1
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Crandall JG, Zhou X, Rokas A, Hittinger CT. Specialization restricts the evolutionary paths available to yeast sugar transporters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.22.604696. [PMID: 39091816 PMCID: PMC11291069 DOI: 10.1101/2024.07.22.604696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Functional innovation at the protein level is a key source of evolutionary novelties. The constraints on functional innovations are likely to be highly specific in different proteins, which are shaped by their unique histories and the extent of global epistasis that arises from their structures and biochemistries. These contextual nuances in the sequence-function relationship have implications both for a basic understanding of the evolutionary process and for engineering proteins with desirable properties. Here, we have investigated the molecular basis of novel function in a model member of an ancient, conserved, and biotechnologically relevant protein family. These Major Facilitator Superfamily sugar porters are a functionally diverse group of proteins that are thought to be highly plastic and evolvable. By dissecting a recent evolutionary innovation in an α-glucoside transporter from the yeast Saccharomyces eubayanus, we show that the ability to transport a novel substrate requires high-order interactions between many protein regions and numerous specific residues proximal to the transport channel. To reconcile the functional diversity of this family with the constrained evolution of this model protein, we generated new, state-of-the-art genome annotations for 332 Saccharomycotina yeast species spanning approximately 400 million years of evolution. By integrating phylogenetic and phenotypic analyses across these species, we show that the model yeast α-glucoside transporters likely evolved from a multifunctional ancestor and became subfunctionalized. The accumulation of additive and epistatic substitutions likely entrenched this subfunction, which made the simultaneous acquisition of multiple interacting substitutions the only reasonably accessible path to novelty.
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Affiliation(s)
- Johnathan G. Crandall
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Xiaofan Zhou
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
- Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Antonis Rokas
- Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Chris Todd Hittinger
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
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2
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Metzger BPH, Park Y, Starr TN, Thornton JW. Epistasis facilitates functional evolution in an ancient transcription factor. eLife 2024; 12:RP88737. [PMID: 38767330 PMCID: PMC11105156 DOI: 10.7554/elife.88737] [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: 05/22/2024] Open
Abstract
A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.
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Affiliation(s)
- Brian PH Metzger
- Department of Ecology and Evolution, University of ChicagoChicagoUnited States
| | - Yeonwoo Park
- Program in Genetics, Genomics, and Systems Biology, University of ChicagoChicagoUnited States
| | - Tyler N Starr
- Department of Biochemistry and Molecular Biophysics, University of ChicagoChicagoUnited States
| | - Joseph W Thornton
- Department of Ecology and Evolution, University of ChicagoChicagoUnited States
- Department of Human Genetics, University of ChicagoChicagoUnited States
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3
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Judge A, Sankaran B, Hu L, Palaniappan M, Birgy A, Prasad BVV, Palzkill T. Network of epistatic interactions in an enzyme active site revealed by large-scale deep mutational scanning. Proc Natl Acad Sci U S A 2024; 121:e2313513121. [PMID: 38483989 PMCID: PMC10962969 DOI: 10.1073/pnas.2313513121] [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: 08/06/2023] [Accepted: 02/14/2024] [Indexed: 03/19/2024] Open
Abstract
Cooperative interactions between amino acids are critical for protein function. A genetic reflection of cooperativity is epistasis, which is when a change in the amino acid at one position changes the sequence requirements at another position. To assess epistasis within an enzyme active site, we utilized CTX-M β-lactamase as a model system. CTX-M hydrolyzes β-lactam antibiotics to provide antibiotic resistance, allowing a simple functional selection for rapid sorting of modified enzymes. We created all pairwise mutations across 17 active site positions in the β-lactamase enzyme and quantitated the function of variants against two β-lactam antibiotics using next-generation sequencing. Context-dependent sequence requirements were determined by comparing the antibiotic resistance function of double mutations across the CTX-M active site to their predicted function based on the constituent single mutations, revealing both positive epistasis (synergistic interactions) and negative epistasis (antagonistic interactions) between amino acid substitutions. The resulting trends demonstrate that positive epistasis is present throughout the active site, that epistasis between residues is mediated through substrate interactions, and that residues more tolerant to substitutions serve as generic compensators which are responsible for many cases of positive epistasis. Additionally, we show that a key catalytic residue (Glu166) is amenable to compensatory mutations, and we characterize one such double mutant (E166Y/N170G) that acts by an altered catalytic mechanism. These findings shed light on the unique biochemical factors that drive epistasis within an enzyme active site and will inform enzyme engineering efforts by bridging the gap between amino acid sequence and catalytic function.
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Affiliation(s)
- Allison Judge
- Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX77030
| | - Banumathi Sankaran
- Department of Molecular Biophysics and Integrated Bioimaging, Berkeley Center for Structural Biology Lawrence Berkeley National Laboratory, Berkeley, CA94720
| | - Liya Hu
- Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX77030
| | - Murugesan Palaniappan
- Department of Pathology and Immunology, Center for Drug Discovery, Baylor College of Medicine, Houston, TX77030
| | - André Birgy
- Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX77030
- Infections, Antimicrobials, Modelling, Evolution, UMR 1137, French Insitute for Medical Research (INSERM), Faculty of Health, Université Paris Cité, Paris75006, France
| | - B. V. Venkataram Prasad
- Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX77030
| | - Timothy Palzkill
- Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX77030
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4
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Radojković M, Ubbink M. Positive epistasis drives clavulanic acid resistance in double mutant libraries of BlaC β-lactamase. Commun Biol 2024; 7:197. [PMID: 38368480 PMCID: PMC10874438 DOI: 10.1038/s42003-024-05868-5] [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: 10/06/2023] [Accepted: 01/26/2024] [Indexed: 02/19/2024] Open
Abstract
Phenotypic effects of mutations are highly dependent on the genetic backgrounds in which they occur, due to epistatic effects. To test how easily the loss of enzyme activity can be compensated for, we screen mutant libraries of BlaC, a β-lactamase from Mycobacterium tuberculosis, for fitness in the presence of carbenicillin and the inhibitor clavulanic acid. Using a semi-rational approach and deep sequencing, we prepare four double-site saturation libraries and determine the relative fitness effect for 1534/1540 (99.6%) of the unique library members at two temperatures. Each library comprises variants of a residue known to be relevant for clavulanic acid resistance as well as residue 105, which regulates access to the active site. Variants with greatly improved fitness were identified within each library, demonstrating that compensatory mutations for loss of activity can be readily found. In most cases, the fittest variants are a result of positive epistasis, indicating strong synergistic effects between the chosen residue pairs. Our study sheds light on a role of epistasis in the evolution of functional residues and underlines the highly adaptive potential of BlaC.
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Affiliation(s)
- Marko Radojković
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Marcellus Ubbink
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
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5
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Duan B, Qiu C, Lockless SW, Sze SH, Kaplan CD. Higher-order epistasis within Pol II trigger loop haplotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576280. [PMID: 38293233 PMCID: PMC10827151 DOI: 10.1101/2024.01.20.576280] [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
RNA polymerase II (Pol II) has a highly conserved domain, the trigger loop (TL), that controls transcription fidelity and speed. We previously probed pairwise genetic interactions between residues within and surrounding the TL and identified widespread incompatibility between TLs of different species when placed in the Saccharomyces cerevisiae Pol II context, indicating epistasis between the TL and its surrounding context. We sought to understand the nature of this incompatibility and probe higher order epistasis internal to the TL. We have employed deep mutational scanning with selected natural TL variants ("haplotypes"), and all possible intermediate substitution combinations between them and the yeast Pol II TL. We identified both positive and negative higher-order residue interactions within example TL haplotypes. Intricate higher-order epistasis formed by TL residues was sometimes only apparent from analysis of intermediate genotypes, emphasizing complexity of epistatic interactions. Furthermore, we distinguished TL substitutions with distinct classes of epistatic patterns, suggesting specific TL residues that potentially influence TL evolution. Our examples of complex residue interactions suggest possible pathways for epistasis to facilitate Pol II evolution.
