1
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Tutaj H, Tomala K, Pirog A, Marszałek M, Korona R. Extreme positive epistasis for fitness in monosomic yeast strains. eLife 2024; 12:RP87455. [PMID: 39417696 DOI: 10.7554/elife.87455] [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: 10/19/2024] Open
Abstract
The loss of a single chromosome in a diploid organism halves the dosage of many genes and is usually accompanied by a substantial decrease in fitness. We asked whether this decrease simply reflects the joint damage caused by individual gene dosage deficiencies. We measured the fitness effects of single heterozygous gene deletions in yeast and combined them for each chromosome. This predicted a negative growth rate, that is, lethality, for multiple monosomies. However, monosomic strains remained alive and grew as if much (often most) of the damage caused by single mutations had disappeared, revealing an exceptionally large and positive epistatic component of fitness. We looked for functional explanations by analyzing the transcriptomes. There was no evidence of increased (compensatory) gene expression on the monosomic chromosomes. Nor were there signs of the cellular stress response that would be expected if monosomy led to protein destabilization and thus cytotoxicity. Instead, all monosomic strains showed extensive upregulation of genes encoding ribosomal proteins, but in an indiscriminate manner that did not correspond to their altered dosage. This response did not restore the stoichiometry required for efficient biosynthesis, which probably became growth limiting, making all other mutation-induced metabolic defects much less important. In general, the modular structure of the cell leads to an effective fragmentation of the total mutational load. Defects outside the module(s) currently defining fitness lose at least some of their relevance, producing the epiphenomenon of positive interactions between individually negative effects.
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Affiliation(s)
- Hanna Tutaj
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Cracow, Poland
| | - Katarzyna Tomala
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Cracow, Poland
| | - Adrian Pirog
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Cracow, Poland
| | - Marzena Marszałek
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Cracow, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Cracow, Poland
| | - Ryszard Korona
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Cracow, Poland
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2
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Alpay BA, Desai MM. Effects of selection stringency on the outcomes of directed evolution. PLoS One 2024; 19:e0311438. [PMID: 39401192 PMCID: PMC11472920 DOI: 10.1371/journal.pone.0311438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 09/18/2024] [Indexed: 10/17/2024] Open
Abstract
Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.
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Affiliation(s)
- Berk A. Alpay
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States of America
| | - Michael M. Desai
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States of America
- Department of Physics, Harvard University, Cambridge, MA, United States of America
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3
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Ardell SM, Martsul A, Johnson MS, Kryazhimskiy S. Environment-independent distribution of mutational effects emerges from microscopic epistasis. Science 2024; 386:87-92. [PMID: 39361740 DOI: 10.1126/science.adn0753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 08/22/2024] [Indexed: 10/05/2024]
Abstract
Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, in which the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.
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Affiliation(s)
- Sarah M Ardell
- Department of Ecology, Behavior and Evolution, University of California, San Diego, La Jolla, CA, USA
| | - Alena Martsul
- Department of Ecology, Behavior and Evolution, University of California, San Diego, La Jolla, CA, USA
| | - Milo S Johnson
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California, San Diego, La Jolla, CA, USA
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4
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Ghiaci P, Jouhten P, Martyushenko N, Roca-Mesa H, Vázquez J, Konstantinidis D, Stenberg S, Andrejev S, Grkovska K, Mas A, Beltran G, Almaas E, Patil KR, Warringer J. Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes. Mol Syst Biol 2024; 20:1109-1133. [PMID: 39174863 PMCID: PMC11450223 DOI: 10.1038/s44320-024-00059-0] [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: 03/21/2024] [Revised: 07/17/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish.
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Affiliation(s)
- Payam Ghiaci
- Department of Chemistry and Molecular Biology, University of Gothenburg, PO Box 462, Gothenburg, 40530, Sweden
- Department of Biorefinery and Energy, High-throughput Centre, Research Institutes of Sweden, Örnsköldsvik, 89250, Sweden
- European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | - Paula Jouhten
- European Molecular Biology Laboratory, Heidelberg, 69117, Germany
- VTT Technical Research Centre of Finland Ltd, Espoo, 02044 VTT, Finland
- Aalto University, Department of Bioproducts and Biosystems, Espoo, 02150, Finland
| | - Nikolay Martyushenko
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Helena Roca-Mesa
- Universitat Rovira i Virgili, Dept. Bioquímica i Biotecnologia, Facultat d'Enologia, Tarragona, 43007, Spain
| | - Jennifer Vázquez
- Universitat Rovira i Virgili, Dept. Bioquímica i Biotecnologia, Facultat d'Enologia, Tarragona, 43007, Spain
- Centro Tecnológico del Vino-VITEC, Carretera de Porrera Km. 1, Falset, 43730, Spain
| | | | - Simon Stenberg
- Department of Chemistry and Molecular Biology, University of Gothenburg, PO Box 462, Gothenburg, 40530, Sweden
| | - Sergej Andrejev
- European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | | | - Albert Mas
- Universitat Rovira i Virgili, Dept. Bioquímica i Biotecnologia, Facultat d'Enologia, Tarragona, 43007, Spain
| | - Gemma Beltran
- Universitat Rovira i Virgili, Dept. Bioquímica i Biotecnologia, Facultat d'Enologia, Tarragona, 43007, Spain
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| | - Kiran R Patil
- European Molecular Biology Laboratory, Heidelberg, 69117, Germany.
- Medical Research Council (MRC) Toxicology Unit, University of Cambridge, Cambridge, CB2 1QR, UK.
| | - Jonas Warringer
- Department of Chemistry and Molecular Biology, University of Gothenburg, PO Box 462, Gothenburg, 40530, Sweden.
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5
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González-González A, Batarseh TN, Rodríguez-Verdugo A, Gaut BS. Patterns of Fitness and Gene Expression Epistasis Generated by Beneficial Mutations in the rho and rpoB Genes of Escherichia coli during High-Temperature Adaptation. Mol Biol Evol 2024; 41:msae187. [PMID: 39235107 PMCID: PMC11414761 DOI: 10.1093/molbev/msae187] [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: 05/12/2024] [Revised: 08/27/2024] [Accepted: 08/30/2024] [Indexed: 09/06/2024] Open
Abstract
Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2 °C). The double mutants exhibited patterns of fitness epistasis that included diminishing returns epistasis at 42.2 °C, stronger diminishing returns between mutations with larger beneficial effects and both negative and positive (sign) epistasis across environments (20.0 °C and 37.0 °C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly connected hub genes in coexpression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both coexpression and protein-protein interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination but that was not the case. Altogether, gene expression and coexpression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression toward the unstressed state.
