1
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Hays M. Genetic conflicts in budding yeast: The 2μ plasmid as a model selfish element. Semin Cell Dev Biol 2024; 161-162:31-41. [PMID: 38598944 DOI: 10.1016/j.semcdb.2024.04.002] [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: 09/26/2023] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Abstract
Antagonistic coevolution, arising from genetic conflict, can drive rapid evolution and biological innovation. Conflict can arise both between organisms and within genomes. This review focuses on budding yeasts as a model system for exploring intra- and inter-genomic genetic conflict, highlighting in particular the 2-micron (2μ) plasmid as a model selfish element. The 2μ is found widely in laboratory strains and industrial isolates of Saccharomyces cerevisiae and has long been known to cause host fitness defects. Nevertheless, the plasmid is frequently ignored in the context of genetic, fitness, and evolution studies. Here, I make a case for further exploring the evolutionary impact of the 2μ plasmid as well as other selfish elements of budding yeasts, discuss recent advances, and, finally, future directions for the field.
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Affiliation(s)
- Michelle Hays
- Department of Genetics, Stanford University, Stanford, CA, United States.
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2
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Wang P, Driscoll WW, Travisano M. Genomic sequencing reveals convergent adaptation during experimental evolution in two budding yeast species. Commun Biol 2024; 7:825. [PMID: 38971878 PMCID: PMC11227552 DOI: 10.1038/s42003-024-06485-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: 02/14/2024] [Accepted: 06/21/2024] [Indexed: 07/08/2024] Open
Abstract
Convergent evolution is central in the origins of multicellularity. Identifying the basis for convergent multicellular evolution is challenging because of the diverse evolutionary origins and environments involved. Haploid Kluyveromyces lactis populations evolve multicellularity during selection for increased settling in liquid media. Strong genomic and phenotypic convergence is observed between K. lactis and previously selected S. cerevisiae populations under similar selection, despite their >100-million-year divergence. We find K. lactis multicellularity is conferred by mutations in genes ACE2 or AIM44, with ACE2 being predominant. They are a subset of the six genes involved in the S. cerevisiae multicellularity. Both ACE2 and AIM44 regulate cell division, indicating that the genetic convergence is likely due to conserved cellular replication mechanisms. Complex population dynamics involving multiple ACE2/AIM44 genotypes are found in most K. lactis lineages. The results show common ancestry and natural selection shape convergence while chance and contingency determine the degree of divergence.
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Affiliation(s)
- Pu Wang
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, 55455, USA.
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, 55108, USA.
| | - William W Driscoll
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, 55108, USA
- Biology Department, Penn State Harrisburg, Harrisburg, PA, 17057, USA
| | - Michael Travisano
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, 55108, USA
- Biotechnology Institute, University of Minnesota, Minneapolis, MN, 55108, USA
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3
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Helenek C, Krzysztoń R, Petreczky J, Wan Y, Cabral M, Coraci D, Balázsi G. Synthetic gene circuit evolution: Insights and opportunities at the mid-scale. Cell Chem Biol 2024:S2451-9456(24)00219-8. [PMID: 38925113 DOI: 10.1016/j.chembiol.2024.05.018] [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: 02/12/2024] [Revised: 05/07/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.
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Affiliation(s)
- Christopher Helenek
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Rafał Krzysztoń
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Julia Petreczky
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yiming Wan
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mariana Cabral
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Damiano Coraci
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA; Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794, USA.
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4
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Rosano D, Sofyali E, Dhiman H, Ghirardi C, Ivanoiu D, Heide T, Vingiani A, Bertolotti A, Pruneri G, Canale E, Dewhurst HF, Saha D, Slaven N, Barozzi I, Li T, Zemlyanskiy G, Phillips H, James C, Győrffy B, Lynn C, Cresswell GD, Rehman F, Noberini R, Bonaldi T, Sottoriva A, Magnani L. Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer Cells. Cancer Discov 2024; 14:866-889. [PMID: 38527495 PMCID: PMC11061610 DOI: 10.1158/2159-8290.cd-23-1161] [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: 10/05/2023] [Revised: 01/10/2024] [Accepted: 02/20/2024] [Indexed: 03/27/2024]
Abstract
Patients with estrogen receptor-positive breast cancer receive adjuvant endocrine therapies (ET) that delay relapse by targeting clinically undetectable micrometastatic deposits. Yet, up to 50% of patients relapse even decades after surgery through unknown mechanisms likely involving dormancy. To investigate genetic and transcriptional changes underlying tumor awakening, we analyzed late relapse patients and longitudinally profiled a rare cohort treated with long-term neoadjuvant ETs until progression. Next, we developed an in vitro evolutionary study to record the adaptive strategies of individual lineages in unperturbed parallel experiments. Our data demonstrate that ETs induce nongenetic cell state transitions into dormancy in a stochastic subset of cells via epigenetic reprogramming. Single lineages with divergent phenotypes awaken unpredictably in the absence of recurrent genetic alterations. Targeting the dormant epigenome shows promising activity against adapting cancer cells. Overall, this study uncovers the contribution of epigenetic adaptation to the evolution of resistance to ETs. SIGNIFICANCE This study advances the understanding of therapy-induced dormancy with potential clinical implications for breast cancer. Estrogen receptor-positive breast cancer cells adapt to endocrine treatment by entering a dormant state characterized by strong heterochromatinization with no recurrent genetic changes. Targeting the epigenetic rewiring impairs the adaptation of cancer cells to ETs. See related commentary by Llinas-Bertran et al., p. 704. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
- Dalia Rosano
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- The Breast Cancer Now Toby Robins Research Center, The Institute of Cancer Research, London, United Kingdom
| | - Emre Sofyali
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Heena Dhiman
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- The Breast Cancer Now Toby Robins Research Center, The Institute of Cancer Research, London, United Kingdom
| | - Chiara Ghirardi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Diana Ivanoiu
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Timon Heide
- Human Technopole, Milan, Italy
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
| | | | | | - Giancarlo Pruneri
- Istituto Nazionale Tumori, Milan, Italy
- Department of Oncology and Haematology-Oncology, University of Milano, Milano, Italy
| | - Eleonora Canale
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Hannah F. Dewhurst
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Debjani Saha
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Neil Slaven
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley
| | - Iros Barozzi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Centre for Cancer Research, Medical University of Vienna, Austria
| | - Tong Li
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Grigory Zemlyanskiy
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Henry Phillips
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Chela James
- Human Technopole, Milan, Italy
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- RCNS Cancer Biomarker Research Group, Budapest, Hungary
- Department of Biophysics, Medical School, University of Pecs, Pecs, Hungary
| | - Claire Lynn
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
| | - George D. Cresswell
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
| | - Farah Rehman
- Charing Cross Hospital, Imperial College NHS Trust, London, United Kingdom
| | - Roberta Noberini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Tiziana Bonaldi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Haematology-Oncology, University of Milano, Milano, Italy
| | - Andrea Sottoriva
- Human Technopole, Milan, Italy
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
| | - Luca Magnani
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- The Breast Cancer Now Toby Robins Research Center, The Institute of Cancer Research, London, United Kingdom
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5
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Yu Q, Ascensao JA, Okada T, Boyd O, Volz E, Hallatschek O. Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. PLoS Pathog 2024; 20:e1012090. [PMID: 38620033 PMCID: PMC11045146 DOI: 10.1371/journal.ppat.1012090] [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: 04/27/2023] [Revised: 04/25/2024] [Accepted: 03/03/2024] [Indexed: 04/17/2024] Open
Abstract
Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.
