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Genome-scale analysis of interactions between genetic perturbations and natural variation.
Hale, Joseph J; Matsui, Takeshi; Goldstein, Ilan; Mullis, Martin N; Roy, Kevin R; Ville, Chris Ne; Miller, Darach; Wang, Charley; Reynolds, Trevor; Steinmetz, Lars M; Levy, Sasha F; Ehrenreich, Ian M.
Afiliación
  • Hale JJ; Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
  • Matsui T; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
  • Goldstein I; Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
  • Mullis MN; Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
  • Roy KR; Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA.
  • Ville CN; Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
  • Miller D; Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
  • Wang C; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
  • Reynolds T; Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
  • Steinmetz LM; Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
  • Levy SF; Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA.
  • Ehrenreich IM; Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
bioRxiv ; 2024 Jan 16.
Article en En | MEDLINE | ID: mdl-38293072
ABSTRACT
Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 7,700 CRISPRi perturbations targeting 1,712 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos