1
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van Loggerenberg W, Sowlati-Hashjin S, Weile J, Hamilton R, Chawla A, Sheykhkarimli D, Gebbia M, Kishore N, Frésard L, Mustajoki S, Pischik E, Di Pierro E, Barbaro M, Floderus Y, Schmitt C, Gouya L, Colavin A, Nussbaum R, Friesema ECH, Kauppinen R, To-Figueras J, Aarsand AK, Desnick RJ, Garton M, Roth FP. Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation. Am J Hum Genet 2023; 110:1769-1786. [PMID: 37729906 PMCID: PMC10577081 DOI: 10.1016/j.ajhg.2023.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/15/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023] Open
Abstract
Defects in hydroxymethylbilane synthase (HMBS) can cause acute intermittent porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ∼⅓ of clinical HMBS variants are missense variants, and most clinically reported HMBS missense variants are designated as "variants of uncertain significance" (VUSs). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino acid substitutions. The resulting variant effect maps generally agreed with biochemical expectations and provide further evidence that HMBS can function as a monomer. Additionally, the maps implicated specific residues as having roles in active site dynamics, which was further supported by molecular dynamics simulations. Most importantly, these maps can help discriminate pathogenic from benign HMBS variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.
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Affiliation(s)
- Warren van Loggerenberg
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | | | - Jochen Weile
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Rayna Hamilton
- Advanced Academic Programs, Johns Hopkins University, Washington, DC 20036, USA
| | - Aditya Chawla
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
| | - Dayag Sheykhkarimli
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
| | - Marinella Gebbia
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
| | - Nishka Kishore
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
| | | | - Sami Mustajoki
- Research Program in Molecular Medicine, Biomedicum-Helsinki, University of Helsinki, 00290 Helsinki, Finland
| | - Elena Pischik
- Research Program in Molecular Medicine, Biomedicum-Helsinki, University of Helsinki, 00290 Helsinki, Finland
| | - Elena Di Pierro
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Unit of Medicine and Metabolic Diseases, 20122 Milano, Italy
| | - Michela Barbaro
- Porphyria Centre Sweden, Centre for Inherited Metabolic Diseases, Karolinska Institutet, Karolinska University Hospital, 17176 Stockholm, Sweden
| | - Ylva Floderus
- Porphyria Centre Sweden, Centre for Inherited Metabolic Diseases, Karolinska Institutet, Karolinska University Hospital, 17176 Stockholm, Sweden
| | - Caroline Schmitt
- Centre français des porphyries, hôpital Louis-Mourier, Assistance Publique-Hopitaux de Paris, 92701 Colombes, France; Centre de recherche sur l'inflammation, Université Paris Cité, UMR1149 INSERM, 75018 Paris, France
| | - Laurent Gouya
- Centre français des porphyries, hôpital Louis-Mourier, Assistance Publique-Hopitaux de Paris, 92701 Colombes, France; Centre de recherche sur l'inflammation, Université Paris Cité, UMR1149 INSERM, 75018 Paris, France
| | | | | | - Edith C H Friesema
- Porphyria Expertcenter Rotterdam, Center for Lysosomal and Metabolic Diseases, Department of Internal Medicine, Erasmus MC, 3015 Rotterdam, the Netherlands
| | - Raili Kauppinen
- Research Program in Molecular Medicine, Biomedicum-Helsinki, University of Helsinki, 00290 Helsinki, Finland
| | - Jordi To-Figueras
- Biochemistry and Molecular Genetics Department, Hospital Clínic, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Aasne K Aarsand
- Norwegian Porphyria Centre, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Robert J Desnick
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Garton
- Institute Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada.
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2
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van Loggerenberg W, Sowlati-Hashjin S, Weile J, Hamilton R, Chawla A, Gebbia M, Kishore N, Frésard L, Mustajoki S, Pischik E, Di Pierro E, Barbaro M, Floderus Y, Schmitt C, Gouya L, Colavin A, Nussbaum R, Friesema ECH, Kauppinen R, To-Figueras J, Aarsand AK, Desnick RJ, Garton M, Roth FP. Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.06.527353. [PMID: 36798224 PMCID: PMC9934555 DOI: 10.1101/2023.02.06.527353] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Defects in hydroxymethylbilane synthase (HMBS) can cause Acute Intermittent Porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ~⅓ of clinical HMBS variants are missense variants, and most clinically-reported HMBS missense variants are designated as "variants of uncertain significance" (VUS). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino-acid substitutions. The resulting variant effect maps generally agreed with biochemical expectation. However, the maps showed variants at the dimerization interface to be unexpectedly well tolerated, and suggested residue roles in active site dynamics that were supported by molecular dynamics simulations. Most importantly, these HMBS variant effect maps can help discriminate pathogenic from benign variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.
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Affiliation(s)
- Warren van Loggerenberg
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Shahin Sowlati-Hashjin
- Institute of Biomedical Engineering, University of Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, ON, Canada
| | - Jochen Weile
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Rayna Hamilton
- Advanced Academic Programs, Johns Hopkins University, Washington, DC, USA
| | - Aditya Chawla
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Marinella Gebbia
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Nishka Kishore
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | | | - Sami Mustajoki
- Research Program in Molecular Medicine, Biomedicum-Helsinki, University of Helsinki
| | - Elena Pischik
- Research Program in Molecular Medicine, Biomedicum-Helsinki, University of Helsinki
| | - Elena Di Pierro
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Unit of Medicine and Metabolic Diseases, Milan, Italy
| | - Michela Barbaro
- Porphyria Centre Sweden, Centre for Inherited Metabolic Diseases, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Ylva Floderus
- Porphyria Centre Sweden, Centre for Inherited Metabolic Diseases, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Caroline Schmitt
- Centre Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, Colombes and Centre de Recherche sur l’Inflammation, UMR1149 INSERM, Université Paris Cité, Paris, France
| | - Laurent Gouya
- Centre Français des Porphyries, Hôpital Louis Mourier, Assistance Publique-Hôpitaux de Paris, Colombes and Centre de Recherche sur l’Inflammation, UMR1149 INSERM, Université Paris Cité, Paris, France
| | | | | | - Edith C. H. Friesema
- Porphyria Expertcenter Rotterdam, Center for Lysosomal and Metabolic Diseases, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Raili Kauppinen
- Research Program in Molecular Medicine, Biomedicum-Helsinki, University of Helsinki
| | - Jordi To-Figueras
- Biochemistry and Molecular Genetics Department, Hospital Clínic, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Aasne K Aarsand
- Norwegian Porphyria Centre, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
| | - Robert J. Desnick
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Garton
- Institute of Biomedical Engineering, University of Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, ON, Canada
| | - Frederick P. Roth
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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3
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Phylogenetic inference of changes in amino acid propensities with single-position resolution. PLoS Comput Biol 2022; 18:e1009878. [PMID: 35180226 PMCID: PMC9106220 DOI: 10.1371/journal.pcbi.1009878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 05/13/2022] [Accepted: 01/28/2022] [Indexed: 11/19/2022] Open
Abstract
Fitness conferred by the same allele may differ between genotypes and environments, and these differences shape variation and evolution. Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions.
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4
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Mattenberger F, Latorre V, Tirosh O, Stern A, Geller R. Globally defining the effects of mutations in a picornavirus capsid. eLife 2021; 10:64256. [PMID: 33432927 PMCID: PMC7861617 DOI: 10.7554/elife.64256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/11/2021] [Indexed: 02/07/2023] Open
Abstract
The capsids of non-enveloped viruses are highly multimeric and multifunctional protein assemblies that play key roles in viral biology and pathogenesis. Despite their importance, a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes of the capsid is lacking. To address this limitation, we globally define the effects of mutations across the capsid of a human picornavirus. Using this resource, we identify structural and sequence determinants that accurately predict mutational fitness effects, refine evolutionary analyses, and define the sequence specificity of key capsid-encoded motifs. Furthermore, capitalizing on the derived sequence requirements for capsid-encoded protease cleavage sites, we implement a bioinformatic approach for identifying novel host proteins targeted by viral proteases. Our findings represent the most comprehensive investigation of mutational fitness effects in a picornavirus capsid to date and illuminate important aspects of viral biology, evolution, and host interactions. A virus is made up of genetic material that is encased with a protective protein coat called the capsid. The capsid also helps the virus to infect host cells by binding to the host receptor proteins and releasing its genetic material. Inside the cell, the virus hitchhikes the infected cell’s machinery to grow or replicate its own genetic material. Viral capsids are the main target of the host’s defence system, and therefore, continuously change in an attempt to escape the immune system by introducing alterations (known as mutations) into the genes encoding viral capsid proteins. Mutations occur randomly, and so while some changes to the viral capsid might confer an advantage, others may have no effect at all, or even weaken the virus. To better understand the effect of capsid mutations on the virus’ ability to infect host cells, Mattenberger et al. studied the Coxsackievirus B3, which is linked to heart problems and acute heart failure in humans. The researchers analysed around 90% of possible amino acid mutations (over 14,800 mutations) and correlated each mutation to how it influenced the virus’ ability to replicate in human cells grown in the laboratory. Based on these results, Mattenberger et al. developed a computer model to predict how a particular mutation might affect the virus. The analysis also identified specific amino acid sequences of capsid proteins that are essential for certain tasks, such as building the capsid. It also included an analysis of sequences in the capsid that allow it to be recognized by another viral protein, which cuts the capsid proteins into the right size from a larger precursor. By looking for similar sequences in human genes, the researchers identified several ones that the virus may attack and inactivate to support its own replication. These findings may help identify potential drug targets to develop new antiviral therapies. For example, proteins of the capsid that are less likely to mutate will provide a better target as they lower the possibility of the virus to become resistant to the treatment. They also highlight new proteins in human cells that could potentially block the virus in cells.
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Affiliation(s)
- Florian Mattenberger
- Institute for Integrative Systems Biology, I2SysBio (Universitat de València-CSIC), Paterna, Spain
| | - Victor Latorre
- Institute for Integrative Systems Biology, I2SysBio (Universitat de València-CSIC), Paterna, Spain
| | - Omer Tirosh
- The Shmunis School of Biomedicine and Cancer Research, Tel-Aviv University, Tel-Aviv, Israel
| | - Adi Stern
- The Shmunis School of Biomedicine and Cancer Research, Tel-Aviv University, Tel-Aviv, Israel
| | - Ron Geller
- Institute for Integrative Systems Biology, I2SysBio (Universitat de València-CSIC), Paterna, Spain
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5
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Hom N, Gentles L, Bloom JD, Lee KK. Deep Mutational Scan of the Highly Conserved Influenza A Virus M1 Matrix Protein Reveals Substantial Intrinsic Mutational Tolerance. J Virol 2019; 93:e00161-19. [PMID: 31019050 PMCID: PMC6580950 DOI: 10.1128/jvi.00161-19] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/09/2019] [Indexed: 12/30/2022] Open
Abstract
Influenza A virus matrix protein M1 is involved in multiple stages of the viral infectious cycle. Despite its functional importance, our present understanding of this essential viral protein is limited. The roles of a small subset of specific amino acids have been reported, but a more comprehensive understanding of the relationship between M1 sequence, structure, and virus fitness remains elusive. In this study, we used deep mutational scanning to measure the effect of every amino acid substitution in M1 on viral replication in cell culture. The map of amino acid mutational tolerance we have generated allows us to identify sites that are functionally constrained in cell culture as well as sites that are less constrained. Several sites that exhibit low tolerance to mutation have been found to be critical for M1 function and production of viable virions. Surprisingly, significant portions of the M1 sequence, especially in the C-terminal domain, whose structure is undetermined, were found to be highly tolerant of amino acid variation, despite having extremely low levels of sequence diversity among natural influenza virus strains. This unexpected discrepancy indicates that not all sites in M1 that exhibit high sequence conservation in nature are under strong constraint during selection for viral replication in cell culture.IMPORTANCE The M1 matrix protein is critical for many stages of the influenza virus infection cycle. Currently, we have an incomplete understanding of this highly conserved protein's function and structure. Key regions of M1, particularly in the C terminus of the protein, remain poorly characterized. In this study, we used deep mutational scanning to determine the extent of M1's tolerance to mutation. Surprisingly, nearly two-thirds of the M1 sequence exhibits a high tolerance for substitutions, contrary to the extremely low sequence diversity observed across naturally occurring M1 isolates. Sites with low mutational tolerance were also identified, suggesting that they likely play critical functional roles and are under selective pressure. These results reveal the intrinsic mutational tolerance throughout M1 and shape future inquiries probing the functions of this essential influenza A virus protein.
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Affiliation(s)
- Nancy Hom
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington, USA
| | - Lauren Gentles
- Department of Microbiology, University of Washington, Seattle, Washington, USA
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jesse D Bloom
- Department of Microbiology, University of Washington, Seattle, Washington, USA
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - Kelly K Lee
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington, USA
- Department of Microbiology, University of Washington, Seattle, Washington, USA
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6
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Laurin-Lemay S, Rodrigue N, Lartillot N, Philippe H. Conditional Approximate Bayesian Computation: A New Approach for Across-Site Dependency in High-Dimensional Mutation-Selection Models. Mol Biol Evol 2019; 35:2819-2834. [PMID: 30203003 DOI: 10.1093/molbev/msy173] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A key question in molecular evolutionary biology concerns the relative roles of mutation and selection in shaping genomic data. Moreover, features of mutation and selection are heterogeneous along the genome and over time. Mechanistic codon substitution models based on the mutation-selection framework are promising approaches to separating these effects. In practice, however, several complications arise, since accounting for such heterogeneities often implies handling models of high dimensionality (e.g., amino acid preferences), or leads to across-site dependence (e.g., CpG hypermutability), making the likelihood function intractable. Approximate Bayesian Computation (ABC) could address this latter issue. Here, we propose a new approach, named Conditional ABC (CABC), which combines the sampling efficiency of MCMC and the flexibility of ABC. To illustrate the potential of the CABC approach, we apply it to the study of mammalian CpG hypermutability based on a new mutation-level parameter implying dependence across adjacent sites, combined with site-specific purifying selection on amino-acids captured by a Dirichlet process. Our proof-of-concept of the CABC methodology opens new modeling perspectives. Our application of the method reveals a high level of heterogeneity of CpG hypermutability across loci and mild heterogeneity across taxonomic groups; and finally, we show that CpG hypermutability is an important evolutionary factor in rendering relative synonymous codon usage. All source code is available as a GitHub repository (https://github.com/Simonll/LikelihoodFreePhylogenetics.git).
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Affiliation(s)
- Simon Laurin-Lemay
- Robert-Cedergren Center for Bioinformatics and Genomics, Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Nicolas Rodrigue
- Department of Biology, Institute of Biochemistry, and School of Mathematics and Statistics, Carleton University, Ottawa, ON, Canada
| | - Nicolas Lartillot
- Laboratoire de Biométrie et Biologie Évolutive, UMR CNRS 5558, Université Lyon 1, Lyon, France
| | - Hervé Philippe
- Robert-Cedergren Center for Bioinformatics and Genomics, Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.,Centre de Théorisation et de Modélisation de la Biodiversité, Station d'Écologie Théorique et Expérimentale, UMR CNRS 5321, Moulis, France
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7
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Soh YS, Moncla LH, Eguia R, Bedford T, Bloom JD. Comprehensive mapping of adaptation of the avian influenza polymerase protein PB2 to humans. eLife 2019; 8:45079. [PMID: 31038123 PMCID: PMC6491042 DOI: 10.7554/elife.45079] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 03/31/2019] [Indexed: 12/11/2022] Open
Abstract
Viruses like influenza are infamous for their ability to adapt to new hosts. Retrospective studies of natural zoonoses and passaging in the lab have identified a modest number of host-adaptive mutations. However, it is unclear if these mutations represent all ways that influenza can adapt to a new host. Here we take a prospective approach to this question by completely mapping amino-acid mutations to the avian influenza virus polymerase protein PB2 that enhance growth in human cells. We identify numerous previously uncharacterized human-adaptive mutations. These mutations cluster on PB2’s surface, highlighting potential interfaces with host factors. Some previously uncharacterized adaptive mutations occur in avian-to-human transmission of H7N9 influenza, showing their importance for natural virus evolution. But other adaptive mutations do not occur in nature because they are inaccessible via single-nucleotide mutations. Overall, our work shows how selection at key molecular surfaces combines with evolutionary accessibility to shape viral host adaptation. Viruses copy themselves by hijacking the cells of an infected host, but this comes with some limitations. Cells from different species have different molecular machinery and so viruses often have to specialize to a narrow group of species. This specialization consists largely of fine-tuning the way that viral proteins interact with host proteins. For instance, in bird flu viruses, a protein known as PB2 does not interact well with the machinery in human cells. Because PB2 proteins form part of the viral polymerase (the structure that copies the viral genome), this prevents bird flu viruses from replicating efficiently in humans. Sometimes however, changes in the PB2 protein allow bird flu viruses to better replicate in humans, potentially leading to deadly flu pandemics. To understand exactly how this happens, researchers have previously used two approaches: examining the changes that have happened in past flu viruses, and monitoring the evolution of bird flu viruses grown in human cells in the lab. However, these approaches can only look at a small number of the many possible genetic changes to the virus. This makes it hard to anticipate the new ways that flu might adapt to human cells in the future. To overcome this problem, Soh et al. systematically created all of the single changes to the bird flu PB2, altering every element of the protein sequence one-by-one. They then tested which of the changes to PB2 helped the virus grow better in human cells. The modifications that made the viruses thrive were on the surface of the protein, suggesting that they might improve interaction with the cell machinery of the host. Some changes have been found in bird flu viruses that have recently jumped into humans in nature, although fortunately none of these viruses have yet spread widely to cause a pandemic. Many factors affect the evolution of viruses, and their ability to infect new species. Understanding which changes in proteins help these microbes adapt to new hosts is an important element that scientists could consider to assess future risks of pandemics.