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Affiliation(s)
- QinQin Yu
- Department of Physics, University of California, Berkeley, California, United States of America
| | - Joao A. Ascensao
- Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Takashi Okada
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- RIKEN iTHEMS, Wako, Saitama, Japan
| | | | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany
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6
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Razo-Mejia M, Mani M, Petrov D. Bayesian inference of relative fitness on high-throughput pooled competition assays. PLoS Comput Biol 2024; 20:e1011937. [PMID: 38489348 PMCID: PMC10971673 DOI: 10.1371/journal.pcbi.1011937] [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: 10/18/2023] [Revised: 03/27/2024] [Accepted: 02/21/2024] [Indexed: 03/17/2024] Open
Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
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Affiliation(s)
- Manuel Razo-Mejia
- Department of Biology, Stanford University, Stanford, California, United States of America
| | - Madhav Mani
- NSF-Simons Center for Quantitative Biology, Northwestern University, Chicago, Illinois, United States of America
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Chicago, Illinois, United States of America
| | - Dmitri Petrov
- Department of Biology, Stanford University, Stanford, California, United States of America
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, United States of America
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
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7
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Park K, Bae Y. Operator model for evolutionary dynamics. Biosystems 2024; 237:105130. [PMID: 38309419 DOI: 10.1016/j.biosystems.2024.105130] [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: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
Drift, selection, and mutation are integral evolutionary factors. In this article, operator model is newly suggested to intuitively represent those evolutionary factors into mathematical operators, and to ultimately offer unconventional methodology for understanding evolutionary dynamics. To be specific, each of the drift, selection, and mutation was respectively interpreted as operator which in essence is a random matrix that acts upon the vector which contains population distribution information. The simulation results from the operator model coincided with the previous theoretical results for beneficial mutation accumulation rate in concurrent and successional regimes for asexually reproducing case. Furthermore, beneficial mutation accumulation in strong drift regime for asexually reproducing case was observed from the simulation while allowing the interactions of mutations with diverse selection coefficients. Lastly, methods to justify, reinforce, apply, and expand the operator model were discussed to scrutinize the implications of the model. With the operator model's unique characteristics, the model is expected to broaden perspective and to offer effective methodology for understanding the evolutionary process.
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Affiliation(s)
- Kangbien Park
- Department of Physics, College of Natural Science, Yonsei University, Seoul, 03722, Republic of Korea.
| | - Yonghee Bae
- Department of Physics, College of Natural Science, Yonsei University, Seoul, 03722, Republic of Korea
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8
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Wong DPGH, Good BH. Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking. Nat Commun 2024; 15:1605. [PMID: 38383538 PMCID: PMC10881964 DOI: 10.1038/s41467-024-45792-0] [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: 09/21/2022] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
Gut microbiota can adapt to their host environment by rapidly acquiring new mutations. However, the dynamics of this process are difficult to characterize in dominant gut species in their complex in vivo environment. Here we show that the fine-scale dynamics of genome-wide transposon libraries can enable quantitative inferences of these in vivo evolutionary forces. By analyzing >400,000 lineages across four human Bacteroides strains in gnotobiotic mice, we observed positive selection on thousands of cryptic variants - most of which were unrelated to their original gene knockouts. The spectrum of fitness benefits varied between species, and displayed diverse tradeoffs over time and in different dietary conditions, enabling inferences of their underlying function. These results suggest that within-host adaptations arise from an intense competition between numerous contending variants, which can strongly influence their emergent evolutionary tradeoffs.
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Affiliation(s)
- Daniel P G H Wong
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA.
- Department of Biology, Stanford University, Stanford, CA, 94305, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, 94158, USA.
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9
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Sintsova A, Ruscheweyh HJ, Field CM, Feer L, Nguyen BD, Daniel B, Hardt WD, Vorholt JA, Sunagawa S. mBARq: a versatile and user-friendly framework for the analysis of DNA barcodes from transposon insertion libraries, knockout mutants, and isogenic strain populations. Bioinformatics 2024; 40:btae078. [PMID: 38341646 PMCID: PMC10885212 DOI: 10.1093/bioinformatics/btae078] [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: 07/31/2023] [Revised: 12/18/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. However, the statistical analysis, visualization, and contextualization of the data resulting from such experiments can be complex and require bioinformatic skills. RESULTS Here, we developed mBARq, a user-friendly tool designed to simplify these steps for diverse experimental setups. The tool is seamlessly integrated with an intuitive web app for interactive data exploration via the STRING and KEGG databases to accelerate scientific discovery. AVAILABILITY AND IMPLEMENTATION The tool is implemented in Python. The source code is freely available (https://github.com/MicrobiologyETHZ/mbarq) and the web app can be accessed at: https://microbiomics.io/tools/mbarq-app.
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Affiliation(s)
- Anna Sintsova
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Hans-Joachim Ruscheweyh
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Christopher M Field
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Lilith Feer
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Bidong D Nguyen
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Benjamin Daniel
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Wolf-Dietrich Hardt
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Julia A Vorholt
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Shinichi Sunagawa
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
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10
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Helsen J, Sherlock G, Dey G. Experimental evolution for cell biology. Trends Cell Biol 2023; 33:903-912. [PMID: 37188561 PMCID: PMC10592577 DOI: 10.1016/j.tcb.2023.04.006] [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] [Received: 01/20/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
Evolutionary cell biology explores the origins, principles, and core functions of cellular features and regulatory networks through the lens of evolution. This emerging field relies heavily on comparative experiments and genomic analyses that focus exclusively on extant diversity and historical events, providing limited opportunities for experimental validation. In this opinion article, we explore the potential for experimental laboratory evolution to augment the evolutionary cell biology toolbox, drawing inspiration from recent studies that combine laboratory evolution with cell biological assays. Primarily focusing on approaches for single cells, we provide a generalizable template for adapting experimental evolution protocols to provide fresh insight into long-standing questions in cell biology.
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Affiliation(s)
- Jana Helsen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Gavin Sherlock
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Gautam Dey
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Germany.
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11
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Hu G, Wang Y, Liu X, Strube ML, Wang B, Kovács ÁT. Species and condition shape the mutational spectrum in experimentally evolved biofilms. mSystems 2023; 8:e0054823. [PMID: 37768063 PMCID: PMC10654089 DOI: 10.1128/msystems.00548-23] [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: 05/27/2023] [Accepted: 08/11/2023] [Indexed: 09/29/2023] Open
Abstract
IMPORTANCE Biofilm formation is a vital factor for the survival and adaptation of bacteria in diverse environmental niches. Experimental evolution combined with the advancement of whole-population genome sequencing provides us a powerful tool to understand the genomic dynamic of evolutionary adaptation to different environments, such as during biofilm development. Previous studies described the genetic and phenotypic changes of selected clones from experimentally evolved Bacillus thuringiensis and Bacillus subtilis that were adapted under abiotic and biotic biofilm conditions. However, the full understanding of the dynamic evolutionary landscapes was lacking. Furthermore, the differences and similarities of adaptive mechanisms in B. thuringiensis and B. subtilis were not identified. To overcome these limitations, we performed longitudinal whole-population genome sequencing to study the underlying genetic dynamics at high resolution. Our study provides the first comprehensive mutational landscape of two bacterial species' biofilms that is adapted to an abiotic and biotic surface.
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Affiliation(s)
- Guohai Hu
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Lyngby, Denmark
| | - Yue Wang
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- BGI Research, Beijing, China
| | - Xin Liu
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- BGI Research, Beijing, China
| | - Mikael Lenz Strube
- Bacterial Ecophysiology and Biotechnology Group, DTU Bioengineering, Technical University of Denmark, Lyngby, Denmark
| | - Bo Wang
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- Shenzhen Key Laboratory of Environmental Microbial Genomics and Application, BGI Research, Shenzhen, China
| | - Ákos T. Kovács
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Lyngby, Denmark
- Institute of Biology, Leiden University, Leiden, The Netherlands
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12
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Razo-Mejia M, Mani M, Petrov D. Bayesian inference of relative fitness on high-throughput pooled competition assays. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.14.562365. [PMID: 37904971 PMCID: PMC10614806 DOI: 10.1101/2023.10.14.562365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
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Affiliation(s)
| | - Madhav Mani
- NSF-Simons Center for Quantitative Biology, Northwestern University
- Department of Molecular Biosciences, Northwestern University
| | - Dmitri Petrov
- Department of Biology, Stanford University
- Stanford Cancer Institute, Stanford University School of Medicine
- Chan Zuckerberg Biohub
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13
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Alexander HK. Quantifying stochastic establishment of mutants in microbial adaptation. MICROBIOLOGY (READING, ENGLAND) 2023; 169:001365. [PMID: 37561015 PMCID: PMC10482372 DOI: 10.1099/mic.0.001365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/10/2023] [Indexed: 08/11/2023]
Abstract
Studies of microbial evolution, especially in applied contexts, have focused on the role of selection in shaping predictable, adaptive responses to the environment. However, chance events - the appearance of novel genetic variants and their establishment, i.e. outgrowth from a single cell to a sizeable population - also play critical initiating roles in adaptation. Stochasticity in establishment has received little attention in microbiology, potentially due to lack of awareness as well as practical challenges in quantification. However, methods for high-replicate culturing, mutant labelling and detection, and statistical inference now make it feasible to experimentally quantify the establishment probability of specific adaptive genotypes. I review methods that have emerged over the past decade, including experimental design and mathematical formulas to estimate establishment probability from data. Quantifying establishment in further biological settings and comparing empirical estimates to theoretical predictions represent exciting future directions. More broadly, recognition that adaptive genotypes may be stochastically lost while rare is significant both for interpreting common lab assays and for designing interventions to promote or inhibit microbial evolution.
