phenotypic heterogeneity across cells from very many human donors cultured in a single, shared in vitro environment, with algorithms (Dropulation and Census-seq) for assigning phenotypes to individual donors. We used natural genetic variation and synthetic (CRISPR-Cas9) genetic perturbations to analyze the vulnerability of neural progenitor cells to infection with Zika virus. These analyses identified a common variant in the antiviral IFITM3 gene that regulated IFITM3 expression and explained most inter-individual variation in NPCs susceptibility to Zika virus infectivity. These and other approaches could provide scalable ways to recognize the impact of genes and genetic variation on cellular phenotypes. HIGHLIGHTSO_LIMeasuring cellular phenotypes in iPSCs and hPSC-derived NPCs from many donors C_LIO_LIEffects of donor sex, cell source, genetic and other variables on hPSC RNA expression C_LIO_LINatural genetic variation and synthetic perturbation screens both identify IFITM3 in NPC susceptibility to Zika virus C_LIO_LIA common genetic variant in IFITM3 explains most inter-individual variation in NPC susceptibility to Zika virus C_LI
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: benchmarking
- approach: statistical model
- approach: experimental assay
- Citations
- 6
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