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Affiliation(s)
- Yq Shirleen Soh
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Louise H Moncla
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Rachel Eguia
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Trevor Bedford
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Jesse D Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Howard Hughes Medical Institute, Seattle, United States
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8
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Machkovech HM, Bloom JD, Subramaniam AR. Comprehensive profiling of translation initiation in influenza virus infected cells. PLoS Pathog 2019; 15:e1007518. [PMID: 30673779 PMCID: PMC6361465 DOI: 10.1371/journal.ppat.1007518] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 02/04/2019] [Accepted: 12/10/2018] [Indexed: 12/11/2022] Open
Abstract
Translation can initiate at alternate, non-canonical start codons in response to stressful stimuli in mammalian cells. Recent studies suggest that viral infection and anti-viral responses alter sites of translation initiation, and in some cases, lead to production of novel immune epitopes. Here we systematically investigate the extent and impact of alternate translation initiation in cells infected with influenza virus. We perform evolutionary analyses that suggest selection against non-canonical initiation at CUG codons in influenza virus lineages that have adapted to mammalian hosts. We then use ribosome profiling with the initiation inhibitor lactimidomycin to experimentally delineate translation initiation sites in a human lung epithelial cell line infected with influenza virus. We identify several candidate sites of alternate initiation in influenza mRNAs, all of which occur at AUG codons that are downstream of canonical initiation codons. One of these candidate downstream start sites truncates 14 amino acids from the N-terminus of the N1 neuraminidase protein, resulting in loss of its cytoplasmic tail and a portion of the transmembrane domain. This truncated neuraminidase protein is expressed on the cell surface during influenza virus infection, is enzymatically active, and is conserved in most N1 viral lineages. We do not detect globally higher levels of alternate translation initiation on host transcripts upon influenza infection or during the anti-viral response, but the subset of host transcripts induced by the anti-viral response is enriched for alternate initiation sites. Together, our results systematically map the landscape of translation initiation during influenza virus infection, and shed light on the evolutionary forces shaping this landscape.
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Affiliation(s)
- Heather M. Machkovech
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America
| | - Jesse D. Bloom
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Arvind R. Subramaniam
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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9
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Hilton SK, Bloom JD. Modeling site-specific amino-acid preferences deepens phylogenetic estimates of viral sequence divergence. Virus Evol 2018; 4:vey033. [PMID: 30425841 PMCID: PMC6220371 DOI: 10.1093/ve/vey033] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Molecular phylogenetics is often used to estimate the time since the divergence of modern gene sequences. For highly diverged sequences, such phylogenetic techniques sometimes estimate surprisingly recent divergence times. In the case of viruses, independent evidence indicates that the estimates of deep divergence times from molecular phylogenetics are sometimes too recent. This discrepancy is caused in part by inadequate models of purifying selection leading to branch-length underestimation. Here we examine the effect on branch-length estimation of using models that incorporate experimental measurements of purifying selection. We find that models informed by experimentally measured site-specific amino-acid preferences estimate longer deep branches on phylogenies of influenza virus hemagglutinin. This lengthening of branches is due to more realistic stationary states of the models, and is mostly independent of the branch-length extension from modeling site-to-site variation in amino-acid substitution rate. The branch-length extension from experimentally informed site-specific models is similar to that achieved by other approaches that allow the stationary state to vary across sites. However, the improvements from all of these site-specific but time homogeneous and site independent models are limited by the fact that a protein’s amino-acid preferences gradually shift as it evolves. Overall, our work underscores the importance of modeling site-specific amino-acid preferences when estimating deep divergence times—but also shows the inherent limitations of approaches that fail to account for how these preferences shift over time.