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Affiliation(s)
- Andrea González-González
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
- Department of Biology, University of Florida, Gainesville, FL, USA
| | - Tiffany N Batarseh
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
- Department of Integrative Biology, UC Berkeley, Berkeley, CA, USA
| | | | - Brandon S Gaut
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
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6
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Izutsu M, Lake DM, Matson ZWD, Dodson JP, Lenski RE. Effects of periodic bottlenecks on the dynamics of adaptive evolution in microbial populations. MICROBIOLOGY (READING, ENGLAND) 2024; 170. [PMID: 39292609 PMCID: PMC11410044 DOI: 10.1099/mic.0.001494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/20/2024]
Abstract
Population bottlenecks can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, each founder that survives a bottleneck can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly. A theoretical model predicted that the rate of fitness gains should be maximized using ~8-fold dilutions. Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation using numerical simulations and experimental populations of Escherichia coli. Our simulations confirm the model's prediction when populations evolve in a regime where beneficial mutations are rare and waiting times between successful mutations are long. However, more extreme dilutions maximize fitness gains in simulations when beneficial mutations are common and clonal interference prevents most of them from fixing. To examine these predictions, we propagated 48 E. coli populations with 2-, 8-, 100-, and 1000-fold dilutions for 150 days. Adaptation began earlier and fitness gains were greater with 100- and 1000-fold dilutions than with 8-fold dilutions, consistent with the simulations when beneficial mutations are common. However, the selection pressures in the 2-fold treatment were qualitatively different from the other treatments, violating a critical assumption of the model and simulations. Thus, varying the dilution factor during periodic bottlenecks can have multiple effects on the dynamics of adaptation caused by differential losses of diversity, different numbers of generations, and altered selection.
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Affiliation(s)
- Minako Izutsu
- Department of Microbiology, Genetics and Immunology, Michigan State University, East Lansing, MI, USA
- Ecology, Evolution and Behavior Program, Michigan State University, East Lansing, MI, USA
- Present address: DeNA Co., Shibuya, Tokyo, Japan
| | - Devin M Lake
- Ecology, Evolution and Behavior Program, Michigan State University, East Lansing, MI, USA
- Department of Integrative Biology, Michigan State University, East Lansing, MI, USA
| | - Zachary W D Matson
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Jack P Dodson
- Department of Microbiology, Genetics and Immunology, Michigan State University, East Lansing, MI, USA
- Present address: Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Richard E Lenski
- Department of Microbiology, Genetics and Immunology, Michigan State University, East Lansing, MI, USA
- Ecology, Evolution and Behavior Program, Michigan State University, East Lansing, MI, USA
- Department of Integrative Biology, Michigan State University, East Lansing, MI, USA
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7
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Abreu CI, Mathur S, Petrov DA. Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments. Nat Ecol Evol 2024; 8:1760-1775. [PMID: 39020024 DOI: 10.1038/s41559-024-02475-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 06/11/2024] [Indexed: 07/19/2024]
Abstract
Evolution in a static laboratory environment often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. Here we find that fitness in fluctuating environments indeed often deviates from the time average, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments. We use a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results show that environmental fluctuations impact fitness and suggest that variance in static environments can explain these impacts.
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Affiliation(s)
- Clare I Abreu
- Department of Biology, Stanford University, Stanford, CA, USA.
| | - Shaili Mathur
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Dmitri A Petrov
- Department of Biology, Stanford University, Stanford, CA, USA.
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8
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Johnston KE, Almhjell PJ, Watkins-Dulaney EJ, Liu G, Porter NJ, Yang J, Arnold FH. A combinatorially complete epistatic fitness landscape in an enzyme active site. Proc Natl Acad Sci U S A 2024; 121:e2400439121. [PMID: 39074291 PMCID: PMC11317637 DOI: 10.1073/pnas.2400439121] [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: 01/20/2024] [Accepted: 06/17/2024] [Indexed: 07/31/2024] Open
Abstract
Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.
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Affiliation(s)
- Kadina E. Johnston
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
| | - Patrick J. Almhjell
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
| | - Ella J. Watkins-Dulaney
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
| | - Grace Liu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
| | - Nicholas J. Porter
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
| | - Jason Yang
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
| | - Frances H. Arnold
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
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9
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Ding K, Chin M, Zhao Y, Huang W, Mai BK, Wang H, Liu P, Yang Y, Luo Y. Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering. Nat Commun 2024; 15:6392. [PMID: 39080249 PMCID: PMC11289365 DOI: 10.1038/s41467-024-50698-y] [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: 05/29/2024] [Accepted: 07/19/2024] [Indexed: 08/02/2024] Open
Abstract
The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.
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Affiliation(s)
- Kerr Ding
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Michael Chin
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Yunlong Zhao
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Wei Huang
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Binh Khanh Mai
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Huanan Wang
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Peng Liu
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
| | - Yang Yang
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA.
- Biomolecular Science and Engineering (BMSE) Program, University of California, Santa Barbara, CA, 93106, USA.
| | - Yunan Luo
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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10
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Ardell S, Martsul A, Johnson MS, Kryazhimskiy S. Environment-independent distribution of mutational effects emerges from microscopic epistasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.18.567655. [PMID: 38014325 PMCID: PMC10680819 DOI: 10.1101/2023.11.18.567655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, where the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global-epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.
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Affiliation(s)
- Sarah Ardell
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Alena Martsul
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Milo S. Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA 94720
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
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11
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Chen P, Zhang J. The loci of environmental adaptation in a model eukaryote. Nat Commun 2024; 15:5672. [PMID: 38971805 PMCID: PMC11227561 DOI: 10.1038/s41467-024-50002-y] [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: 12/19/2023] [Accepted: 06/25/2024] [Indexed: 07/08/2024] Open
Abstract
While the underlying genetic changes have been uncovered in some cases of adaptive evolution, the lack of a systematic study prevents a general understanding of the genomic basis of adaptation. For example, it is unclear whether protein-coding or noncoding mutations are more important to adaptive evolution and whether adaptations to different environments are brought by genetic changes distributed in diverse genes and biological processes or concentrated in a core set. We here perform laboratory evolution of 3360 Saccharomyces cerevisiae populations in 252 environments of varying levels of stress. We find the yeast adaptations to be primarily fueled by large-effect coding mutations overrepresented in a relatively small gene set, despite prevalent antagonistic pleiotropy across environments. Populations generally adapt faster in more stressful environments, partly because of greater benefits of the same mutations in more stressful environments. These and other findings from this model eukaryote help unravel the genomic principles of environmental adaptation.
