1
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Martin NS, Schaper S, Camargo CQ, Louis AA. Non-Poissonian Bursts in the Arrival of Phenotypic Variation Can Strongly Affect the Dynamics of Adaptation. Mol Biol Evol 2024; 41:msae085. [PMID: 38693911 PMCID: PMC11156200 DOI: 10.1093/molbev/msae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/01/2024] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.
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Affiliation(s)
- Nora S Martin
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
| | - Steffen Schaper
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
| | - Chico Q Camargo
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
- Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4QF, UK
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
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2
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Luo L, Lv J. An evolutionary theory on virus mutation in COVID-19. Virus Res 2024; 344:199358. [PMID: 38508401 PMCID: PMC10979259 DOI: 10.1016/j.virusres.2024.199358] [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] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 03/22/2024]
Abstract
With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.
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Affiliation(s)
- Liaofu Luo
- Faculty of Physical Science and Technology, Inner Mongolia University, 235 West College Road, Hohhot 010021, China.
| | - Jun Lv
- College of Science, Inner Mongolia University of Technology, 49 Aymin Street, Hohhot 010051, China.
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3
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García-Galindo P, Ahnert SE, Martin NS. The non-deterministic genotype-phenotype map of RNA secondary structure. J R Soc Interface 2023; 20:20230132. [PMID: 37608711 PMCID: PMC10445035 DOI: 10.1098/rsif.2023.0132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic (ND) phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of ND GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the ND map. This framework is validated by demonstrating close correspondence between the ND quantities and sample averages of their deterministic counterparts. When using the ND framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.
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Affiliation(s)
- Paula García-Galindo
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Sebastian E. Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
- The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
| | - Nora S. Martin
- Rudolf Peierls Centre for Theoretical Physics, Beecroft Building, Parks Road, Oxford OX1 3PU, UK
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4
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Mohanty V, Greenbury SF, Sarkany T, Narayanan S, Dingle K, Ahnert SE, Louis AA. Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve. J R Soc Interface 2023; 20:20230169. [PMID: 37491910 PMCID: PMC10369032 DOI: 10.1098/rsif.2023.0169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/27/2023] [Indexed: 07/27/2023] Open
Abstract
Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organized as bricklayer's graphs, so-called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype maps for RNA secondary structure and the hydrophobic-polar (HP) model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.
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Affiliation(s)
- Vaibhav Mohanty
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
- Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
- MD-PhD Program, Harvard Medical School, Boston, MA, USA and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sam F. Greenbury
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, British Library, London, UK
| | - Tasmin Sarkany
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Shyam Narayanan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamaludin Dingle
- Department of Mathematics and Natural Sciences, Centre for Applied Mathematics and Bioinformatics (CAMB), Gulf University of Science and Technology, Kuwait
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Sebastian E. Ahnert
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK
- Department of Chemical Engineering and Biotechnology, Cavendish Laboratory, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, British Library, London, UK
| | - Ard A. Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
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5
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Cotto O, Day T. A null model for the distribution of fitness effects of mutations. Proc Natl Acad Sci U S A 2023; 120:e2218200120. [PMID: 37252948 PMCID: PMC10266029 DOI: 10.1073/pnas.2218200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/28/2023] [Indexed: 06/01/2023] Open
Abstract
The distribution of fitness effects (DFE) of new mutations is key to our understanding of many evolutionary processes. Theoreticians have developed several models to help understand the patterns seen in empirical DFEs. Many such models reproduce the broad patterns seen in empirical DFEs but these models often rely on structural assumptions that cannot be tested empirically. Here, we investigate how much of the underlying "microscopic" biological processes involved in the mapping of new mutations to fitness can be inferred from "macroscopic" observations of the DFE. We develop a null model by generating random genotype-to-fitness maps and show that the null DFE is that with the largest possible information entropy. We further show that, subject to one simple constraint, this null DFE is a Gompertz distribution. Finally, we illustrate how the predictions of this null DFE match empirically measured DFEs from several datasets, as well as DFEs simulated from Fisher's geometric model. This suggests that a match between models and empirical data is often not a very strong indication of the mechanisms underlying the mapping of mutation to fitness.
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Affiliation(s)
- Olivier Cotto
- Department of Mathematics and Statistics, Queens University, Kingston, ON, K7L 3N6, Canada
- Department of Biology, Queens University, Kingston, ON, K7L 3N6, Canada
- Plant Health Institute Montpellier, Université Montpellier, Institut National de Recherche pour l’Agriculture, l’alimentation et l’Environnement, Centre de coopération Internationale en Recherche Agronomique pour le Développement, Institut de Recherche pour le Développement, Institut Agro, Montpellier, F-34398, France
| | - Troy Day
- Department of Mathematics and Statistics, Queens University, Kingston, ON, K7L 3N6, Canada
- Department of Biology, Queens University, Kingston, ON, K7L 3N6, Canada
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6
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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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7
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Santos-Moreno J, Tasiudi E, Kusumawardhani H, Stelling J, Schaerli Y. Robustness and innovation in synthetic genotype networks. Nat Commun 2023; 14:2454. [PMID: 37117168 PMCID: PMC10147661 DOI: 10.1038/s41467-023-38033-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/13/2023] [Indexed: 04/30/2023] Open
Abstract
Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.
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Affiliation(s)
- Javier Santos-Moreno
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015, Lausanne, Switzerland
- Department of Medicine and Life Sciences, Pompeu Fabra University, 00803, Barcelona, Spain
| | - Eve Tasiudi
- Department of Biosystems Science and Engineering, ETH Zurich and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Hadiastri Kusumawardhani
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015, Lausanne, Switzerland
| | - Joerg Stelling
- Department of Biosystems Science and Engineering, ETH Zurich and SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
| | - Yolanda Schaerli
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015, Lausanne, Switzerland.
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8
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Random and Natural Non-Coding RNA Have Similar Structural Motif Patterns but Differ in Bulge, Loop, and Bond Counts. Life (Basel) 2023; 13:life13030708. [PMID: 36983865 PMCID: PMC10054693 DOI: 10.3390/life13030708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/15/2023] [Accepted: 02/27/2023] [Indexed: 03/08/2023] Open
Abstract
An important question in evolutionary biology is whether (and in what ways) genotype–phenotype (GP) map biases can influence evolutionary trajectories. Untangling the relative roles of natural selection and biases (and other factors) in shaping phenotypes can be difficult. Because the RNA secondary structure (SS) can be analyzed in detail mathematically and computationally, is biologically relevant, and a wealth of bioinformatic data are available, it offers a good model system for studying the role of bias. For quite short RNA (length L≤126), it has recently been shown that natural and random RNA types are structurally very similar, suggesting that bias strongly constrains evolutionary dynamics. Here, we extend these results with emphasis on much larger RNA with lengths up to 3000 nucleotides. By examining both abstract shapes and structural motif frequencies (i.e., the number of helices, bonds, bulges, junctions, and loops), we find that large natural and random structures are also very similar, especially when contrasted to typical structures sampled from the spaces of all possible RNA structures. Our motif frequency study yields another result, where the frequencies of different motifs can be used in machine learning algorithms to classify random and natural RNA with high accuracy, especially for longer RNA (e.g., ROC AUC 0.86 for L = 1000). The most important motifs for classification are the number of bulges, loops, and bonds. This finding may be useful in using SS to detect candidates for functional RNA within ‘junk’ DNA regions.