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Affiliation(s)
- Sarah K Hilton
- Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center.,Department of Genome Sciences, University of Washington, USA
| | - Jesse D Bloom
- Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center.,Department of Genome Sciences, University of Washington, USA.,Howard Hughes Medical Institute, Seattle, WA, USA
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10
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Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants. Proc Natl Acad Sci U S A 2018; 115:E8276-E8285. [PMID: 30104379 PMCID: PMC6126756 DOI: 10.1073/pnas.1806133115] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
A key goal in the study of influenza virus evolution is to forecast which viral strains will persist and which ones will die out. Here we experimentally measure the effects of all amino acid mutations to the hemagglutinin protein from a human H3N2 influenza strain on viral growth in cell culture. We show that these measurements have utility for distinguishing among viral strains that do and do not succeed in nature. Overall, our work suggests that new high-throughput experimental approaches may be useful for understanding virus evolution in nature. Human influenza virus rapidly accumulates mutations in its major surface protein hemagglutinin (HA). The evolutionary success of influenza virus lineages depends on how these mutations affect HA’s functionality and antigenicity. Here we experimentally measure the effects on viral growth in cell culture of all single amino acid mutations to the HA from a recent human H3N2 influenza virus strain. We show that mutations that are measured to be more favorable for viral growth are enriched in evolutionarily successful H3N2 viral lineages relative to mutations that are measured to be less favorable for viral growth. Therefore, despite the well-known caveats about cell-culture measurements of viral fitness, such measurements can still be informative for understanding evolution in nature. We also compare our measurements for H3 HA to similar data previously generated for a distantly related H1 HA and find substantial differences in which amino acids are preferred at many sites. For instance, the H3 HA has less disparity in mutational tolerance between the head and stalk domains than the H1 HA. Overall, our work suggests that experimental measurements of mutational effects can be leveraged to help understand the evolutionary fates of viral lineages in nature—but only when the measurements are made on a viral strain similar to the ones being studied in nature.
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11
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Lyons DM, Lauring AS. Mutation and Epistasis in Influenza Virus Evolution. Viruses 2018; 10:E407. [PMID: 30081492 PMCID: PMC6115771 DOI: 10.3390/v10080407] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/30/2018] [Accepted: 07/30/2018] [Indexed: 12/25/2022] Open
Abstract
Influenza remains a persistent public health challenge, because the rapid evolution of influenza viruses has led to marginal vaccine efficacy, antiviral resistance, and the annual emergence of novel strains. This evolvability is driven, in part, by the virus's capacity to generate diversity through mutation and reassortment. Because many new traits require multiple mutations and mutations are frequently combined by reassortment, epistatic interactions between mutations play an important role in influenza virus evolution. While mutation and epistasis are fundamental to the adaptability of influenza viruses, they also constrain the evolutionary process in important ways. Here, we review recent work on mutational effects and epistasis in influenza viruses.
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Affiliation(s)
- Daniel M Lyons
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Adam S Lauring
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA.
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12
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Dolan PT, Whitfield ZJ, Andino R. Mechanisms and Concepts in RNA Virus Population Dynamics and Evolution. Annu Rev Virol 2018; 5:69-92. [PMID: 30048219 DOI: 10.1146/annurev-virology-101416-041718] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
RNA viruses are unique in their evolutionary capacity, exhibiting high mutation rates and frequent recombination. They rapidly adapt to environmental changes, such as shifts in immune pressure or pharmacological challenge. The evolution of RNA viruses has been brought into new focus with the recent developments of genetic and experimental tools to explore and manipulate the evolutionary dynamics of viral populations. These studies have uncovered new mechanisms that enable viruses to overcome evolutionary challenges in the environment and have emphasized the intimate relationship of viral populations with evolution. Here, we review some of the emerging viral and host mechanisms that underlie the evolution of RNA viruses. We also discuss new studies that demonstrate that the relationship between evolutionary dynamics and virus biology spans many spatial and temporal scales, affecting transmission dynamics within and between hosts as well as pathogenesis.