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Affiliation(s)
- Piaopiao Chen
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, 48109, USA
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, 48109, USA.
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12
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Alpay BA, Desai MM. Effects of selection stringency on the outcomes of directed evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.09.598029. [PMID: 38895455 PMCID: PMC11185767 DOI: 10.1101/2024.06.09.598029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.
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Affiliation(s)
- Berk A. Alpay
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Michael M. Desai
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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13
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Diaz-Colunga J, Skwara A, Vila JCC, Bajic D, Sanchez A. Global epistasis and the emergence of function in microbial consortia. Cell 2024; 187:3108-3119.e30. [PMID: 38776921 DOI: 10.1016/j.cell.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/06/2023] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, 28049 Madrid, Spain; Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007 Salamanca, Spain.
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Biotechnology, Delft University of Technology, Delft 2628 CD, the Netherlands.
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, 28049 Madrid, Spain; Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007 Salamanca, Spain.
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14
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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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15
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Mori M, Patsalo V, Euler C, Williamson JR, Scott M. Proteome partitioning constraints in long-term laboratory evolution. Nat Commun 2024; 15:4087. [PMID: 38744842 PMCID: PMC11094134 DOI: 10.1038/s41467-024-48447-2] [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: 08/04/2023] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.
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Affiliation(s)
- Matteo Mori
- Department of Physics, University of California at San Diego, La Jolla, CA, USA
| | - Vadim Patsalo
- Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Christian Euler
- Department of Chemical Engineering, University of Waterloo, Waterloo, ON, Canada
| | - James R Williamson
- Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Matthew Scott
- Waterloo Centre for Microbial Research and the Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
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16
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Hinz A, Amado A, Kassen R, Bank C, Wong A. Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli. Mol Biol Evol 2024; 41:msae086. [PMID: 38709811 PMCID: PMC11110942 DOI: 10.1093/molbev/msae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/11/2024] [Accepted: 04/30/2024] [Indexed: 05/08/2024] Open
Abstract
The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.
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Affiliation(s)
- Aaron Hinz
- Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada
| | - André Amado
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
- Division of Theoretical Ecology and Evolution, Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Evolutionary Dynamics Group, Gulbenkian Science Institute, Oeiras, Portugal
| | - Rees Kassen
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada
| | - Claudia Bank
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
- Division of Theoretical Ecology and Evolution, Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Evolutionary Dynamics Group, Gulbenkian Science Institute, Oeiras, Portugal
| | - Alex Wong
- Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
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17
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Boffi NM, Guo Y, Rycroft CH, Amir A. How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution. eLife 2024; 12:RP87895. [PMID: 38376390 PMCID: PMC10942580 DOI: 10.7554/elife.87895] [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: 02/21/2024] Open
Abstract
The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population's mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn.
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Affiliation(s)
- Nicholas M Boffi
- Courant Institute of Mathematical Sciences, New York UniversityNew YorkUnited States
| | - Yipei Guo
- Janelia Research CampusAshburnUnited States
| | - Chris H Rycroft
- Department of Mathematics, University of Wisconsin–MadisonMadisonUnited States
- Mathematics Group, Lawrence Berkeley National LaboratoryBerkeleyUnited States
| | - Ariel Amir
- Weizmann Institute of ScienceRehovotIsrael
- John A. Paulson School of Engineering and Applied Sciences, Harvard UniversityCambridgeUnited States
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18
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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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19
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Zeng M, Sarker B, Rondthaler SN, Vu V, Andrews LB. Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape. ACS Synth Biol 2024; 13:568-589. [PMID: 38206199 DOI: 10.1021/acssynbio.3c00543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.
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Affiliation(s)
- Min Zeng
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Stephen N Rondthaler
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Vanessa Vu
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B Andrews
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
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20
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Couce A, Limdi A, Magnan M, Owen SV, Herren CM, Lenski RE, Tenaillon O, Baym M. Changing fitness effects of mutations through long-term bacterial evolution. Science 2024; 383:eadd1417. [PMID: 38271521 DOI: 10.1126/science.add1417] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/12/2023] [Indexed: 01/27/2024]
Abstract
The distribution of fitness effects of new mutations shapes evolution, but it is challenging to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 generations of evolution, we quantify the fitness effects of insertion mutations in every gene. Macroscopically, the fraction of deleterious mutations changed little over time whereas the beneficial tail declined sharply, approaching an exponential distribution. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel; altered essentiality is only partly explained by structural variation. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of the importance of loss-of-function mutations. Taken together, these results reveal the dynamic-but statistically predictable-nature of mutational fitness effects.
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Affiliation(s)
- Alejandro Couce
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain
| | - Anurag Limdi
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Melanie Magnan
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
| | - Siân V Owen
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Cristina M Herren
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Department of Marine and Environmental Sciences, Northeastern University, Boston, MA 02115, USA
| | - Richard E Lenski
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
- Program in Ecology, Evolution, and Behavior, Michigan State University, East Lansing, MI 48824, USA
| | - Olivier Tenaillon
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
- Université Paris Cité, Inserm, Institut Cochin, F-75014 Paris, France
| | - Michael Baym
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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21
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Schartl M, Lu Y. Validity of Xiphophorus fish as models for human disease. Dis Model Mech 2024; 17:dmm050382. [PMID: 38299666 PMCID: PMC10855230 DOI: 10.1242/dmm.050382] [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: 02/02/2024] Open
Abstract
Platyfish and swordtails of the genus Xiphophorus provide a well-established model for melanoma research and have become well known for this feature. Recently, modelling approaches for other human diseases in Xiphophorus have been developed or are emerging. This Review provides a comprehensive summary of these models and discusses how findings from basic biological and molecular studies and their translation to medical research demonstrate that Xiphophorus models have face, construct and predictive validity for studying a broad array of human diseases. These models can thus improve our understanding of disease mechanisms to benefit patients.
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Affiliation(s)
- Manfred Schartl
- The Xiphophorus Genetic Stock Center, Department of Chemistry and Biochemistry, Texas State University, San Marcos, TX 78666, USA
- Developmental Biochemistry, Theodor-Boveri Institute, Biocenter, University of Würzburg, Würzburg 97074, Germany
| | - Yuan Lu
- The Xiphophorus Genetic Stock Center, Department of Chemistry and Biochemistry, Texas State University, San Marcos, TX 78666, USA
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22
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Eccleston RC, Manko E, Campino S, Clark TG, Furnham N. A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations. eLife 2023; 12:e84756. [PMID: 38132182 PMCID: PMC10807863 DOI: 10.7554/elife.84756] [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: 11/07/2022] [Accepted: 12/21/2023] [Indexed: 12/23/2023] Open
Abstract
Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.