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Affiliation(s)
- Helen K. Alexander
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, UK
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14
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Devi A, Speyer G, Lynch M. The divergence of mean phenotypes under persistent directional selection. Genetics 2023; 224:iyad091. [PMID: 37200616 PMCID: PMC10552002 DOI: 10.1093/genetics/iyad091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 02/26/2023] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Numerous organismal traits, particularly at the cellular level, are likely to be under persistent directional selection across phylogenetic lineages. Unless all mutations affecting such traits have large enough effects to be efficiently selected in all species, gradients in mean phenotypes are expected to arise as a consequence of differences in the power of random genetic drift, which varies by approximately five orders of magnitude across the Tree of Life. Prior theoretical work examining the conditions under which such gradients can arise focused on the simple situation in which all genomic sites affecting the trait have identical and constant mutational effects. Here, we extend this theory to incorporate the more biologically realistic situation in which mutational effects on a trait differ among nucleotide sites. Pursuit of such modifications leads to the development of semi-analytic expressions for the ways in which selective interference arises via linkage effects in single-effects models, which then extend to more complex scenarios. The theory developed clarifies the conditions under which mutations of different selective effects mutually interfere with each others' fixation and shows how variance in effects among sites can substantially modify and extend the expected scaling relationships between mean phenotypes and effective population sizes.
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Affiliation(s)
- Archana Devi
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287, USA
| | - Gil Speyer
- Knowledge Enterprise, Arizona State University, Tempe, AZ 85287, USA
| | - Michael Lynch
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287, USA
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15
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Stevenson ZC, Moerdyk-Schauwecker MJ, Banse SA, Patel DS, Lu H, Phillips PC. High-throughput library transgenesis in Caenorhabditis elegans via Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS). eLife 2023; 12:RP84831. [PMID: 37401921 PMCID: PMC10328503 DOI: 10.7554/elife.84831] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023] Open
Abstract
High-throughput transgenesis using synthetic DNA libraries is a powerful method for systematically exploring genetic function. Diverse synthesized libraries have been used for protein engineering, identification of protein-protein interactions, characterization of promoter libraries, developmental and evolutionary lineage tracking, and various other exploratory assays. However, the need for library transgenesis has effectively restricted these approaches to single-cell models. Here, we present Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS), a simple yet powerful approach to large-scale transgenesis that overcomes typical limitations encountered in multicellular systems. TARDIS splits the transgenesis process into a two-step process: creation of individuals carrying experimentally introduced sequence libraries, followed by inducible extraction and integration of individual sequences/library components from the larger library cassette into engineered genomic sites. Thus, transformation of a single individual, followed by lineage expansion and functional transgenesis, gives rise to thousands of genetically unique transgenic individuals. We demonstrate the power of this system using engineered, split selectable TARDIS sites in Caenorhabditis elegans to generate (1) a large set of individually barcoded lineages and (2) transcriptional reporter lines from predefined promoter libraries. We find that this approach increases transformation yields up to approximately 1000-fold over current single-step methods. While we demonstrate the utility of TARDIS using C. elegans, in principle the process is adaptable to any system where experimentally generated genomic loci landing pads and diverse, heritable DNA elements can be generated.
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Affiliation(s)
| | | | - Stephen A Banse
- Institute of Ecology and Evolution, University of OregonEugeneUnited States
| | - Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of TechnologyAtlantaUnited States
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of TechnologyAtlantaUnited States
| | - Patrick C Phillips
- Institute of Ecology and Evolution, University of OregonEugeneUnited States
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16
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Rezenman S, Knafo M, Tsigalnitski I, Barad S, Jona G, Levi D, Dym O, Reich Z, Kapon R. gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution. PLoS One 2023; 18:e0286696. [PMID: 37285353 DOI: 10.1371/journal.pone.0286696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/19/2023] [Indexed: 06/09/2023] Open
Abstract
Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones' relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system's application and resolution by applying it to track tens of thousands of Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.
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Affiliation(s)
- Shahar Rezenman
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Maor Knafo
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ivgeni Tsigalnitski
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Shiri Barad
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ghil Jona
- Life Sciences Core Facilities, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Dikla Levi
- Life Sciences Core Facilities, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Orly Dym
- The Dana and Yossie Hollander Center for Structural Proteomics, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ziv Reich
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ruti Kapon
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
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17
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Theodosiou L, Farr AD, Rainey PB. Barcoding Populations of Pseudomonas fluorescens SBW25. J Mol Evol 2023; 91:254-262. [PMID: 37186220 PMCID: PMC10275814 DOI: 10.1007/s00239-023-10103-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/13/2023] [Indexed: 05/17/2023]
Abstract
In recent years, evolutionary biologists have developed an increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.
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Affiliation(s)
- Loukas Theodosiou
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany.
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding, Cologne, Germany.
| | - Andrew D Farr
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Paul B Rainey
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Laboratory of Biophysics and Evolution, CBI, ESPCI Paris, Université PSL, CNRS, Paris, France
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18
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Spealman P, De T, Chuong JN, Gresham D. Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution. J Mol Evol 2023; 91:356-368. [PMID: 37012421 PMCID: PMC10275804 DOI: 10.1007/s00239-023-10102-7] [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: 09/28/2022] [Accepted: 02/21/2023] [Indexed: 04/05/2023]
Abstract
Copy number variants (CNVs), comprising gene amplifications and deletions, are a pervasive class of heritable variation. CNVs play a key role in rapid adaptation in both natural, and experimental, evolution. However, despite the advent of new DNA sequencing technologies, detection and quantification of CNVs in heterogeneous populations has remained challenging. Here, we summarize recent advances in the use of CNV reporters that provide a facile means of quantifying de novo CNVs at a specific locus in the genome, and nanopore sequencing, for resolving the often complex structures of CNVs. We provide guidance for the engineering and analysis of CNV reporters and practical guidelines for single-cell analysis of CNVs using flow cytometry. We summarize recent advances in nanopore sequencing, discuss the utility of this technology, and provide guidance for the bioinformatic analysis of these data to define the molecular structure of CNVs. The combination of reporter systems for tracking and isolating CNV lineages and long-read DNA sequencing for characterizing CNV structures enables unprecedented resolution of the mechanisms by which CNVs are generated and their evolutionary dynamics.
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Affiliation(s)
- Pieter Spealman
- Department of Biology, New York University, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - Titir De
- Department of Biology, New York University, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - Julie N Chuong
- Department of Biology, New York University, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - David Gresham
- Department of Biology, New York University, New York, NY, 10003, USA.
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA.
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19
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Karlsson K, Przybilla MJ, Kotler E, Khan A, Xu H, Karagyozova K, Sockell A, Wong WH, Liu K, Mah A, Lo YH, Lu B, Houlahan KE, Ma Z, Suarez CJ, Barnes CP, Kuo CJ, Curtis C. Deterministic evolution and stringent selection during preneoplasia. Nature 2023; 618:383-393. [PMID: 37258665 PMCID: PMC10247377 DOI: 10.1038/s41586-023-06102-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/19/2023] [Indexed: 06/02/2023]
Abstract
The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.