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Affiliation(s)
- Patrick T Dolan
- Department of Biology, Stanford University, Stanford, California 94305, USA.,Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA;
| | - Zachary J Whitfield
- Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA;
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA;
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13
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Mavor D, Barlow KA, Asarnow D, Birman Y, Britain D, Chen W, Green EM, Kenner LR, Mensa B, Morinishi LS, Nelson CA, Poss EM, Suresh P, Tian R, Arhar T, Ary BE, Bauer DP, Bergman ID, Brunetti RM, Chio CM, Dai SA, Dickinson MS, Elledge SK, Helsell CVM, Hendel NL, Kang E, Kern N, Khoroshkin MS, Kirkemo LL, Lewis GR, Lou K, Marin WM, Maxwell AM, McTigue PF, Myers-Turnbull D, Nagy TL, Natale AM, Oltion K, Pourmal S, Reder GK, Rettko NJ, Rohweder PJ, Schwarz DMC, Tan SK, Thomas PV, Tibble RW, Town JP, Tsai MK, Ugur FS, Wassarman DR, Wolff AM, Wu TS, Bogdanoff D, Li J, Thorn KS, O'Conchúir S, Swaney DL, Chow ED, Madhani HD, Redding S, Bolon DN, Kortemme T, DeRisi JL, Kampmann M, Fraser JS. Extending chemical perturbations of the ubiquitin fitness landscape in a classroom setting reveals new constraints on sequence tolerance. Biol Open 2018; 7:7/7/bio036103. [PMID: 30037883 PMCID: PMC6078352 DOI: 10.1242/bio.036103] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Although the primary protein sequence of ubiquitin (Ub) is extremely stable over evolutionary time, it is highly tolerant to mutation during selection experiments performed in the laboratory. We have proposed that this discrepancy results from the difference between fitness under laboratory culture conditions and the selective pressures in changing environments over evolutionary timescales. Building on our previous work (Mavor et al., 2016), we used deep mutational scanning to determine how twelve new chemicals (3-Amino-1,2,4-triazole, 5-fluorocytosine, Amphotericin B, CaCl2, Cerulenin, Cobalt Acetate, Menadione, Nickel Chloride, p-Fluorophenylalanine, Rapamycin, Tamoxifen, and Tunicamycin) reveal novel mutational sensitivities of ubiquitin residues. Collectively, our experiments have identified eight new sensitizing conditions for Lys63 and uncovered a sensitizing condition for every position in Ub except Ser57 and Gln62. By determining the ubiquitin fitness landscape under different chemical constraints, our work helps to resolve the inconsistencies between deep mutational scanning experiments and sequence conservation over evolutionary timescales.
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Affiliation(s)
- David Mavor
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Kyle A Barlow
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Daniel Asarnow
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Yuliya Birman
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Derek Britain
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Weilin Chen
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Evan M Green
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Lillian R Kenner
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Bruk Mensa
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Leanna S Morinishi
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Charlotte A Nelson
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Erin M Poss
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Pooja Suresh
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Ruilin Tian
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Taylor Arhar
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Beatrice E Ary
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - David P Bauer
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Ian D Bergman
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Rachel M Brunetti
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Cynthia M Chio
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Shizhong A Dai
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Miles S Dickinson
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Susanna K Elledge
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Cole V M Helsell
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Nathan L Hendel
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Emily Kang
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Nadja Kern
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Matvei S Khoroshkin
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Lisa L Kirkemo
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Greyson R Lewis
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Kevin Lou
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Wesley M Marin
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Alison M Maxwell
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Peter F McTigue
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | | | - Tamas L Nagy
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Andrew M Natale
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Keely Oltion
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Sergei Pourmal
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Gabriel K Reder
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Nicholas J Rettko
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Peter J Rohweder
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Daniel M C Schwarz
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Sophia K Tan
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Paul V Thomas
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Ryan W Tibble
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Jason P Town
- Bioinformatics Graduate Group, University of California, San Francisco 94158, USA
| | - Mary K Tsai
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Fatima S Ugur
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Douglas R Wassarman
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Alexander M Wolff
- Biophysics Graduate Group, University of California, San Francisco 94158, USA
| | - Taia S Wu
- Chemistry and Chemical Biology Graduate Program, University of California, San Francisco 94158, USA
| | - Derek Bogdanoff
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA
| | - Jennifer Li
- Department of Chemistry Undergraduate Program, University of California, Davis 95616, USA
| | - Kurt S Thorn
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA
| | - Shane O'Conchúir
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biology (QBI), San Francisco 94158, USA
| | - Danielle L Swaney
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biology (QBI), San Francisco 94158, USA
| | - Eric D Chow
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA
| | - Hiten D Madhani
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA
| | - Sy Redding
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA
| | - Daniel N Bolon
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester 01655, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biology (QBI), San Francisco 94158, USA
| | - Joseph L DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA
| | - Martin Kampmann
- Department of Biochemistry and Biophysics, University of California, San Francisco 94158, USA .,Institute for Neurodegenerative Diseases, University of California, San Francisco 94158, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biology (QBI), San Francisco 94158, USA
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14
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Abstract
The deterministic force of natural selection and stochastic influence of drift shape RNA virus evolution. New deep-sequencing and microfluidics technologies allow us to quantify the effect of mutations and trace the evolution of viral populations with single-genome and single-nucleotide resolution. Such experiments can reveal the topography of the genotype-fitness landscapes that shape the path of viral evolution. By combining historical analyses, like phylogenetic approaches, with high-throughput and high-resolution evolutionary experiments, we can observe parallel patterns of evolution that drive important phenotypic transitions. These developments provide a framework for quantifying and anticipating potential evolutionary events. Here, we examine emerging technologies that can map the selective landscapes of viruses, focusing on their application to pathogenic viruses. We identify areas where these technologies can bolster our ability to study the evolution of viruses and to anticipate and possibly intervene in evolutionary events and prevent viral disease.
