Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session A05: Ecological and Evolutionary Dynamics IFocus Recordings Available
|
Hide Abstracts |
Sponsoring Units: DBIO Chair: Daniel Weissman, Emory Room: McCormick Place W-178A |
Monday, March 14, 2022 8:00AM - 8:36AM |
A05.00001: Leveraging evolutionary trade-offs and phage selection pressure to alter bacterial infections Invited Speaker: Paul Turner One possible strategy to combat the antibiotic resistance crisis is a renewed approach to 'phage therapy,' where these administered viruses not only kill the target bacteria, but also predictably select for phage resistance that reduces virulence and/or increases antibiotic sensitivity (evolutionary trade-offs). By utilizing virulence factors as receptor binding sites, the phages exert selection for bacteria to evolve phage resistance by modifying (or losing) the virulence factor, potentially reducing bacterial pathogenicity. We present examples of phages that have evolved to kill target bacteria while selecting for phage resistance that coincides with phenotypic traits that are beneficial to biomedicine. In vitro data on phage-bacteria (co)evolutionary dynamics are often recapitulated in phenotypic, genetic and metagenomics analyses of microbes in longitudinal patient samples before, during and after emergency phage therapy treatments. |
Monday, March 14, 2022 8:36AM - 8:48AM |
A05.00002: AI-driven predictions of binding trends of SARS-CoV-2 variants from atomistic simulations Sara Capponi Binding processes are fundamental for cellular functions such as immune response activation, cell regulation, and signal transduction among others. However, experimental measures of binding free energies often depend on the system set up while in silico calculation at atomistic level of the binding process can be computationally demanding due to the long timescale of typical binding/unbinding events. The aim of our study is to leverage machine learning approaches to address this challenge and estimate binding affinity trends between two proteins using short atomistic simulations. Our technique uses a neural network algorithm applied to a series of images generated by the simulation data and representing the distance between two molecules in time. The algorithm is capable of distinguishing with high accuracy low vs high binding affinity of non-hydrophobic mutations, indicating that our method excels on the inference of the binding affinity trends for charged and/or polar amino acid mutations. Moreover, it shows high accuracy in prediction using a small subset of the simulated data. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2. |
Monday, March 14, 2022 8:48AM - 9:00AM |
A05.00003: Evolutionary Tradeoff of Enterovirus A71 Thermostability and Cell Entry Benjamin A Catching, Ming T Yeh, Sara Capponi, Raul Andino, Simone Bianco
|
Monday, March 14, 2022 9:00AM - 9:12AM |
A05.00004: Inferring polygenic selection for HIV escape from T cell responses Yirui Gao, John P Barton Polygenic selection refers to natural selection that acts through multiple mutations. As one example, HIV mutation to escape from human T cell responses can be thought of as polygenic adaptation in the sense that any nonsynonymous mutations within the epitope can allow the virus to escape immune recognition and hence enhance its fitness [1]. Here we build a model describing this polygenic immune selection. We also extend the Marginal Path Likelihood [2] method to this model to infer selection from evolutionary histories. Simulations show the validity of our method. Applying our approach to within-host HIV evolutionary histories, we observe strong selection to escape from T cell responses across the HIV genome. |
Monday, March 14, 2022 9:12AM - 9:24AM |
A05.00005: Modeling time-series of noisy two-cycle ecological oscillators Vahini Reddy Nareddy, Jonathan L Machta, Karen Abbott, Shadi Esmaeili-Wellman, Alan Hastings In this work, we investigate how time-series of an ecological oscillator can be modeled and predicted when the true dynamics of oscillator is unknown. Two-cycle ecological oscillators have two phases of oscillations: high values at even times or high values at odd times. In the presence of noise, exact high and low values vary from cycle to cycle, and the two-cycles at times may change their phase of oscillation. We develop two discrete-state models and a continuous-state model to study their predictive ability given the noisy time-series data. For discrete-state models, we have a two-state system with two phases of oscillation as two states and a three-state system with an additional third state to incorporate transition dynamics between the phases of oscillations. We will present forecast skill results for the three developed models (two-state, three-state and continuous-state) and a machine learning forecasting tool. We will also discuss maximum likelihood inference methods for the developed models and the comparison of obtained forecast skill with mutual information between the data used for models and the time-series data. |
Monday, March 14, 2022 9:24AM - 9:36AM |