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Affiliation(s)
- Bingbing Duan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
| | - Chenxi Qiu
- Department of Genetics, Harvard Medical School, Boston, MA 02215
| | - Steve W Lockless
- Department of Biology, Texas A&M University, College Station, TX 77843
| | - Sing-Hoi Sze
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX 77843
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX 77843
| | - Craig D Kaplan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
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6
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Zhang H, Bull RA, Quadeer AA, McKay MR. HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies. Virus Evol 2023; 9:vead068. [PMID: 38107333 PMCID: PMC10722114 DOI: 10.1093/ve/vead068] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/27/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a non-covalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remain largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modeling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.
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Affiliation(s)
- Hang Zhang
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, SAR, China
| | - Rowena A Bull
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- The Kirby Institute for Infection and Immunity, Sydney, NSW 2052, Australia
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
| | - Matthew R McKay
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
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7
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Zhang H, Quadeer AA, McKay MR. Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis. Nat Commun 2023; 14:7457. [PMID: 37978179 PMCID: PMC10656532 DOI: 10.1038/s41467-023-42550-6] [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: 01/17/2023] [Accepted: 10/13/2023] [Indexed: 11/19/2023] Open
Abstract
Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Employing a sequence-based fitness landscape model whose predictions correlate highly with experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. Evolutionary modelling further supports that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.
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Affiliation(s)
- Hang Zhang
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
| | - Matthew R McKay
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia.
- Department of Microbiology and Immunology, University of Melbourne, at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
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8
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Gao G, Chen M, Mo R, Li N, Xu Y, Lu Y. Linking New Alleles at the Oscillator Loci to Flowering and Expansion of Asian Rice. Genes (Basel) 2023; 14:2027. [PMID: 38002970 PMCID: PMC10671530 DOI: 10.3390/genes14112027] [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: 09/10/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023] Open
Abstract
The central oscillator is believed to be the key mechanism by which plants adapt to new environments. However, impacts from hybridization, the natural environment, and human selection have rarely been assessed on the oscillator of a crop. Here, from clearly identified alleles at oscillator loci (OsCCA1/LHY, OsPRR95, OsPRR37, OsPRR59, and OsPRR1) in ten diverse genomes of Oryza sativa, additional accessions, and functional analysis, we show that rice's oscillator was rebuilt primarily by new alleles from recombining parental sequences and subsequent 5' or/and coding mutations. New alleles may exhibit altered transcript levels from that of a parental allele and are transcribed variably among genetic backgrounds and natural environments in RIL lines. Plants carrying more expressed OsCCA1_a and less transcribed OsPRR1_e flower early in the paddy field. 5' mutations are instrumental in varied transcription, as shown by EMSA tests on one deletion at the 5' region of highly transcribed OsPRR1_a. Compared to relatively balanced mutations at oscillator loci of Arabidopsis thaliana, 5' mutations of OsPRR37 (and OsCCA1 to a less degree) were under negative selection while those of OsPRR1 alleles were under strong positive selection. Together, range expansion of Asian rice can be elucidated by human selection on OsPRR1 alleles via local flowering time-yield relationships.
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Affiliation(s)
- Guangtong Gao
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nan Xin Cun, Beijing 100093, China; (G.G.); (M.C.); (N.L.); (Y.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Maoxian Chen
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nan Xin Cun, Beijing 100093, China; (G.G.); (M.C.); (N.L.); (Y.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rong Mo
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nan Xin Cun, Beijing 100093, China; (G.G.); (M.C.); (N.L.); (Y.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nan Li
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nan Xin Cun, Beijing 100093, China; (G.G.); (M.C.); (N.L.); (Y.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunzhang Xu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nan Xin Cun, Beijing 100093, China; (G.G.); (M.C.); (N.L.); (Y.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
| | - Yingqing Lu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nan Xin Cun, Beijing 100093, China; (G.G.); (M.C.); (N.L.); (Y.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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9
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Yousefi M, Andrejka L, Szamecz M, Winslow MM, Petrov DA, Boross G. Fully accessible fitness landscape of oncogene-negative lung adenocarcinoma. Proc Natl Acad Sci U S A 2023; 120:e2303224120. [PMID: 37695905 PMCID: PMC10515140 DOI: 10.1073/pnas.2303224120] [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: 03/06/2023] [Accepted: 07/12/2023] [Indexed: 09/13/2023] Open
Abstract
Cancer genomes are almost invariably complex with genomic alterations cooperating during each step of carcinogenesis. In cancers that lack a single dominant oncogene mutation, cooperation between the inactivation of multiple tumor suppressor genes can drive tumor initiation and growth. Here, we shed light on how the sequential acquisition of genomic alterations generates oncogene-negative lung tumors. We couple tumor barcoding with combinatorial and multiplexed somatic genome editing to characterize the fitness landscapes of three tumor suppressor genes NF1, RASA1, and PTEN, the inactivation of which jointly drives oncogene-negative lung adenocarcinoma initiation and growth. The fitness landscape was surprisingly accessible, with each additional mutation leading to growth advantage. Furthermore, the fitness landscapes remained fully accessible across backgrounds with the inactivation of additional tumor suppressor genes. These results suggest that while predicting cancer evolution will be challenging, acquiring the multiple alterations that drive the growth of oncogene-negative tumors can be facilitated by the lack of constraints on mutational order.
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Affiliation(s)
- Maryam Yousefi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
| | - Laura Andrejka
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
| | - Márton Szamecz
- Eötvös Loránd University, Faculty of Informatics, Budapest1053, Hungary
| | - Monte M. Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, CA94305
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA94305
- Department of Pathology, Stanford University School of Medicine, Stanford, CA94305
| | - Dmitri A. Petrov
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA94305
- Department of Biology, Stanford University, Stanford, CA94305
| | - Gábor Boross
- Department of Biology, Stanford University, Stanford, CA94305
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10
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Backe SJ, Mollapour M, Woodford MR. Saccharomyces cerevisiae as a tool for deciphering Hsp90 molecular chaperone function. Essays Biochem 2023; 67:781-795. [PMID: 36912239 PMCID: PMC10497724 DOI: 10.1042/ebc20220224] [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: 12/21/2022] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 03/14/2023]
Abstract
Yeast is a valuable model organism for their ease of genetic manipulation, rapid growth rate, and relative similarity to higher eukaryotes. Historically, Saccharomyces cerevisiae has played a major role in discovering the function of complex proteins and pathways that are important for human health and disease. Heat shock protein 90 (Hsp90) is a molecular chaperone responsible for the stabilization and activation of hundreds of integral members of the cellular signaling network. Much important structural and functional work, including many seminal discoveries in Hsp90 biology are the direct result of work carried out in S. cerevisiae. Here, we have provided a brief overview of the S. cerevisiae model system and described how this eukaryotic model organism has been successfully applied to the study of Hsp90 chaperone function.
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Affiliation(s)
- Sarah J. Backe
- Department of Urology, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
- Upstate Cancer Center, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
| | - Mehdi Mollapour
- Department of Urology, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
- Upstate Cancer Center, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
- Department of Biochemistry and Molecular Biology, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
| | - Mark R. Woodford
- Department of Urology, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
- Upstate Cancer Center, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
- Department of Biochemistry and Molecular Biology, SUNY Upstate Medical University, Syracuse, NY 13210, U.S.A
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11
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Wagner A. Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein. Nat Commun 2023; 14:3624. [PMID: 37336901 PMCID: PMC10279741 DOI: 10.1038/s41467-023-39321-8] [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: 09/26/2022] [Accepted: 06/05/2023] [Indexed: 06/21/2023] Open
Abstract
Can evolvability-the ability to produce adaptive heritable variation-itself evolve through adaptive Darwinian evolution? If so, then Darwinian evolution may help create the conditions that enable Darwinian evolution. Here I propose a framework that is suitable to address this question with available experimental data on adaptive landscapes. I introduce the notion of an evolvability-enhancing mutation, which increases the likelihood that subsequent mutations in an evolving organism, protein, or RNA molecule are adaptive. I search for such mutations in the experimentally characterized and combinatorially complete fitness landscapes of a protein and an RNA molecule. I find that such evolvability-enhancing mutations indeed exist. They constitute a small fraction of all mutations, which shift the distribution of fitness effects of subsequent mutations towards less deleterious mutations, and increase the incidence of beneficial mutations. Evolving populations which experience such mutations can evolve significantly higher fitness. The study of evolvability-enhancing mutations opens many avenues of investigation into the evolution of evolvability.
