1
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Rolff J, Bonhoeffer S, Kloft C, Leistner R, Regoes R, Hochberg ME. Forecasting antimicrobial resistance evolution. Trends Microbiol 2024; 32:736-745. [PMID: 38238231 DOI: 10.1016/j.tim.2023.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 08/09/2024]
Abstract
Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.
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Affiliation(s)
- Jens Rolff
- Evolutionary Biology, Institute of Biology, Freie Universität Berlin, Berlin, Germany.
| | | | - Charlotte Kloft
- Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
| | - Rasmus Leistner
- Charité-Universitätsmedizin Berlin Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Berlin, Germany
| | - Roland Regoes
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
| | - Michael E Hochberg
- ISEM, Université de Montpellier, CNRS, IRD, EPHE, 34095 Montpellier, France; Santa Fe Institute, Santa Fe, NM 87501, USA
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2
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Ardell S, Martsul A, Johnson MS, Kryazhimskiy S. Environment-independent distribution of mutational effects emerges from microscopic epistasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.18.567655. [PMID: 38014325 PMCID: PMC10680819 DOI: 10.1101/2023.11.18.567655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, where the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global-epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.
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Affiliation(s)
- Sarah Ardell
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Alena Martsul
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Milo S. Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA 94720
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
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3
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Bozzuto C, Ives AR. Predictability of ecological and evolutionary dynamics in a changing world. Proc Biol Sci 2024; 291:20240980. [PMID: 38981521 DOI: 10.1098/rspb.2024.0980] [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: 11/06/2023] [Accepted: 06/07/2024] [Indexed: 07/11/2024] Open
Abstract
Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.
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Affiliation(s)
- Claudio Bozzuto
- Wildlife Analysis GmbH, Oetlisbergstrasse 38 , 8053 Zurich, Switzerland
| | - Anthony R Ives
- Department of Integrative Biology, University of Wisconsin-Madison , Madison, WI 53706, USA
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4
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Nguyen A, Zhao H, Myagmarsuren D, Srinivasan S, Wu D, Chen J, Piszczek G, Schuck P. Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2. eLife 2024; 13:RP94836. [PMID: 38941236 PMCID: PMC11213569 DOI: 10.7554/elife.94836] [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: 06/30/2024] Open
Abstract
Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.
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Affiliation(s)
- Ai Nguyen
- Laboratory of Dynamics of Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
| | - Huaying Zhao
- Laboratory of Dynamics of Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
| | - Dulguun Myagmarsuren
- Laboratory of Dynamics of Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
| | - Sanjana Srinivasan
- Laboratory of Dynamics of Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
| | - Di Wu
- Biophysics Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States
| | - Jiji Chen
- Advanced Imaging and Microscopy Resource, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
| | - Grzegorz Piszczek
- Biophysics Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States
| | - Peter Schuck
- Laboratory of Dynamics of Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
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5
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Schmidlin, Apodaca, Newell, Sastokas, Kinsler, Geiler-Samerotte. Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.17.562616. [PMID: 37905147 PMCID: PMC10614906 DOI: 10.1101/2023.10.17.562616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.
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Affiliation(s)
- Schmidlin
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Apodaca
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Newell
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Sastokas
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Kinsler
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
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6
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Smith E. Beyond fitness: The information imparted in population states by selection throughout lifecycles. Theor Popul Biol 2024; 157:86-117. [PMID: 38615922 DOI: 10.1016/j.tpb.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/25/2024] [Accepted: 04/05/2024] [Indexed: 04/16/2024]
Abstract
We approach the questions, what part of evolutionary change results from selection, and what is the adaptive information flow into a population undergoing selection, as a problem of quantifying the divergence of typical trajectories realized under selection from the expected dynamics of their counterparts under a null stochastic-process model representing the absence of selection. This approach starts with a formulation of adaptation in terms of information and from that identifies selection from the genetic parameters that generate information flow; it is the reverse of a historical approach that defines selection in terms of fitness, and then identifies adaptive characters as those amplified in relative frequency by fitness. Adaptive information is a relative entropy on distributions of histories computed directly from the generators of stochastic evolutionary population processes, which in large population limits can be approximated by its leading exponential dependence as a large-deviation function. We study a particular class of generators that represent the genetic dependence of explicit transitions around reproductive cycles in terms of stoichiometry, familiar from chemical reaction networks. Following Smith (2023), which showed that partitioning evolutionary events among genetically distinct realizations of lifecycles yields a more consistent causal analysis through the Price equation than the construction from units of selection and fitness, here we show that it likewise yields more complete evolutionary information measures.
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Affiliation(s)
- Eric Smith
- Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1-IE-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan; School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, GA, 30332, USA; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA.
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7
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585703. [PMID: 38746108 PMCID: PMC11092427 DOI: 10.1101/2024.03.19.585703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .
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8
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Mohebbi F, Zelikovsky A, Mangul S, Chowell G, Skums P. Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks. Nat Commun 2024; 15:2838. [PMID: 38565543 PMCID: PMC10987511 DOI: 10.1038/s41467-024-47304-6] [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: 09/28/2023] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.
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Affiliation(s)
- Fatemeh Mohebbi
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Serghei Mangul
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.
- School of Computing, College of Engineering, University of Connecticut, Storrs, CT, USA.
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9
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Abs E, Chase AB, Manzoni S, Ciais P, Allison SD. Microbial evolution-An under-appreciated driver of soil carbon cycling. GLOBAL CHANGE BIOLOGY 2024; 30:e17268. [PMID: 38562029 DOI: 10.1111/gcb.17268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.
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Affiliation(s)
- Elsa Abs
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA
- Laboratoire Des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Alexander B Chase
- Department of Earth Sciences, Southern Methodist University, Dallas, Texas, USA
| | - Stefano Manzoni
- Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Philippe Ciais
- Laboratoire Des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Steven D Allison
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA
- Department of Earth System Science, University of California, Irvine, Irvine, California, USA
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10
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Lou J, Liang W, Cao L, Hu I, Zhao S, Chen Z, Chan RWY, Cheung PPH, Zheng H, Liu C, Li Q, Chong MKC, Zhang Y, Yeoh EK, Chan PKS, Zee BCY, Mok CKP, Wang MH. Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations. Nat Commun 2024; 15:2546. [PMID: 38514647 PMCID: PMC10958014 DOI: 10.1038/s41467-024-46918-0] [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: 12/06/2023] [Accepted: 03/12/2024] [Indexed: 03/23/2024] Open
Abstract
Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.