A05.00006: Dynamics of viral mutation and evolution Greyson R Lewis We discuss our model of viral-host interaction, and the dynamics that is introduced by the multiple forces acting on the virus in that system. Two of the main forces, cell permissivity and immune response, result in two opposing strategies for viruses, one with either a large “match” to the cells or a small one, and these two, together with the other forces, give rise to a rich phase diagram. We find a first-order phase transition as a function of increasing cell permissivity at fixed immunity, featuring bi-modal quasispecies distributions at the phase boundary, and higher-order transitions in other areas of phase space as a function of varying immunity at fixed cell permissivity. We calculate the evolution of the viral load inside and outside cells, as well as the mutation of the viral quasispecies distribution, both as a function of time. The dynamics reveals two time scales of viral infection in our model, one portraying fast onset and the other of varying length to steady state. We map the three phases in our phase diagram based on their properties to acute (such as the flu and Covid-like diseases), chronic (hepatitis), and opportunistic diseases at very low immunity. |
Monday, March 14, 2022 9:36AM - 9:48AM |
A05.00007: Diversity-generating host-disease coevolution with adaptive immunity Madeleine J Bonsma-Fisher, Sidhartha Goyal Bacteria are under constant threat from viruses, and some bacteria possess adaptive immune systems that provide protection through a genomic 'memory' of past viral infections. In conjunction with viral evolution, this creates a diverse population of bacteria where each cell has a unique viral genomic imprint. The fate of the bacterial population depends on the rate at which memories are updated to track evolving viruses. How does the dynamic fitness landscape generated by adaptive immunity impact population diversity and the rate of evolution? We find with a simple stochastic population dynamics model that viruses experience a changing fitness that both drives and reigns in diversity: new virus mutants that escape immune targeting have high fitness and experience selective dynamics associated with low diversity, while established virus clones experience immune targeting and lose their fitness advantage, going extinct following neutral dynamics associated with high diversity. Both diversity and the speed of evolution depend sub-linearly on viral mutation rate in contrast to a linear dependence under neutral dynamics. The effectiveness of adaptive immunity is captured by bacterial average immunity which depends inversely on diversity and is a crucial experimental observable. |
Monday, March 14, 2022 9:48AM - 10:00AM |
A05.00008: The scales of viral-host co-evolution. Aleksandra M Walczak Does host-pathogen co-evolution constrain the space viral trajectories? I will show that co-evolution between immune systems and viruses in a finite-dimensional antigenic space can be described by an antigenic wave pushed forward and canalized by host-pathogen interactions. This leads to a new emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. |
Monday, March 14, 2022 10:00AM - 10:12AM |
A05.00009: Predicted adaptation of microbial population growth deceleration in feast-and-famine environments Justus Fink, Michael Manhart A major challenge is to predict the success of a mutant lineage. It is the life-history of the organism that integrates multiple traits into the overall relative fitness. The feast-and-famine lifestyle often found for microbial populations in natural environments can be clearly defined and controlled but measuring the precise direction of selection in multidimensional trait space remains difficult. Here we give a model for co-culture experiments between two strains competing for a single limiting resource where cell growth slows down with nutrient concentration. We derive the selection pressure as a function of growth traits across all frequencies and environmental conditions. This explicit relationship reveals that selection increases growth rate and minimizes deceleration as two separate traits, but does not act on resource efficiency. For evolution under generic feast-and-famine conditions we predict the maximum possible adaption in the rate-limiting resource threshold K. We find that that the evolved trait scales with the effective population size and is expected to adapt to orders of magnitude below the environmental concentration. We apply these results to quantify the necessary environment to enable selection, to optimize selection pressure in serial transfer evolution experiments and show how to detect the degree of adaptation from the shape of growth curves. Our findings on the mechanisms of selection serve to interpret the resource threshold evolution in the LTEE and realign the observed trait variation in natural isolates. |
Monday, March 14, 2022 10:12AM - 10:24AM |