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Affiliation(s)
- Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland.
- The Santa Fe Institute, Santa Fe, NM, USA.
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12
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Yousefi M, Andrejka L, Winslow MM, Petrov DA, Boross G. Fully accessible fitness landscape of oncogene-negative lung adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526178. [PMID: 36778226 PMCID: PMC9915475 DOI: 10.1101/2023.01.30.526178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Cancer genomes are almost invariably complex with genomic alterations cooperating during each step of carcinogenesis. In cancers that lack a single dominant oncogene mutation, cooperation between the inactivation of multiple tumor suppressor genes can drive tumor initiation and growth. Here, we shed light on how the sequential acquisition of genomic alterations generates oncogene-negative lung tumors. We couple tumor barcoding with combinatorial and multiplexed somatic genome editing to characterize the fitness landscapes of three tumor suppressor genes NF1, RASA1, and PTEN, the inactivation of which jointly drives oncogene-negative lung adenocarcinoma initiation and growth. The fitness landscape was surprisingly accessible, with each additional mutation leading to growth advantage. Furthermore, the fitness landscapes remained fully accessible across backgrounds with additional tumor suppressor mutations. These results suggest that while predicting cancer evolution will be challenging, acquiring the multiple alterations required for the growth of oncogene-negative tumors can be facilitated by the lack of constraints on mutational order.
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13
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Zhang J. What Has Genomics Taught An Evolutionary Biologist? GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1-12. [PMID: 36720382 PMCID: PMC10373158 DOI: 10.1016/j.gpb.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 01/06/2023] [Accepted: 01/19/2023] [Indexed: 01/30/2023]
Abstract
Genomics, an interdisciplinary field of biology on the structure, function, and evolution of genomes, has revolutionized many subdisciplines of life sciences, including my field of evolutionary biology, by supplying huge data, bringing high-throughput technologies, and offering a new approach to biology. In this review, I describe what I have learned from genomics and highlight the fundamental knowledge and mechanistic insights gained. I focus on three broad topics that are central to evolutionary biology and beyond-variation, interaction, and selection-and use primarily my own research and study subjects as examples. In the next decade or two, I expect that the most important contributions of genomics to evolutionary biology will be to provide genome sequences of nearly all known species on Earth, facilitate high-throughput phenotyping of natural variants and systematically constructed mutants for mapping genotype-phenotype-fitness landscapes, and assist the determination of causality in evolutionary processes using experimental evolution.
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Affiliation(s)
- Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
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14
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Phillips AM, Maurer DP, Brooks C, Dupic T, Schmidt AG, Desai MM. Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody. eLife 2023; 12:83628. [PMID: 36625542 PMCID: PMC9995116 DOI: 10.7554/elife.83628] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | - Daniel P Maurer
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Caelan Brooks
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Aaron G Schmidt
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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15
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Iyengar BR, Wagner A. Bacterial Hsp90 predominantly buffers but does not potentiate the phenotypic effects of deleterious mutations during fluorescent protein evolution. Genetics 2022; 222:iyac154. [PMID: 36227141 PMCID: PMC9713429 DOI: 10.1093/genetics/iyac154] [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: 07/29/2022] [Accepted: 09/26/2022] [Indexed: 12/13/2022] Open
Abstract
Chaperones facilitate the folding of other ("client") proteins and can thus affect the adaptive evolution of these clients. Specifically, chaperones affect the phenotype of proteins via two opposing mechanisms. On the one hand, they can buffer the effects of mutations in proteins and thus help preserve an ancestral, premutation phenotype. On the other hand, they can potentiate the effects of mutations and thus enhance the phenotypic changes caused by a mutation. We study that how the bacterial Hsp90 chaperone (HtpG) affects the evolution of green fluorescent protein. To this end, we performed directed evolution of green fluorescent protein under low and high cellular concentrations of Hsp90. Specifically, we evolved green fluorescent protein under both stabilizing selection for its ancestral (green) phenotype and directional selection toward a new (cyan) phenotype. While Hsp90 did only affect the rate of adaptive evolution transiently, it did affect the phenotypic effects of mutations that occurred during adaptive evolution. Specifically, Hsp90 allowed strongly deleterious mutations to accumulate in evolving populations by buffering their effects. Our observations show that the role of a chaperone for adaptive evolution depends on the organism and the trait being studied.
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Affiliation(s)
- Bharat Ravi Iyengar
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, 1015 Lausanne, Switzerland
- Institute for Evolution and Biodiversity, Westfalian Wilhelms—University of Münster, 48149 Münster, Germany
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, 1015 Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, NM 87501, USA
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, 7600 Stellenbosch, South Africa
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16
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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17
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Chattopadhyay G, Bhowmick J, Manjunath K, Ahmed S, Goyal P, Varadarajan R. Mechanistic insights into global suppressors of protein folding defects. PLoS Genet 2022; 18:e1010334. [PMID: 36037221 PMCID: PMC9491731 DOI: 10.1371/journal.pgen.1010334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/09/2022] [Accepted: 07/11/2022] [Indexed: 01/14/2023] Open
Abstract
Most amino acid substitutions in a protein either lead to partial loss-of-function or are near neutral. Several studies have shown the existence of second-site mutations that can rescue defects caused by diverse loss-of-function mutations. Such global suppressor mutations are key drivers of protein evolution. However, the mechanisms responsible for such suppression remain poorly understood. To address this, we characterized multiple suppressor mutations both in isolation and in combination with inactive mutants. We examined six global suppressors of the bacterial toxin CcdB, the known M182T global suppressor of TEM-1 β-lactamase, the N239Y global suppressor of p53-DBD and three suppressors of the SARS-CoV-2 spike Receptor Binding Domain. When coupled to inactive mutants, they promote increased in-vivo solubilities as well as regain-of-function phenotypes. In the case of CcdB, where novel suppressors were isolated, we determined the crystal structures of three such suppressors to obtain insight into the specific molecular interactions responsible for the observed effects. While most individual suppressors result in small stability enhancements relative to wildtype, which can be combined to yield significant stability increments, thermodynamic stabilisation is neither necessary nor sufficient for suppressor action. Instead, in diverse systems, we observe that individual global suppressors greatly enhance the foldability of buried site mutants, primarily through increase in refolding rate parameters measured in vitro. In the crowded intracellular environment, mutations that slow down folding likely facilitate off-pathway aggregation. We suggest that suppressor mutations that accelerate refolding can counteract this, enhancing the yield of properly folded, functional protein in vivo.
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Affiliation(s)
| | - Jayantika Bhowmick
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore,
India
| | - Kavyashree Manjunath
- Centre for Chemical Biology and Therapeutics, Institute For Stem Cell
Science and Regenerative Medicine, Bangalore, India
| | - Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore,
India
| | - Parveen Goyal
- Institute for Stem Cell Science and Regenerative Medicine, Bangalore,
India
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18
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Benjamin R, Giacoletto CJ, FitzHugh ZT, Eames D, Buczek L, Wu X, Newsome J, Han MV, Pearson T, Wei Z, Banerjee A, Brown L, Valente LJ, Shen S, Deng HW, Schiller MR. GigaAssay - An adaptable high-throughput saturation mutagenesis assay platform. Genomics 2022; 114:110439. [PMID: 35905834 PMCID: PMC9420302 DOI: 10.1016/j.ygeno.2022.110439] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/12/2022] [Accepted: 07/24/2022] [Indexed: 11/17/2022]
Abstract
High-throughput assay systems have had a large impact on understanding the mechanisms of basic cell functions. However, high-throughput assays that directly assess molecular functions are limited. Herein, we describe the "GigaAssay", a modular high-throughput one-pot assay system for measuring molecular functions of thousands of genetic variants at once. In this system, each cell was infected with one virus from a library encoding thousands of Tat mutant proteins, with each viral particle encoding a random unique molecular identifier (UMI). We demonstrate proof of concept by measuring transcription of a GFP reporter in an engineered reporter cell line driven by binding of the HIV Tat transcription factor to the HIV long terminal repeat. Infected cells were flow-sorted into 3 bins based on their GFP fluorescence readout. The transcriptional activity of each Tat mutant was calculated from the ratio of signals from each bin. The use of UMIs in the GigaAssay produced a high average accuracy (95%) and positive predictive value (98%) determined by comparison to literature benchmark data, known C-terminal truncations, and blinded independent mutant tests. Including the substitution tolerance with structure/function analysis shows restricted substitution types spatially concentrated in the Cys-rich region. Tat has abundant intragenic epistasis (10%) when single and double mutants are compared.