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Affiliation(s)
- Jingzhi Lou
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- Beth Bioinformatics Co. Ltd, Hong Kong SAR, China
| | - Weiwen Liang
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lirong Cao
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Inchi Hu
- Department of Statistics, George Mason University, Fairfax, VA, USA
| | - Shi Zhao
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Zigui Chen
- Department of Microbiology, CUHK, Hong Kong SAR, China
| | - Renee Wan Yi Chan
- Department of Paediatrics, CUHK, Hong Kong SAR, China
- Hong Kong Hub of Paediatric Excellence, CUHK, Hong Kong SAR, China
| | | | - Hong Zheng
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Caiqi Liu
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Qi Li
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Marc Ka Chun Chong
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Yexian Zhang
- Beth Bioinformatics Co. Ltd, Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Eng-Kiong Yeoh
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- Centre for Health Systems and Policy Research, CUHK, Hong Kong SAR, China
| | - Paul Kay-Sheung Chan
- Department of Microbiology, CUHK, Hong Kong SAR, China
- Stanley Ho Centre for Emerging Infectious Diseases, CUHK, Hong Kong SAR, China
| | - Benny Chung Ying Zee
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Chris Ka Pun Mok
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
- Li Ka Shing Institute of Health Sciences, Faculty of Medicine, CUHK, Hong Kong SAR, China.
| | - Maggie Haitian Wang
- JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
- CUHK Shenzhen Research Institute, Shenzhen, China.
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11
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Han L, Yogurtcu ON, Rodriguez Messan M, Valega-Mackenzie W, Nukala U, Yang H. Dosage optimization for reducing tumor burden using a phenotype-structured population model with a drug-resistance continuum. MATHEMATICAL MEDICINE AND BIOLOGY : A JOURNAL OF THE IMA 2024; 41:35-52. [PMID: 38408192 DOI: 10.1093/imammb/dqae003] [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: 07/10/2023] [Revised: 11/11/2023] [Accepted: 02/20/2024] [Indexed: 02/28/2024]
Abstract
Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.
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Affiliation(s)
- Lifeng Han
- Department of Mathematics, Tulane University, 6823 St. Charles Avenue, New Orleans, LA 70115, USA
| | - Osman N Yogurtcu
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Marisabel Rodriguez Messan
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Wencel Valega-Mackenzie
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Ujwani Nukala
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Hong Yang
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
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12
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Bitter MC, Berardi S, Oken H, Huynh A, Schmidt P, Petrov DA. Continuously fluctuating selection reveals extreme granularity and parallelism of adaptive tracking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.16.562586. [PMID: 37904939 PMCID: PMC10614893 DOI: 10.1101/2023.10.16.562586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown. We generated high-frequency, genome-wide allele frequency data from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December, a period of ∼12 generations. Adaptation throughout the fundamental ecological phases of population expansion, peak density, and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of allele frequency change to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveal fitness-relevance of standing variation that is likely to be masked by inference approaches based on static population sampling, or insufficiently resolved time-series data. We propose such fine-scaled temporally fluctuating selection may be an important force maintaining functional genetic variation in natural populations and an important stochastic force affecting levels of standing genetic variation genome-wide.
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13
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King ES, Tadele DS, Pierce B, Hinczewski M, Scott JG. Diverse mutant selection windows shape spatial heterogeneity in evolving populations. PLoS Comput Biol 2024; 20:e1011878. [PMID: 38386690 PMCID: PMC10914271 DOI: 10.1371/journal.pcbi.1011878] [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: 04/10/2023] [Revised: 03/05/2024] [Accepted: 01/31/2024] [Indexed: 02/24/2024] Open
Abstract
Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.
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Affiliation(s)
- Eshan S. King
- Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Dagim S. Tadele
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, Ohio, United States of America
- Oslo University Hospital, Ullevål, Department of Medical Genetics, Oslo, Norway
| | - Beck Pierce
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Jacob G. Scott
- Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Physics, Case Western Reserve University, Cleveland, Ohio, United States of America
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, United States of America
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14
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Zambon A, Zecchina R, Tiana G. Structure of the space of folding protein sequences defined by large language models. Phys Biol 2024; 21:026002. [PMID: 38237200 DOI: 10.1088/1478-3975/ad205c] [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: 11/15/2023] [Accepted: 01/18/2024] [Indexed: 02/01/2024]
Abstract
Proteins populate a manifold in the high-dimensional sequence space whose geometrical structure guides their natural evolution. Leveraging recently-developed structure prediction tools based on transformer models, we first examine the protein sequence landscape as defined by an effective energy that is a proxy of sequence foldability. This landscape shares characteristics with optimization challenges encountered in machine learning and constraint satisfaction problems. Our analysis reveals that natural proteins predominantly reside in wide, flat minima within this energy landscape. To investigate further, we employ statistical mechanics algorithms specifically designed to explore regions with high local entropy in relatively flat landscapes. Our findings indicate that these specialized algorithms can identify valleys with higher entropy compared to those found using traditional methods such as Monte Carlo Markov Chains. In a proof-of-concept case, we find that these highly entropic minima exhibit significant similarities to natural sequences, especially in critical key sites and local entropy. Additionally, evaluations through Molecular Dynamics suggests that the stability of these sequences closely resembles that of natural proteins. Our tool combines advancements in machine learning and statistical physics, providing new insights into the exploration of sequence landscapes where wide, flat minima coexist alongside a majority of narrower minima.
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Affiliation(s)
- A Zambon
- Department of Physics and Center for Complexity and Biosystems, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy
| | - R Zecchina
- Bocconi University, via Roentgen 1, 20136 Milano, Italy
| | - G Tiana
- Department of Physics and Center for Complexity and Biosystems, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy
- INFN, Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
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15
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Lässig M, Mustonen V, Nourmohammad A. Steering and controlling evolution - from bioengineering to fighting pathogens. Nat Rev Genet 2023; 24:851-867. [PMID: 37400577 PMCID: PMC11137064 DOI: 10.1038/s41576-023-00623-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.