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Affiliation(s)
- Ruth Charlotte Eccleston
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Emilia Manko
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Susana Campino
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Taane G Clark
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
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23
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Larsen TJ, Jahan I, Brock DA, Strassmann JE, Queller DC. Reduced social function in experimentally evolved Dictyostelium discoideum implies selection for social conflict in nature. Proc Biol Sci 2023; 290:20231722. [PMID: 38113942 PMCID: PMC10730294 DOI: 10.1098/rspb.2023.1722] [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: 07/31/2023] [Accepted: 11/21/2023] [Indexed: 12/21/2023] Open
Abstract
Many microbes interact with one another, but the difficulty of directly observing these interactions in nature makes interpreting their adaptive value complicated. The social amoeba Dictyostelium discoideum forms aggregates wherein some cells are sacrificed for the benefit of others. Within chimaeric aggregates containing multiple unrelated lineages, cheaters can gain an advantage by undercontributing, but the extent to which wild D. discoideum has adapted to cheat is not fully clear. In this study, we experimentally evolved D. discoideum in an environment where there were no selective pressures to cheat or resist cheating in chimaeras. Dictyostelium discoideum lines grown in this environment evolved reduced competitiveness within chimaeric aggregates and reduced ability to migrate during the slug stage. By contrast, we did not observe a reduction in cell number, a trait for which selection was not relaxed. The observed loss of traits that our laboratory conditions had made irrelevant suggests that these traits were adaptations driven and maintained by selective pressures D. discoideum faces in its natural environment. Our results suggest that D. discoideum faces social conflict in nature, and illustrate a general approach that could be applied to searching for social or non-social adaptations in other microbes.
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Affiliation(s)
- Tyler J. Larsen
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Israt Jahan
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Debra A. Brock
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Joan E. Strassmann
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - David C. Queller
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
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24
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Eble H, Joswig M, Lamberti L, Ludington WB. Master regulators of biological systems in higher dimensions. Proc Natl Acad Sci U S A 2023; 120:e2300634120. [PMID: 38096409 PMCID: PMC10743376 DOI: 10.1073/pnas.2300634120] [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: 01/12/2023] [Accepted: 10/23/2023] [Indexed: 12/18/2023] Open
Abstract
A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.
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Affiliation(s)
- Holger Eble
- Chair of Discrete Mathematics/Geometry, Technical University Berlin, Berlin10623, Germany
| | - Michael Joswig
- Chair of Discrete Mathematics/Geometry, Technical University Berlin, Berlin10623, Germany
- Max Planck Institute for Mathematics in the Sciences, Leipzig04103, Germany
| | - Lisa Lamberti
- Department of Biosystems Science and Engineering, Federal Institute of Technology (ETH Zürich), Basel4058, Switzerland
- Swiss Institute of Bioinformatics, Basel4058, Switzerland
| | - William B. Ludington
- Department of Biosphere Sciences and Engineering, Carnegie Institution for Science, Baltimore, MD21218
- Department of Biology, Johns Hopkins University, Baltimore, MD21218
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25
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Mahilkar A, Nagendra P, Venkataraman P, Deshmukh S, Saini S. Rapid evolution of pre-zygotic reproductive barriers in allopatric populations. Microbiol Spectr 2023; 11:e0195023. [PMID: 37787555 PMCID: PMC10714765 DOI: 10.1128/spectrum.01950-23] [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: 05/09/2023] [Accepted: 08/14/2023] [Indexed: 10/04/2023] Open
Abstract
IMPORTANCE A population diversifies into two or more species-such a process is known as speciation. In sexually reproducing microorganisms, which barriers arise first-pre-mating or post-mating? In this work, we quantify the relative strengths of these barriers and demonstrate that pre-mating barriers arise first in allopatrically evolving populations of yeast, Saccharomyces cerevisiae. These defects arise because of the altered kinetics of mating of the participating groups. Thus, our work provides an understanding of how adaptive changes can lead to diversification among microbial populations.
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Affiliation(s)
- Anjali Mahilkar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Powai, Maharashtra, India
| | - Prachitha Nagendra
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Powai, Maharashtra, India
| | - Pavithra Venkataraman
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Powai, Maharashtra, India
| | - Saniya Deshmukh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Powai, Maharashtra, India
| | - Supreet Saini
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Powai, Maharashtra, India
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26
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Diaz-Colunga J, Sanchez A, Ogbunugafor CB. Environmental modulation of global epistasis in a drug resistance fitness landscape. Nat Commun 2023; 14:8055. [PMID: 38052815 DOI: 10.1038/s41467-023-43806-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/21/2023] [Indexed: 12/07/2023] Open
Abstract
Interactions between mutations (epistasis) can add substantial complexity to genotype-phenotype maps, hampering our ability to predict evolution. Yet, recent studies have shown that the fitness effect of a mutation can often be predicted from the fitness of its genetic background using simple, linear relationships. This phenomenon, termed global epistasis, has been leveraged to reconstruct fitness landscapes and infer adaptive trajectories in a wide variety of contexts. However, little attention has been paid to how patterns of global epistasis may be affected by environmental variation, despite this variation frequently being a major driver of evolution. This is particularly relevant for the evolution of drug resistance, where antimicrobial drugs may change the environment faced by pathogens and shape their adaptive trajectories in ways that can be difficult to predict. By analyzing a fitness landscape of four mutations in a gene encoding an essential enzyme of P. falciparum (a parasite cause of malaria), here we show that patterns of global epistasis can be strongly modulated by the concentration of a drug in the environment. Expanding on previous theoretical results, we demonstrate that this modulation can be quantitatively explained by how specific gene-by-gene interactions are modified by drug dose. Importantly, our results highlight the need to incorporate potential environmental variation into the global epistasis framework in order to predict adaptation in dynamic environments.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, 06511, USA.
- Department of Microbial Biotechnology, Spanish National Center for Biotechnology CNB-CSIC, 28049, Madrid, Spain.
- Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007, Salamanca, Spain.
| | - Alvaro Sanchez
- Department of Microbial Biotechnology, Spanish National Center for Biotechnology CNB-CSIC, 28049, Madrid, Spain.
- Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007, Salamanca, Spain.
| | - C Brandon Ogbunugafor
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, 06511, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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27
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Papkou A, Garcia-Pastor L, Escudero JA, Wagner A. A rugged yet easily navigable fitness landscape. Science 2023; 382:eadh3860. [PMID: 37995212 DOI: 10.1126/science.adh3860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/29/2023] [Indexed: 11/25/2023]
Abstract
Fitness landscape theory predicts that rugged landscapes with multiple peaks impair Darwinian evolution, but experimental evidence is limited. In this study, we used genome editing to map the fitness of >260,000 genotypes of the key metabolic enzyme dihydrofolate reductase in the presence of the antibiotic trimethoprim, which targets this enzyme. The resulting landscape is highly rugged and harbors 514 fitness peaks. However, its highest peaks are accessible to evolving populations via abundant fitness-increasing paths. Different peaks share large basins of attraction that render the outcome of adaptive evolution highly contingent on chance events. Our work shows that ruggedness need not be an obstacle to Darwinian evolution but can reduce its predictability. If true in general, the complexity of optimization problems on realistic landscapes may require reappraisal.
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Affiliation(s)
- Andrei Papkou
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Lucia Garcia-Pastor
- Departamento de Sanidad Animal and VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain
| | - José Antonio Escudero
- Departamento de Sanidad Animal and VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, NM, USA
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28
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Skwara A, Gowda K, Yousef M, Diaz-Colunga J, Raman AS, Sanchez A, Tikhonov M, Kuehn S. Statistically learning the functional landscape of microbial communities. Nat Ecol Evol 2023; 7:1823-1833. [PMID: 37783827 PMCID: PMC11088814 DOI: 10.1038/s41559-023-02197-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/11/2023] [Indexed: 10/04/2023]
Abstract
Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.
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Affiliation(s)
- Abigail Skwara
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Karna Gowda
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Mahmoud Yousef
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Juan Diaz-Colunga
- Department of Microbial Biotechnology, National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Arjun S Raman
- Department of Pathology, University of Chicago, Chicago, IL, USA
- Duchossois Family Institute, University of Chicago, Chicago, IL, USA
| | - Alvaro Sanchez
- Department of Microbial Biotechnology, National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Mikhail Tikhonov
- Department of Physics, Washington University in St. Louis, St. Louis, MO, USA.
| | - Seppe Kuehn
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA.
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
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29
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Chen V, Johnson MS, Hérissant L, Humphrey PT, Yuan DC, Li Y, Agarwala A, Hoelscher SB, Petrov DA, Desai MM, Sherlock G. Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments. eLife 2023; 12:e92899. [PMID: 37861305 PMCID: PMC10629826 DOI: 10.7554/elife.92899] [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: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/21/2023] Open
Abstract
Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in 'non-home' environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.
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Affiliation(s)
- Vivian Chen
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Milo S Johnson
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityBostonUnited States
| | - Lucas Hérissant
- Department of Genetics, Stanford UniversityStanfordUnited States
| | - Parris T Humphrey
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - David C Yuan
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Yuping Li
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Atish Agarwala
- Department of Physics, Stanford UniversityStanfordUnited States
| | | | - Dmitri A Petrov
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityBostonUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Gavin Sherlock
- Department of Genetics, Stanford UniversityStanfordUnited States
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30
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Balakrishnan R, Cremer J. Conditionally unutilized proteins and their profound effects on growth and adaptation across microbial species. Curr Opin Microbiol 2023; 75:102366. [PMID: 37625262 DOI: 10.1016/j.mib.2023.102366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/12/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023]
Abstract
Protein synthesis is an important determinant of microbial growth and response that demands a high amount of metabolic and biosynthetic resources. Despite these costs, microbial species from different taxa and habitats massively synthesize proteins that are not utilized in the conditions they currently experience. Based on resource allocation models, recent studies have begun to reconcile the costs and benefits of these conditionally unutilized proteins (CUPs) in the context of varying environmental conditions. Such massive synthesis of CUPs is crucial to consider in different areas of modern microbiology, from the systematic investigation of cell physiology, via the prediction of evolution in laboratory and natural environments, to the rational design of strains in biotechnology applications.
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Affiliation(s)
- Rohan Balakrishnan
- Department of Physics, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
| | - Jonas Cremer
- Department of Biology, Stanford University, 318 Campus Drive, Stanford, CA 93105, USA.
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31
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Abreu CI, Mathur S, Petrov DA. Strong environmental memory revealed by experimental evolution in static and fluctuating environments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557739. [PMID: 37745585 PMCID: PMC10515930 DOI: 10.1101/2023.09.14.557739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Evolution in a static environment, such as a laboratory setting with constant and uniform conditions, often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time-average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time-average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. We find that fitness in fluctuating environments indeed often deviates from the expectation based on static components, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments, even if the components of the focal fluctuating environment are excluded from this variance. We employ a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results demonstrate that environmental fluctuations have large impacts on fitness and suggest that variance in static environments can explain these impacts.
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Affiliation(s)
- Clare I. Abreu
- Department of Biology, Stanford University; Stanford CA, USA
| | - Shaili Mathur
- Department of Biology, Stanford University; Stanford CA, USA
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32
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Chen L, Zhang Z, Li Z, Li R, Huo R, Chen L, Wang D, Luo X, Chen K, Liao C, Zheng M. Learning protein fitness landscapes with deep mutational scanning data from multiple sources. Cell Syst 2023; 14:706-721.e5. [PMID: 37591206 DOI: 10.1016/j.cels.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 08/19/2023]
Abstract
One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme that leverages the existing deep mutational scanning data from diverse proteins to aid in understanding the fitness landscape of a new protein. Proof-of-concept trials are designed to validate this training scheme in three aspects: random and positional extrapolation for single-variant effects, zero-shot fitness predictions for new proteins, and extrapolation for higher-order variant effects from single-variant effects. Moreover, our study identified previously overlooked strong baselines, and their unexpectedly good performance brings our attention to the pitfalls of MLDE. Overall, these results may improve our understanding of the association between different protein fitness profiles and shed light on developing better machine learning-assisted approaches to the directed evolution of proteins. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Lin Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zehong Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenghao Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Rui Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Ruifeng Huo
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Cangsong Liao
- University of Chinese Academy of Sciences, Beijing 100049, China; Chemical Biology Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Science, Shanghai 201203, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China.