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9
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Salazar-Ciudad I, Cano-Fernández H. Evo-devo beyond development: Generalizing evo-devo to all levels of the phenotypic evolution. Bioessays 2023; 45:e2200205. [PMID: 36739577 DOI: 10.1002/bies.202200205] [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: 10/24/2022] [Revised: 12/25/2022] [Accepted: 01/12/2023] [Indexed: 02/06/2023]
Abstract
A foundational idea of evo-devo is that morphological variation is not isotropic, that is, it does not occur in all directions. Instead, some directions of morphological variation are more likely than others from DNA-level variation and these largely depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution at all phenotypic levels. At other phenotypic levels there is no development, but there are processes that can be seen, in analogy to development, as constructing the phenotype (e.g., protein folding, learning for behavior, etc.). We argue that to explain the direction of evolution two types of arguments need to be combined: generative arguments about which phenotypic variation arises in each generation and selective arguments about which of it passes to the next generation. We explain how a full consideration of the two types of arguments improves the explanatory power of evolutionary theory. Also see the video abstract here: https://youtu.be/Egbvma_uaKc.
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Affiliation(s)
- Isaac Salazar-Ciudad
- Centre de Recerca Matemàtica, Cerdanyola del Vallès, Spain.,Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Hugo Cano-Fernández
- Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
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10
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Sappington A, Mohanty V. Probabilistic Genotype-Phenotype Maps Reveal Mutational Robustness of RNA Folding, Spin Glasses, and Quantum Circuits. ARXIV 2023:arXiv:2301.01847v1. [PMID: 36713233 PMCID: PMC9882568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Recent studies of genotype-phenotype (GP) maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype maps deterministically to a single phenotype. Here, we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic framework for investigating robustness. We study three model systems to show that our generalized framework can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in spin glass ground state finding, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on a 7-qubit IBM quantum computer. In all three cases, we observe a novel biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes.
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Affiliation(s)
- Anna Sappington
- Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, MA 02115 and Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Vaibhav Mohanty
- Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, MA 02115 and Massachusetts Institute of Technology, Cambridge, MA 02139
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11
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Mohanty V, Louis AA. Robustness and stability of spin-glass ground states to perturbed interactions. Phys Rev E 2023; 107:014126. [PMID: 36797942 DOI: 10.1103/physreve.107.014126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 12/16/2022] [Indexed: 06/18/2023]
Abstract
Across many problems in science and engineering, it is important to consider how much the output of a given system changes due to perturbations of the input. Here, we investigate the glassy phase of ±J spin glasses at zero temperature by calculating the robustness of the ground states to flips in the sign of single interactions. For random graphs and the Sherrington-Kirkpatrick model, we find relatively large sets of bond configurations that generate the same ground state. These sets can themselves be analyzed as subgraphs of the interaction domain, and we compute many of their topological properties. In particular, we find that the robustness, equivalent to the average degree, of these subgraphs is much higher than one would expect from a random model. Most notably, it scales in the same logarithmic way with the size of the subgraph as has been found in genotype-phenotype maps for RNA secondary structure folding, protein quaternary structure, gene regulatory networks, as well as for models for genetic programming. The similarity between these disparate systems suggests that this scaling may have a more universal origin.
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Affiliation(s)
- Vaibhav Mohanty
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, OX1 3NP, United Kingdom
- MD-PhD Program and Program in Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts 02125, USA and Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, OX1 3NP, United Kingdom
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12
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Dreher Y, Fichtler J, Karfusehr C, Jahnke K, Xin Y, Keller A, Göpfrich K. Genotype-phenotype mapping with polyominos made from DNA origami tiles. Biophys J 2022; 121:4840-4848. [PMID: 36088535 PMCID: PMC9811662 DOI: 10.1016/j.bpj.2022.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/18/2022] [Accepted: 09/06/2022] [Indexed: 01/07/2023] Open
Abstract
The correlation between genetic information and characteristics of a living cell-its genotype and its phenotype-constitutes the basis of genetics. Here, we experimentally realize a primitive form of genotype-phenotype mapping with DNA origami. The DNA origami can polymerize into two-dimensional lattices (phenotype) via blunt-end stacking facilitated by edge staples at the seam of the planar DNA origami. There are 80 binding positions for edge staples, which allow us to translate an 80-bit long binary code (genotype) onto the DNA origami. The presence of an edge staple thus corresponds to a "1" and its absence to a "0." The interactions of our DNA-based system can be reproduced by a polyomino model. Polyomino growth simulations qualitatively reproduce our experimental results. We show that not only the absolute number of base stacks but also their sequence position determine the cluster size and correlation length of the orientation of single DNA origami within the cluster. Importantly, the mutation of a few bits can result in major morphology changes of the DNA origami cluster, while more often, major sequence changes have no impact. Our experimental realization of a correlation between binary information ("genotype") and cluster morphology ("phenotype") thus reproduces key properties of genotype-phenotype maps known from living systems.
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Affiliation(s)
- Yannik Dreher
- Max Planck Institute for Medical Research, Biophysical Engineering Group, Heidelberg, Germany; Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Julius Fichtler
- Max Planck Institute for Medical Research, Biophysical Engineering Group, Heidelberg, Germany
| | - Christoph Karfusehr
- Max Planck Institute for Medical Research, Biophysical Engineering Group, Heidelberg, Germany; Max Planck School Matter to Life, Heidelberg University, Heidelberg, Germany
| | - Kevin Jahnke
- Max Planck Institute for Medical Research, Biophysical Engineering Group, Heidelberg, Germany; Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Yang Xin
- Paderborn University, Technical and Macromolecular Chemistry, Paderborn, Germany
| | - Adrian Keller
- Paderborn University, Technical and Macromolecular Chemistry, Paderborn, Germany
| | - Kerstin Göpfrich
- Max Planck Institute for Medical Research, Biophysical Engineering Group, Heidelberg, Germany; Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
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13
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Dingle K, Novev JK, Ahnert SE, Louis AA. Predicting phenotype transition probabilities via conditional algorithmic probability approximations. J R Soc Interface 2022; 19:20220694. [PMID: 36514888 PMCID: PMC9748496 DOI: 10.1098/rsif.2022.0694] [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: 12/15/2022] Open
Abstract
Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with the increasing phenotype descriptional complexity. This means that phenotypes with many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here, we use similar arguments to bound the probability P(x → y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula: see text], where [Formula: see text] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilizing detailed knowledge of the GP map.