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Affiliation(s)
- Kasper Karlsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Science for Life Laboratory and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Moritz J Przybilla
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Wellcome Sanger Institute & University of Cambridge, Hinxton, UK
| | - Eran Kotler
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Aziz Khan
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Hang Xu
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kremena Karagyozova
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra Sockell
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Wing H Wong
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine Liu
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Amanda Mah
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuan-Hung Lo
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Bingxin Lu
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Kathleen E Houlahan
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Zhicheng Ma
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Calvin J Kuo
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christina Curtis
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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20
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Johnson MS, Venkataram S, Kryazhimskiy S. Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes. J Mol Evol 2023; 91:263-280. [PMID: 36651964 PMCID: PMC10276077 DOI: 10.1007/s00239-022-10083-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/15/2022] [Indexed: 01/19/2023]
Abstract
Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.
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Affiliation(s)
- Milo S Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, 94720, USA
| | - Sandeep Venkataram
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, 92093, USA.
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21
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Li F, Mahadevan A, Sherlock G. An improved algorithm for inferring mutational parameters from bar-seq evolution experiments. BMC Genomics 2023; 24:246. [PMID: 37149606 PMCID: PMC10164349 DOI: 10.1186/s12864-023-09345-x] [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: 11/08/2022] [Accepted: 04/27/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Genetic barcoding provides a high-throughput way to simultaneously track the frequencies of large numbers of competing and evolving microbial lineages. However making inferences about the nature of the evolution that is taking place remains a difficult task. RESULTS Here we describe an algorithm for the inference of fitness effects and establishment times of beneficial mutations from barcode sequencing data, which builds upon a Bayesian inference method by enforcing self-consistency between the population mean fitness and the individual effects of mutations within lineages. By testing our inference method on a simulation of 40,000 barcoded lineages evolving in serial batch culture, we find that this new method outperforms its predecessor, identifying more adaptive mutations and more accurately inferring their mutational parameters. CONCLUSION Our new algorithm is particularly suited to inference of mutational parameters when read depth is low. We have made Python code for our serial dilution evolution simulations, as well as both the old and new inference methods, available on GitHub ( https://github.com/FangfeiLi05/FitMut2 ), in the hope that it can find broader use by the microbial evolution community.
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Affiliation(s)
- Fangfei Li
- Department of Genetics, Stanford University, Stanford, California, US
| | - Aditya Mahadevan
- Department of Physics, Stanford University, Stanford, California, US
| | - Gavin Sherlock
- Department of Genetics, Stanford University, Stanford, California, US.
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22
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Lo RS, Cromie GA, Tang M, Teng K, Owens K, Sirr A, Kutz JN, Morizono H, Caldovic L, Ah Mew N, Gropman A, Dudley AM. The functional impact of 1,570 individual amino acid substitutions in human OTC. Am J Hum Genet 2023; 110:863-879. [PMID: 37146589 PMCID: PMC10183466 DOI: 10.1016/j.ajhg.2023.03.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/30/2023] [Indexed: 05/07/2023] Open
Abstract
Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Individuals with neonatal onset appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma, and death, outcomes ameliorated by rapid diagnosis and treatment. Here, we develop a high-throughput functional assay for human OTC and individually measure the impact of 1,570 variants, 84% of all SNV-accessible missense mutations. Comparison to existing clinical significance calls, demonstrated that our assay distinguishes known benign from pathogenic variants and variants with neonatal onset from late-onset disease presentation. This functional stratification allowed us to identify score ranges corresponding to clinically relevant levels of impairment of OTC activity. Examining the results of our assay in the context of protein structure further allowed us to identify a 13 amino acid domain, the SMG loop, whose function appears to be required in human cells but not in yeast. Finally, inclusion of our data as PS3 evidence under the current ACMG guidelines, in a pilot reclassification of 34 variants with complete loss of activity, would change the classification of 22 from variants of unknown significance to clinically actionable likely pathogenic variants. These results illustrate how large-scale functional assays are especially powerful when applied to rare genetic diseases.
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Affiliation(s)
- Russell S Lo
- Pacific Northwest Research Institute, Seattle, WA, USA
| | | | - Michelle Tang
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Kevin Teng
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Katherine Owens
- Pacific Northwest Research Institute, Seattle, WA, USA; Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Amy Sirr
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Hiroki Morizono
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Ljubica Caldovic
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Nicholas Ah Mew
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Andrea Gropman
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA; Department of Neurology, Division of Neurogenetics and Neurodevelopmental Disabilities, Children's National Hospital, Washington, DC, USA; Center for Neuroscience Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA
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23
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Zhou S, Wu Y, Zhao Y, Zhang Z, Jiang L, Liu L, Zhang Y, Tang J, Yuan YJ. Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization. Natl Sci Rev 2023; 10:nwad073. [PMID: 37223244 PMCID: PMC10202648 DOI: 10.1093/nsr/nwad073] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/07/2022] [Accepted: 02/02/2023] [Indexed: 11/12/2023] Open
Abstract
Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary processes. Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural variations. Here, we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes (synII, synIII, synV, circular synVI, synIXR and synX). Remarkably, we find that the rearrangement events exhibit a specific landscape of frequency. We further reveal that the landscape is shaped by the combined effects of chromatin accessibility and spatial contact probability. The rearrangements tend to occur in 3D spatially proximal and chromatin-accessible regions. The enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution, and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.
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Affiliation(s)
- Sijie Zhou
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Yi Wu
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Yu Zhao
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA
| | - Zhen Zhang
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Lin Liu
- Epigenetic Group, FrasergenBioinformatics Co., Ltd., Wuhan 430000, China
| | - Yan Zhang
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
- Department of Computer Science, University of South Carolina, Columbia, SC 29208, USA
| | - Ying-Jin Yuan
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
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24
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Szukala A, Lovegrove‐Walsh J, Luqman H, Fior S, Wolfe TM, Frajman B, Schönswetter P, Paun O. Polygenic routes lead to parallel altitudinal adaptation in Heliosperma pusillum (Caryophyllaceae). Mol Ecol 2023; 32:1832-1847. [PMID: 35152499 PMCID: PMC10946620 DOI: 10.1111/mec.16393] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/29/2021] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
Abstract
Understanding how organisms adapt to the environment is a major goal of modern biology. Parallel evolution-the independent evolution of similar phenotypes in different populations-provides a powerful framework to investigate the evolutionary potential of populations, the constraints of evolution, its repeatability and therefore its predictability. Here, we quantified the degree of gene expression and functional parallelism across replicated ecotype formation in Heliosperma pusillum (Caryophyllaceae), and gained insights into the architecture of adaptive traits. Population structure analyses and demographic modelling support a previously formulated hypothesis of parallel polytopic divergence of montane and alpine ecotypes. We detect a large proportion of differentially expressed genes (DEGs) underlying divergence within each replicate ecotype pair, with a strikingly low number of shared DEGs across pairs. Functional enrichment of DEGs reveals that the traits affected by significant expression divergence are largely consistent across ecotype pairs, in strong contrast to the nonshared genetic basis. The remarkable redundancy of differential gene expression indicates a polygenic architecture for the diverged adaptive traits. We conclude that polygenic traits appear key to opening multiple routes for adaptation, widening the adaptive potential of organisms.