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Affiliation(s)
- Ronald Benjamin
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Christopher J Giacoletto
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA; School of Life Sciences, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA; Heligenics Inc., 833 Las Vegas Blvd. North, Suite B, Las Vegas, NV 89101, USA
| | - Zachary T FitzHugh
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Danielle Eames
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Lindsay Buczek
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Xiaogang Wu
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Jacklyn Newsome
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Mira V Han
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA; School of Life Sciences, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Tony Pearson
- School of Life Sciences, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA; Heligenics Inc., 833 Las Vegas Blvd. North, Suite B, Las Vegas, NV 89101, USA
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, GITC 4214C, University Heights, Newark, NJ 07102, USA
| | - Atoshi Banerjee
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Lancer Brown
- Heligenics Inc., 833 Las Vegas Blvd. North, Suite B, Las Vegas, NV 89101, USA
| | - Liz J Valente
- Heligenics Inc., 833 Las Vegas Blvd. North, Suite B, Las Vegas, NV 89101, USA
| | - Shirley Shen
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA
| | - Hong-Wen Deng
- Center for Biomedical Informatics & Genomics Tulane University, 1440 Canal Street, Suite 1621, New Orleans, LA 70112, USA
| | - Martin R Schiller
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA; School of Life Sciences, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA; Heligenics Inc., 833 Las Vegas Blvd. North, Suite B, Las Vegas, NV 89101, USA.
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19
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Chen DS, Clark AG, Wolfner MF. Octopaminergic/tyraminergic Tdc2 neurons regulate biased sperm usage in female Drosophila melanogaster. Genetics 2022; 221:6613932. [PMID: 35736370 DOI: 10.1093/genetics/iyac097] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/04/2022] [Indexed: 11/14/2022] Open
Abstract
In polyandrous internally fertilizing species, a multiply-mated female can use stored sperm from different males in a biased manner to fertilize her eggs. The female's ability to assess sperm quality and compatibility is essential for her reproductive success, and represents an important aspect of postcopulatory sexual selection. In Drosophila melanogaster, previous studies demonstrated that the female nervous system plays an active role in influencing progeny paternity proportion, and suggested a role for octopaminergic/tyraminergic Tdc2 neurons in this process. Here, we report that inhibiting Tdc2 neuronal activity causes females to produce a higher-than-normal proportion of first-male progeny. This difference is not due to differences in sperm storage or release, but instead is attributable to the suppression of second-male sperm usage bias that normally occurs in control females. We further show that a subset of Tdc2 neurons innervating the female reproductive tract is largely responsible for the progeny proportion phenotype that is observed when Tdc2 neurons are inhibited globally. On the contrary, overactivation of Tdc2 neurons does not further affect sperm storage and release or progeny proportion. These results suggest that octopaminergic/tyraminergic signaling allows a multiply-mated female to bias sperm usage, and identify a new role for the female nervous system in postcopulatory sexual selection.
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Affiliation(s)
- Dawn S Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca NY 14853, USA
| | - Andrew G Clark
- Department of Molecular Biology and Genetics, Cornell University, Ithaca NY 14853, USA
| | - Mariana F Wolfner
- Department of Molecular Biology and Genetics, Cornell University, Ithaca NY 14853, USA
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20
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Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape. Nat Ecol Evol 2022; 6:427-438. [PMID: 35210579 DOI: 10.1038/s41559-022-01675-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.
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21
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Gonzalez Somermeyer L, Fleiss A, Mishin AS, Bozhanova NG, Igolkina AA, Meiler J, Alaball Pujol ME, Putintseva EV, Sarkisyan KS, Kondrashov FA. Heterogeneity of the GFP fitness landscape and data-driven protein design. eLife 2022; 11:75842. [PMID: 35510622 PMCID: PMC9119679 DOI: 10.7554/elife.75842] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/25/2022] [Indexed: 11/24/2022] Open
Abstract
Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.
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Affiliation(s)
| | - Aubin Fleiss
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom
| | - Alexander S Mishin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of SciencesMoscowRussian Federation
| | - Nina G Bozhanova
- Department of Chemistry, Center for Structural Biology, Vanderbilt UniversityNashvilleUnited States
| | - Anna A Igolkina
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenterViennaAustria
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt UniversityNashvilleUnited States,Institute for Drug Discovery, Medical School, Leipzig UniversityLeipzigGermany
| | - Maria-Elisenda Alaball Pujol
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom
| | | | - Karen S Sarkisyan
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of SciencesMoscowRussian Federation
| | - Fyodor A Kondrashov
- Institute of Science and Technology AustriaKlosterneuburgAustria,Evolutionary and Synthetic Biology Unit, Okinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
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22
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Phillips AM, Lawrence KR, Moulana A, Dupic T, Chang J, Johnson MS, Cvijovic I, Mora T, Walczak AM, Desai MM. Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies. eLife 2021; 10:71393. [PMID: 34491198 PMCID: PMC8476123 DOI: 10.7554/elife.71393] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/05/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past two decades, several broadly neutralizing antibodies (bnAbs) that confer protection against diverse influenza strains have been isolated. Structural and biochemical characterization of these bnAbs has provided molecular insight into how they bind distinct antigens. However, our understanding of the evolutionary pathways leading to bnAbs, and thus how best to elicit them, remains limited. Here, we measure equilibrium dissociation constants of combinatorially complete mutational libraries for two naturally isolated influenza bnAbs (CR9114, 16 heavy-chain mutations; CR6261, 11 heavy-chain mutations), reconstructing all possible evolutionary intermediates back to the unmutated germline sequences. We find that these two libraries exhibit strikingly different patterns of breadth: while many variants of CR6261 display moderate affinity to diverse antigens, those of CR9114 display appreciable affinity only in specific, nested combinations. By examining the extensive pairwise and higher order epistasis between mutations, we find key sites with strong synergistic interactions that are highly similar across antigens for CR6261 and different for CR9114. Together, these features of the binding affinity landscapes strongly favor sequential acquisition of affinity to diverse antigens for CR9114, while the acquisition of breadth to more similar antigens for CR6261 is less constrained. These results, if generalizable to other bnAbs, may explain the molecular basis for the widespread observation that sequential exposure favors greater breadth, and such mechanistic insight will be essential for predicting and eliciting broadly protective immune responses.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Katherine R Lawrence
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.,Quantitative Biology Initiative, Harvard University, Cambridge, United States.,Department of Physics, Massachusetts Institute of Technology, Cambridge, United States
| | - Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Jeffrey Chang
- Department of Physics, Harvard University, Cambridge, United States
| | - Milo S Johnson
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Ivana Cvijovic
- Department of Applied Physics, Stanford University, Stanford, United States
| | - Thierry Mora
- Laboratoire de physique de ÍÉcole Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Aleksandra M Walczak
- Laboratoire de physique de ÍÉcole Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.,Quantitative Biology Initiative, Harvard University, Cambridge, United States.,Department of Physics, Harvard University, Cambridge, United States
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23
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Zheng J, Guo N, Wagner A. Mistranslation reduces mutation load in evolving proteins through negative epistasis with DNA mutations. Mol Biol Evol 2021; 38:4792-4804. [PMID: 34255074 PMCID: PMC8557407 DOI: 10.1093/molbev/msab206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Translational errors during protein synthesis cause phenotypic mutations that are several orders of magnitude more frequent than DNA mutations. Such phenotypic mutations may affect adaptive evolution through their interactions with DNA mutations. To study how mistranslation may affect the adaptive evolution of evolving proteins, we evolved populations of green fluorescent protein (GFP) in either high-mistranslation or low-mistranslation Escherichia coli hosts. In both hosts, we first evolved GFP under purifying selection for the ancestral phenotype green fluorescence, and then under directional selection toward the new phenotype yellow fluorescence. High-mistranslation populations evolved modestly higher yellow fluorescence during each generation of evolution than low-mistranslation populations. We demonstrate by high-throughput sequencing that elevated mistranslation reduced the accumulation of deleterious DNA mutations under both purifying and directional selection. It did so by amplifying the fitness effects of deleterious DNA mutations through negative epistasis with phenotypic mutations. In contrast, mistranslation did not affect the incidence of beneficial mutations. Our findings show that phenotypic mutations interact epistatically with DNA mutations. By reducing a population’s mutation load, mistranslation can affect an important determinant of evolvability.