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Affiliation(s)
- Kamaludin Dingle
- Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge CB2 1TN, UK,Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA,Department of Mathematics and Natural Sciences, Centre for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, 32093, Kuwait
| | - Javor K. Novev
- Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge CB2 1TN, UK
| | - Sebastian E. Ahnert
- Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge CB2 1TN, UK
| | - Ard A. Louis
- Department of Physics, Rudolf Peierls Centre for Theoretical Physics, Oxford University, Oxford OX1 2JD, UK
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14
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van der Laan L, Rooney K, Trooster TM, Mannens MM, Sadikovic B, Henneman P. DNA methylation episignatures: insight into copy number variation. Epigenomics 2022; 14:1373-1388. [PMID: 36537268 DOI: 10.2217/epi-2022-0287] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
In this review we discuss epigenetic disorders that result from aberrations in genes linked to epigenetic regulation. We describe current testing methods for the detection of copy number variants (CNVs) in Mendelian disorders, dosage sensitivity, reciprocal phenotypes and the challenges of test selection and overlapping clinical features in genetic diagnosis. We discuss aberrations of DNA methylation and propose a role for episignatures as a novel clinical testing method in CNV disorders. Finally, we postulate that episignature mapping in CNV disorders may provide novel insights into the molecular mechanisms of disease and unlock key findings of the genome-wide impact on disease gene networks.
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Affiliation(s)
- Liselot van der Laan
- Department of Human Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, Amsterdam, 1105 AZ, The Netherlands
| | - Kathleen Rooney
- Department of Pathology & Laboratory Medicine, Western University, London, Ontario, N5A 3K7, Canada.,Verspeeten Clinical Genome Centre, London Health Science Centre, London, Ontario, N6A 5W9, Canada
| | - Tessa Ma Trooster
- Department of Human Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, Amsterdam, 1105 AZ, The Netherlands
| | - Marcel Mam Mannens
- Department of Human Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, Amsterdam, 1105 AZ, The Netherlands
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Western University, London, Ontario, N5A 3K7, Canada.,Verspeeten Clinical Genome Centre, London Health Science Centre, London, Ontario, N6A 5W9, Canada
| | - Peter Henneman
- Department of Human Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, Amsterdam, 1105 AZ, The Netherlands
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15
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The structure of genotype-phenotype maps makes fitness landscapes navigable. Nat Ecol Evol 2022; 6:1742-1752. [PMID: 36175543 DOI: 10.1038/s41559-022-01867-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 08/01/2022] [Indexed: 11/09/2022]
Abstract
Fitness landscapes are often described in terms of 'peaks' and 'valleys', indicating an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high dimensional, which results in counter-intuitive structural properties of genotype-phenotype maps. Here we show that these properties, such as the presence of pervasive neutral networks, make fitness landscapes navigable. For three biologically realistic genotype-phenotype map models-RNA secondary structure, protein tertiary structure and protein complexes-we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through fitness valleys. This in turn indicates that true fitness valleys are very rare. By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be used under evolutionary dynamics. Our findings have broad implications for the prediction of natural evolutionary outcomes and for directed evolution.
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16
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Jena B, Saxena S, Nayak GK, Balestrieri A, Gupta N, Khanna NN, Laird JR, Kalra MK, Fouda MM, Saba L, Suri JS. Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. Cancers (Basel) 2022; 14:4052. [PMID: 36011048 PMCID: PMC9406706 DOI: 10.3390/cancers14164052] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.
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Affiliation(s)
- Biswajit Jena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Gopal Krishna Nayak
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | | | - Neha Gupta
- Department of IT, Bharati Vidyapeeth’s College of Engineering, New Delhi 110056, India
| | - Narinder N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, 09124 Cagliari, Italy
| | - Jasjit S. Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
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17
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Brun-Usan M, Zimm R, Uller T. Beyond genotype-phenotype maps: Toward a phenotype-centered perspective on evolution. Bioessays 2022; 44:e2100225. [PMID: 35863907 DOI: 10.1002/bies.202100225] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 11/08/2022]
Abstract
Evolutionary biology is paying increasing attention to the mechanisms that enable phenotypic plasticity, evolvability, and extra-genetic inheritance. Yet, there is a concern that these phenomena remain insufficiently integrated within evolutionary theory. Understanding their evolutionary implications would require focusing on phenotypes and their variation, but this does not always fit well with the prevalent genetic representation of evolution that screens off developmental mechanisms. Here, we instead use development as a starting point, and represent it in a way that allows genetic, environmental and epigenetic sources of phenotypic variation to be independent. We show why this representation helps to understand the evolutionary consequences of both genetic and non-genetic phenotype determinants, and discuss how this approach can instigate future areas of empirical and theoretical research.
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Affiliation(s)
- Miguel Brun-Usan
- Department of Biology, Lund University, 22362, Lund, Sweden.,Institute for Life Sciences/Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, UK
| | - Roland Zimm
- Ecole Normale Supérieure de Lyon, Institute de Génomique Fonctionnelle de Lyon, Lyon, France
| | - Tobias Uller
- Institute for Life Sciences/Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, UK
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18
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Manrubia S. The simple emergence of complex molecular function. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20200422. [PMID: 35599566 DOI: 10.1098/rsta.2020.0422] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
At odds with a traditional view of molecular evolution that seeks a descent-with-modification relationship between functional sequences, new functions can emerge de novo with relative ease. At early times of molecular evolution, random polymers could have sufficed for the appearance of incipient chemical activity, while the cellular environment harbours a myriad of proto-functional molecules. The emergence of function is facilitated by several mechanisms intrinsic to molecular organization, such as redundant mapping of sequences into structures, phenotypic plasticity, modularity or cooperative associations between genomic sequences. It is the availability of niches in the molecular ecology that filters new potentially functional proposals. New phenotypes and subsequent levels of molecular complexity could be attained through combinatorial explorations of currently available molecular variants. Natural selection does the rest. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Susanna Manrubia
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Systems Biology Department, National Biotechnology Centre (CSIC), c/Darwin 3, 28049 Madrid, Spain
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19
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DiFrisco J, Wagner GP, Love AC. Reframing research on evolutionary novelty and co-option: Character identity mechanisms versus deep homology. Semin Cell Dev Biol 2022; 145:3-12. [PMID: 35400563 DOI: 10.1016/j.semcdb.2022.03.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 01/31/2022] [Accepted: 03/23/2022] [Indexed: 11/27/2022]
Abstract
A central topic in research at the intersection of development and evolution is the origin of novel traits. Despite progress on understanding how developmental mechanisms underlie patterns of diversity in the history of life, the problem of novelty continues to challenge researchers. Here we argue that research on evolutionary novelty and the closely associated phenomenon of co-option can be reframed fruitfully by: (1) specifying a conceptual model of mechanisms that underwrite character identity, (2) providing a richer and more empirically precise notion of co-option that goes beyond common appeals to "deep homology", and (3) attending to the nature of experimental interventions that can determine whether and how the co-option of identity mechanisms can help to explain novel character origins. This reframing has the potential to channel future investigation to make substantive progress on the problem of evolutionary novelty. To illustrate this potential, we apply our reframing to two case studies: treehopper helmets and beetle horns.
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Affiliation(s)
| | - Günter P Wagner
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Yale Systems Biology Institute, Yale University, New Haven, CT, USA; Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, New Haven, CT, USA; Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA
| | - Alan C Love
- Department of Philosophy, University of Minnesota, Minneapolis, MN, USA; Minnesota Center for Philosophy of Sciences, University of Minnesota, Minneapolis, MN, USA.