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Affiliation(s)
- Aglaia Szukala
- Department of Botany and Biodiversity ResearchUniversity of ViennaViennaAustria
- Vienna Graduate School of Population GeneticsViennaAustria
| | | | - Hirzi Luqman
- Department of Environmental System ScienceETH ZürichZürichSwitzerland
| | - Simone Fior
- Department of Environmental System ScienceETH ZürichZürichSwitzerland
| | - Thomas M. Wolfe
- Institute for Forest EntomologyForest Pathology and Forest Protection, BOKUViennaAustria
| | - Božo Frajman
- Department of BotanyUniversity of InnsbruckInnsbruckAustria
| | | | - Ovidiu Paun
- Department of Botany and Biodiversity ResearchUniversity of ViennaViennaAustria
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25
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Yang L, Caradonna TM, Schmidt AG, Chakraborty AK. Mechanisms that promote the evolution of cross-reactive antibodies upon vaccination with designed influenza immunogens. Cell Rep 2023; 42:112160. [PMID: 36867533 PMCID: PMC10184763 DOI: 10.1016/j.celrep.2023.112160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/18/2022] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Immunogens that elicit broadly neutralizing antibodies targeting the conserved receptor-binding site (RBS) on influenza hemagglutinin may serve as candidates for a universal influenza vaccine. Here, we develop a computational model to interrogate antibody evolution by affinity maturation after immunization with two types of immunogens: a heterotrimeric "chimera" hemagglutinin that is enriched for the RBS epitope relative to other B cell epitopes and a cocktail composed of three non-epitope-enriched homotrimers of the monomers that comprise the chimera. Experiments in mice find that the chimera outperforms the cocktail for eliciting RBS-directed antibodies. We show that this result follows from an interplay between how B cells engage these antigens and interact with diverse helper T cells and requires T cell-mediated selection of germinal center B cells to be a stringent constraint. Our results shed light on antibody evolution and highlight how immunogen design and T cells modulate vaccination outcomes.
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Affiliation(s)
- Leerang Yang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Aaron G Schmidt
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | - Arup K Chakraborty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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26
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Moulana A, Dupic T, Phillips AM, Chang J, Roffler AA, Greaney AJ, Starr TN, Bloom JD, Desai MM. The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution. eLife 2023; 12:e83442. [PMID: 36803543 PMCID: PMC9949795 DOI: 10.7554/elife.83442] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
The Omicron BA.1 variant of SARS-CoV-2 escapes convalescent sera and monoclonal antibodies that are effective against earlier strains of the virus. This immune evasion is largely a consequence of mutations in the BA.1 receptor binding domain (RBD), the major antigenic target of SARS-CoV-2. Previous studies have identified several key RBD mutations leading to escape from most antibodies. However, little is known about how these escape mutations interact with each other and with other mutations in the RBD. Here, we systematically map these interactions by measuring the binding affinity of all possible combinations of these 15 RBD mutations (215=32,768 genotypes) to 4 monoclonal antibodies (LY-CoV016, LY-CoV555, REGN10987, and S309) with distinct epitopes. We find that BA.1 can lose affinity to diverse antibodies by acquiring a few large-effect mutations and can reduce affinity to others through several small-effect mutations. However, our results also reveal alternative pathways to antibody escape that does not include every large-effect mutation. Moreover, epistatic interactions are shown to constrain affinity decline in S309 but only modestly shape the affinity landscapes of other antibodies. Together with previous work on the ACE2 affinity landscape, our results suggest that the escape of each antibody is mediated by distinct groups of mutations, whose deleterious effects on ACE2 affinity are compensated by another distinct group of mutations (most notably Q498R and N501Y).
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Jeffrey Chang
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Anne A Roffler
- Biological and Biomedical Sciences, Harvard Medical SchoolBostonUnited States
| | - Allison J Greaney
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome Sciences, University of WashingtonSeattleUnited States
- Medical Scientist Training Program, University of WashingtonSeattleUnited States
| | - Tyler N Starr
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome Sciences, University of WashingtonSeattleUnited States
- Howard Hughes Medical InstituteSeattleUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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27
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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: 49] [Impact Index Per Article: 49.0] [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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28
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Phillips AM, Maurer DP, Brooks C, Dupic T, Schmidt AG, Desai MM. Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody. eLife 2023; 12:83628. [PMID: 36625542 PMCID: PMC9995116 DOI: 10.7554/elife.83628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | - Daniel P Maurer
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Caelan Brooks
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Aaron G Schmidt
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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29
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Venkataram S, Kuo HY, Hom EFY, Kryazhimskiy S. Mutualism-enhancing mutations dominate early adaptation in a two-species microbial community. Nat Ecol Evol 2023; 7:143-154. [PMID: 36593292 DOI: 10.1038/s41559-022-01923-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/03/2022] [Indexed: 01/03/2023]
Abstract
Species interactions drive evolution while evolution shapes these interactions. The resulting eco-evolutionary dynamics and their repeatability depend on how adaptive mutations available to community members affect fitness and ecologically relevant traits. However, the diversity of adaptive mutations is not well characterized, and we do not know how this diversity is affected by the ecological milieu. Here we use barcode lineage tracking to address this question in a community of yeast Saccharomyces cerevisiae and alga Chlamydomonas reinhardtii that have a net commensal relationship that results from a balance between competitive and mutualistic interactions. We find that yeast has access to many adaptive mutations with diverse ecological consequences, in particular those that increase and reduce the yields of both species. The presence of the alga does not change which mutations are adaptive in yeast (that is, there is no fitness trade-off for yeast between growing alone or with alga), but rather shifts selection to favour yeast mutants that increase the yields of both species and make the mutualism stronger. Thus, in the presence of the alga, adaptative mutations contending for fixation in yeast are more likely to enhance the mutualism, even though cooperativity is not directly favoured by natural selection in our system. Our results demonstrate that ecological interactions not only alter the trajectory of evolution but also dictate its repeatability; in particular, weak mutualisms can repeatably evolve to become stronger.
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Affiliation(s)
- Sandeep Venkataram
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, USA
| | - Huan-Yu Kuo
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, USA.,Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Erik F Y Hom
- Department of Biology and Center for Biodiversity and Conservation Research, University of Mississippi, University, MS, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, USA.
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30
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Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations. Nat Commun 2022; 13:7916. [PMID: 36564390 PMCID: PMC9789051 DOI: 10.1038/s41467-022-35484-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Mutation-mediated treatment resistance is one of the primary challenges for modern antibiotic and anti-cancer therapy. Yet, many resistance mutations have a substantial fitness cost and are subject to purifying selection. How emerging resistant lineages may escape purifying selection via subsequent compensatory mutations is still unclear due to the difficulty of tracking such evolutionary rescue dynamics in space and time. Here, we introduce a system of fluorescence-coupled synthetic mutations to show that the probability of evolutionary rescue, and the resulting long-term persistence of drug resistant mutant lineages, is dramatically increased in dense microbial populations. By tracking the entire evolutionary trajectory of thousands of resistant lineages in expanding yeast colonies we uncover an underlying quasi-stable equilibrium between the opposing forces of radial expansion and natural selection, a phenomenon we term inflation-selection balance. Tailored computational models and agent-based simulations corroborate the fundamental nature of the observed effects and demonstrate the potential impact on drug resistance evolution in cancer. The described phenomena should be considered when predicting multi-step evolutionary dynamics in any mechanically compact cellular population, including pathogenic microbial biofilms and solid tumors. The insights gained will be especially valuable for the quantitative understanding of response to treatment, including emerging evolution-based therapy strategies.
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31
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Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1. Nat Commun 2022; 13:7011. [PMID: 36384919 PMCID: PMC9668218 DOI: 10.1038/s41467-022-34506-z] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/26/2022] [Indexed: 11/17/2022] Open
Abstract
The Omicron BA.1 variant emerged in late 2021 and quickly spread across the world. Compared to the earlier SARS-CoV-2 variants, BA.1 has many mutations, some of which are known to enable antibody escape. Many of these antibody-escape mutations individually decrease the spike receptor-binding domain (RBD) affinity for ACE2, but BA.1 still binds ACE2 with high affinity. The fitness and evolution of the BA.1 lineage is therefore driven by the combined effects of numerous mutations. Here, we systematically map the epistatic interactions between the 15 mutations in the RBD of BA.1 relative to the Wuhan Hu-1 strain. Specifically, we measure the ACE2 affinity of all possible combinations of these 15 mutations (215 = 32,768 genotypes), spanning all possible evolutionary intermediates from the ancestral Wuhan Hu-1 strain to BA.1. We find that immune escape mutations in BA.1 individually reduce ACE2 affinity but are compensated by epistatic interactions with other affinity-enhancing mutations, including Q498R and N501Y. Thus, the ability of BA.1 to evade immunity while maintaining ACE2 affinity is contingent on acquiring multiple interacting mutations. Our results implicate compensatory epistasis as a key factor driving substantial evolutionary change for SARS-CoV-2 and are consistent with Omicron BA.1 arising from a chronic infection.