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Affiliation(s)
- Jia Zheng
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland
| | - Ning Guo
- Zwirnereistrasse 11, Wallisellen, Zurich, Switzerland
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland.,The Santa Fe Institute, Santa Fe, New Mexico, USA
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24
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Sandler G, Wright SI, Agrawal AF. Patterns and Causes of Signed Linkage Disequilibria in Flies and Plants. Mol Biol Evol 2021; 38:4310-4321. [PMID: 34097067 PMCID: PMC8476167 DOI: 10.1093/molbev/msab169] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Most empirical studies of linkage disequilibrium (LD) study its magnitude, ignoring its sign. Here, we examine patterns of signed LD in two population genomic data sets, one from Capsella grandiflora and one from Drosophila melanogaster. We consider how processes such as drift, admixture, Hill–Robertson interference, and epistasis may contribute to these patterns. We report that most types of mutations exhibit positive LD, particularly, if they are predicted to be less deleterious. We show with simulations that this pattern arises easily in a model of admixture or distance-biased mating, and that genome-wide differences across site types are generally expected due to differences in the strength of purifying selection even in the absence of epistasis. We further explore how signed LD decays on a finer scale, showing that loss of function mutations exhibit particularly positive LD across short distances, a pattern consistent with intragenic antagonistic epistasis. Controlling for genomic distance, signed LD in C. grandiflora decays faster within genes, compared with between genes, likely a by-product of frequent recombination in gene promoters known to occur in plant genomes. Finally, we use information from published biological networks to explore whether there is evidence for negative synergistic epistasis between interacting radical missense mutations. In D. melanogaster networks, we find a modest but significant enrichment of negative LD, consistent with the possibility of intranetwork negative synergistic epistasis.
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Affiliation(s)
- George Sandler
- Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada
| | - Stephen I Wright
- Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada.,Center for Analysis of Genome Evolution and Function, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada
| | - Aneil F Agrawal
- Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada.,Center for Analysis of Genome Evolution and Function, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada
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25
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Abstract
RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance). IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.
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Routh S, Acharyya A, Dhar R. A two-step PCR assembly for construction of gene variants across large mutational distances. Biol Methods Protoc 2021; 6:bpab007. [PMID: 33928191 PMCID: PMC8062255 DOI: 10.1093/biomethods/bpab007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/09/2021] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Construction of empirical fitness landscapes has transformed our understanding of genotype-phenotype relationships across genes. However, most empirical fitness landscapes have been constrained to the local genotype neighbourhood of a gene primarily due to our limited ability to systematically construct genotypes that differ by a large number of mutations. Although a few methods have been proposed in the literature, these techniques are complex owing to several steps of construction or contain a large number of amplification cycles that increase chances of non-specific mutations. A few other described methods require amplification of the whole vector, thereby increasing the chances of vector backbone mutations that can have unintended consequences for study of fitness landscapes. Thus, this has substantially constrained us from traversing large mutational distances in the genotype network, thereby limiting our understanding of the interactions between multiple mutations and the role these interactions play in evolution of novel phenotypes. In the current work, we present a simple but powerful approach that allows us to systematically and accurately construct gene variants at large mutational distances. Our approach relies on building-up small fragments containing targeted mutations in the first step followed by assembly of these fragments into the complete gene fragment by polymerase chain reaction (PCR). We demonstrate the utility of our approach by constructing variants that differ by up to 11 mutations in a model gene. Our work thus provides an accurate method for construction of multi-mutant variants of genes and therefore will transform the studies of empirical fitness landscapes by enabling exploration of genotypes that are far away from a starting genotype.
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Affiliation(s)
- Shreya Routh
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Anamika Acharyya
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
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27
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Rauscher R, Bampi GB, Guevara-Ferrer M, Santos LA, Joshi D, Mark D, Strug LJ, Rommens JM, Ballmann M, Sorscher EJ, Oliver KE, Ignatova Z. Positive epistasis between disease-causing missense mutations and silent polymorphism with effect on mRNA translation velocity. Proc Natl Acad Sci U S A 2021; 118:e2010612118. [PMID: 33468668 PMCID: PMC7848603 DOI: 10.1073/pnas.2010612118] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Epistasis refers to the dependence of a mutation on other mutation(s) and the genetic context in general. In the context of human disorders, epistasis complicates the spectrum of disease symptoms and has been proposed as a major contributor to variations in disease outcome. The nonadditive relationship between mutations and the lack of complete understanding of the underlying physiological effects limit our ability to predict phenotypic outcome. Here, we report positive epistasis between intragenic mutations in the cystic fibrosis transmembrane conductance regulator (CFTR)-the gene responsible for cystic fibrosis (CF) pathology. We identified a synonymous single-nucleotide polymorphism (sSNP) that is invariant for the CFTR amino acid sequence but inverts translation speed at the affected codon. This sSNP in cis exhibits positive epistatic effects on some CF disease-causing missense mutations. Individually, both mutations alter CFTR structure and function, yet when combined, they lead to enhanced protein expression and activity. The most robust effect was observed when the sSNP was present in combination with missense mutations that, along with the primary amino acid change, also alter the speed of translation at the affected codon. Functional studies revealed that synergistic alteration in ribosomal velocity is the underlying mechanism; alteration of translation speed likely increases the time window for establishing crucial domain-domain interactions that are otherwise perturbed by each individual mutation.
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Affiliation(s)
- Robert Rauscher
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Giovana B Bampi
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Marta Guevara-Ferrer
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Leonardo A Santos
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Disha Joshi
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - David Mark
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Lisa J Strug
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto M5G 0A4, Canada
- Department of Statistical Sciences, Computer Science and Division of Biostatistics, University of Toronto, Toronto M5G 0A4, Canada
| | - Johanna M Rommens
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto M5G 0A4, Canada
| | | | - Eric J Sorscher
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - Kathryn E Oliver
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - Zoya Ignatova
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany;
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28
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Zhang TH, Dai L, Barton JP, Du Y, Tan Y, Pang W, Chakraborty AK, Lloyd-Smith JO, Sun R. Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease. PLoS Genet 2020; 16:e1009009. [PMID: 33085662 PMCID: PMC7605711 DOI: 10.1371/journal.pgen.1009009] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 11/02/2020] [Accepted: 07/24/2020] [Indexed: 12/12/2022] Open
Abstract
Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.
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Affiliation(s)
- Tian-hao Zhang
- Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - John P. Barton
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, USA
| | - Yushen Du
- School of Medicine, ZheJiang University, Hangzhou, 210000, China
- Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
| | - Yuxiang Tan
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wenwen Pang
- Department of Public Health Laboratory Science, West China School of Public Health, Sichuan University, Chengdu 610041, China
| | - Arup K. Chakraborty
- Institute for Medical Engineering and Science, Departments of Chemical Engineering, Physics, & Chemistry, Massachusetts Institute of Technology, MA 21309, USA
- Ragon Institute of MGH, MIT, & Harvard, Cambridge, MA 21309, USA
| | - James O. Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
| | - Ren Sun
- Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
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29
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Satokangas I, Martin SH, Helanterä H, Saramäki J, Kulmuni J. Multi-locus interactions and the build-up of reproductive isolation. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190543. [PMID: 32654649 PMCID: PMC7423273 DOI: 10.1098/rstb.2019.0543] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2020] [Indexed: 12/15/2022] Open
Abstract
All genes interact with other genes, and their additive effects and epistatic interactions affect an organism's phenotype and fitness. Recent theoretical and empirical work has advanced our understanding of the role of multi-locus interactions in speciation. However, relating different models to one another and to empirical observations is challenging. This review focuses on multi-locus interactions that lead to reproductive isolation (RI) through reduced hybrid fitness. We first review theoretical approaches and show how recent work incorporating a mechanistic understanding of multi-locus interactions recapitulates earlier models, but also makes novel predictions concerning the build-up of RI. These include high variance in the build-up rate of RI among taxa, the emergence of strong incompatibilities producing localized barriers to introgression, and an effect of population size on the build-up of RI. We then review recent experimental approaches to detect multi-locus interactions underlying RI using genomic data. We argue that future studies would benefit from overlapping methods like ancestry disequilibrium scans, genome scans of differentiation and analyses of hybrid gene expression. Finally, we highlight a need for further overlap between theoretical and empirical work, and approaches that predict what kind of patterns multi-locus interactions resulting in incompatibilities will leave in genome-wide polymorphism data. This article is part of the theme issue 'Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers'.