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20
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Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution. Proc Natl Acad Sci U S A 2022; 119:e2113883119. [PMID: 35275794 PMCID: PMC8931234 DOI: 10.1073/pnas.2113883119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
SignificanceWhy does evolution favor symmetric structures when they only represent a minute subset of all possible forms? Just as monkeys randomly typing into a computer language will preferentially produce outputs that can be generated by shorter algorithms, so the coding theorem from algorithmic information theory predicts that random mutations, when decoded by the process of development, preferentially produce phenotypes with shorter algorithmic descriptions. Since symmetric structures need less information to encode, they are much more likely to appear as potential variation. Combined with an arrival-of-the-frequent mechanism, this algorithmic bias predicts a much higher prevalence of low-complexity (high-symmetry) phenotypes than follows from natural selection alone and also explains patterns observed in protein complexes, RNA secondary structures, and a gene regulatory network.
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21
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Spang A, Mahendrarajah TA, Offre P, Stairs CW. Evolving perspective on the origin and diversification of cellular life and the virosphere. Genome Biol Evol 2022; 14:6537539. [PMID: 35218347 PMCID: PMC9169541 DOI: 10.1093/gbe/evac034] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2022] [Indexed: 11/14/2022] Open
Abstract
The tree of life (TOL) is a powerful framework to depict the evolutionary history of cellular organisms through time, from our microbial origins to the diversification of multicellular eukaryotes that shape the visible biosphere today. During the past decades, our perception of the TOL has fundamentally changed, in part, due to profound methodological advances, which allowed a more objective approach to studying organismal and viral diversity and led to the discovery of major new branches in the TOL as well as viral lineages. Phylogenetic and comparative genomics analyses of these data have, among others, revolutionized our understanding of the deep roots and diversity of microbial life, the origin of the eukaryotic cell, eukaryotic diversity, as well as the origin, and diversification of viruses. In this review, we provide an overview of some of the recent discoveries on the evolutionary history of cellular organisms and their viruses and discuss a variety of complementary techniques that we consider crucial for making further progress in our understanding of the TOL and its interconnection with the virosphere.
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Affiliation(s)
- Anja Spang
- NIOZ, Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry, Utrecht University, The Netherlands and 1790 AB Den Burg.,Department of Cell- and Molecular Biology, Science for Life Laboratory, Uppsala University, Sweden SE-75123, Uppsala
| | - Tara A Mahendrarajah
- NIOZ, Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry, Utrecht University, The Netherlands and 1790 AB Den Burg
| | - Pierre Offre
- NIOZ, Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry, Utrecht University, The Netherlands and 1790 AB Den Burg
| | - Courtney W Stairs
- Department of Biology, Lund University, Sweden Sölvegatan 35, 223 62 Lund
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22
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Dingle K, Ghaddar F, Šulc P, Louis AA. Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes. Mol Biol Evol 2022; 39:msab280. [PMID: 34542628 PMCID: PMC8763027 DOI: 10.1093/molbev/msab280] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Morphospaces-representations of phenotypic characteristics-are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavoring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that noncoding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon a random sampling of sequences. We show that: 1) only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored; 2) remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far; and 3) perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon a uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather a strong phenotype bias in the RNA genotype-phenotype map, a type of developmental bias or "findability constraint," which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to "find."
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Affiliation(s)
- Kamaludin Dingle
- Centre for Applied Mathematics and Bioinformatics, Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally, Kuwait
| | - Fatme Ghaddar
- Centre for Applied Mathematics and Bioinformatics, Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally, Kuwait
| | - Petr Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics at the Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom
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23
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Wechsler D, Bascompte J. Cheating in mutualisms promotes diversity and complexity. Am Nat 2021; 199:393-405. [DOI: 10.1086/717865] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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24
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Nakhle F, Harfouche AL. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. PATTERNS (NEW YORK, N.Y.) 2021; 2:100323. [PMID: 34553170 PMCID: PMC8441561 DOI: 10.1016/j.patter.2021.100323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
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Affiliation(s)
- Farid Nakhle
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
| | - Antoine L. Harfouche
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
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25
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Wölfl B, te Rietmole H, Salvioli M, Kaznatcheev A, Thuijsman F, Brown JS, Burgering B, Staňková K. The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. DYNAMIC GAMES AND APPLICATIONS 2021; 12:313-342. [PMID: 35601872 PMCID: PMC9117378 DOI: 10.1007/s13235-021-00397-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 05/05/2023]
Abstract
Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one's fitness not only depends on one's own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer's eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.
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Affiliation(s)
- Benjamin Wölfl
- Department of Mathematics, University of Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Hedy te Rietmole
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Monica Salvioli
- Department of Mathematics, University of Trento, Trento, Italy
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Artem Kaznatcheev
- Department of Biology, University of Pennsylvania, Philadelphia, USA
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Frank Thuijsman
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL USA
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL USA
| | - Boudewijn Burgering
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
- The Oncode Institute, Utrecht, The Netherlands
| | - Kateřina Staňková
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
- Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
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26
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Jouffrey V, Leonard AS, Ahnert SE. Gene duplication and subsequent diversification strongly affect phenotypic evolvability and robustness. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201636. [PMID: 34168886 PMCID: PMC8220273 DOI: 10.1098/rsos.201636] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 03/17/2021] [Indexed: 06/13/2023]
Abstract
We study the effects of non-determinism and gene duplication on the structure of genotype-phenotype (GP) maps by introducing a non-deterministic version of the Polyomino self-assembly model. This model has previously been used in a variety of contexts to model the assembly and evolution of protein quaternary structure. Firstly, we show the limit of the current deterministic paradigm which leads to built-in anti-correlation between evolvability and robustness at the genotypic level. We develop a set of metrics to measure structural properties of GP maps in a non-deterministic setting and use them to evaluate the effects of gene duplication and subsequent diversification. Our generalized versions of evolvability and robustness exhibit positive correlation for a subset of genotypes. This positive correlation is only possible because non-deterministic phenotypes can contribute to both robustness and evolvability. Secondly, we show that duplication increases robustness and reduces evolvability initially, but that the subsequent diversification that duplication enables has a stronger, inverse effect, greatly increasing evolvability and reducing robustness relative to their original values.
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Affiliation(s)
- V. Jouffrey
- Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
- Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
| | - A. S. Leonard
- Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
- Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
| | - S. E. Ahnert
- Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
- Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
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27
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Castle SD, Grierson CS, Gorochowski TE. Towards an engineering theory of evolution. Nat Commun 2021; 12:3326. [PMID: 34099656 PMCID: PMC8185075 DOI: 10.1038/s41467-021-23573-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/04/2021] [Indexed: 02/07/2023] Open
Abstract
Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution's potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology.
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Affiliation(s)
- Simeon D Castle
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Claire S Grierson
- School of Biological Sciences, University of Bristol, Bristol, UK
- BrisSynBio, University of Bristol, Bristol, UK
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Bristol, UK.
- BrisSynBio, University of Bristol, Bristol, UK.