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32
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Brettner L, Ho WC, Schmidlin K, Apodaca S, Eder R, Geiler-Samerotte K. Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes. Curr Opin Genet Dev 2022; 75:101951. [PMID: 35797741 DOI: 10.1016/j.gde.2022.101951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/01/2022] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.
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33
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Emergent evolutionary forces in spatial models of luminal growth and their application to the human gut microbiota. Proc Natl Acad Sci U S A 2022; 119:e2114931119. [PMID: 35787046 PMCID: PMC9282425 DOI: 10.1073/pnas.2114931119] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The genetic composition of the gut microbiota is constantly reshaped by ecological and evolutionary forces. These strain-level dynamics are challenging to understand because they depend on complex spatial growth processes that take place within a host. Here we introduce a population genetic framework to predict how stochastic evolutionary forces emerge from simple models of microbial growth in spatially extended environments like the intestinal lumen. Our framework shows how fluid flow and longitudinal variation in growth rate combine to shape the frequencies of genetic variants in simulated fecal samples, yielding analytical expressions for the effective generation times, selection coefficients, and rates of genetic drift. We find that over longer timescales, the emergent evolutionary dynamics can often be captured by well-mixed models that lack explicit spatial structure, even when there is substantial spatial variation in species-level composition. By applying these results to the human colon, we find that continuous fluid flow and simple forms of wall growth alone are unlikely to create sufficient bottlenecks to allow large fluctuations in mutant frequencies within a host. We also find that the effective generation times may be significantly shorter than expected from traditional average growth rate estimates. Our results provide a starting point for quantifying genetic turnover in spatially extended settings like the gut microbiota and may be relevant for other microbial ecosystems where unidirectional fluid flow plays an important role.
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34
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The teenage years of yeast population genomics trace history, admixing and getting wilder. Curr Opin Genet Dev 2022; 75:101942. [PMID: 35753210 DOI: 10.1016/j.gde.2022.101942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/22/2022]
Abstract
Population genomics studies the evolutionary processes that shape intraspecies genetic variations. In this review, I explore the insights into yeast-population genomics that have emerged from recent advances in sequencing. Genomes of the model Saccharomyces cerevisiae and many new yeast species from around the world are being used to address various aspects of population biology, including geographical origin, the level of introgression, domestication signatures, and outcrossing frequency. New long-read sequencing has enabled a greater capacity to quantify these variations at a finer resolution from complete de novo genomes at the population scale to phasing subgenomes of different origins. These resources provide a platform to dissect the relationship between phenotypes across environmental niches.
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35
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Jagdish T, Nguyen Ba AN. Microbial experimental evolution in a massively multiplexed and high-throughput era. Curr Opin Genet Dev 2022; 75:101943. [PMID: 35752001 DOI: 10.1016/j.gde.2022.101943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
Abstract
Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.
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Affiliation(s)
- Tanush Jagdish
- Department of Molecular and Cellular Biology and The Program for Systems Synthetic and Quantitative Biology, Harvard University, Cambridge, United States.
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, Canada; Department of Cell and Systems Biology, University of Toronto, Toronto, Canada.
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36
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Tavakolian N, Frazão JG, Bendixsen D, Stelkens R, Li CB. Shepherd: Accurate Clustering for Correcting DNA Barcode Errors. Bioinformatics 2022; 38:3710-3716. [PMID: 35708611 PMCID: PMC9344852 DOI: 10.1093/bioinformatics/btac395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/26/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation DNA barcodes are short, random nucleotide sequences introduced into cell populations to track the relative counts of hundreds of thousands of individual lineages over time. Lineage tracking is widely applied, e.g. to understand evolutionary dynamics in microbial populations and the progression of breast cancer in humans. Barcode sequences are unknown upon insertion and must be identified using next-generation sequencing technology, which is error prone. In this study, we frame the barcode error correction task as a clustering problem with the aim to identify true barcode sequences from noisy sequencing data. We present Shepherd, a novel clustering method that is based on an indexing system of barcode sequences using k-mers, and a Bayesian statistical test incorporating a substitution error rate to distinguish true from error sequences. Results When benchmarking with synthetic data, Shepherd provides barcode count estimates that are significantly more accurate than state-of-the-art methods, producing 10–150 times fewer spurious lineages. For empirical data, Shepherd produces results that are consistent with the improvements seen on synthetic data. These improvements enable higher resolution lineage tracking and more accurate estimates of biologically relevant quantities, e.g. the detection of small effect mutations. Availability and implementation A Python implementation of Shepherd is freely available at: https://www.github.com/Nik-Tavakolian/Shepherd. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nik Tavakolian
- Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden
| | | | - Devin Bendixsen
- Department of Zoology, Stockholm University, Stockholm, 10691, Sweden
| | - Rike Stelkens
- Department of Zoology, Stockholm University, Stockholm, 10691, Sweden
| | - Chun-Biu Li
- Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden
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37
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Bakerlee CW, Nguyen Ba AN, Shulgina Y, Rojas Echenique JI, Desai MM. Idiosyncratic epistasis leads to global fitness-correlated trends. Science 2022; 376:630-635. [PMID: 35511982 PMCID: PMC10124986 DOI: 10.1126/science.abm4774] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Epistasis can markedly affect evolutionary trajectories. In recent decades, protein-level fitness landscapes have revealed extensive idiosyncratic epistasis among specific mutations. By contrast, other work has found ubiquitous and apparently nonspecific patterns of global diminishing-returns and increasing-costs epistasis among mutations across the genome. Here, we used a hierarchical CRISPR gene drive system to construct all combinations of 10 missense mutations from across the genome in budding yeast and measured their fitness in six environments. We show that the resulting fitness landscapes exhibit global fitness-correlated trends but that these trends emerge from specific idiosyncratic interactions. We thus provide experimental validation of recent theoretical work arguing that fitness-correlated trends can emerge as the generic consequence of idiosyncratic epistasis.
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Affiliation(s)
- Christopher W Bakerlee
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.,Quantitative Biology Initiative, Harvard University, Cambridge, MA, USA.,Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.,Quantitative Biology Initiative, Harvard University, Cambridge, MA, USA.,Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada.,Department of Biology, University of Toronto Mississauga, Mississauga, Ontario, Canada
| | - Yekaterina Shulgina
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Jose I Rojas Echenique
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.,Quantitative Biology Initiative, Harvard University, Cambridge, MA, USA.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA, USA.,Department of Physics, Harvard University, Cambridge, MA, USA
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38
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Avecilla G, Chuong JN, Li F, Sherlock G, Gresham D, Ram Y. Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics. PLoS Biol 2022; 20:e3001633. [PMID: 35622868 PMCID: PMC9140244 DOI: 10.1371/journal.pbio.3001633] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 04/14/2022] [Indexed: 11/24/2022] Open
Abstract
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood-free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright-Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10-4.7 to 10-4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods-barcode lineage tracking and pairwise fitness assays-which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.
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Affiliation(s)
- Grace Avecilla
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Julie N. Chuong
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Fangfei Li
- Department of Genetics, Stanford University, California, Stanford, United States of America
| | - Gavin Sherlock
- Department of Genetics, Stanford University, California, Stanford, United States of America
| | - David Gresham
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Yoav Ram
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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39
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Nguyen Ba AN, Lawrence KR, Rego-Costa A, Gopalakrishnan S, Temko D, Michor F, Desai MM. Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast. eLife 2022; 11:73983. [PMID: 35147078 PMCID: PMC8979589 DOI: 10.7554/elife.73983] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/11/2022] [Indexed: 11/25/2022] Open
Abstract
Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.