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Affiliation(s)
- Michael Lässig
- Institute for Biological Physics, University of Cologne, Cologne, Germany.
| | - Ville Mustonen
- Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Armita Nourmohammad
- Department of Physics, University of Washington, Seattle, WA, USA.
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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16
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Firneno TJ, Semenov G, Dopman EB, Taylor SA, Larson EL, Gompert Z. Quantitative Analyses of Coupling in Hybrid Zones. Cold Spring Harb Perspect Biol 2023; 15:a041434. [PMID: 37739809 PMCID: PMC10691479 DOI: 10.1101/cshperspect.a041434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
In hybrid zones, whether barrier loci experience selection mostly independently or as a unit depends on the ratio of selection to recombination as captured by the coupling coefficient. Theory predicts a sharper transition between an uncoupled and coupled system when more loci affect hybrid fitness. However, the extent of coupling in hybrid zones has rarely been quantified. Here, we use simulations to characterize the relationship between the coupling coefficient and variance in clines across genetic loci. We then reanalyze 25 hybrid zone data sets and find that cline variances and estimated coupling coefficients form a smooth continuum from high variance and weak coupling to low variance and strong coupling. Our results are consistent with low rates of hybridization and a strong genome-wide barrier to gene flow when the coupling coefficient is much greater than 1, but also suggest that this boundary might be approached gradually and at a near constant rate over time.
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Affiliation(s)
- Thomas J Firneno
- Department of Biology, University of Denver, Denver, Colorado 80208, USA
| | - Georgy Semenov
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado 80211, USA
| | - Erik B Dopman
- Department of Biology, Tufts University, Medford, Massachusetts 02155, USA
| | - Scott A Taylor
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado 80211, USA
| | - Erica L Larson
- Department of Biology, University of Denver, Denver, Colorado 80208, USA
| | - Zachariah Gompert
- Department of Biology, Utah State University, Logan, Utah 84321, USA
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17
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Meijers M, Ruchnewitz D, Eberhardt J, Łuksza M, Lässig M. Population immunity predicts evolutionary trajectories of SARS-CoV-2. Cell 2023; 186:5151-5164.e13. [PMID: 37875109 PMCID: PMC10964984 DOI: 10.1016/j.cell.2023.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/26/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023]
Abstract
The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany.
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18
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Miele L, Evans RML, Cunniffe NJ, Torres-Barceló C, Bevacqua D. Evolutionary Epidemiology Consequences of Trait-Dependent Control of Heterogeneous Parasites. Am Nat 2023; 202:E130-E146. [PMID: 37963120 DOI: 10.1086/726062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
AbstractDisease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. In particular, we consider a parasite expressing continuous levels of a trait-determining resource exploitation and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimize both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.
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19
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Vila JC, Goldford J, Estrela S, Bajic D, Sanchez-Gorostiaga A, Damian-Serrano A, Lu N, Marsland R, Rebolleda-Gomez M, Mehta P, Sanchez A. Metabolic similarity and the predictability of microbial community assembly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564019. [PMID: 37961608 PMCID: PMC10634833 DOI: 10.1101/2023.10.25.564019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
When microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.
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Affiliation(s)
- Jean C.C. Vila
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Joshua Goldford
- Division of Geophysical and Planetary sciences,California Institute of Technology, Pasadena, CA, USA
| | - Sylvie Estrela
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Djordje Bajic
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
- Section of Industrial Microbiology, Department of Biotechnology, Technical University Delft, Delft, The Netherlands
| | - Alicia Sanchez-Gorostiaga
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
- Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), Alcalá de Henares, Spain
| | - Alejandro Damian-Serrano
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Department of Biology, University of Oregon, Eugene, OR, USA
| | - Nanxi Lu
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
| | - Robert Marsland
- Department of Physics, Boston University, Boston, MA 02215, USA
| | - Maria Rebolleda-Gomez
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California
| | - Pankaj Mehta
- Department of Physics, Boston University, Boston, MA 02215, USA
| | - Alvaro Sanchez
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
- Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC; Madrid, Spain
- New address: Institute of Functional Biology & Genomics IBFG, CSIC & University of Salamanca; Salamanca, Spain
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20
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Yitbarek S, Guittar J, Knutie SA, Ogbunugafor CB. Deconstructing taxa x taxa xenvironment interactions in the microbiota: A theoretical examination. iScience 2023; 26:107875. [PMID: 37860776 PMCID: PMC10583047 DOI: 10.1016/j.isci.2023.107875] [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: 05/04/2022] [Revised: 03/21/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023] Open
Abstract
A major objective of microbial ecology is to identify how the composition of microbial taxa shapes host phenotypes. However, most studies focus on pairwise interactions and ignore the potentially significant effects of higher-order microbial interactions.Here, we quantify the effects of higher-order interactions among taxa on host infection risk. We apply our approach to an in silico dataset that is built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite across a breadth of nutrient environmental contexts.We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Furthermore, we show that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes.
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Affiliation(s)
- Senay Yitbarek
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - John Guittar
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA
| | - Sarah A. Knutie
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
| | - C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
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21
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Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
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Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
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22
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Flanagan LM, Horton JS, Taylor TB. Mutational hotspots lead to robust but suboptimal adaptive outcomes in certain environments. MICROBIOLOGY (READING, ENGLAND) 2023; 169:001395. [PMID: 37815519 PMCID: PMC10634368 DOI: 10.1099/mic.0.001395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
The observed mutational spectrum of adaptive outcomes can be constrained by many factors. For example, mutational biases can narrow the observed spectrum by increasing the rate of mutation at isolated sites in the genome. In contrast, complex environments can shift the observed spectrum by defining fitness consequences of mutational routes. We investigate the impact of different nutrient environments on the evolution of motility in Pseudomonas fluorescens Pf0-2x (an engineered non-motile derivative of Pf0-1) in the presence and absence of a strong mutational hotspot. Previous work has shown that this mutational hotspot can be built and broken via six silent mutations, which provide rapid access to a mutation that rescues swimming motility and confers the strongest swimming phenotype in specific environments. Here, we evolved a hotspot and non-hotspot variant strain of Pf0-2x for motility under nutrient-rich (LB) and nutrient-limiting (M9) environmental conditions. We observed the hotspot strain consistently evolved faster across all environmental conditions and its mutational spectrum was robust to environmental differences. However, the non-hotspot strain had a distinct mutational spectrum that changed depending on the nutrient environment. Interestingly, while alternative adaptive mutations in nutrient-rich environments were equal to, or less effective than, the hotspot mutation, the majority of these mutations in nutrient-limited conditions produced superior swimmers. Our competition experiments mirrored these findings, underscoring the role of environment in defining both the mutational spectrum and the associated phenotype strength. This indicates that while mutational hotspots working in concert with natural selection can speed up access to robust adaptive mutations (which can provide a competitive advantage in evolving populations), they can limit exploration of the mutational landscape, restricting access to potentially stronger phenotypes in specific environments.
