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Suárez-Martín I, Risso VA, Romero-Zaliz R, Sanchez-Ruiz JM. Efficient Searches in Protein Sequence Space Through AI-Driven Iterative Learning. Int J Mol Sci 2025; 26:4741. [PMID: 40429882 DOI: 10.3390/ijms26104741] [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: 04/17/2025] [Revised: 05/08/2025] [Accepted: 05/13/2025] [Indexed: 05/29/2025] Open
Abstract
The protein sequence space is vast. This fact, together with the prevalence of epistasis, hampers the engineering of novel enzymes through library screening and is a major obstacle to any attempt to predict natural protein evolution. Recently, specialized methodologies have been used to determine fitness data on ~260,000 sequences for the gene of the enzyme dihydrofolate reductase and antibody affinity data for all combinations of the mutations present in the receptor-binding domain (RBD) of the Omicron strain of SARS-CoV-2 (~30,000 variants). We show that upon iterative training on a total of just a few hundred variants, various state-of-the-art AI tools (multi-layer perceptron, random forest, and XGBoost algorithms) find very high fitness variants of the enzyme and predict the antibody evasion patterns of the RBD. This work provides a basis for efficient, widely applicable, low-throughput experimental approaches to assess viral protein evolution and to engineer enzymes for biotechnological applications.
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Affiliation(s)
- Ignacio Suárez-Martín
- Unidad de Excelencia de Química Aplicada a Biomedicina y Medioambiente (UEQ), Departamento de Química Física, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
- Centro de Investigación en Tecnologías de la Información y las Telecomunicaciones (CITIC-UGR), Universidad de Granada, 18071 Granada, Spain
| | - Valeria A Risso
- Unidad de Excelencia de Química Aplicada a Biomedicina y Medioambiente (UEQ), Departamento de Química Física, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
| | - Rocío Romero-Zaliz
- Centro de Investigación en Tecnologías de la Información y las Telecomunicaciones (CITIC-UGR), Universidad de Granada, 18071 Granada, Spain
- Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingenierías Informática y de la Telecomunicación, Universidad de Granada, 18071 Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), Universidad de Granada, 18071 Granada, Spain
| | - Jose M Sanchez-Ruiz
- Unidad de Excelencia de Química Aplicada a Biomedicina y Medioambiente (UEQ), Departamento de Química Física, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
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2
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Park Y, Metzger BPH, Thornton JW. The simplicity of protein sequence-function relationships. Nat Commun 2024; 15:7953. [PMID: 39261454 PMCID: PMC11390738 DOI: 10.1038/s41467-024-51895-5] [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: 02/07/2024] [Accepted: 08/20/2024] [Indexed: 09/13/2024] Open
Abstract
How complex are the rules by which a protein's sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence-which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis-or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird's-eye view of sequence space. This technique yields the simplest explanation of sequence-function relationships and is more robust than existing methods to measurement noise, missing data, and model misspecification. We reanalyze 20 experimental datasets and find that context-independent amino acid effects and pairwise interactions, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of phenotypic variance and over 92% in every case. Only a tiny fraction of genotypes are strongly affected by higher-order epistasis. Sequence-function relationships are also sparse: a miniscule fraction of amino acids and interactions account for 90% of phenotypic variance. Sequence-function causality across these datasets is therefore simple, opening the way for tractable approaches to characterize proteins' genetic architecture.
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Affiliation(s)
- Yeonwoo Park
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
- Center for RNA Research, Institute for Basic Science, Seoul, Republic of Korea
| | - Brian P H Metzger
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Joseph W Thornton
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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3
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Guerrero RF, Dorji T, Harris RM, Shoulders MD, Ogbunugafor CB. Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications. eLife 2024; 12:RP88480. [PMID: 38833384 PMCID: PMC11149929 DOI: 10.7554/elife.88480] [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] [Indexed: 06/06/2024] Open
Abstract
The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).
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Affiliation(s)
- Rafael F Guerrero
- Department of Biological Sciences, North Carolina State UniversityRaleighUnited States
| | - Tandin Dorji
- Department of Mathematics and Statistics, University of VermontBurlingtonUnited States
| | - Ra'Mal M Harris
- Department of Chemistry, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Matthew D Shoulders
- Department of Chemistry, Massachusetts Institute of TechnologyCambridgeUnited States
| | - C Brandon Ogbunugafor
- Department of Chemistry, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Ecology and Evolutionary Biology, Yale UniversityNew HavenUnited States
- Santa Fe InstituteSanta FeUnited States
- Public Health Modeling Unit, Yale School of Public HealthNew HavenUnited States
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4
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Park Y, Metzger BP, Thornton JW. The simplicity of protein sequence-function relationships. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.02.556057. [PMID: 37732229 PMCID: PMC10508729 DOI: 10.1101/2023.09.02.556057] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
How complicated is the genetic architecture of proteins - the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a protein's function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence - causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions - or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a protein's entire genotype-phenotype map. This method yields a maximally efficient explanation of a protein's genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.