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Affiliation(s)
- I. Satokangas
- Organismal & Evolutionary Biology Research Programme, University of Helsinki, Viikinkaari 1, PO Box 65, 00014 Helsinki, Finland
| | - S. H. Martin
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh EH9 3FL, UK
| | - H. Helanterä
- Ecology and Genetics research unit, University of Oulu, PO Box 3000, 90014 Oulu, Finland
| | - J. Saramäki
- Department of Computer Science, Aalto University, PO Box 11000, 00076 Aalto, Espoo, Finland
| | - J. Kulmuni
- Organismal & Evolutionary Biology Research Programme, University of Helsinki, Viikinkaari 1, PO Box 65, 00014 Helsinki, Finland
- Tvärminne Zoological Station, University of Helsinki, J. A. Palménin tie 260, 10900 Hanko, Finland
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30
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Alpay BA, Demetci P, Istrail S, Aguiar D. Combinatorial and statistical prediction of gene expression from haplotype sequence. Bioinformatics 2020; 36:i194-i202. [PMID: 32657373 PMCID: PMC7355230 DOI: 10.1093/bioinformatics/btaa318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) have discovered thousands of significant genetic effects on disease phenotypes. By considering gene expression as the intermediary between genotype and disease phenotype, expression quantitative trait loci studies have interpreted many of these variants by their regulatory effects on gene expression. However, there remains a considerable gap between genotype-to-gene expression association and genotype-to-gene expression prediction. Accurate prediction of gene expression enables gene-based association studies to be performed post hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for subsequent experimental investigation. RESULTS In this work, we develop gene expression prediction methods that relax the independence and additivity assumptions between genetic markers. First, we consider gene expression prediction from a regression perspective and develop the HAPLEXR algorithm which combines haplotype clusterings with allelic dosages. Second, we introduce the new gene expression classification problem, which focuses on identifying expression groups rather than continuous measurements; we formalize the selection of an appropriate number of expression groups using the principle of maximum entropy. Third, we develop the HAPLEXD algorithm that models haplotype sharing with a modified suffix tree data structure and computes expression groups by spectral clustering. In both models, we penalize model complexity by prioritizing genetic clusters that indicate significant effects on expression. We compare HAPLEXR and HAPLEXD with three state-of-the-art expression prediction methods and two novel logistic regression approaches across five GTEx v8 tissues. HAPLEXD exhibits significantly higher classification accuracy overall; HAPLEXR shows higher prediction accuracy on approximately half of the genes tested and the largest number of best predicted genes (r2>0.1) among all methods. We show that variant and haplotype features selected by HAPLEXR are smaller in size than competing methods (and thus more interpretable) and are significantly enriched in functional annotations related to gene regulation. These results demonstrate the importance of explicitly modeling non-dosage dependent and intragenic epistatic effects when predicting expression. AVAILABILITY AND IMPLEMENTATION Source code and binaries are freely available at https://github.com/rapturous/HAPLEX. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Berk A Alpay
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Pinar Demetci
- Department of Computer Science and Center for Computational Biology, Brown University, Providence, RI 02912, USA
| | - Sorin Istrail
- Department of Computer Science and Center for Computational Biology, Brown University, Providence, RI 02912, USA
| | - Derek Aguiar
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
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31
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Form and relationship of the social networks of the New Testament. SOCIAL NETWORK ANALYSIS AND MINING 2019. [DOI: 10.1007/s13278-019-0577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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32
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Esposito D, Weile J, Shendure J, Starita LM, Papenfuss AT, Roth FP, Fowler DM, Rubin AF. MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect. Genome Biol 2019; 20:223. [PMID: 31679514 PMCID: PMC6827219 DOI: 10.1186/s13059-019-1845-6] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 10/01/2019] [Indexed: 11/10/2022] Open
Abstract
Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.
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Affiliation(s)
- Daniel Esposito
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Jochen Weile
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Lea M Starita
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Anthony T Papenfuss
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia
- Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
- Department of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Frederick P Roth
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Canadian Institute for Advanced Research, Toronto, ON, Canada.
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Canadian Institute for Advanced Research, Toronto, ON, Canada.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia.
- Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
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33
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Kemble H, Nghe P, Tenaillon O. Recent insights into the genotype-phenotype relationship from massively parallel genetic assays. Evol Appl 2019; 12:1721-1742. [PMID: 31548853 PMCID: PMC6752143 DOI: 10.1111/eva.12846] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/21/2019] [Accepted: 07/02/2019] [Indexed: 12/20/2022] Open
Abstract
With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.
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Affiliation(s)
- Harry Kemble
- Infection, Antimicrobials, Modelling, Evolution, INSERM, Unité Mixte de Recherche 1137Université Paris Diderot, Université Paris NordParisFrance
- École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), UMR CNRS‐ESPCI CBI 8231PSL Research UniversityParis Cedex 05France
| | - Philippe Nghe
- École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), UMR CNRS‐ESPCI CBI 8231PSL Research UniversityParis Cedex 05France
| | - Olivier Tenaillon
- Infection, Antimicrobials, Modelling, Evolution, INSERM, Unité Mixte de Recherche 1137Université Paris Diderot, Université Paris NordParisFrance
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34
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Li X, Lalić J, Baeza-Centurion P, Dhar R, Lehner B. Changes in gene expression predictably shift and switch genetic interactions. Nat Commun 2019; 10:3886. [PMID: 31467279 PMCID: PMC6715729 DOI: 10.1038/s41467-019-11735-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 07/29/2019] [Indexed: 11/18/2022] Open
Abstract
Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often—but not always—be predicted by hierarchical mechanistic models. Non-additive genetic interactions are plastic and can complicate genetic prediction. Here, using deep mutagenesis of the lambda repressor, Li et al. reveal that changes in gene expression can alter the strength and direction of genetic interactions between mutations in many genes and develop mathematical models for predicting them.
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Affiliation(s)
- Xianghua Li
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Jasna Lalić
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Pablo Baeza-Centurion
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Riddhiman Dhar
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain. .,ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
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35
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Atkinson JT, Jones AM, Zhou Q, Silberg JJ. Circular permutation profiling by deep sequencing libraries created using transposon mutagenesis. Nucleic Acids Res 2019; 46:e76. [PMID: 29912470 PMCID: PMC6061844 DOI: 10.1093/nar/gky255] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 03/28/2018] [Indexed: 12/17/2022] Open
Abstract
Deep mutational scanning has been used to create high-resolution DNA sequence maps that illustrate the functional consequences of large numbers of point mutations. However, this approach has not yet been applied to libraries of genes created by random circular permutation, an engineering strategy that is used to create open reading frames that express proteins with altered contact order. We describe a new method, termed circular permutation profiling with DNA sequencing (CPP-seq), which combines a one-step transposon mutagenesis protocol for creating libraries with a functional selection, deep sequencing and computational analysis to obtain unbiased insight into a protein's tolerance to circular permutation. Application of this method to an adenylate kinase revealed that CPP-seq creates two types of vectors encoding each circularly permuted gene, which differ in their ability to express proteins. Functional selection of this library revealed that >65% of the sampled vectors that express proteins are enriched relative to those that cannot translate proteins. Mapping enriched sequences onto structure revealed that the mobile AMP binding and rigid core domains display greater tolerance to backbone fragmentation than the mobile lid domain, illustrating how CPP-seq can be used to relate a protein's biophysical characteristics to the retention of activity upon permutation.