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Affiliation(s)
| | - James S. Horton
- Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK
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23
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Charlebois DA. Quantitative systems-based prediction of antimicrobial resistance evolution. NPJ Syst Biol Appl 2023; 9:40. [PMID: 37679446 PMCID: PMC10485028 DOI: 10.1038/s41540-023-00304-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.
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Affiliation(s)
- Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, AB, T6G-2E1, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G-2E9, Canada.
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24
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King ES, Pierce B, Hinczewski M, Scott JG. Diverse mutant selection windows shape spatial heterogeneity in evolving populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.531899. [PMID: 37732215 PMCID: PMC10508720 DOI: 10.1101/2023.03.09.531899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N*2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.
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Affiliation(s)
- Eshan S. King
- Systems Biology and Bioinformatics Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Beck Pierce
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
| | - Jacob G. Scott
- Systems Biology and Bioinformatics Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
- Department of Translational Hematology and Oncology Research and Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
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25
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Vieira MC, Palm AKE, Stamper CT, Tepora ME, Nguyen KD, Pham TD, Boyd SD, Wilson PC, Cobey S. Germline-encoded specificities and the predictability of the B cell response. PLoS Pathog 2023; 19:e1011603. [PMID: 37624867 PMCID: PMC10484431 DOI: 10.1371/journal.ppat.1011603] [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: 02/01/2023] [Revised: 09/07/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles' propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination.
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Affiliation(s)
- Marcos C. Vieira
- Department of Ecology and Evolution, University of Chicago, Chicago, United States of America
| | - Anna-Karin E. Palm
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, United States of America
| | - Christopher T. Stamper
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden
- Committee on Immunology, University of Chicago, Chicago, United States of America
| | - Micah E. Tepora
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, United States of America
| | - Khoa D. Nguyen
- Department of Pathology, Stanford University School of Medicine, Stanford, United States of America
| | - Tho D. Pham
- Department of Pathology, Stanford University School of Medicine, Stanford, United States of America
| | - Scott D. Boyd
- Department of Pathology, Stanford University School of Medicine, Stanford, United States of America
| | - Patrick C. Wilson
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, United States of America
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York City, United States of America
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, United States of America
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26
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Gunnarsson PA, Babu MM. Predicting evolutionary outcomes through the probability of accessing sequence variants. SCIENCE ADVANCES 2023; 9:eade2903. [PMID: 37506212 PMCID: PMC10381947 DOI: 10.1126/sciadv.ade2903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 06/27/2023] [Indexed: 07/30/2023]
Abstract
Natural selection can only operate on available genetic variation. Thus, determining the probability of accessing different sequence variants from a starting sequence can help predict evolutionary trajectories and outcomes. We define the concept of "variant accessibility" as the probability that a set of genotypes encoding a particular protein function will arise through mutations before subject to natural selection. This probability is shaped by the mutational biases of nucleotides and the structure of the genetic code. Using the influenza A virus as a model, we discuss how a more accessible but less fit variant can emerge as an adaptation rather than a more fit variant. We describe a genotype-accessibility landscape, complementary to the genotype-fitness landscape, that informs the likelihood of a starting sequence reaching different parts of genotype space. The proposed framework lays the foundation for predicting the emergence of adaptive genotypes in evolving systems such as viruses and tumors.
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Affiliation(s)
- P. Alexander Gunnarsson
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
- Department of Structural Biology and Center of Excellence for Data-Driven Discovery, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - M. Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
- Department of Structural Biology and Center of Excellence for Data-Driven Discovery, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
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27
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Luo XG, Kuipers J, Beerenwinkel N. Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat Commun 2023; 14:3676. [PMID: 37344522 DOI: 10.1038/s41467-023-39400-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.
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Affiliation(s)
- Xiang Ge Luo
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland.
