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Hale JJ, Matsui T, Goldstein I, Mullis MN, Roy KR, Ville CN, Miller D, Wang C, Reynolds T, Steinmetz LM, Levy SF, Ehrenreich IM. Genome-scale analysis of interactions between genetic perturbations and natural variation. Nat Commun 2024; 15:4234. [PMID: 38762544 PMCID: PMC11102447 DOI: 10.1038/s41467-024-48626-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
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 8046 CRISPRi perturbations targeting 1721 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.
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Affiliation(s)
- Joseph J Hale
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Takeshi Matsui
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Ilan Goldstein
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Martin N Mullis
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Kevin R Roy
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher Ne Ville
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Charley Wang
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Trevor Reynolds
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Lars M Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
- BacStitch DNA, Los Altos, CA, USA.
| | - Ian M Ehrenreich
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA.
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Hale JJ, Matsui T, Goldstein I, Mullis MN, Roy KR, Ville CN, Miller D, Wang C, Reynolds T, Steinmetz LM, Levy SF, Ehrenreich IM. Genome-scale analysis of interactions between genetic perturbations and natural variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.06.539663. [PMID: 38293072 PMCID: PMC10827069 DOI: 10.1101/2023.05.06.539663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
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.
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Affiliation(s)
- Joseph J. Hale
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Takeshi Matsui
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Ilan Goldstein
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin N. Mullis
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Kevin R. Roy
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Chris Ne Ville
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Charley Wang
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Trevor Reynolds
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Lars M. Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sasha F. Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
- Present address: BacStitch DNA, Los Altos, California, USA
| | - Ian M. Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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Matkarimov BT, Saparbaev MK. Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible. PeerJ 2023; 11:e16671. [PMID: 38107580 PMCID: PMC10725672 DOI: 10.7717/peerj.16671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023] Open
Abstract
Background Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits. Methods Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes. Results The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions. Conclusions According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.
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Affiliation(s)
- Bakhyt T. Matkarimov
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- L.N.Gumilev Eurasian National University, Astana, Kazakhstan
| | - Murat K. Saparbaev
- Groupe «Mechanisms of DNA Repair and Carcinogenesis», CNRS UMR9019, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Al-Farabi Kazakh National University, Almaty, Kazakhstan
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Ang RML, Chen SAA, Kern AF, Xie Y, Fraser HB. Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing. CELL GENOMICS 2023; 3:100260. [PMID: 37082144 PMCID: PMC10112194 DOI: 10.1016/j.xgen.2023.100260] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/27/2022] [Accepted: 01/06/2023] [Indexed: 04/22/2023]
Abstract
The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.
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Affiliation(s)
- Roy Moh Lik Ang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Yihua Xie
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Corresponding author
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Everman ER, Macdonald SJ, Kelly JK. The genetic basis of adaptation to copper pollution in Drosophila melanogaster. Front Genet 2023; 14:1144221. [PMID: 37082199 PMCID: PMC10110907 DOI: 10.3389/fgene.2023.1144221] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/21/2023] [Indexed: 04/22/2023] Open
Abstract
Introduction: Heavy metal pollutants can have long lasting negative impacts on ecosystem health and can shape the evolution of species. The persistent and ubiquitous nature of heavy metal pollution provides an opportunity to characterize the genetic mechanisms that contribute to metal resistance in natural populations. Methods: We examined variation in resistance to copper, a common heavy metal contaminant, using wild collections of the model organism Drosophila melanogaster. Flies were collected from multiple sites that varied in copper contamination risk. We characterized phenotypic variation in copper resistance within and among populations using bulked segregant analysis to identify regions of the genome that contribute to copper resistance. Results and Discussion: Copper resistance varied among wild populations with a clear correspondence between resistance level and historical exposure to copper. We identified 288 SNPs distributed across the genome associated with copper resistance. Many SNPs had population-specific effects, but some had consistent effects on copper resistance in all populations. Significant SNPs map to several novel candidate genes involved in refolding disrupted proteins, energy production, and mitochondrial function. We also identified one SNP with consistent effects on copper resistance in all populations near CG11825, a gene involved in copper homeostasis and copper resistance. We compared the genetic signatures of copper resistance in the wild-derived populations to genetic control of copper resistance in the Drosophila Synthetic Population Resource (DSPR) and the Drosophila Genetic Reference Panel (DGRP), two copper-naïve laboratory populations. In addition to CG11825, which was identified as a candidate gene in the wild-derived populations and previously in the DSPR, there was modest overlap of copper-associated SNPs between the wild-derived populations and laboratory populations. Thirty-one SNPs associated with copper resistance in wild-derived populations fell within regions of the genome that were associated with copper resistance in the DSPR in a prior study. Collectively, our results demonstrate that the genetic control of copper resistance is highly polygenic, and that several loci can be clearly linked to genes involved in heavy metal toxicity response. The mixture of parallel and population-specific SNPs points to a complex interplay between genetic background and the selection regime that modifies the effects of genetic variation on copper resistance.
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Affiliation(s)
| | - Stuart J. Macdonald
- Molecular Biosciences, University of Kansas, Lawrence, KS, United States
- Center for Computational Biology, University of Kansas, Lawrence, KS, United States
| | - John K. Kelly
- Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States
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Schell R, Hale JJ, Mullis MN, Matsui T, Foree R, Ehrenreich IM. Genetic basis of a spontaneous mutation’s expressivity. Genetics 2022; 220:6515283. [PMID: 35078232 PMCID: PMC8893249 DOI: 10.1093/genetics/iyac013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/19/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Genetic background often influences the phenotypic consequences of mutations, resulting in variable expressivity. How standing genetic variants collectively cause this phenomenon is not fully understood. Here, we comprehensively identify loci in a budding yeast cross that impact the growth of individuals carrying a spontaneous missense mutation in the nuclear-encoded mitochondrial ribosomal gene MRP20. Initial results suggested that a single large effect locus influences the mutation’s expressivity, with one allele causing inviability in mutants. However, further experiments revealed this simplicity was an illusion. In fact, many additional loci shape the mutation’s expressivity, collectively leading to a wide spectrum of mutational responses. These results exemplify how complex combinations of alleles can produce a diversity of qualitative and quantitative responses to the same mutation.
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Affiliation(s)
- Rachel Schell
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Joseph J Hale
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin N Mullis
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Takeshi Matsui
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Ryan Foree
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Ian M Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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Goldstein I, Ehrenreich IM. The complex role of genetic background in shaping the effects of spontaneous and induced mutations. Yeast 2020; 38:187-196. [PMID: 33125810 PMCID: PMC7984271 DOI: 10.1002/yea.3530] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/09/2020] [Accepted: 10/24/2020] [Indexed: 12/27/2022] Open
Abstract
Spontaneous and induced mutations frequently show different phenotypic effects across genetically distinct individuals. It is generally appreciated that these background effects mainly result from genetic interactions between the mutations and segregating loci. However, the architectures and molecular bases of these genetic interactions are not well understood. Recent work in a number of model organisms has tried to advance knowledge of background effects both by using large‐scale screens to find mutations that exhibit this phenomenon and by identifying the specific loci that are involved. Here, we review this body of research, emphasizing in particular the insights it provides into both the prevalence of background effects across different mutations and the mechanisms that cause these background effects. A large fraction of mutations show different effects in distinct individuals. These background effects are mainly caused by epistasis with segregating loci. Mapping studies show a diversity of genetic architectures can be involved. Genetically complex changes in gene expression are often, but not always, causative.
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Affiliation(s)
- Ilan Goldstein
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, California, 90089-2910, USA
| | - Ian M Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, California, 90089-2910, USA
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