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Affiliation(s)
- Joshua T Atkinson
- Systems, Synthetic, and Physical Biology Graduate Program, Rice University, 6100 Main MS-180, Houston, TX 77005, USA
| | - Alicia M Jones
- Department of BioSciences, Rice University, MS-140, 6100 Main Street, Houston, TX 77005, USA
| | - Quan Zhou
- Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Jonathan J Silberg
- Department of BioSciences, Rice University, MS-140, 6100 Main Street, Houston, TX 77005, USA.,Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
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36
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Abstract
Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.
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37
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Polte C, Wedemeyer D, Oliver KE, Wagner J, Bijvelds MJC, Mahoney J, de Jonge HR, Sorscher EJ, Ignatova Z. Assessing cell-specific effects of genetic variations using tRNA microarrays. BMC Genomics 2019; 20:549. [PMID: 31307398 PMCID: PMC6632033 DOI: 10.1186/s12864-019-5864-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background By definition, effect of synonymous single-nucleotide variants (SNVs) on protein folding and function are neutral, as they alter the codon and not the encoded amino acid. Recent examples indicate tissue-specific and transfer RNA (tRNA)-dependent effects of some genetic variations arguing against neutrality of synonymous SNVs for protein biogenesis. Results We performed systematic analysis of tRNA abunandance across in various models used in cystic fibrosis (CF) research and drug development, including Fischer rat thyroid (FRT) cells, patient-derived primary human bronchial epithelia (HBE) from lung biopsies, primary human nasal epithelia (HNE) from nasal curettage, intestinal organoids, and airway progenitor-directed differentiation of human induced pluripotent stem cells (iPSCs). These were compared to an immortalized CF bronchial cell model (CFBE41o−) and two widely used laboratory cell lines, HeLa and HEK293. We discovered that specific synonymous SNVs exhibited differential effects which correlated with variable concentrations of cognate tRNAs. Conclusions Our results highlight ways in which the presence of synonymous SNVs may alter local kinetics of mRNA translation; and thus, impact protein biogenesis and function. This effect is likely to influence results from mechansistic analysis and/or drug screeining efforts, and establishes importance of cereful model system selection based on genetic variation profile. Electronic supplementary material The online version of this article (10.1186/s12864-019-5864-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Christine Polte
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany
| | - Daniel Wedemeyer
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany
| | - Kathryn E Oliver
- Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Johannes Wagner
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany
| | - Marcel J C Bijvelds
- Gastroenterology and Hepatology Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - John Mahoney
- Cystic Fibrosis Foundation CFFT Lab, Lexington, MA, 02421, USA
| | - Hugo R de Jonge
- Gastroenterology and Hepatology Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Eric J Sorscher
- Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Zoya Ignatova
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany.
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38
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Rotem A, Serohijos AWR, Chang CB, Wolfe JT, Fischer AE, Mehoke TS, Zhang H, Tao Y, Lloyd Ung W, Choi JM, Rodrigues JV, Kolawole AO, Koehler SA, Wu S, Thielen PM, Cui N, Demirev PA, Giacobbi NS, Julian TR, Schwab K, Lin JS, Smith TJ, Pipas JM, Wobus CE, Feldman AB, Weitz DA, Shakhnovich EI. Evolution on the Biophysical Fitness Landscape of an RNA Virus. Mol Biol Evol 2019; 35:2390-2400. [PMID: 29955873 PMCID: PMC6188569 DOI: 10.1093/molbev/msy131] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.
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Affiliation(s)
- Assaf Rotem
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Adrian W R Serohijos
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA.,Département de Biochimie et Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, Montréal, QC, Canada
| | - Connie B Chang
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT
| | - Joshua T Wolfe
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Audrey E Fischer
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Thomas S Mehoke
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Huidan Zhang
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,Key Laboratory of Cell Biology, Department of Cell Biology, Ministry of Public Health, China Medical University, Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education, China Medical University, Shenyang, China
| | - Ye Tao
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - W Lloyd Ung
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Jeong-Mo Choi
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - João V Rodrigues
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - Abimbola O Kolawole
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | - Stephan A Koehler
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Susan Wu
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Peter M Thielen
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Naiwen Cui
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Plamen A Demirev
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | | | - Timothy R Julian
- Environmental Health Sciences and the Hopkins Water Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland
| | - Kellogg Schwab
- Environmental Health Sciences and the Hopkins Water Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jeffrey S Lin
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Thomas J Smith
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch at Galveston, Galveston, TX
| | - James M Pipas
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Christiane E Wobus
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | - Andrew B Feldman
- Department of Emergency Medicine, Johns Hopkins Medicine, Baltimore, MD
| | - David A Weitz
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
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39
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Domingo J, Baeza-Centurion P, Lehner B. The Causes and Consequences of Genetic Interactions (Epistasis). Annu Rev Genomics Hum Genet 2019; 20:433-460. [PMID: 31082279 DOI: 10.1146/annurev-genom-083118-014857] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.
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Affiliation(s)
- Júlia Domingo
- Systems Biology Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, 08003 Barcelona, Spain; , ,
| | - Pablo Baeza-Centurion
- Systems Biology Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, 08003 Barcelona, Spain; , ,
| | - Ben Lehner
- Systems Biology Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, 08003 Barcelona, Spain; , , .,Universitat Pompeu Fabra, 08003 Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
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40
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Abstract
For nearly a century adaptive landscapes have provided overviews of the evolutionary process and yet they remain metaphors. We redefine adaptive landscapes in terms of biological processes rather than descriptive phenomenology. We focus on the underlying mechanisms that generate emergent properties such as epistasis, dominance, trade-offs and adaptive peaks. We illustrate the utility of landscapes in predicting the course of adaptation and the distribution of fitness effects. We abandon aged arguments concerning landscape ruggedness in favor of empirically determining landscape architecture. In so doing, we transform the landscape metaphor into a scientific framework within which causal hypotheses can be tested.
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Affiliation(s)
- Xiao Yi
- BioTechnology Institute, University of Minnesota, St. Paul, MN
| | - Antony M Dean
- BioTechnology Institute, University of Minnesota, St. Paul, MN
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN
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41
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Horovitz A, Fleisher RC, Mondal T. Double-mutant cycles: new directions and applications. Curr Opin Struct Biol 2019; 58:10-17. [PMID: 31029859 DOI: 10.1016/j.sbi.2019.03.025] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 03/20/2019] [Indexed: 11/17/2022]
Abstract
Double-mutant cycle (DMC) analysis is a powerful approach for detecting and quantifying the energetics of both direct and long-range interactions in proteins and other chemical systems. It can also be used to unravel higher-order interactions (e.g. three-body effects) that lead to cooperativity in protein folding and function. In this review, we describe new applications of DMC analysis based on advances in native mass spectrometry and high-throughput methods such as next generation sequencing and protein complementation assays. These developments have facilitated carrying out high-throughput DMC analysis, which can be used to characterize increasingly higher-order interactions and very large interaction networks in proteins. Such studies have provided insights into the extent of cooperativity (epistasis) in protein structures. High-throughput DMC studies have also been used to validate correlated mutation analysis and can provide restraints for protein docking.
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Affiliation(s)
- Amnon Horovitz
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Rachel C Fleisher
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tridib Mondal
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
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42
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Gonzalez CE, Ostermeier M. Pervasive Pairwise Intragenic Epistasis among Sequential Mutations in TEM-1 β-Lactamase. J Mol Biol 2019; 431:1981-1992. [PMID: 30922874 DOI: 10.1016/j.jmb.2019.03.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/25/2019] [Accepted: 03/13/2019] [Indexed: 12/25/2022]
Abstract
Interactions between mutations play a central role in shaping the fitness landscape, but a clear picture of intragenic epistasis has yet to emerge. To further reveal the prevalence and patterns of intragenic epistasis, we present a survey of epistatic interactions between sequential mutations in TEM-1 β-lactamase. We measured the fitness effect of ~12,000 pairs of consecutive amino acid substitutions and used our previous study of the fitness effects of single amino acid substitutions to calculate epistasis for over 8000 mutation pairs. Since sequential mutations are prone to physically interact, we postulated that our study would be surveying specific epistasis instead of nonspecific epistasis. We found widespread negative epistasis, especially in beta-strands, and a high frequency of negative sign epistasis among individually beneficial mutations. Negative epistasis (52%) occurred 7.6 times as frequently as positive epistasis (6.8%). Buried residues experienced more negative epistasis that surface-exposed residues. However, TEM-1 exhibited a couple of hotspots for positive epistasis, most notably L221/ R222 at which many combinations of mutations positively interacted. This study is the first to systematically examine pairwise epistasis throughout an entire protein performing its native function in its native host.