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Affiliation(s)
- Yeonwoo Park
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637
- Current affiliation: Center for RNA Research, Institute for Basic Science, Seoul, Republic of Korea 08826
| | - Brian P.H. Metzger
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
- Current affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN 47907
| | - Joseph W. Thornton
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
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5
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Ogbunugafor CB, Guerrero RF, Miller-Dickson MD, Shakhnovich EI, Shoulders MD. Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance. Phys Rev E 2023; 108:054408. [PMID: 38115433 PMCID: PMC10935598 DOI: 10.1103/physreve.108.054408] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/19/2023] [Indexed: 12/21/2023]
Abstract
Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.
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Affiliation(s)
- C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Rafael F. Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Eugene I. Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Matthew D. Shoulders
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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6
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Guerrero RF, Dorji T, Harris RM, Shoulders MD, Ogbunugafor CB. Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.08.536116. [PMID: 37066376 PMCID: PMC10104179 DOI: 10.1101/2023.04.08.536116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).
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Affiliation(s)
| | - Tandin Dorji
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT
| | - Ra’Mal M. Harris
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
| | | | - C. Brandon Ogbunugafor
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
- DDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT
- Santa Fe Institute, Santa Fe, NM
- Public Health Modeling Unit, Yale School of Public Health, New Haven, CT
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7
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Feng L, Yang W, Ding M, Hou L, Gragnoli C, Griffin C, Wu R. A personalized pharmaco-epistatic network model of precision medicine. Drug Discov Today 2023; 28:103608. [PMID: 37149282 DOI: 10.1016/j.drudis.2023.103608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/12/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.
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Affiliation(s)
- Li Feng
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wuyue Yang
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Mengdong Ding
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Luke Hou
- Ward Melville High School, East Setauket, NY 11733, USA
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Division of Endocrinology, Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Christipher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
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8
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Ogbunugafor CB, Guerrero RF, Shakhnovich EI, Shoulders MD. Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.09.535490. [PMID: 37066177 PMCID: PMC10104174 DOI: 10.1101/2023.04.09.535490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.
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Affiliation(s)
- C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
- Santa Fe Institute, Santa Fe, NM
| | - Rafael F. Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh, NC
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9
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Sohail MS, Louie RHY, Hong Z, Barton JP, McKay MR. Inferring Epistasis from Genetic Time-series Data. Mol Biol Evol 2022; 39:6710201. [PMID: 36130322 PMCID: PMC9558069 DOI: 10.1093/molbev/msac199] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.
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Affiliation(s)
- Muhammad Saqib Sohail
- Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, People’s Republic of China
| | - Raymond H Y Louie
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Zhenchen Hong
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
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10
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Ogbunugafor CB. The mutation effect reaction norm (mu-rn) highlights environmentally dependent mutation effects and epistatic interactions. Evolution 2022; 76:37-48. [PMID: 34989399 DOI: 10.1111/evo.14428] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/23/2021] [Indexed: 11/27/2022]
Abstract
Since the modern synthesis, the fitness effects of mutations and epistasis have been central yet provocative concepts in evolutionary and population genetics. Studies of how the interactions between parcels of genetic information can change as a function of environmental context have added a layer of complexity to these discussions. Here I introduce the "mutation effect reaction norm" (Mu-RN), a new instrument through which one can analyze the phenotypic consequences of mutations and interactions across environmental contexts. It embodies the fusion of measurements of genetic interactions with the reaction norm, a classic depiction of the performance of genotypes across environments. I demonstrate the utility of the Mu-RN through the signature of a "compensatory ratchet" mutation that undermines reverse evolution of antimicrobial resistance. More broadly, I argue that the mutation effect reaction norm may help us resolve the dynamism and unpredictability of evolution, with implications for theoretical biology, genetic modification technology, and public health. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- C Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA
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11
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Park S, Supek F, Lehner B. Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers. Nat Commun 2021; 12:7051. [PMID: 34862370 PMCID: PMC8642467 DOI: 10.1038/s41467-021-27242-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/09/2021] [Indexed: 11/10/2022] Open
Abstract
The classic two-hit model posits that both alleles of a tumor suppressor gene (TSG) must be inactivated to cause cancer. In contrast, for some oncogenes and haploinsufficient TSGs, a single genetic alteration can suffice to increase tumor fitness. Here, by quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers. Third order genetic interactions identify the causes of some of these switches in dominance and dosage sensitivity as mutations in other genes in the same biological pathway. The correct genetic model for a gene thus depends on the other mutations in a genome, with a second hit in the same gene or an alteration in a different gene in the same pathway sometimes representing alternative evolutionary paths to cancer.
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Affiliation(s)
- Solip Park
- Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain.
| | - Fran Supek
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
| | - Ben Lehner
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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