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28
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Cano AV, Gitschlag BL, Rozhoňová H, Stoltzfus A, McCandlish DM, Payne JL. Mutation bias and the predictability of evolution. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220055. [PMID: 37004719 PMCID: PMC10067271 DOI: 10.1098/rstb.2022.0055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Alejandro V Cano
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Bryan L Gitschlag
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Hana Rozhoňová
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Arlin Stoltzfus
- Office of Data and Informatics, Material Measurement Laboratory, National Institute of Standards and Technology, Rockville, MD 20899, USA
- Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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29
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Ghenu AH, Amado A, Gordo I, Bank C. Epistasis decreases with increasing antibiotic pressure but not temperature. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220058. [PMID: 37004727 PMCID: PMC10067269 DOI: 10.1098/rstb.2022.0058] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Ana-Hermina Ghenu
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - André Amado
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Isabel Gordo
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
| | - Claudia Bank
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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30
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Servajean R, Bitbol AF. Impact of population size on early adaptation in rugged fitness landscapes. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220045. [PMID: 37004726 PMCID: PMC10067268 DOI: 10.1098/rstb.2022.0045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Owing to stochastic fluctuations arising from finite population size, known as genetic drift, the ability of a population to explore a rugged fitness landscape depends on its size. In the weak mutation regime, while the mean steady-state fitness increases with population size, we find that the height of the first fitness peak encountered when starting from a random genotype displays various behaviours versus population size, even among small and simple rugged landscapes. We show that the accessibility of the different fitness peaks is key to determining whether this height overall increases or decreases with population size. Furthermore, there is often a finite population size that maximizes the height of the first fitness peak encountered when starting from a random genotype. This holds across various classes of model rugged landscapes with sparse peaks, and in some experimental and experimentally inspired ones. Thus, early adaptation in rugged fitness landscapes can be more efficient and predictable for relatively small population sizes than in the large-size limit. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
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Affiliation(s)
- Richard Servajean
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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31
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Crocker J, Payne JL, Walczak AM, Wittkopp PJ. Interdisciplinary approaches to predicting evolutionary biology. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220042. [PMID: 37004718 PMCID: PMC10067262 DOI: 10.1098/rstb.2022.0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Affiliation(s)
- Justin Crocker
- European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Joshua L Payne
- Computational Biology Group, Institute of Integrative Biology, Building CHN H 75.1, Universitätstrasse 16, CH-8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Aleksandra M Walczak
- Laboratoire de Physique de l'École Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université Paris Cité, 75005 Paris, France
| | - Patricia J Wittkopp
- Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109-1382, USA
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32
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Diaz-Colunga J, Skwara A, Gowda K, Diaz-Uriarte R, Tikhonov M, Bajic D, Sanchez A. Global epistasis on fitness landscapes. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220053. [PMID: 37004717 PMCID: PMC10067270 DOI: 10.1098/rstb.2022.0053] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns—ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Karna Gowda
- Department of Ecology & Evolution & Center for the Physics of Evolving Systems, The University of Chicago, Chicago, IL 60637, USA
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, School of Medicine, Universidad Autónoma de Madrid, Madrid 28029, Spain
- Instituto de Investigaciones Biomédicas ‘Alberto Sols’ (UAM-CSIC), Madrid 28029, Spain
| | - Mikhail Tikhonov
- Department of Physics, Washington University of St Louis, St Louis, MO 63130, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Department of Microbial Biotechnology, Campus de Cantoblanco, CNB-CSIC, Madrid 28049, Spain
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33
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Venkataram S, Kryazhimskiy S. Evolutionary repeatability of emergent properties of ecological communities. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220047. [PMID: 37004728 PMCID: PMC10067272 DOI: 10.1098/rstb.2022.0047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/07/2022] [Indexed: 04/04/2023] Open
Abstract
Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Sandeep Venkataram
- Department of Ecology, Behavior and Evolution, UC San Diego, La Jolla, CA 92093, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, UC San Diego, La Jolla, CA 92093, USA
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34
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Schlötterer C. How predictable is adaptation from standing genetic variation? Experimental evolution in Drosophila highlights the central role of redundancy and linkage disequilibrium. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220046. [PMID: 37004724 PMCID: PMC10067264 DOI: 10.1098/rstb.2022.0046] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Experimental evolution is well-suited to test the predictability of evolution without the confounding effects of inaccurate forecasts about future environments. Most of the literature about parallel (and thus predictable) evolution has been carried out in asexual microorganisms, which adapt by de novo mutations. Nevertheless, parallel evolution has also been studied in sexual species at the genomic level. Here, I review the evidence for parallel evolution in Drosophila, the best-studied obligatory outcrossing model for adaptation from standing genetic variation in the laboratory. Similar to asexual microorganisms, evidence for parallel evolution varies between the focal hierarchical levels. Selected phenotypes consistently respond in a very predicable way, but the underlying allele frequency changes are much less predictable. The most important insight is that the predictability of the genomic selection response for polygenic traits depends highly on the founder population and to a much lesser extent on the selection regime. This implies that predicting adaptive genomic response is challenging and requires a good understanding of the adaptive architecture (including linkage disequilibrium) in the ancestral populations. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Christian Schlötterer
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, 1210 Wien, Austria
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35
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Dichio V, Zeng HL, Aurell E. Statistical genetics in and out of quasi-linkage equilibrium. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2023; 86:052601. [PMID: 36944245 DOI: 10.1088/1361-6633/acc5fa] [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: 03/08/2022] [Accepted: 03/21/2023] [Indexed: 06/18/2023]
Abstract
This review is about statistical genetics, an interdisciplinary topic between statistical physics and population biology. The focus is on the phase ofquasi-linkage equilibrium(QLE). Our goals here are to clarify under which conditions the QLE phase can be expected to hold in population biology and how the stability of the QLE phase is lost. The QLE state, which has many similarities to a thermal equilibrium state in statistical mechanics, was discovered by M Kimura for a two-locus two-allele model, and was extended and generalized to the global genome scale byNeher&Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman theory describes a population evolving due to the mutations, recombination, natural selection and possibly genetic drift. A QLE phase exists at sufficiently high recombination rate (r) and/or mutation ratesµwith respect to selection strength. We show how in QLE it is possible to infer the epistatic parameters of the fitness function from the knowledge of the (dynamical) distribution of genotypes in a population. We further consider the breakdown of the QLE regime for high enough selection strength. We review recent results for the selection-mutation and selection-recombination dynamics. Finally, we identify and characterize a new phase which we call the non-random coexistence where variability persists in the population without either fixating or disappearing.
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Affiliation(s)
- Vito Dichio
- Sorbonne Université, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Hong-Li Zeng
- School of Science, Nanjing University of Posts and Telecommunications, New Energy Technology Engineering Laboratory of Jiangsu Province, Nanjing 210023, People's Republic of China
| | - Erik Aurell
- Department of Computational Science and Technology, KTH-Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden
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36
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Campbell AM, Hauton C, Baker-Austin C, van Aerle R, Martinez-Urtaza J. An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens. Curr Opin Biotechnol 2023; 80:102898. [PMID: 36739640 DOI: 10.1016/j.copbio.2023.102898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/22/2022] [Accepted: 01/02/2023] [Indexed: 02/05/2023]
Abstract
It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.
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Affiliation(s)
- Amy M Campbell
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK; Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Chris Hauton
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK
| | - Craig Baker-Austin
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Ronny van Aerle
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Jaime Martinez-Urtaza
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK; Department of Genetics and Microbiology, Autonomous University of Barcelona, Barcelona, Spain.