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Affiliation(s)
- Patrick T Dolan
- Department of Biology, Stanford University, E200 Clark Center, 318 Campus Drive, Stanford, CA 94305, USA; Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, GH-S572, UCSF Box 2280, San Francisco, CA 94143-2280, USA
| | - Zachary J Whitfield
- Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, GH-S572, UCSF Box 2280, San Francisco, CA 94143-2280, USA
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, GH-S572, UCSF Box 2280, San Francisco, CA 94143-2280, USA.
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15
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Haddox HK, Dingens AS, Hilton SK, Overbaugh J, Bloom JD. Mapping mutational effects along the evolutionary landscape of HIV envelope. eLife 2018; 7:34420. [PMID: 29590010 PMCID: PMC5910023 DOI: 10.7554/elife.34420] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 03/15/2018] [Indexed: 01/04/2023] Open
Abstract
The immediate evolutionary space accessible to HIV is largely determined by how single amino acid mutations affect fitness. These mutational effects can shift as the virus evolves. However, the prevalence of such shifts in mutational effects remains unclear. Here, we quantify the effects on viral growth of all amino acid mutations to two HIV envelope (Env) proteins that differ at >100 residues. Most mutations similarly affect both Envs, but the amino acid preferences of a minority of sites have clearly shifted. These shifted sites usually prefer a specific amino acid in one Env, but tolerate many amino acids in the other. Surprisingly, shifts are only slightly enriched at sites that have substituted between the Envs—and many occur at residues that do not even contact substitutions. Therefore, long-range epistasis can unpredictably shift Env’s mutational tolerance during HIV evolution, although the amino acid preferences of most sites are conserved between moderately diverged viral strains. The virus that causes AIDS, or HIV, has a protein called Env on its surface, which is essential for the virus to infect cells. Env can also be recognized by the immune system, which then targets the virus for destruction or blocks it from infecting cells. Unfortunately, Env evolves very quickly, which means that HIV can evade our defenses. However, there are limits to how much this protein can change, since it still needs to perform its essential role in helping viruses enter cells. In the century since HIV first appeared in human populations, the virus has evolved considerably. There are now many HIV strains that infect people, and they bear Env proteins with substantially different sequences. However, it is not clear if these changes in sequence have resulted in Envs from distinct strains being able to tolerate different mutations. To examine this question, Haddox et al. compared how the Envs from two strains of HIV react to modifications in their sequences. They created all possible individual mutations in the proteins, and the resulting collections of mutated viruses were then tested for their ability to infect cells in the laboratory. Most mutations had similar effects in both Env proteins. This allowed Haddox et al. to identify portions of the protein that easily accommodate changes, and portions that must remain unchanged for viruses to remain infectious—at least in the laboratory. Some of these mutations are under different types of pressures when the virus faces the immune system, and those were identified using computational approaches. However, some mutations were tolerated differently by the two Env proteins. Therefore, viral strains differ in how their Env proteins can evolve. The parts of Env that showed differences in mutational tolerance between the strains were not necessarily the parts that differ in sequence. This shows that changes in sequence in one part of the protein can modify how other portions evolve. It remains to be determined whether changes in tolerance to mutations translate into differences in how the virus can escape immunity. This is an important question given that the rapid evolution of Env is a major obstacle to creating a vaccine for HIV.
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Affiliation(s)
- Hugh K Haddox
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Molecular and Cellular Biology PhD program, University of Washington, Seattle, United States
| | - Adam S Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Molecular and Cellular Biology PhD program, University of Washington, Seattle, United States
| | - Sarah K Hilton
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Department of Genome Sciences, University of Washington, Seattle, United States
| | - Julie Overbaugh
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States.,Epidemiology Program, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States.,Department of Genome Sciences, University of Washington, Seattle, United States
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