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33
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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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34
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Cotto O, Day T. A null model for the distribution of fitness effects of mutations. Proc Natl Acad Sci U S A 2023; 120:e2218200120. [PMID: 37252948 PMCID: PMC10266029 DOI: 10.1073/pnas.2218200120] [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: 10/25/2022] [Accepted: 04/28/2023] [Indexed: 06/01/2023] Open
Abstract
The distribution of fitness effects (DFE) of new mutations is key to our understanding of many evolutionary processes. Theoreticians have developed several models to help understand the patterns seen in empirical DFEs. Many such models reproduce the broad patterns seen in empirical DFEs but these models often rely on structural assumptions that cannot be tested empirically. Here, we investigate how much of the underlying "microscopic" biological processes involved in the mapping of new mutations to fitness can be inferred from "macroscopic" observations of the DFE. We develop a null model by generating random genotype-to-fitness maps and show that the null DFE is that with the largest possible information entropy. We further show that, subject to one simple constraint, this null DFE is a Gompertz distribution. Finally, we illustrate how the predictions of this null DFE match empirically measured DFEs from several datasets, as well as DFEs simulated from Fisher's geometric model. This suggests that a match between models and empirical data is often not a very strong indication of the mechanisms underlying the mapping of mutation to fitness.
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Affiliation(s)
- Olivier Cotto
- Department of Mathematics and Statistics, Queens University, Kingston, ON, K7L 3N6, Canada
- Department of Biology, Queens University, Kingston, ON, K7L 3N6, Canada
- Plant Health Institute Montpellier, Université Montpellier, Institut National de Recherche pour l’Agriculture, l’alimentation et l’Environnement, Centre de coopération Internationale en Recherche Agronomique pour le Développement, Institut de Recherche pour le Développement, Institut Agro, Montpellier, F-34398, France
| | - Troy Day
- Department of Mathematics and Statistics, Queens University, Kingston, ON, K7L 3N6, Canada
- Department of Biology, Queens University, Kingston, ON, K7L 3N6, Canada
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35
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Martínez AA, Lang GI. Identifying Targets of Selection in Laboratory Evolution Experiments. J Mol Evol 2023; 91:345-355. [PMID: 36810618 PMCID: PMC11197053 DOI: 10.1007/s00239-023-10096-2] [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: 12/07/2022] [Accepted: 02/01/2023] [Indexed: 02/24/2023]
Abstract
Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.
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Affiliation(s)
| | - Gregory I Lang
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA.
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36
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Johnson MS, Reddy G, Desai MM. Epistasis and evolution: recent advances and an outlook for prediction. BMC Biol 2023; 21:120. [PMID: 37226182 PMCID: PMC10206586 DOI: 10.1186/s12915-023-01585-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/30/2023] [Indexed: 05/26/2023] Open
Abstract
As organisms evolve, the effects of mutations change as a result of epistatic interactions with other mutations accumulated along the line of descent. This can lead to shifts in adaptability or robustness that ultimately shape subsequent evolution. Here, we review recent advances in measuring, modeling, and predicting epistasis along evolutionary trajectories, both in microbial cells and single proteins. We focus on simple patterns of global epistasis that emerge in this data, in which the effects of mutations can be predicted by a small number of variables. The emergence of these patterns offers promise for efforts to model epistasis and predict evolution.
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Affiliation(s)
- Milo S Johnson
- Department of Integrative Biology, University of California, Berkeley, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Gautam Reddy
- Physics & Informatics Laboratories, NTT Research, Inc., Sunnyvale, CA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology and Department of Physics, Harvard University, Cambridge, MA, USA.
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37
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Ghenu AH, Amado A, Gordo I, Bank C. Epistasis decreases with increasing antibiotic pressure but not temperature. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220058. [PMID: 37004727 PMCID: PMC10067269 DOI: 10.1098/rstb.2022.0058] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Ana-Hermina Ghenu
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - André Amado
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Isabel Gordo
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
| | - Claudia Bank
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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38
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Diaz-Colunga J, Skwara A, Gowda K, Diaz-Uriarte R, Tikhonov M, Bajic D, Sanchez A. Global epistasis on fitness landscapes. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220053. [PMID: 37004717 PMCID: PMC10067270 DOI: 10.1098/rstb.2022.0053] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 11/23/2022] [Indexed: 04/04/2023] Open
Abstract
Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns-ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Karna Gowda
- Department of Ecology & Evolution & Center for the Physics of Evolving Systems, The University of Chicago, Chicago, IL 60637, USA
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, School of Medicine, Universidad Autónoma de Madrid, Madrid 28029, Spain
- Instituto de Investigaciones Biomédicas ‘Alberto Sols’ (UAM-CSIC), Madrid 28029, Spain
| | - Mikhail Tikhonov
- Department of Physics, Washington University of St Louis, St Louis, MO 63130, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Department of Microbial Biotechnology, Campus de Cantoblanco, CNB-CSIC, Madrid 28049, Spain
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39
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Farr AD, Pesce D, Das SG, Zwart MP, de Visser JAGM. The Fitness of Beta-Lactamase Mutants Depends Nonlinearly on Resistance Level at Sublethal Antibiotic Concentrations. mBio 2023:e0009823. [PMID: 37129484 DOI: 10.1128/mbio.00098-23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
Adaptive evolutionary processes are constrained by the availability of mutations which cause a fitness benefit and together make up the fitness landscape, which maps genotype space onto fitness under specified conditions. Experimentally derived fitness landscapes have demonstrated a predictability to evolution by identifying limited "mutational routes" that evolution by natural selection may take between low and high-fitness genotypes. However, such studies often utilize indirect measures to determine fitness. We estimated the competitive fitness of mutants relative to all single-mutation neighbors to describe the fitness landscape of three mutations in a β-lactamase enzyme. Fitness assays were performed at sublethal concentrations of the antibiotic cefotaxime in a structured and unstructured environment. In the unstructured environment, the antibiotic selected for higher-resistance types-but with an equivalent fitness for a subset of mutants, despite substantial variation in resistance-resulting in a stratified fitness landscape. In contrast, in a structured environment with a low antibiotic concentration, antibiotic-susceptible genotypes had a relative fitness advantage, which was associated with antibiotic-induced filamentation. These results cast doubt that highly resistant genotypes have a unique selective advantage in environments with subinhibitory concentrations of antibiotics and demonstrate that direct fitness measures are required for meaningful predictions of the accessibility of evolutionary routes. IMPORTANCE The evolution of antibiotic-resistant bacterial populations underpins the ongoing antibiotic resistance crisis. We aim to understand how antibiotic-degrading enzymes can evolve to cause increased resistance, how this process is constrained, and whether it can be predictable. To this end, competition experiments were performed with a combinatorially complete set of mutants of a β-lactamase gene subject to subinhibitory concentrations of the antibiotic cefotaxime. While some mutations confer on their hosts high resistance to cefotaxime, in competition these mutations do not always confer a selective advantage. Specifically, high-resistance mutants had equivalent fitnesses despite different resistance levels and even had selective disadvantages under conditions involving spatial structure. Together, our findings suggest that the relationship between resistance level and fitness at subinhibitory concentrations is complex; predicting the evolution of antibiotic resistance requires knowledge of the conditions that select for resistant genotypes and the selective advantage evolved types have over their predecessors.