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37
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Uchida Y, Takeda H, Furusawa C, Irie N. Stability in gene expression and body-plan development leads to evolutionary conservation. EvoDevo 2023; 14:4. [PMID: 36918942 PMCID: PMC10015717 DOI: 10.1186/s13227-023-00208-w] [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: 11/06/2022] [Accepted: 03/01/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Phenotypic evolution is mainly explained by selection for phenotypic variation arising from factors including mutation and environmental noise. Recent theoretical and experimental studies have suggested that phenotypes with greater developmental stability tend to have a constant phenotype and gene expression level within a particular genetic and environmental condition, and this positively correlates with stronger evolutionary conservation, even after the accumulation of genetic changes. This could reflect a novel mechanism that contributes to evolutionary conservation; however, it remains unclear whether developmental stability is the cause, or whether at least it contributes to their evolutionary conservation. Here, using Japanese medaka lines, we tested experimentally whether developmental stages and gene expression levels with greater stability led to their evolutionary conservation. RESULTS We first measured the stability of each gene expression level and developmental stage (defined here as the whole embryonic transcriptome) in the inbred F0 medaka population. We then measured their evolutionary conservation in the F3 generation by crossing the F0 line with the distantly related Japanese medaka line (Teradomori), followed by two rounds of intra-generational crossings. The results indicated that the genes and developmental stages that had smaller variations in the F0 generation showed lower diversity in the hybrid F3 generation, which implies a causal relationship between stability and evolutionary conservation. CONCLUSIONS These findings suggest that the stability in phenotypes, including the developmental stages and gene expression levels, leads to their evolutionary conservation; this most likely occurs due to their low potential to generate phenotypic variation. In addition, since the highly stable developmental stages match with the body-plan-establishment stage, it also implies that the developmental stability potentially contributed to the strict conservation of animal body plan.
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Affiliation(s)
- Yui Uchida
- Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.
| | - Hiroyuki Takeda
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Chikara Furusawa
- Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.,Universal Biology Institute, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Naoki Irie
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan. .,Universal Biology Institute, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
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38
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Natalino M, Fumasoni M. Experimental approaches to study evolutionary cell biology using yeasts. Yeast 2023; 40:123-133. [PMID: 36896914 DOI: 10.1002/yea.3848] [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: 11/07/2022] [Revised: 02/16/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023] Open
Abstract
The past century has witnessed tremendous advances in understanding how cells function. Nevertheless, how cellular processes have evolved is still poorly understood. Many studies have highlighted surprising molecular diversity in how cells from diverse species execute the same processes, and advances in comparative genomics are likely to reveal much more molecular diversity than was believed possible until recently. Extant cells remain therefore the product of an evolutionary history that we vastly ignore. Evolutionary cell biology has emerged as a discipline aiming to address this knowledge gap by combining evolutionary, molecular, and cellular biology thinking. Recent studies have shown how even essential molecular processes, such as DNA replication, can undergo fast adaptive evolution under certain laboratory conditions. These developments open new lines of research where the evolution of cellular processes can be investigated experimentally. Yeasts naturally find themselves at the forefront of this research line. Not only do they allow the observation of fast evolutionary adaptation, but they also provide numerous genomic, synthetic, and cellular biology tools already developed by a large community. Here we propose that yeasts can serve as an "evolutionary cell lab" to test hypotheses, principles, and ideas in evolutionary cell biology. We discuss various experimental approaches available for this purpose, and how biology at large can benefit from them.
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39
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Nacif CL, Kratochwil CF, Kautt AF, Nater A, Machado-Schiaffino G, Meyer A, Henning F. Molecular parallelism in the evolution of a master sex-determining role for the anti-Mullerian hormone receptor 2 gene (amhr2) in Midas cichlids. Mol Ecol 2023; 32:1398-1410. [PMID: 35403749 DOI: 10.1111/mec.16466] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/28/2022] [Accepted: 03/25/2022] [Indexed: 12/01/2022]
Abstract
The evolution of sex chromosomes and their differentiation from autosomes is a major event during genome evolution that happened many times in several lineages. The repeated evolution and lability of sex-determination mechanisms in fishes makes this a well-suited system to test for general patterns in evolution. According to current theory, differentiation is triggered by the suppression of recombination following the evolution of a new master sex-determining gene. However, the molecular mechanisms that establish recombination suppression are known from few examples, owing to the intrinsic difficulties of assembling sex-determining regions (SDRs). The development of forward-genetics and long-read sequencing have generated a wealth of data questioning central aspects of the current theory. Here, we demonstrate that sex in Midas cichlids is determined by an XY system, and identify and assemble the SDR by combining forward-genetics, long-read sequencing and optical mapping. We show how long-reads aid in the detection of artefacts in genotype-phenotype mapping that arise from incomplete genome assemblies. The male-specific region is restricted to a 100-kb segment on chromosome 4 that harbours transposable elements and a Y-specific duplicate of the anti-Mullerian receptor 2 gene, which has evolved master sex-determining functions repeatedly. Our data suggest that amhr2Y originated by an interchromosomal translocation from chromosome 20 to 4 pre-dating the split of Midas and Flier cichlids. In the latter, it is pseudogenized and translocated to another chromosome. Duplication of anti-Mullerian genes is a common route to establishing new sex determiners, highlighting the role of molecular parallelism in the evolution of sex determination.
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Affiliation(s)
- Camila L Nacif
- Department of Genetics, Institute of Biology, Federal University of Rio de Janeiro, Cidade Universitária, Rio de Janeiro, Brazil
| | | | - Andreas F Kautt
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Alexander Nater
- Department of Biology, University of Konstanz, Konstanz, Germany
| | | | - Axel Meyer
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Frederico Henning
- Department of Genetics, Institute of Biology, Federal University of Rio de Janeiro, Cidade Universitária, Rio de Janeiro, Brazil.,Department of Biology, University of Konstanz, Konstanz, Germany
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40
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Lemmen KD, Zhou L, Papakostas S, Declerck SAJ. An experimental test of the growth rate hypothesis as a predictive framework for microevolutionary adaptation. Ecology 2023; 104:e3853. [PMID: 36054549 PMCID: PMC10078216 DOI: 10.1002/ecy.3853] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 02/01/2023]
Abstract
The growth rate hypothesis (GRH) posits that the relative body phosphorus content of an organism is positively related to somatic growth rate, as protein synthesis, which is necessary for growth, requires P-rich rRNA. This hypothesis has strong support at the interspecific level. Here, we explore the use of the GRH to predict microevolutionary responses in consumer body stoichiometry. For this, we subjected populations of the rotifer Brachionus calyciflorus to selection for fast population growth rate (PGR) in P-rich (HPF) and P-poor (LPF) food environments. With common garden transplant experiments, we demonstrate that in HP populations evolution toward increased PGR was concomitant with an increase in relative phosphorus content. In contrast, LP populations evolved higher PGR without an increase in relative phosphorus content. We conclude that the GRH has the potential to predict microevolutionary change, but that its application is contingent on the environmental context. Our results highlight the potential of cryptic evolution in determining the performance response of populations to elemental limitation of their food resources.