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28
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Romero‐Mujalli D, Rochow M, Kahl S, Paraskevopoulou S, Folkertsma R, Jeltsch F, Tiedemann R. Adaptive and nonadaptive plasticity in changing environments: Implications for sexual species with different life history strategies. Ecol Evol 2021; 11:6341-6357. [PMID: 34141222 PMCID: PMC8207414 DOI: 10.1002/ece3.7485] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/03/2021] [Accepted: 03/03/2021] [Indexed: 11/07/2022] Open
Abstract
Populations adapt to novel environmental conditions by genetic changes or phenotypic plasticity. Plastic responses are generally faster and can buffer fitness losses under variable conditions. Plasticity is typically modeled as random noise and linear reaction norms that assume simple one-to-one genotype-phenotype maps and no limits to the phenotypic response. Most studies on plasticity have focused on its effect on population viability. However, it is not clear, whether the advantage of plasticity depends solely on environmental fluctuations or also on the genetic and demographic properties (life histories) of populations. Here we present an individual-based model and study the relative importance of adaptive and nonadaptive plasticity for populations of sexual species with different life histories experiencing directional stochastic climate change. Environmental fluctuations were simulated using differentially autocorrelated climatic stochasticity or noise color, and scenarios of directional climate change. Nonadaptive plasticity was simulated as a random environmental effect on trait development, while adaptive plasticity as a linear, saturating, or sinusoidal reaction norm. The last two imposed limits to the plastic response and emphasized flexible interactions of the genotype with the environment. Interestingly, this assumption led to (a) smaller phenotypic than genotypic variance in the population (many-to-one genotype-phenotype map) and the coexistence of polymorphisms, and (b) the maintenance of higher genetic variation-compared to linear reaction norms and genetic determinism-even when the population was exposed to a constant environment for several generations. Limits to plasticity led to genetic accommodation, when costs were negligible, and to the appearance of cryptic variation when limits were exceeded. We found that adaptive plasticity promoted population persistence under red environmental noise and was particularly important for life histories with low fecundity. Populations producing more offspring could cope with environmental fluctuations solely by genetic changes or random plasticity, unless environmental change was too fast.
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Affiliation(s)
- Daniel Romero‐Mujalli
- Evolutionary Biology/Systematic ZoologyUniversity of PotsdamPotsdamGermany
- Plant Ecology and Nature ConservationUniversity of PotsdamPotsdamGermany
- Foundation, Zoology InstituteUniversity of Veterinary Medicine HannoverHannoverGermany
| | - Markus Rochow
- Evolutionary Biology/Systematic ZoologyUniversity of PotsdamPotsdamGermany
| | - Sandra Kahl
- Berlin‐Brandenburg Institute of Advanced Biodiversity Research (BBIB)BerlinGermany
- Biodiversity Research/Systematic BotanyInstitute of Biochemistry und BiologyUniversity of PotsdamPotsdamGermany
| | - Sofia Paraskevopoulou
- Evolutionary Biology/Systematic ZoologyUniversity of PotsdamPotsdamGermany
- Faculty of Life SciencesSchool of ZoologyTel Aviv UniversityTel AvivIsrael
| | - Remco Folkertsma
- Evolutionary Adaptive GenomicsUniversity of PotsdamPotsdamGermany
| | - Florian Jeltsch
- Plant Ecology and Nature ConservationUniversity of PotsdamPotsdamGermany
- Berlin‐Brandenburg Institute of Advanced Biodiversity Research (BBIB)BerlinGermany
| | - Ralph Tiedemann
- Evolutionary Biology/Systematic ZoologyUniversity of PotsdamPotsdamGermany
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29
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Manrubia S, Cuesta JA, Aguirre J, Ahnert SE, Altenberg L, Cano AV, Catalán P, Diaz-Uriarte R, Elena SF, García-Martín JA, Hogeweg P, Khatri BS, Krug J, Louis AA, Martin NS, Payne JL, Tarnowski MJ, Weiß M. From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics. Phys Life Rev 2021; 38:55-106. [PMID: 34088608 DOI: 10.1016/j.plrev.2021.03.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
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Affiliation(s)
- Susanna Manrubia
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain; Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BiFi), Universidad de Zaragoza, Spain; UC3M-Santander Big Data Institute (IBiDat), Getafe, Madrid, Spain
| | - Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Centro de Astrobiología, CSIC-INTA, ctra. de Ajalvir km 4, 28850 Torrejón de Ardoz, Madrid, Spain
| | - Sebastian E Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | | | - Alejandro V Cano
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Investigaciones Biomédicas "Alberto Sols" (UAM-CSIC), Madrid, Spain
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas, I(2)SysBio (CSIC-UV), València, Spain; The Santa Fe Institute, Santa Fe, NM, USA
| | | | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics Group, Utrecht University, the Netherlands
| | - Bhavin S Khatri
- The Francis Crick Institute, London, UK; Department of Life Sciences, Imperial College London, London, UK
| | - Joachim Krug
- Institute for Biological Physics, University of Cologne, Köln, Germany
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
| | - Nora S Martin
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Marcel Weiß
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
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Wheeler LC, Wing BA, Smith SD. Structure and contingency determine mutational hotspots for flower color evolution. Evol Lett 2020; 5:61-74. [PMID: 33552536 PMCID: PMC7857289 DOI: 10.1002/evl3.212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/26/2020] [Accepted: 11/25/2020] [Indexed: 01/26/2023] Open
Abstract
Evolutionary genetic studies have uncovered abundant evidence for genomic hotspots of phenotypic evolution, as well as biased patterns of mutations at those loci. However, the theoretical basis for this concentration of particular types of mutations at particular loci remains largely unexplored. In addition, historical contingency is known to play a major role in evolutionary trajectories, but has not been reconciled with the existence of such hotspots. For example, do the appearance of hotspots and the fixation of different types of mutations at those loci depend on the starting state and/or on the nature and direction of selection? Here, we use a computational approach to examine these questions, focusing the anthocyanin pigmentation pathway, which has been extensively studied in the context of flower color transitions. We investigate two transitions that are common in nature, the transition from blue to purple pigmentation and from purple to red pigmentation. Both sets of simulated transitions occur with a small number of mutations at just four loci and show strikingly similar peaked shapes of evolutionary trajectories, with the mutations of the largest effect occurring early but not first. Nevertheless, the types of mutations (biochemical vs. regulatory) as well as their direction and magnitude are contingent on the particular transition. These simulated color transitions largely mirror findings from natural flower color transitions, which are known to occur via repeated changes at a few hotspot loci. Still, some types of mutations observed in our simulated color evolution are rarely observed in nature, suggesting that pleiotropic effects further limit the trajectories between color phenotypes. Overall, our results indicate that the branching structure of the pathway leads to a predictable concentration of evolutionary change at the hotspot loci, but the types of mutations at these loci and their order is contingent on the evolutionary context.