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Affiliation(s)
- Andrew D Farr
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Diego Pesce
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
| | - Suman G Das
- Institute for Biological Physics, University of Cologne, Cologne, Germany
| | - Mark P Zwart
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - J Arjan G M de Visser
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
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40
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Tomar SS, Hua-Van A, Le Rouzic A. A population genetics theory for piRNA-regulated transposable elements. Theor Popul Biol 2023; 150:1-13. [PMID: 36863578 DOI: 10.1016/j.tpb.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 03/04/2023]
Abstract
Transposable elements (TEs) are self-reproducing selfish DNA sequences that can invade the genome of virtually all living species. Population genetics models have shown that TE copy numbers generally reach a limit, either because the transposition rate decreases with the number of copies (transposition regulation) or because TE copies are deleterious, and thus purged by natural selection. Yet, recent empirical discoveries suggest that TE regulation may mostly rely on piRNAs, which require a specific mutational event (the insertion of a TE copy in a piRNA cluster) to be activated - the so-called TE regulation "trap model". We derived new population genetics models accounting for this trap mechanism, and showed that the resulting equilibria differ substantially from previous expectations based on a transposition-selection equilibrium. We proposed three sub-models, depending on whether or not genomic TE copies and piRNA cluster TE copies are selectively neutral or deleterious, and we provide analytical expressions for maximum and equilibrium copy numbers, as well as cluster frequencies for all of them. In the full neutral model, the equilibrium is achieved when transposition is completely silenced, and this equilibrium does not depend on the transposition rate. When genomic TE copies are deleterious but not cluster TE copies, no long-term equilibrium is possible, and active TEs are eventually eliminated after an active incomplete invasion stage. When all TE copies are deleterious, a transposition-selection equilibrium exists, but the invasion dynamics is not monotonic, and the copy number peaks before decreasing. Mathematical predictions were in good agreement with numerical simulations, except when genetic drift and/or linkage disequilibrium dominates. Overall, the trap-model dynamics appeared to be substantially more stochastic and less repeatable than traditional regulation models.
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Affiliation(s)
- Siddharth S Tomar
- Université Paris-Saclay, CNRS, IRD, UMR EGCE, 12 Route 128, Gif-sur-Yvette, 91190, France.
| | - Aurélie Hua-Van
- Université Paris-Saclay, CNRS, IRD, UMR EGCE, 12 Route 128, Gif-sur-Yvette, 91190, France.
| | - Arnaud Le Rouzic
- Université Paris-Saclay, CNRS, IRD, UMR EGCE, 12 Route 128, Gif-sur-Yvette, 91190, France.
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41
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Xiong W, Chen Y, Ma S. Unified model-free interaction screening via CV-entropy filter. Comput Stat Data Anal 2023; 180:107684. [PMID: 36910335 PMCID: PMC9997997 DOI: 10.1016/j.csda.2022.107684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
For many practical high-dimensional problems, interactions have been increasingly found to play important roles beyond main effects. A representative example is gene-gene interaction. Joint analysis, which analyzes all interactions and main effects in a single model, can be seriously challenged by high dimensionality. For high-dimensional data analysis in general, marginal screening has been established as effective for reducing computational cost, increasing stability, and improving estimation/selection performance. Most of the existing marginal screening methods are designed for the analysis of main effects only. The existing screening methods for interaction analysis are often limited by making stringent model assumptions, lacking robustness, and/or requiring predictors to be continuous (and hence lacking flexibility). A unified marginal screening approach tailored to interaction analysis is developed, which can be applied to regression, classification, and survival analysis. Predictors are allowed to be continuous and discrete. The proposed approach is built on Coefficient of Variation (CV) filters based on information entropy. Statistical properties are rigorously established. It is shown that the CV filters are almost insensitive to the distribution tails of predictors, correlation structure among predictors, and sparsity level of signals. An efficient two-stage algorithm is developed to make the proposed approach scalable to ultrahigh-dimensional data. Simulations and the analysis of TCGA LUAD data further establish the practical superiority of the proposed approach.
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Affiliation(s)
- Wei Xiong
- School of Statistics, University of International Business and Economics, Beijing 100872, PR China
| | - Yaxian Chen
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, USA
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42
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Fluctuating selection and the determinants of genetic variation. Trends Genet 2023; 39:491-504. [PMID: 36890036 DOI: 10.1016/j.tig.2023.02.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 03/08/2023]
Abstract
Recent studies of cosmopolitan Drosophila populations have found hundreds to thousands of genetic loci with seasonally fluctuating allele frequencies, bringing temporally fluctuating selection to the forefront of the historical debate surrounding the maintenance of genetic variation in natural populations. Numerous mechanisms have been explored in this longstanding area of research, but these exciting empirical findings have prompted several recent theoretical and experimental studies that seek to better understand the drivers, dynamics, and genome-wide influence of fluctuating selection. In this review, we evaluate the latest evidence for multilocus fluctuating selection in Drosophila and other taxa, highlighting the role of potential genetic and ecological mechanisms in maintaining these loci and their impacts on neutral genetic variation.
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43
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Xiong W, Pan H, Wang J, Tian M. An efficient model-free approach to interaction screening for high dimensional data. Stat Med 2023; 42:1583-1605. [PMID: 36857779 DOI: 10.1002/sim.9688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 12/02/2022] [Accepted: 02/06/2023] [Indexed: 03/03/2023]
Abstract
An innovated model-free interaction screening procedure called the MCVIS is proposed for high dimensional data analysis. Specifically, we adopt the introduced MCV index for quantifying the importance of an interaction effect among predictors. Our proposed method is fully nonparametric and is capable of successfully selecting interactions even if the signal of parental main effects is weak. The MCVIS procedure has many distinctive features: (i) it can work with discrete, categorical and continuous covariates; (ii) it can deal with both categorical and continuous response, even handle the missing response; (iii) it is robust for heavy-tailed distributions, thus well accommodates heterogeneity typically caused by high dimensionality; (iv) it enjoys the sure screening and ranking consistency properties, therefore achieves dimension reduction without information loss. In another respect, computational feasibility is a top concern in high dimensional data analysis, by transforming our MCV into several variants, the MCVIS procedure is simple and fast to implement. Extensive numerical experiments and comparisons confirm the effectiveness and wide applicability of our MCVIS procedure. We further illustrate the proposed methodology by empirical study of two real datasets. Supplementary materials are available online.