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Affiliation(s)
- Kimberley D Lemmen
- Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, Netherlands
| | - Libin Zhou
- Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, Netherlands
| | | | - Steven A J Declerck
- Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, Netherlands.,Department of Biology, Laboratory of Aquatic Ecology, Evolution and Conservation, KU Leuven, Leuven, Belgium
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41
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Pennell M, Rodriguez OL, Watson CT, Greiff V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol 2023; 44:7-21. [PMID: 36470826 DOI: 10.1016/j.it.2022.11.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 12/04/2022]
Abstract
The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Affiliation(s)
- Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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42
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Wortel MT, Agashe D, Bailey SF, Bank C, Bisschop K, Blankers T, Cairns J, Colizzi ES, Cusseddu D, Desai MM, van Dijk B, Egas M, Ellers J, Groot AT, Heckel DG, Johnson ML, Kraaijeveld K, Krug J, Laan L, Lässig M, Lind PA, Meijer J, Noble LM, Okasha S, Rainey PB, Rozen DE, Shitut S, Tans SJ, Tenaillon O, Teotónio H, de Visser JAGM, Visser ME, Vroomans RMA, Werner GDA, Wertheim B, Pennings PS. Towards evolutionary predictions: Current promises and challenges. Evol Appl 2023; 16:3-21. [PMID: 36699126 PMCID: PMC9850016 DOI: 10.1111/eva.13513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022] Open
Abstract
Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.
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Affiliation(s)
- Meike T. Wortel
- Swammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Deepa Agashe
- National Centre for Biological SciencesBangaloreIndia
| | | | - Claudia Bank
- Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Gulbenkian Science InstituteOeirasPortugal
| | - Karen Bisschop
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
- Laboratory of Aquatic Biology, KU Leuven KulakKortrijkBelgium
| | - Thomas Blankers
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
| | | | - Enrico Sandro Colizzi
- Origins CenterGroningenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | | | - Bram van Dijk
- Max Planck Institute for Evolutionary BiologyPlönGermany
| | - Martijn Egas
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Jacintha Ellers
- Department of Ecological ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Astrid T. Groot
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | | | | | - Ken Kraaijeveld
- Leiden Centre for Applied BioscienceUniversity of Applied Sciences LeidenLeidenThe Netherlands
| | - Joachim Krug
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Liedewij Laan
- Department of Bionanoscience, Kavli Institute of NanoscienceTU DelftDelftThe Netherlands
| | - Michael Lässig
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Peter A. Lind
- Department Molecular BiologyUmeå UniversityUmeåSweden
| | - Jeroen Meijer
- Theoretical Biology and Bioinformatics, Department of BiologyUtrecht UniversityUtrechtThe Netherlands
| | - Luke M. Noble
- Institute de Biologie, École Normale Supérieure, CNRS, InsermParisFrance
| | | | - Paul B. Rainey
- Department of Microbial Population BiologyMax Planck Institute for Evolutionary BiologyPlönGermany
- Laboratoire Biophysique et Évolution, CBI, ESPCI Paris, Université PSL, CNRSParisFrance
| | - Daniel E. Rozen
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | - Shraddha Shitut
- Origins CenterGroningenThe Netherlands
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | | | | | | | | | - Marcel E. Visser
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
| | - Renske M. A. Vroomans
- Origins CenterGroningenThe Netherlands
- Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands
| | | | - Bregje Wertheim
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
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43
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Mononen T, Kuosmanen T, Cairns J, Mustonen V. Understanding cellular growth strategies via optimal control. J R Soc Interface 2023; 20:20220744. [PMID: 36596459 PMCID: PMC9810423 DOI: 10.1098/rsif.2022.0744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Evolutionary prediction and control are increasingly interesting research topics that are expanding to new areas of application. Unravelling and anticipating successful adaptations to different selection pressures becomes crucial when steering rapidly evolving cancer or microbial populations towards a chosen target. Here we introduce and apply a rich theoretical framework of optimal control to understand adaptive use of traits, which in turn allows eco-evolutionarily informed population control. Using adaptive metabolism and microbial experimental evolution as a case study, we show how demographic stochasticity alone can lead to lag time evolution, which appears as an emergent property in our model. We further show that the cycle length used in serial transfer experiments has practical importance as it may cause unintentional selection for specific growth strategies and lag times. Finally, we show how frequency-dependent selection can be incorporated to the state-dependent optimal control framework allowing the modelling of complex eco-evolutionary dynamics. Our study demonstrates the utility of optimal control theory in elucidating organismal adaptations and the intrinsic decision making of cellular communities with high adaptive potential.
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Affiliation(s)
- Tommi Mononen
- Department of Computer Science, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland
| | - Teemu Kuosmanen
- Department of Computer Science, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland
| | - Johannes Cairns
- Department of Computer Science, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland
| | - Ville Mustonen
- Department of Computer Science, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland,Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland
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44
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Iwasawa J, Maeda T, Shibai A, Kotani H, Kawada M, Furusawa C. Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape. PLoS Biol 2022; 20:e3001920. [PMID: 36512529 PMCID: PMC9746992 DOI: 10.1371/journal.pbio.3001920] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
The fitness landscape represents the complex relationship between genotype or phenotype and fitness under a given environment, the structure of which allows the explanation and prediction of evolutionary trajectories. Although previous studies have constructed fitness landscapes by comprehensively studying the mutations in specific genes, the high dimensionality of genotypic changes prevents us from developing a fitness landscape capable of predicting evolution for the whole cell. Herein, we address this problem by inferring the phenotype-based fitness landscape for antibiotic resistance evolution by quantifying the multidimensional phenotypic changes, i.e., time-series data of resistance for eight different drugs. We show that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred phenotype-fitness landscape. We further discuss how inferred phenotype-fitness landscapes could contribute to the prediction and control of evolution. This approach bridges the gap between phenotypic/genotypic changes and fitness while contributing to a better understanding of drug resistance evolution.