A05.00010: Preventing co-infection: a viral strategy with short-term benefits and long-term drawbacks Michael Hunter, Diana Fusco Viral co-infection occurs when multiple distinct viral particles infect the same host. This can impact viral evolution through intracellular interactions, complementation, reassortment and recombination. In nature many viral species are found to have a wide range of mechanisms to prevent co-infection, raising the question of how this strategic choice impacts viral evolution. We find that genetic drift is suppressed when co-infection is allowed, which in turn facilitates the fixation of beneficial mutations and the removal of deleterious ones. Interestingly, we also find that the growth rate (dis)advantage associated with variations in life history parameters can be dramatically different from the (dis)advantage measured in direct competition simulations. Finally, we find that a mutant which prevents co-infection displays a substantial competitive advantage over a co-infecting population even if it displays a much lower growth rate in isolation. Our findings suggest that while preventing co-infection can negatively impact the long-term evolution of a viral population, in the short-term it is ultimately a winning strategy. |
Monday, March 14, 2022 10:24AM - 10:36AM |
A05.00011: Enhanced Biodiversity in Time-dependent Environments Tom Burkart, Erwin Frey Natural ecosystems, in particular on the microbial scale, are typically inhabited by a large number of species. In contrast, purely competitive nonlinear population dynamics models in general predict only one surviving species (competitive exclusion principle). In these models, the environmental parameters, such as the resource abundance, are often assumed to be constant. Thus, the impact of a time-dependent environment on the ecosystem biodiversity is not captured by such models. In nature, however, most environments are in fact changing periodically, such as the mammalian gut microbiome in response to the circadian rhythm. Here, we show that including a periodic time dependence in a generic population dynamics model can enhance the biodiversity of the system. We demonstrate that, in general, nonlinear interactions between species combined with a temporally varying environment can enhance biodiversity in the class of competing species models. Our results offer a mechanism to explain how biodiversity and genetic variation could have been maintained in various systems, ranging from mammalian gut microbiota to early Earth environments. |
Monday, March 14, 2022 10:36AM - 10:48AM |
A05.00012: Time to fixation in changing environments Sachin Kaushik Although many experimental and theoretical studies on natural selection have been carried out in a constant environment, as natural environments typically vary in time, it is important to ask if and how the results of these investigations are affected by a changing environment. Here, we study the properties of the conditional fixation time defined as the time to fixation of a new mutant that is destined to fix in a finite, randomly mating diploid population with intermediate dominance that is evolving in a periodically changing environment. It is known that in a static environment, the conditional mean fixation time of a co-dominant beneficial mutant is equal to that of a deleterious mutant with the same magnitude of selection coefficient. We find that this symmetry is not preserved, even when the environment is changing slowly. More generally, we find that the conditional mean fixation time of an initially beneficial mutant in a slowly changing environment depends weakly on the dominance coefficient and remains close to the corresponding result in the static environment. However, for an initially deleterious mutant under moderate and slowly varying selection, the fixation time differs substantially from that in a constant environment when the mutant is recessive. As fixation times are intimately related to the levels and patterns of genetic diversity, our results suggest that for beneficial sweeps, these quantities are only mildly affected by temporal variation in environment. In contrast, environmental change is likely to impact the patterns due to recessive deleterious sweeps strongly. |
Monday, March 14, 2022 10:48AM - 11:00AM |
A05.00013: Metabolic preferences of marine copiotrophs are explained by the structure of central metabolism Matti Gralka, Otto X Cordero Bacterial communities in the ocean collectively drive biogeochemical cycles by digesting complex organic matter and recycling essential nutrients. To understand the taxonomic distribution and patterns of metabolic niches in these bacteria, we catalogued the metabolic capabilities of a library of 160 marine heterotrophic bacteria across 64 substrates. We found that the metabolic niches were oriented along a primary axis corresponding to the relative preference for sugars (i.e., glycolytic substrates) and organic and amino acids (i.e., gluconeogenic substrates). This preference was roughly conserved at the order level, but with marked differences between clades, down to the genus level. Comparative genomics revealed that a preference for glycolytic substrates was strongly correlated with the number of carbohydrate-degrading enzymes and negatively correlated with whole-genome GC content. By contrast, we identified only a small number of genes and no central metabolic pathways that were correlated with metabolic preference. This suggests that the fundamental metabolic preference for sugars or acids are encoded in the regulatory network rather than gene content, a hypothesis supported by recent theory suggesting intrinsic trade-offs in central metabolism at the root of these preferences. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700