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Affiliation(s)
- Lucas C Wheeler
- Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA
| | - Boswell A Wing
- Department of Geological Sciences University of Colorado Boulder CO USA
| | - Stacey D Smith
- Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA
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31
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Hoeger PH. Genes and phenotypes in vascular malformations. Clin Exp Dermatol 2020; 46:495-502. [PMID: 33368487 DOI: 10.1111/ced.14513] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/05/2020] [Accepted: 10/27/2020] [Indexed: 12/18/2022]
Abstract
Vascular malformations (VMs) are caused by localized defects of vascular development. Most VMs are due to sporadic, postzygotic mutations, while some are the result of autosomal dominant germline mutations. Genotype-phenotype correlation is influenced by many factors. Individual genes can induce different phenotypes (pleiotropy), and similar phenotypes can be due to different genes/mutations (redundancy). The phenotypic spectrum of somatic mutations is wide, and depends on variant allele frequency, timing during embryogenesis, cell type(s) involved and type of mutation. The phenotype of germline mutations is determined by penetrance and expressivity, and is influenced by epigenetic factors (DNA methylation, histone modification) or 'second-hit' somatic mutations. Except for disorders with pathognomonic phenotypes such as Proteus syndrome or a characteristic constellation of symptoms such as CLOVES [congenital lipomatous (fatty) overgrowth, vascular malformations, epidermal naevi and scoliosis/skeletal/spinal anomalies] or PIK3CA-related overgrowth spectrum syndrome, differential diagnosis of VM is therefore difficult. It will be greatly facilitated with increasing analytic sensitivity of sequencing techniques such as next-generation sequencing. High-sensitivity molecular techniques are a prerequisite for targeted pharmacotherapy, i.e. selective therapeutic inhibition of activating mutations underlying VM, which has shown promising results in preliminary studies.
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Affiliation(s)
- P H Hoeger
- Department of Paediatric Dermatology, Catholic Children's Hospital Wilhelmstift, Hamburg, Germany
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Rigato E, Fusco G. A heuristic model of the effects of phenotypic robustness in adaptive evolution. Theor Popul Biol 2020; 136:22-30. [PMID: 33221334 DOI: 10.1016/j.tpb.2020.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 10/15/2020] [Accepted: 11/09/2020] [Indexed: 10/23/2022]
Abstract
A recent theoretical, deterministic model of the effects of phenotypic robustness on adaptive evolutionary dynamics showed that a certain level of phenotypic robustness (critical robustness) is a required condition for adaptation to occur and to be maintained during evolution in most real organismal systems. We built an individual-based heuristic model to verify the soundness of these theoretical results through computer simulation, testing expectations under a range of scenarios for the relevant parameters of the evolutionary dynamics. These include the mutation probability, the presence of stochastic effects, the introduction of environmental influences and the possibility for some features of the population (like selection coefficients and phenotypic robustness) to change themselves during adaptation. Overall, we found a good match between observed and expected results, even for evolutionary parameter values that violate some of the assumptions of the deterministic model, and that robustness can itself evolve. However, from more than one simulation it appears that very high robustness values, higher than the critical value, can limit or slow-down adaptation. This possible trade-off was not predicted by the deterministic model.
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Affiliation(s)
- Emanuele Rigato
- Department of Biology, University of Padova, Via U. Bassi 58/B, 35131 Padova, Italy
| | - Giuseppe Fusco
- Department of Biology, University of Padova, Via U. Bassi 58/B, 35131 Padova, Italy.
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Abstract
Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems. This feat has inspired computer scientists since the advent of computing and led to optimization tools that can evolve complex neural networks for machines-an approach known as "neuroevolution." After a few successes in designing evolvable representations for high-dimensional artifacts, the field has been recently revitalized by going beyond optimization: to many, the wonder of evolution is less in the perfect optimization of each species than in the creativity of such a simple iterative process, that is, in the diversity of species. This modern view of artificial evolution is moving the field away from microevolution, following a fitness gradient in a niche, to macroevolution, filling many niches with highly different species. It already opened promising applications, like evolving gait repertoires, video game levels for different tastes, and diverse designs for aerodynamic bikes.
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Cano AV, Payne JL. Mutation bias interacts with composition bias to influence adaptive evolution. PLoS Comput Biol 2020; 16:e1008296. [PMID: 32986712 PMCID: PMC7571706 DOI: 10.1371/journal.pcbi.1008296] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 10/19/2020] [Accepted: 08/30/2020] [Indexed: 11/19/2022] Open
Abstract
Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias—a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths—that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness. Mutation is often depicted as a random process due its unpredictable nature. However, such randomness does not imply uniformly distributed outcomes, because some DNA sequence changes happen more frequently than others. Mutation bias can be an orienting factor in adaptive evolution, influencing the mutational trajectories populations follow toward higher-fitness genotypes. Because these trajectories are typically just a small subset of all possible mutational trajectories, they can exhibit composition bias—an enrichment of a particular kind of DNA sequence change, such as transition or transversion mutations. Here, we use empirical data from eukaryotic transcriptional regulation to study how mutation bias and composition bias interact to influence adaptive evolution.
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Affiliation(s)
- Alejandro V. Cano
- Institute of Integrative Biology, ETH, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joshua L. Payne
- Institute of Integrative Biology, ETH, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- * E-mail:
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36
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Weiß M, Ahnert SE. Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure. J R Soc Interface 2020; 17:20190784. [PMID: 32429824 DOI: 10.1098/rsif.2019.0784] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
In genotype-phenotype (GP) maps, the genotypes that map to the same phenotype are usually not randomly distributed across the space of genotypes, but instead are predominantly connected through one-point mutations, forming network components that are commonly referred to as neutral components (NCs). Because of their impact on evolutionary processes, the characteristics of these NCs, like their size or robustness, have been studied extensively. Here, we introduce a framework that allows the estimation of NC size and robustness in the GP map of RNA secondary structure. The advantage of this framework is that it only requires small samples of genotypes and their local environment, which also allows experimental realizations. We verify our framework by applying it to the exhaustively analysable GP map of RNA sequence length L = 15, and benchmark it against an existing method by applying it to longer, naturally occurring functional non-coding RNA sequences. Although it is specific to the RNA secondary structure GP map in the first place, our framework can probably be transferred and adapted to other sequence-to-structure GP maps.
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Affiliation(s)
- Marcel Weiß
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.,Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
| | - Sebastian E Ahnert
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.,Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
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37
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Pathogenomics and Management of Fusarium Diseases in Plants. Pathogens 2020; 9:pathogens9050340. [PMID: 32369942 PMCID: PMC7281180 DOI: 10.3390/pathogens9050340] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 04/25/2020] [Accepted: 04/28/2020] [Indexed: 12/16/2022] Open
Abstract
There is an urgency to supplant the heavy reliance on chemical control of Fusarium diseases in different economically important, staple food crops due to development of resistance in the pathogen population, the high cost of production to the risk-averse grower, and the concomitant environmental impacts. Pathogenomics has enabled (i) the creation of genetic inventories which identify those putative genes, regulators, and effectors that are associated with virulence, pathogenicity, and primary and secondary metabolism; (ii) comparison of such genes among related pathogens; (iii) identification of potential genetic targets for chemical control; and (iv) better characterization of the complex dynamics of host–microbe interactions that lead to disease. This type of genomic data serves to inform host-induced gene silencing (HIGS) technology for targeted disruption of transcription of select genes for the control of Fusarium diseases. This review discusses the various repositories and browser access points for comparison of genomic data, the strategies for identification and selection of pathogenicity- and virulence-associated genes and effectors in different Fusarium species, HIGS and successful Fusarium disease control trials with a consideration of loss of RNAi, off-target effects, and future challenges in applying HIGS for management of Fusarium diseases.