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Affiliation(s)
- Junichiro Iwasawa
- Department of Physics, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Tomoya Maeda
- Graduate School of Agriculture Research, Faculty of Agriculture, Hokkaido University, Sapporo, Japan
| | - Atsushi Shibai
- Center for Biosystems Dynamics Research, RIKEN, Suita, Japan
| | - Hazuki Kotani
- Center for Biosystems Dynamics Research, RIKEN, Suita, Japan
| | - Masako Kawada
- Center for Biosystems Dynamics Research, RIKEN, Suita, Japan
| | - Chikara Furusawa
- Department of Physics, Graduate School of Science, University of Tokyo, Tokyo, Japan
- Center for Biosystems Dynamics Research, RIKEN, Suita, Japan
- Universal Biology Institute, Graduate School of Science, University of Tokyo, Tokyo, Japan
- * E-mail:
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45
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Bikard D. How has microbiology changed 200 years after Pasteur's birth? C R Biol 2022; 345:21-33. [PMID: 36852594 DOI: 10.5802/crbiol.85] [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: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/11/2022]
Abstract
The last two centuries have seen major scientific and technological advances that have turned the field of microbiology upside down. If Louis Pasteur came out of his vault to celebrate his two hundredth birthday with us, would he recognize the field of study of which he was one of the founders? Are the objectives of the discipline still the same? What is the influence of new technologies on our scientific approach? What are the new horizons and future challenges?
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46
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Jouhten P, Konstantinidis D, Pereira F, Andrejev S, Grkovska K, Castillo S, Ghiachi P, Beltran G, Almaas E, Mas A, Warringer J, Gonzalez R, Morales P, Patil KR. Predictive evolution of metabolic phenotypes using model-designed environments. Mol Syst Biol 2022; 18:e10980. [PMID: 36201279 PMCID: PMC9536503 DOI: 10.15252/msb.202210980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/04/2022] Open
Abstract
Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.
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Affiliation(s)
- Paula Jouhten
- European Molecular Biology LaboratoryHeidelbergGermany,VTT Technical Research Centre of Finland LtdEspooFinland,Department of Bioproducts and BiosystemsAalto UniversityEspooFinland
| | | | | | | | | | | | - Payam Ghiachi
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Gemma Beltran
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Eivind Almaas
- Department of Biotechnology and Food ScienceNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Albert Mas
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Jonas Warringer
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Ramon Gonzalez
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Pilar Morales
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Kiran R Patil
- European Molecular Biology LaboratoryHeidelbergGermany,Medical Research Council (MRC) Toxicology UnitUniversity of CambridgeCambridgeUK
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47
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Accumulation and maintenance of information in evolution. Proc Natl Acad Sci U S A 2022; 119:e2123152119. [PMID: 36037343 PMCID: PMC9457054 DOI: 10.1073/pnas.2123152119] [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] [Indexed: 11/18/2022] Open
Abstract
Selection accumulates information in the genome-it guides stochastically evolving populations toward states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback-Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright-Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation, and recombination. Finally, the cost of maintaining information depends on how it is encoded: Specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap.
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48
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Williams BJM, Ogbunugafor CB, Althouse BM, Hébert-Dufresne L. Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin. PNAS NEXUS 2022; 1:pgac143. [PMID: 36060623 PMCID: PMC9434636 DOI: 10.1093/pnasnexus/pgac143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.
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Affiliation(s)
- Blake J M Williams
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
| | - C Brandon Ogbunugafor
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
- Department of Ecology and Evolutionary Biology, Yale University , New Haven, CT 06511, USA
- Santa Fe Institute , Santa Fe, NM 87501, USA
- Public Health Modeling Unit, Yale School of Public Health , New Haven, CT 06510, USA
| | - Benjamin M Althouse
- Institute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation , Seattle, WA 98109, USA
- Information School, University of Washington , Seattle, WA 98195, USA
- Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont , Burlington VT 05405, USA
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49
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Bisschop K, Blankers T, Mariën J, Wortel MT, Egas M, Groot AT, Visser ME, Ellers J. Population bottleneck has only marginal effect on fitness evolution and its repeatability in dioecious Caenorhabditis elegans. Evolution 2022; 76:1896-1904. [PMID: 35795889 PMCID: PMC9545033 DOI: 10.1111/evo.14556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 06/16/2022] [Accepted: 06/22/2022] [Indexed: 01/22/2023]
Abstract
The predictability of evolution is expected to depend on the relative contribution of deterministic and stochastic processes. This ratio is modulated by effective population size. Smaller effective populations harbor less genetic diversity and stochastic processes are generally expected to play a larger role, leading to less repeatable evolutionary trajectories. Empirical insight into the relationship between effective population size and repeatability is limited and focused mostly on asexual organisms. Here, we tested whether fitness evolution was less repeatable after a population bottleneck in obligately outcrossing populations of Caenorhabditis elegans. Replicated populations founded by 500, 50, or five individuals (no/moderate/strong bottleneck) were exposed to a novel environment with a different bacterial prey. As a proxy for fitness, population size was measured after one week of growth before and after 15 weeks of evolution. Surprisingly, we found no significant differences among treatments in their fitness evolution. Even though the strong bottleneck reduced the relative contribution of selection to fitness variation, this did not translate to a significant reduction in the repeatability of fitness evolution. Thus, although a bottleneck reduced the contribution of deterministic processes, we conclude that the predictability of evolution may not universally depend on effective population size, especially in sexual organisms.
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Affiliation(s)
- Karen Bisschop
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdam1090 GEThe Netherlands,Origins CenterGroningenThe Netherlands,Terrestrial Ecology UnitGhent UniversityGhent9000Belgium,Laboratory of Aquatic BiologyKU Leuven KulakKortrijk8500Belgium
| | - Thomas Blankers
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdam1090 GEThe Netherlands,Origins CenterGroningenThe Netherlands
| | - Janine Mariën
- Animal EcologyVU AmsterdamAmsterdam1081 HVThe Netherlands
| | - Meike T. Wortel
- Swammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdam1090 GEThe Netherlands
| | - Martijn Egas
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdam1090 GEThe Netherlands
| | - Astrid T. Groot
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdam1090 GEThe Netherlands
| | - Marcel E. Visser
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)Wageningen6700 ABThe Netherlands
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50
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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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