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Affiliation(s)
- Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | - Artur Rego-Costa
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | | | | | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University
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40
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Robustness: linking strain design to viable bioprocesses. Trends Biotechnol 2022; 40:918-931. [PMID: 35120750 DOI: 10.1016/j.tibtech.2022.01.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/05/2022] [Accepted: 01/05/2022] [Indexed: 12/18/2022]
Abstract
Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.
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41
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Shoemaker WR, Chen D, Garud NR. Comparative Population Genetics in the Human Gut Microbiome. Genome Biol Evol 2022; 14:evab116. [PMID: 34028530 PMCID: PMC8743038 DOI: 10.1093/gbe/evab116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2021] [Indexed: 11/13/2022] Open
Abstract
Genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of coexisting species in the microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we reanalyze patterns of purifying selection across ∼40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.
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Affiliation(s)
- William R Shoemaker
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
| | - Daisy Chen
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
- Department of Human Genetics, University of California, Los Angeles, California, USA
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42
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Abstract
The use of DNA barcodes for determining changes in genotype frequencies has been instrumental to increase the scale at which we can phenotype strain libraries by using next-generation sequencing technologies. Here, we describe the determination of strain fitness for thousands of yeast strains simultaneously in a single assay using recent innovations that increase the precision of these measurements, such as the inclusion of unique-molecular identifiers (UMIs) and purification by solid-phase reverse immobilization (SPRI) beads.
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Affiliation(s)
- Claire A Chochinov
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Alex N Nguyen Ba
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada.
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada.
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43
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Ardell SM, Kryazhimskiy S. The population genetics of collateral resistance and sensitivity. eLife 2021; 10:73250. [PMID: 34889185 PMCID: PMC8765753 DOI: 10.7554/elife.73250] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 12/06/2021] [Indexed: 12/05/2022] Open
Abstract
Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments. Drugs known as antibiotics are the main treatment for most serious infections caused by bacteria. However, many bacteria are acquiring genetic mutations that make them resistant to the effects of one or more types of antibiotics, making them harder to eliminate. One way to tackle drug-resistant bacteria is to develop new types of antibiotics; however, in recent years, the rate at which new antibiotics have become available has been dwindling. Using two or more existing drugs, one after another, can also be an effective way to eliminate resistant bacteria. The success of any such ‘multi-drug’ treatment lies in being able to predict whether mutations that make the bacteria resistant to one drug simultaneously make it sensitive to another, a phenomenon known as collateral sensitivity. Different resistance mutations may have different collateral effects: some may increase the bacteria’s sensitivity to the second drug, while others might make the bacteria more resistant. However, it is currently unclear how to design robust multi-drug treatments that take this diversity of collateral effects into account. Here, Ardell and Kryazhimskiy used a concept called JDFE (short for the joint distribution of fitness effects) to describe the diversity of collateral effects in a population of bacteria exposed to a single drug. This information was then used to mathematically model how collateral effects evolved in the population over time. Ardell and Kryazhimskiy showed that this approach can predict how likely a population is to become collaterally sensitive or collaterally resistant to a second antibiotic. Drug pairs can then be ranked according to the risk of collateral resistance emerging, so long as information on the variety of resistance mutations available to the bacteria are included in the model. Each year, more than 700,000 people die from infections caused by bacteria that are resistant to one or more antibiotics. The findings of Ardell and Kryazhimskiy may eventually help clinicians design multi-drug treatments that effectively eliminate bacterial infections and help to prevent more bacteria from evolving resistance to antibiotics. However, to achieve this goal, more research is needed to fully understand the range collateral effects caused by resistance mutations.
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Affiliation(s)
- Sarah M Ardell
- Division of Biological Sciences, University of California, San Diego, La Jolla, United States
| | - Sergey Kryazhimskiy
- Division of Biological Sciences, University of California, San Diego, La Jolla, United States
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44
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The Role of Ancestral Duplicated Genes in Adaptation to Growth on Lactate, a Non-Fermentable Carbon Source for the Yeast Saccharomyces cerevisiae. Int J Mol Sci 2021; 22:ijms222212293. [PMID: 34830177 PMCID: PMC8622941 DOI: 10.3390/ijms222212293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 11/26/2022] Open
Abstract
The cell central metabolism has been shaped throughout evolutionary times when facing challenges from the availability of resources. In the budding yeast, Saccharomyces cerevisiae, a set of duplicated genes originating from an ancestral whole-genome and several coetaneous small-scale duplication events drive energy transfer through glucose metabolism as the main carbon source either by fermentation or respiration. These duplicates (~a third of the genome) have been dated back to approximately 100 MY, allowing for enough evolutionary time to diverge in both sequence and function. Gene duplication has been proposed as a molecular mechanism of biological innovation, maintaining balance between mutational robustness and evolvability of the system. However, some questions concerning the molecular mechanisms behind duplicated genes transcriptional plasticity and functional divergence remain unresolved. In this work we challenged S. cerevisiae to the use of lactic acid/lactate as the sole carbon source and performed a small adaptive laboratory evolution to this non-fermentative carbon source, determining phenotypic and transcriptomic changes. We observed growth adaptation to acidic stress, by reduction of growth rate and increase in biomass production, while the transcriptomic response was mainly driven by repression of the whole-genome duplicates, those implied in glycolysis and overexpression of ROS response. The contribution of several duplicated pairs to this carbon source switch and acidic stress is also discussed.
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45
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Reconstruction of evolving gene variants and fitness from short sequencing reads. Nat Chem Biol 2021; 17:1188-1198. [PMID: 34635842 PMCID: PMC8551035 DOI: 10.1038/s41589-021-00876-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 08/09/2021] [Indexed: 12/23/2022]
Abstract
Directed evolution can generate proteins with tailor-made activities. However, full-length genotypes, their frequencies and fitnesses are difficult to measure for evolving gene-length biomolecules using most high-throughput DNA sequencing methods, as short read lengths can lose mutation linkages in haplotypes. Here we present Evoracle, a machine learning method that accurately reconstructs full-length genotypes (R2 = 0.94) and fitness using short-read data from directed evolution experiments, with substantial improvements over related methods. We validate Evoracle on phage-assisted continuous evolution (PACE) and phage-assisted non-continuous evolution (PANCE) of adenine base editors and OrthoRep evolution of drug-resistant enzymes. Evoracle retains strong performance (R2 = 0.86) on data with complete linkage loss between neighboring nucleotides and large measurement noise, such as pooled Sanger sequencing data (~US$10 per timepoint), and broadens the accessibility of training machine learning models on gene variant fitnesses. Evoracle can also identify high-fitness variants, including low-frequency 'rising stars', well before they are identifiable from consensus mutations.
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46
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Bakerlee CW, Phillips AM, Nguyen Ba AN, Desai MM. Dynamics and variability in the pleiotropic effects of adaptation in laboratory budding yeast populations. eLife 2021; 10:e70918. [PMID: 34596043 PMCID: PMC8579951 DOI: 10.7554/elife.70918] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/29/2021] [Indexed: 12/15/2022] Open
Abstract
Evolutionary adaptation to a constant environment is driven by the accumulation of mutations which can have a range of unrealized pleiotropic effects in other environments. These pleiotropic consequences of adaptation can influence the emergence of specialists or generalists, and are critical for evolution in temporally or spatially fluctuating environments. While many experiments have examined the pleiotropic effects of adaptation at a snapshot in time, very few have observed the dynamics by which these effects emerge and evolve. Here, we propagated hundreds of diploid and haploid laboratory budding yeast populations in each of three environments, and then assayed their fitness in multiple environments over 1000 generations of evolution. We find that replicate populations evolved in the same condition share common patterns of pleiotropic effects across other environments, which emerge within the first several hundred generations of evolution. However, we also find dynamic and environment-specific variability within these trends: variability in pleiotropic effects tends to increase over time, with the extent of variability depending on the evolution environment. These results suggest shifting and overlapping contributions of chance and contingency to the pleiotropic effects of adaptation, which could influence evolutionary trajectories in complex environments that fluctuate across space and time.