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Affiliation(s)
- Courtney E Gonzalez
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA
| | - Marc Ostermeier
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA.
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43
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Adams RM, Kinney JB, Walczak AM, Mora T. Epistasis in a Fitness Landscape Defined by Antibody-Antigen Binding Free Energy. Cell Syst 2019; 8:86-93.e3. [PMID: 30611676 PMCID: PMC6487650 DOI: 10.1016/j.cels.2018.12.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 10/12/2018] [Accepted: 12/07/2018] [Indexed: 12/16/2022]
Abstract
Epistasis is the phenomenon by which the effect of a mutation depends on its genetic background. While it is usually defined in terms of organismal fitness, for single proteins, it must reflect physical interactions among residues. Here, we systematically extract the specific contribution pairwise epistasis makes to the physical affinity of antibody-antigen binding relevant to affinity maturation, a process of accelerated Darwinian evolution. We find that, among competing definitions of affinity, the binding free energy is the most appropriate to describe epistasis. We show that epistasis is pervasive, accounting for 25%-35% of variability, of which a large fraction is beneficial. This work suggests that epistasis both constrains, through negative epistasis, and enlarges, through positive epistasis, the set of possible evolutionary paths that can produce high-affinity sequences during repeated rounds of mutation and selection.
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Affiliation(s)
- Rhys M Adams
- CNRS, Laboratoire de Physique Théorique, UPMC (Sorbonne University), and École Normale Supérieure (PSL), 24 rue Lhomond, Paris 75005, France; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 1 Bungtown Rd., Cold Spring Harbor, NY 11724, USA
| | - Justin B Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 1 Bungtown Rd., Cold Spring Harbor, NY 11724, USA
| | - Aleksandra M Walczak
- CNRS, Laboratoire de Physique Théorique, UPMC (Sorbonne University), and École Normale Supérieure (PSL), 24 rue Lhomond, Paris 75005, France.
| | - Thierry Mora
- CNRS, Laboratoire de Physique Statistique, UPMC (Sorbonne University), Paris-Diderot University, and École Normale Supérieure (PSL), 24, rue Lhomond, Paris 75005, France.
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44
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Fragata I, Blanckaert A, Dias Louro MA, Liberles DA, Bank C. Evolution in the light of fitness landscape theory. Trends Ecol Evol 2019; 34:69-82. [DOI: 10.1016/j.tree.2018.10.009] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/16/2018] [Accepted: 10/17/2018] [Indexed: 01/28/2023]
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45
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Hartman EC, Lobba MJ, Favor AH, Robinson SA, Francis MB, Tullman-Ercek D. Experimental Evaluation of Coevolution in a Self-Assembling Particle. Biochemistry 2018; 58:1527-1538. [DOI: 10.1021/acs.biochem.8b00948] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Emily C. Hartman
- Department of Chemistry, University of California, Berkeley, California 94720-1460, United States
| | - Marco J. Lobba
- Department of Chemistry, University of California, Berkeley, California 94720-1460, United States
| | - Andrew H. Favor
- Department of Chemistry, University of California, Berkeley, California 94720-1460, United States
| | - Stephanie A. Robinson
- Department of Chemistry, University of California, Berkeley, California 94720-1460, United States
| | - Matthew B. Francis
- Department of Chemistry, University of California, Berkeley, California 94720-1460, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratories, Berkeley, California 94720-1460, United States
| | - Danielle Tullman-Ercek
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Technological Institute E136, Evanston, Illinois 60208-3120, United States
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46
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Good BH, Hallatschek O. Effective models and the search for quantitative principles in microbial evolution. Curr Opin Microbiol 2018; 45:203-212. [PMID: 30530175 PMCID: PMC6599682 DOI: 10.1016/j.mib.2018.11.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/17/2018] [Accepted: 11/15/2018] [Indexed: 12/14/2022]
Abstract
Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.
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Affiliation(s)
- Benjamin H Good
- Department of Physics, University of California, Berkeley, United States; Department of Bioengineering, University of California, Berkeley, United States.
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, United States; Department of Integrative Biology, University of California, Berkeley, United States
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47
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Passagem-Santos D, Zacarias S, Perfeito L. Power law fitness landscapes and their ability to predict fitness. Heredity (Edinb) 2018; 121:482-498. [PMID: 30190560 PMCID: PMC6180038 DOI: 10.1038/s41437-018-0143-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 08/15/2018] [Accepted: 08/16/2018] [Indexed: 12/19/2022] Open
Abstract
Whether or not evolution by natural selection is predictable depends on the existence of general patterns shaping the way mutations interact with the genetic background. This interaction, also known as epistasis, has been observed during adaptation (macroscopic epistasis) and in individual mutations (microscopic epistasis). Interestingly, a consistent negative correlation between the fitness effect of beneficial mutations and background fitness (known as diminishing returns epistasis) has been observed across different species and conditions. We tested whether the adaptation pattern of an additional species, Schizosaccharomyces pombe, followed the same trend. We used strains that differed by the presence of large karyotype differences and observed the same pattern of fitness convergence. Using these data along with published datasets, we measured the ability of different models to describe adaptation rates. We found that a phenotype-fitness landscape shaped like a power law is able to correctly predict adaptation dynamics in a variety of species and conditions. Furthermore we show that this model can provide a link between the observed macroscopic and microscopic epistasis. It may be very useful in the development of algorithms able to predict the adaptation of microorganisms from measures of the current phenotypes. Overall, our results suggest that even though adaptation quickly slows down, populations adapting to lab conditions may be quite far from a fitness peak.
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48
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Pairwise and higher-order genetic interactions during the evolution of a tRNA. Nature 2018; 558:117-121. [PMID: 29849145 PMCID: PMC6193533 DOI: 10.1038/s41586-018-0170-7] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Accepted: 04/09/2018] [Indexed: 01/09/2023]
Abstract
A central question in genetics and evolution is the extent to which the outcomes of mutations change depending on the genetic context in which they occur1-3. Pairwise interactions between mutations have been systematically mapped within4-18 and between 19 genes, and have been shown to contribute substantially to phenotypic variation among individuals 20 . However, the extent to which genetic interactions themselves are stable or dynamic across genotypes is unclear21, 22. Here we quantify more than 45,000 genetic interactions between the same 87 pairs of mutations across more than 500 closely related genotypes of a yeast tRNA. Notably, all pairs of mutations interacted in at least 9% of genetic backgrounds and all pairs switched from interacting positively to interacting negatively in different genotypes (false discovery rate < 0.1). Higher-order interactions are also abundant and dynamic across genotypes. The epistasis in this tRNA means that all individual mutations switch from detrimental to beneficial, even in closely related genotypes. As a consequence, accurate genetic prediction requires mutation effects to be measured across different genetic backgrounds and the use of higher-order epistatic terms.
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49
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Li C, Zhang J. Multi-environment fitness landscapes of a tRNA gene. Nat Ecol Evol 2018; 2:1025-1032. [PMID: 29686238 PMCID: PMC5966336 DOI: 10.1038/s41559-018-0549-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Accepted: 03/27/2018] [Indexed: 11/09/2022]
Abstract
A fitness landscape (FL) describes the genotype-fitness relationship in a given environment. To explain and predict evolution, it is imperative to measure the FL in multiple environments because the natural environment changes frequently. Using a high-throughput method that combines precise gene replacement with next-generation sequencing, we determine the in vivo FL of a yeast tRNA gene comprising over 23,000 genotypes in four environments. Although genotype-by-environment interaction (G×E) is abundantly detected, its pattern is so simple that we can transform an existing FL to that in a new environment with fitness measures of only a few genotypes in the new environment. Under each environment, we observe prevalent, negatively biased epistasis between mutations (G×G). Epistasis-by-environment interaction (G×G×E) is also prevalent, but trends in epistasis difference between environments are predictable. Our study thus reveals simple rules underlying seemingly complex FLs, opening the door to understanding and predicting FLs in general.
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Affiliation(s)
- Chuan Li
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.,Department of Biology, Stanford University, Stanford, CA, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
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50
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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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