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Affiliation(s)
- Wei Xiong
- School of Statistics, University of International Business and Economics, Beijing, China
| | - Han Pan
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Jianrong Wang
- School of Statistics, University of International Business and Economics, Beijing, China
| | - Maozai Tian
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China
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44
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Sanchez A, Bajic D, Diaz-Colunga J, Skwara A, Vila JCC, Kuehn S. The community-function landscape of microbial consortia. Cell Syst 2023; 14:122-134. [PMID: 36796331 DOI: 10.1016/j.cels.2022.12.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/17/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.
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Affiliation(s)
- Alvaro Sanchez
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA; Department of Microbial Biotechnology, CNB-CSIC, Campus de Cantoblanco, Madrid, Spain.
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Seppe Kuehn
- Center for the Physics of Evolving Systems, The Unviersity of Chicago, Chicago, IL, USA; Department of Ecology and Evolution, The University of Chicago, Chicago, IL, USA
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45
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Ascensao JA, Wetmore KM, Good BH, Arkin AP, Hallatschek O. Quantifying the local adaptive landscape of a nascent bacterial community. Nat Commun 2023; 14:248. [PMID: 36646697 PMCID: PMC9842643 DOI: 10.1038/s41467-022-35677-5] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/16/2022] [Indexed: 01/17/2023] Open
Abstract
The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes ("L" and "S") shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
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46
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Life Continuously Acquires New Information in Dialogue with the Environment. Bioinformatics 2023. [DOI: 10.1007/978-3-662-65036-3_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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47
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Matsui Y, Nagai M, Ying BW. Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions. Front Microbiol 2023; 14:1145673. [PMID: 37032868 PMCID: PMC10073601 DOI: 10.3389/fmicb.2023.1145673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/02/2023] [Indexed: 04/11/2023] Open
Abstract
The genomic, environmental, and evolutionary interruptions caused the changes in bacterial growth, which were stringently associated with changes in gene expression. The growth and gene expression changes remained unclear in response to these interruptions that occurred combinative. As a pilot study, whether and how bacterial growth was affected by the individual and dual interruptions of genome reduction, environmental stress, and adaptive evolution were investigated. Growth assay showed that the presence of the environmental stressors, i.e., threonine and chloramphenicol, significantly decreased the growth rate of the wild-type Escherichia coli, whereas not that of the reduced genome. It indicated a canceling effect in bacterial growth due to the dual interruption of the genomic and environmental changes. Experimental evolution of the reduced genome released the canceling effect by improving growth fitness. Intriguingly, the transcriptome architecture maintained a homeostatic chromosomal periodicity regardless of the genomic, environmental, and evolutionary interruptions. Negative epistasis in transcriptome reorganization was commonly observed in response to the dual interruptions, which might contribute to the canceling effect. It was supported by the changes in the numbers of differentially expressed genes (DEGs) and the enriched regulons and functions. Gene network analysis newly constructed 11 gene modules, one out of which was correlated to the growth rate. Enrichment of DEGs in these modules successfully categorized them into three types, i.e., conserved, responsive, and epistatic. Taken together, homeostasis in transcriptome architecture was essential to being alive, and it might be attributed to the negative epistasis in transcriptome reorganization and the functional differentiation in gene modules. The present study directly connected bacterial growth fitness with transcriptome reorganization and provided a global view of how microorganisms responded to genomic, environmental, and evolutionary interruptions for survival from wild nature.
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48
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Snead AA, Alda F. Time-Series Sequences for Evolutionary Inferences. Integr Comp Biol 2022; 62:1771-1783. [PMID: 36104153 DOI: 10.1093/icb/icac146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 01/05/2023] Open
Affiliation(s)
- Anthony A Snead
- Department of Biological Sciences, University of Alabama, 300 Hackberry Lane, Tuscaloosa, AL 35487, USA
| | - Fernando Alda
- Department of Biology, Geology and Environmental Science, University of Tennessee at Chattanooga, 615 McCallie Ave, Chattanooga, TN 37403, USA
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49
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Mina M, Iyer A, Ciriello G. Epistasis and evolutionary dependencies in human cancers. Curr Opin Genet Dev 2022; 77:101989. [PMID: 36182742 DOI: 10.1016/j.gde.2022.101989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 01/27/2023]
Abstract
Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.
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Affiliation(s)
- Marco Mina
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Arvind Iyer
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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50
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Persson K, Stenberg S, Tamás MJ, Warringer J. Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis. G3 (BETHESDA, MD.) 2022; 12:6694849. [PMID: 36083011 PMCID: PMC9635671 DOI: 10.1093/g3journal/jkac240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 08/29/2022] [Indexed: 05/31/2023]
Abstract
Adaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins enhancing the evolvability, has therefore attracted interest. However, our current understanding of how genes influence adaptation kinetics is limited, partly because accurately measuring adaptation for many cell populations is challenging. We used a high-throughput adaptive laboratory evolution platform to track the adaptation of >18,000 cell populations corresponding to single-gene deletion strains in the haploid yeast deletion collection. We report that the preadaptation fitness of gene knockouts near-perfectly (R2= 0.91) predicts their adaptation to arsenic, leaving at the most a marginal role for dedicated evolvability gene functions. We tracked the adaptation of another >23,000 gene knockout populations to a diverse range of selection pressures and generalized the almost perfect (R2=0.72-0.98) capacity of preadaptation fitness to predict adaptation. We also reconstructed mutations in FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wild-type cells, in gene deletions covering a broad fitness range and show that the predictability of arsenic adaptation can be understood as a by global epistasis, where excluding arsenic is more beneficial to arsenic unfit cells. The paucity of genes with a meaningful evolvability effect on adaptation diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.
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Affiliation(s)
- Karl Persson
- Corresponding author: Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
| | - Simon Stenberg
- Department of Chemistry and Molecular Biology, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Markus J Tamás
- Department of Chemistry and Molecular Biology, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Jonas Warringer
- Corresponding author: Department of Chemistry and Molecular Biology, University of Gothenburg, PO Box 462, 40530 Gothenburg, Sweden.
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