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Affiliation(s)
- Christopher W Bakerlee
- Department of Molecular and Cellular Biology, Harvard UniversityCambridgeUnited States
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Cell and Systems Biology, University of TorontoTorontoCanada
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard University, CambridgeCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard University, CambridgeCambridgeUnited States
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47
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Phillips AM, Lawrence KR, Moulana A, Dupic T, Chang J, Johnson MS, Cvijovic I, Mora T, Walczak AM, Desai MM. Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies. eLife 2021; 10:71393. [PMID: 34491198 PMCID: PMC8476123 DOI: 10.7554/elife.71393] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/05/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past two decades, several broadly neutralizing antibodies (bnAbs) that confer protection against diverse influenza strains have been isolated. Structural and biochemical characterization of these bnAbs has provided molecular insight into how they bind distinct antigens. However, our understanding of the evolutionary pathways leading to bnAbs, and thus how best to elicit them, remains limited. Here, we measure equilibrium dissociation constants of combinatorially complete mutational libraries for two naturally isolated influenza bnAbs (CR9114, 16 heavy-chain mutations; CR6261, 11 heavy-chain mutations), reconstructing all possible evolutionary intermediates back to the unmutated germline sequences. We find that these two libraries exhibit strikingly different patterns of breadth: while many variants of CR6261 display moderate affinity to diverse antigens, those of CR9114 display appreciable affinity only in specific, nested combinations. By examining the extensive pairwise and higher order epistasis between mutations, we find key sites with strong synergistic interactions that are highly similar across antigens for CR6261 and different for CR9114. Together, these features of the binding affinity landscapes strongly favor sequential acquisition of affinity to diverse antigens for CR9114, while the acquisition of breadth to more similar antigens for CR6261 is less constrained. These results, if generalizable to other bnAbs, may explain the molecular basis for the widespread observation that sequential exposure favors greater breadth, and such mechanistic insight will be essential for predicting and eliciting broadly protective immune responses.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Katherine R Lawrence
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.,Quantitative Biology Initiative, Harvard University, Cambridge, United States.,Department of Physics, Massachusetts Institute of Technology, Cambridge, United States
| | - Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Jeffrey Chang
- Department of Physics, Harvard University, Cambridge, United States
| | - Milo S Johnson
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Ivana Cvijovic
- Department of Applied Physics, Stanford University, Stanford, United States
| | - Thierry Mora
- Laboratoire de physique de ÍÉcole Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Aleksandra M Walczak
- Laboratoire de physique de ÍÉcole Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.,Quantitative Biology Initiative, Harvard University, Cambridge, United States.,Department of Physics, Harvard University, Cambridge, United States
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48
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Changes in the distribution of fitness effects and adaptive mutational spectra following a single first step towards adaptation. Nat Commun 2021; 12:5193. [PMID: 34465770 PMCID: PMC8408183 DOI: 10.1038/s41467-021-25440-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/11/2021] [Indexed: 01/17/2023] Open
Abstract
Historical contingency and diminishing returns epistasis have been typically studied for relatively divergent genotypes and/or over long evolutionary timescales. Here, we use Saccharomyces cerevisiae to study the extent of diminishing returns and the changes in the adaptive mutational spectra following a single first adaptive mutational step. We further evolve three clones that arose under identical conditions from a common ancestor. We follow their evolutionary dynamics by lineage tracking and determine adaptive outcomes using fitness assays and whole genome sequencing. We find that diminishing returns manifests as smaller fitness gains during the 2nd step of adaptation compared to the 1st step, mainly due to a compressed distribution of fitness effects. We also find that the beneficial mutational spectra for the 2nd adaptive step are contingent on the 1st step, as we see both shared and diverging adaptive strategies. Finally, we find that adaptive loss-of-function mutations, such as nonsense and frameshift mutations, are less common in the second step of adaptation than in the first step. Analyses of both natural and experimental evolution suggest that adaptation depends on the evolutionary past and adaptive potential decreases over time. Here, by tracking yeast adaptation with DNA barcoding, the authors show that such evolutionary phenomena can be observed even after a single adaptive step.
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49
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Santos-Lopez A, Marshall CW, Haas AL, Turner C, Rasero J, Cooper VS. The roles of history, chance, and natural selection in the evolution of antibiotic resistance. eLife 2021; 10:e70676. [PMID: 34431477 PMCID: PMC8412936 DOI: 10.7554/elife.70676] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/24/2021] [Indexed: 12/20/2022] Open
Abstract
History, chance, and selection are the fundamental factors that drive and constrain evolution. We designed evolution experiments to disentangle and quantify effects of these forces on the evolution of antibiotic resistance. Previously, we showed that selection of the pathogen Acinetobacter baumannii in both structured and unstructured environments containing the antibiotic ciprofloxacin produced distinct genotypes and phenotypes, with lower resistance in biofilms as well as collateral sensitivity to β-lactam drugs (Santos-Lopez et al., 2019). Here we study how this prior history influences subsequent evolution in new β-lactam antibiotics. Selection was imposed by increasing concentrations of ceftazidime and imipenem and chance differences arose as random mutations among replicate populations. The effects of history were reduced by increasingly strong selection in new drugs, but not erased, at times revealing important contingencies. A history of selection in structured environments constrained resistance to new drugs and led to frequent loss of resistance to the initial drug by genetic reversions and not compensatory mutations. This research demonstrates that despite strong selective pressures of antibiotics leading to genetic parallelism, history can etch potential vulnerabilities to orthogonal drugs.
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Affiliation(s)
- Alfonso Santos-Lopez
- Department of Microbiology and Molecular Genetics, School of Medicine, University of PittsburghPittsburghUnited States
| | - Christopher W Marshall
- Department of Microbiology and Molecular Genetics, School of Medicine, University of PittsburghPittsburghUnited States
| | - Allison L Haas
- Department of Microbiology and Molecular Genetics, School of Medicine, University of PittsburghPittsburghUnited States
| | - Caroline Turner
- Department of Microbiology and Molecular Genetics, School of Medicine, University of PittsburghPittsburghUnited States
| | - Javier Rasero
- Department of Psychology, Carnegie Mellon UniversityPittsburghUnited States
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, School of Medicine, University of PittsburghPittsburghUnited States
- Center for Evolutionary Biology and Medicine, University of PittsburghPittsburghUnited States
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50
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Tung S, Bakerlee CW, Phillips AM, Nguyen Ba AN, Desai MM. The genetic basis of differential autodiploidization in evolving yeast populations. G3 GENES|GENOMES|GENETICS 2021; 11:6291244. [PMID: 34849811 PMCID: PMC8496219 DOI: 10.1093/g3journal/jkab192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/26/2021] [Indexed: 11/13/2022]
Abstract
Abstract
Spontaneous whole-genome duplication, or autodiploidization, is a common route to adaptation in experimental evolution of haploid budding yeast populations. The rate at which autodiploids fix in these populations appears to vary across strain backgrounds, but the genetic basis of these differences remains poorly characterized. Here, we show that the frequency of autodiploidization differs dramatically between two closely related laboratory strains of Saccharomyces cerevisiae, BY4741 and W303. To investigate the genetic basis of this difference, we crossed these strains to generate hundreds of unique F1 segregants and tested the tendency of each segregant to autodiplodize across hundreds of generations of laboratory evolution. We find that variants in the SSD1 gene are the primary genetic determinant of differences in autodiploidization. We then used multiple laboratory and wild strains of S. cerevisiae to show that clonal populations of strains with a functional copy of SSD1 autodiploidize more frequently in evolution experiments, while knocking out this gene or replacing it with the W303 allele reduces autodiploidization propensity across all genetic backgrounds tested. These results suggest a potential strategy for modifying rates of spontaneous whole-genome duplications in laboratory evolution experiments in haploid budding yeast. They may also have relevance to other settings in which eukaryotic genome stability plays an important role, such as biomanufacturing and the treatment of pathogenic fungal diseases and cancers.
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Affiliation(s)
- Sudipta Tung
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- The Lakshmi Mittal And Family South Asia Institute, Harvard University, Cambridge, MA 02138, USA
| | - Christopher W Bakerlee
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
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