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38
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An agent-based lattice model for the emergence of anti-microbial resistance. J Theor Biol 2020; 486:110080. [DOI: 10.1016/j.jtbi.2019.110080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 11/08/2019] [Accepted: 11/11/2019] [Indexed: 11/19/2022]
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39
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González R, Butković A, Elena SF. From foes to friends: Viral infections expand the limits of host phenotypic plasticity. Adv Virus Res 2020; 106:85-121. [PMID: 32327149 DOI: 10.1016/bs.aivir.2020.01.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Phenotypic plasticity enables organisms to survive in the face of unpredictable environmental stress. Intimately related to the notion of phenotypic plasticity is the concept of the reaction norm that places phenotypic plasticity in the context of a genotype-specific response to environmental gradients. Whether reaction norms themselves evolve and which factors might affect their shape has been the object of intense debates among evolutionary biologists along the years. Since their discovery, viruses have been considered as pathogens. However, new viromic techniques and a shift in conceptual paradigms are showing that viruses are mostly non-pathogenic ubiquitous entities. Recent studies have shown how viral infections can even be beneficial for their hosts. This may happen especially in the context of stressed hosts, where the virus infection can induce beneficial changes in the host's physiological homeostasis, hence changing the shape of the reaction norm. Despite the fact that underlying physiological mechanisms and evolutionary dynamics are still not well understood, such beneficial interactions are being discovered in a growing number of plant-virus systems. Here, we aim to review these disperse studies and place them into the context of phenotypic plasticity and the evolution of reaction norms. This is an emerging field that is posing many questions that still need to be properly answered. The answers would clearly interest virologists, plant pathologists and evolutionary biologists and likely they will suggest possible future biotechnological applications, including the development of crops with higher survival rates and yield under adverse environmental situations.
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Affiliation(s)
- Rubén González
- Instituto de Biología Integrativa de Sistemas, CSIC-Universitat de València, Valencia, Spain.
| | - Anamarija Butković
- Instituto de Biología Integrativa de Sistemas, CSIC-Universitat de València, Valencia, Spain
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas, CSIC-Universitat de València, Valencia, Spain; The Santa Fe Institute, Santa Fe, NM, United States.
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40
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Nichol D, Robertson-Tessi M, Anderson ARA, Jeavons P. Model genotype-phenotype mappings and the algorithmic structure of evolution. J R Soc Interface 2019; 16:20190332. [PMID: 31690233 PMCID: PMC6893500 DOI: 10.1098/rsif.2019.0332] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/04/2019] [Indexed: 12/13/2022] Open
Abstract
Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.
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Affiliation(s)
- Daniel Nichol
- Department of Computer Science, University of Oxford, Oxford, UK
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander R. A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Peter Jeavons
- Department of Computer Science, University of Oxford, Oxford, UK
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41
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Tarasov S. Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits. Syst Biol 2019; 68:698-716. [PMID: 30668800 PMCID: PMC6701457 DOI: 10.1093/sysbio/syz005] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/06/2019] [Accepted: 01/15/2019] [Indexed: 11/12/2022] Open
Abstract
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes-hierarchical and hidden-which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks (GRNs) underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models (SMM) equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of SMM with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of GRNs and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing "tail color problem," in which the trait is scored for species with tails of different colors or no tails. It also presents a previously unknown issue called the "two-scientist paradox," in which the nature of coding the trait and the hidden processes driving the trait's evolution are confounded; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This article gives practical examples of using the new framework for phylogenetic inference and comparative analysis.
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Affiliation(s)
- Sergei Tarasov
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN 37996, USA
- Department of Biological Sciences, Virginia Tech, 4076 Derring Hall, 926 West Campus Drive, Blacksburg, VA 24061, USA
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42
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Xue B, Sartori P, Leibler S. Environment-to-phenotype mapping and adaptation strategies in varying environments. Proc Natl Acad Sci U S A 2019; 116:13847-13855. [PMID: 31221749 PMCID: PMC6628789 DOI: 10.1073/pnas.1903232116] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Biological organisms exhibit diverse strategies for adapting to varying environments. For example, a population of organisms may express the same phenotype in all environments ("unvarying strategy") or follow environmental cues and express alternative phenotypes to match the environment ("tracking strategy"), or diversify into coexisting phenotypes to cope with environmental uncertainty ("bet-hedging strategy"). We introduce a general framework for studying how organisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the long-term growth rate of a population. The previously studied strategies emerge as special cases of our model: The tracking strategy is favorable when environmental cues are accurate, whereas when cues are noisy, organisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation, which can even be random.
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Affiliation(s)
- BingKan Xue
- The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540;
- Laboratory of Living Matter, The Rockefeller University, New York, NY 10065
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065
| | - Pablo Sartori
- The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540
- Laboratory of Living Matter, The Rockefeller University, New York, NY 10065
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065
| | - Stanislas Leibler
- The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540;
- Laboratory of Living Matter, The Rockefeller University, New York, NY 10065
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065
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43
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Abstract
Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.
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Affiliation(s)
- Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America
| | - Hyunbum Jang
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America
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Catalán P, Elena SF, Cuesta JA, Manrubia S. Parsimonious Scenario for the Emergence of Viroid-Like Replicons De Novo. Viruses 2019; 11:v11050425. [PMID: 31075860 PMCID: PMC6563258 DOI: 10.3390/v11050425] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 04/30/2019] [Accepted: 05/02/2019] [Indexed: 01/12/2023] Open
Abstract
Viroids are small, non-coding, circular RNA molecules that infect plants. Different hypotheses for their evolutionary origin have been put forward, such as an early emergence in a precellular RNA World or several de novo independent evolutionary origins in plants. Here, we discuss the plausibility of de novo emergence of viroid-like replicons by giving theoretical support to the likelihood of different steps along a parsimonious evolutionary pathway. While Avsunviroidae-like structures are relatively easy to obtain through evolution of a population of random RNA sequences of fixed length, rod-like structures typical of Pospiviroidae are difficult to fix. Using different quantitative approaches, we evaluated the likelihood that RNA sequences fold into a rod-like structure and bear specific sequence motifs facilitating interactions with other molecules, e.g., RNA polymerases, RNases, and ligases. By means of numerical simulations, we show that circular RNA replicons analogous to Pospiviroidae emerge if evolution is seeded with minimal circular RNAs that grow through the gradual addition of nucleotides. Further, these rod-like replicons often maintain their structure if independent functional modules are acquired that impose selective constraints. The evolutionary scenario we propose here is consistent with the structural and biochemical properties of viroids described to date.
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Affiliation(s)
- Pablo Catalán
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QD, UK.
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, Paterna, 46980 València, Spain.
- The Santa Fe Institute, Santa Fe, NM 87501, USA.
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
- Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911 Leganés, Spain.
- Instituto de Biocomputación y Física de Sistemas Complejos (BiFi), Universidad de Zaragoza, 50018 Zaragoza, Spain.
- Institute of Financial Big Data (IFiBiD), Universidad Carlos III de Madrid⁻Banco de Santander, 28903 Getafe, Spain.
| | - Susanna Manrubia
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
- National Biotechnology Centre (CSIC), 28049 Madrid, Spain.
