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Yu Q, Ascensao JA, Okada T, Boyd O, Volz E, Hallatschek O. Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. PLoS Pathog 2024; 20:e1012090. [PMID: 38620033 PMCID: PMC11045146 DOI: 10.1371/journal.ppat.1012090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/25/2024] [Accepted: 03/03/2024] [Indexed: 04/17/2024] Open
Abstract
Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.
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Affiliation(s)
- QinQin Yu
- Department of Physics, University of California, Berkeley, California, United States of America
| | - Joao A. Ascensao
- Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Takashi Okada
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- RIKEN iTHEMS, Wako, Saitama, Japan
| | | | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany
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Ascensao JA, Lok K, Hallatschek O. Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations. bioRxiv 2024:2024.02.23.581776. [PMID: 38562700 PMCID: PMC10983864 DOI: 10.1101/2024.02.23.581776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Large stochastic population abundance fluctuations are ubiquitous across the tree of life1-7, impacting the predictability of population dynamics and influencing eco-evolutionary outcomes. It has generally been thought that these large abundance fluctuations do not strongly impact evolution (in contrast to genetic drift), as the relative frequencies of alleles in the population will be unaffected if the abundance of all alleles fluctuate in unison. However, we argue that large abundance fluctuations can lead to significant genotype frequency fluctuations if different genotypes within a population experience these fluctuations asynchronously. By serially diluting mixtures of two closely related E. coli strains, we show that such asynchrony can occur, leading to giant frequency fluctuations that far exceed expectations from models of genetic drift. We develop a flexible, effective model that explains the abundance fluctuations as arising from correlated offspring numbers between individuals, and the large frequency fluctuations result from even slight decoupling in offspring numbers between genotypes. This model accurately describes the observed abundance and frequency fluctuation scaling behaviors. Our findings suggest chaotic dynamics underpin these giant fluctuations, causing initially similar trajectories to diverge exponentially; subtle environmental changes can be magnified, leading to batch correlations in identical growth conditions. Furthermore, we present evidence that such decoupling noise is also present in mixed-genotype S. cerevisiae populations. We demonstrate that such decoupling noise can strongly influence evolutionary outcomes, in a manner distinct from genetic drift. Given the generic nature of asynchronous fluctuations, we anticipate they are widespread in biological populations, significantly affecting evolutionary and ecological dynamics.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California Berkeley, Berkeley, CA, USA
| | - Kristen Lok
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
- Present affiliation: Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Oskar Hallatschek
- Department of Physics, University of California Berkeley, Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103 Leipzig, Germany
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Ascensao JA, Denk J, Lok K, Yu Q, Wetmore KM, Hallatschek O. Rediversification following ecotype isolation reveals hidden adaptive potential. Curr Biol 2024; 34:855-867.e6. [PMID: 38325377 PMCID: PMC10911448 DOI: 10.1016/j.cub.2024.01.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 11/09/2023] [Accepted: 01/10/2024] [Indexed: 02/09/2024]
Abstract
Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about whether communities can regenerate ecological diversity following ecotype removal or extinction and how the rediversified communities would compare to the original ones. Here, we show that simple two-ecotype communities from the E. coli long-term evolution experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different from the original community in ways relevant to the mechanism of ecotype coexistence-for example, in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, although the rediversified community showed unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species where there are even more potential niches, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Jonas Denk
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Kristen Lok
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
- Present affiliation: Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - QinQin Yu
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Present affiliation: Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Oskar Hallatschek
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103 Leipzig, Germany
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Ascensao JA, Denk J, Lok K, Yu Q, Wetmore KM, Hallatschek O. Rediversification Following Ecotype Isolation Reveals Hidden Adaptive Potential. bioRxiv 2023:2023.05.03.539206. [PMID: 37205326 PMCID: PMC10187175 DOI: 10.1101/2023.05.03.539206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about if communities can regenerate ecological diversity following species removal or extinction, and how the rediversified communities would compare to the original ones. Here we show that simple two-ecotype communities from the E. coli Long Term Evolution Experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different compared to the original community in ways relevant to the mechanism of ecotype coexistence, for example in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, but with unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Jonas Denk
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Kristen Lok
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - QinQin Yu
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Present affiliation: Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Oskar Hallatschek
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103 Leipzig, Germany
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Ascensao JA, Wetmore KM, Good BH, Arkin AP, Hallatschek O. Quantifying the local adaptive landscape of a nascent bacterial community. Nat Commun 2023; 14:248. [PMID: 36646697 PMCID: PMC9842643 DOI: 10.1038/s41467-022-35677-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/16/2022] [Indexed: 01/17/2023] Open
Abstract
The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes ("L" and "S") shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
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Ascensao JA, Datta P, Hancioglu B, Sontag E, Gennaro ML, Igoshin OA. Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis. PLoS Comput Biol 2016; 12:e1004741. [PMID: 26900694 PMCID: PMC4762938 DOI: 10.1371/journal.pcbi.1004741] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 01/07/2016] [Indexed: 01/27/2023] Open
Abstract
Understanding how dynamical responses of biological networks are constrained by underlying network topology is one of the fundamental goals of systems biology. Here we employ monotone systems theory to formulate a theorem stating necessary conditions for non-monotonic time-response of a biochemical network to a monotonic stimulus. We apply this theorem to analyze the non-monotonic dynamics of the σB-regulated glyoxylate shunt gene expression in Mycobacterium tuberculosis cells exposed to hypoxia. We first demonstrate that the known network structure is inconsistent with observed dynamics. To resolve this inconsistency we employ the formulated theorem, modeling simulations and optimization along with follow-up dynamic experimental measurements. We show a requirement for post-translational modulation of σB activity in order to reconcile the network dynamics with its topology. The results of this analysis make testable experimental predictions and demonstrate wider applicability of the developed methodology to a wide class of biological systems.
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Affiliation(s)
- Joao A. Ascensao
- Department of Bioengineering and Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
| | - Pratik Datta
- Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey, United States of America
| | - Baris Hancioglu
- Department of Bioengineering and Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
| | - Eduardo Sontag
- Department of Mathematics and Center for Quantitative Biology, Rutgers University, Piscataway, New Jersey, United States of America
| | - Maria L. Gennaro
- Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey, United States of America
| | - Oleg A. Igoshin
- Department of Bioengineering and Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
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Ascensao JA, Dolan ME, Hill DP, Blake JA. Methodology for the inference of gene function from phenotype data. BMC Bioinformatics 2014; 15:405. [PMID: 25495798 PMCID: PMC4302099 DOI: 10.1186/s12859-014-0405-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Accepted: 12/02/2014] [Indexed: 12/14/2022] Open
Abstract
Background Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. Results We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. Conclusions We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0405-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Joao A Ascensao
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA. .,Rice University, 6100 Main Street, Houston, TX, USA.
| | - Mary E Dolan
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA.
| | - David P Hill
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA.
| | - Judith A Blake
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA.
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