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45
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Collins-Hed AI, Ardell DH. Match fitness landscapes for macromolecular interaction networks: Selection for translational accuracy and rate can displace tRNA-binding interfaces of non-cognate aminoacyl-tRNA synthetases. Theor Popul Biol 2019; 129:68-80. [PMID: 31042487 DOI: 10.1016/j.tpb.2019.03.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 01/26/2019] [Accepted: 03/13/2019] [Indexed: 12/21/2022]
Abstract
Advances in structural biology of aminoacyl-tRNA synthetases (aaRSs) have revealed incredible diversity in how aaRSs bind their tRNA substrates. The causes of this diversity remain mysterious. We developed a new class of highly rugged fitness landscape models called match landscapes, through which genes encode the assortative interactions of their gene products through the complementarity and identifiability of their structural features. We used results from coding theory to prove bounds and equalities on fitness in match landscapes assuming additive interaction energies, macroscopic aminoacylation kinetics including proofreading, site-specific modifiers of interaction, and selection for translational accuracy in multiple, perfectly encoded site-types. Using genotypes based on extended Hamming codes we show that over a wide array of interface sizes and numbers of encoded cognate pairs, selection for translational accuracy alone is insufficient to displace the tRNA-binding interfaces of aaRSs. Yet, under combined selection for translational accuracy and rate, site-specific modifiers are selected to adaptively displace the tRNA-binding interfaces of non-cognate aaRS-tRNA pairs. We describe a remarkable correspondence between the lengths of perfect RNA (quaternary) codes and the modal sizes of small non-coding RNA families.
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Affiliation(s)
- Andrea I Collins-Hed
- Quantitative and Systems Biology Program, University of California, Merced, CA, 95306, United States
| | - David H Ardell
- Quantitative and Systems Biology Program, University of California, Merced, CA, 95306, United States; Molecular and Cell Biology Department, School of Natural Sciences, University of California, Merced, CA, 95306, United States.
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Fragata I, Blanckaert A, Dias Louro MA, Liberles DA, Bank C. Evolution in the light of fitness landscape theory. Trends Ecol Evol 2019; 34:69-82. [DOI: 10.1016/j.tree.2018.10.009] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/16/2018] [Accepted: 10/17/2018] [Indexed: 01/28/2023]
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García-Martín JA, Catalán P, Manrubia S, Cuesta JA. Statistical theory of phenotype abundance distributions: A test through exact enumeration of genotype spaces. ACTA ACUST UNITED AC 2018. [DOI: 10.1209/0295-5075/123/28001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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48
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Aguirre J, Catalán P, Cuesta JA, Manrubia S. On the networked architecture of genotype spaces and its critical effects on molecular evolution. Open Biol 2018; 8:180069. [PMID: 29973397 PMCID: PMC6070719 DOI: 10.1098/rsob.180069] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 06/12/2018] [Indexed: 12/26/2022] Open
Abstract
Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a network-of-networks multilayered structure of the map from genotype to function that we begin to unveil.
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Affiliation(s)
- Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Programa de Biología de Sistemas, Centro Nacional de Biotecnología (CSIC), Madrid, Spain
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza, Spain
- UC3M-BS Institute of Financial Big Data (IFiBiD), Universidad Carlos III de Madrid, Getafe, Madrid, Spain
| | - Susanna Manrubia
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Programa de Biología de Sistemas, Centro Nacional de Biotecnología (CSIC), Madrid, Spain
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Aguilar‐Rodríguez J, Peel L, Stella M, Wagner A, Payne JL. The architecture of an empirical genotype-phenotype map. Evolution 2018; 72:1242-1260. [PMID: 29676774 PMCID: PMC6055911 DOI: 10.1111/evo.13487] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 04/03/2018] [Indexed: 12/15/2022]
Abstract
Recent advances in high-throughput technologies are bringing the study of empirical genotype-phenotype (GP) maps to the fore. Here, we use data from protein-binding microarrays to study an empirical GP map of transcription factor (TF) -binding preferences. In this map, each genotype is a DNA sequence. The phenotype of this DNA sequence is its ability to bind one or more TFs. We study this GP map using genotype networks, in which nodes represent genotypes with the same phenotype, and edges connect nodes if their genotypes differ by a single small mutation. We describe the structure and arrangement of genotype networks within the space of all possible binding sites for 525 TFs from three eukaryotic species encompassing three kingdoms of life (animal, plant, and fungi). We thus provide a high-resolution depiction of the architecture of an empirical GP map. Among a number of findings, we show that these genotype networks are "small-world" and assortative, and that they ubiquitously overlap and interface with one another. We also use polymorphism data from Arabidopsis thaliana to show how genotype network structure influences the evolution of TF-binding sites in vivo. We discuss our findings in the context of regulatory evolution.
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Affiliation(s)
- José Aguilar‐Rodríguez
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Current Address: Department of Biology, Stanford University, StanfordCA, USA; Department of Chemical and Systems Biology, Stanford UniversityStanfordCAUSA
| | - Leto Peel
- Institute of Information and Communication Technologies, Electronics and Applied MathematicsUniversité Catholique de LouvainLouvain‐la‐NeuveBelgium
- Namur Center for Complex SystemsUniversity of NamurNamurBelgium
| | - Massimo Stella
- Institute for Complex Systems Simulation, Department of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUnited Kingdom
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- The Santa Fe InstituteSanta FeNew MexicoUSA
| | - Joshua L. Payne
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Institute for Integrative Biology, ETHZurichSwitzerland
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Abstract
Cells regulate the activity of genes in a variety of ways. For example, they regulate transcription through DNA binding proteins called transcription factors, and they regulate mRNA stability and processing through RNA binding proteins. Based on current knowledge, transcriptional regulation is more widespread and is involved in many more evolutionary adaptations than posttranscriptional regulation. The reason could be that transcriptional regulation is studied more intensely. We suggest instead that transcriptional regulation harbors an intrinsic evolutionary advantage: when mutations change transcriptional regulation, they are more likely to bring forth novel patterns of such regulation. That is, transcriptional regulation is more evolvable. Our analysis suggests a reason why a specific kind of gene regulation is especially abundant in the living world. Much of gene regulation is carried out by proteins that bind DNA or RNA molecules at specific sequences. One class of such proteins is transcription factors, which bind short DNA sequences to regulate transcription. Another class is RNA binding proteins, which bind short RNA sequences to regulate RNA maturation, transport, and stability. Here, we study the robustness and evolvability of these regulatory mechanisms. To this end, we use experimental binding data from 172 human and fruit fly transcription factors and RNA binding proteins as well as human polymorphism data to study the evolution of binding sites in vivo. We find little difference between the robustness of regulatory protein–RNA interactions and transcription factor–DNA interactions to DNA mutations. In contrast, we find that RNA-mediated regulation is less evolvable than transcriptional regulation, because mutations are less likely to create interactions of an RNA molecule with a new RNA binding protein than they are to create interactions of a gene regulatory region with a new transcription factor. Our observations are consistent with the high level of conservation observed for interactions between RNA binding proteins and their target molecules as well as the evolutionary plasticity of regulatory regions bound by transcription factors. They may help explain why transcriptional regulation is implicated in many more evolutionary adaptations and innovations than RNA-mediated gene regulation.
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