Quantifying the local adaptive landscape of a nascent bacterial community

Ascensao, Joao A; Wetmore, Kelly M.; Good, Benjamin H; Arkin, Adam P.; Hallatschek, Oskar

doi:10.1038/s41467-022-35677-5

Cited by 255 publications

(35 citation statements)

References 74 publications

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“…The DNA barcodes are sequenced at the end of each growth cycle to quantify the relative abundance of each of the barcodes. We point the reader to Kinsler et al [4] for specific details on these assays for S. cerevisiae and to Ascensao et al [3] for equivalent assays for E. coli . Figure 1(B) presents a typical barcode trajectory where the black trajectories represent the so-called neutral lineages , genetically equivalent to the untagged ancestor strain that initially dominates the culture.…”

Section: Resultsmentioning

confidence: 99%

“…Empirically, each barcode frequency trajectory follows an exponential function of the form 1,3,4 where is the frequency of barcode b at the end of cycle number t, s ( b ) is the relative fitness with respect to the reference strain—the quantity we want to infer from the data is the mean fitness of the culture at the end of cycle number t , and τ is the time pass between cycle t and t + 1. We can rewrite Equation 3 as Equation 4 separates the measurements—the barcode frequencies—from the unobserved (sometimes referred to as latent) parameters we want to infer from the data—the population mean fitness and the barcode relative fitness.…”

Section: Resultsmentioning

confidence: 99%

“…We present a Bayesian inference pipeline to quantify the uncertainty about the parametric information we can extract from high-throughput competitive fitness assays given a model of the data generation process and experimental data. In these assays, the fitness of an ensemble of genotypes is determined relative to a reference genotype 3,4 . Figure 1(A) shows a schematic of the experimental procedure in which an initial pool of barcoded strains are mixed with a reference strain and inoculated into fresh media.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian inference of relative fitness on high-throughput pooled competition assays

Razo-Mejia,

Mani,

Petrov

2023

Preprint

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The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian inference of relative fitness on high-throughput pooled competition assays

Razo-Mejia,

Mani,

Petrov

2023

Preprint

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“…We sought to infer the fitness effects of individual lineages, so that we could then determine if putatively selected lineages are influencing the estimation of the time-varying effective population sizes. We first used a deterministic method to estimate lineage fitness effects, similar to the method described in [76].…”

Section: Methodsmentioning

confidence: 99%

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Ascensao

Okada

et al. 2022

Preprint

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Random genetic drift in the population-level dynamics of an infectious disease outbreak results from the randomness of inter-host transmission and the randomness of host recovery or death. The strength of genetic drift has been found to be high for SARS-CoV-2 due to superspreading, and this is expected to substantially impact the disease epidemiology and evolution. Noise that results from the measurement process, such as biases in data collection across time, geographical areas, etc., can potentially confound estimates of genetic drift as both processes contribute "noise" to the data. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. 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. 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. Corrections taking into account epidemiological dynamics (susceptible-infected-recovered or susceptible-exposed-infected-recovered models) do not explain the discrepancy. Moreover, the levels of genetic drift that we observe are higher than the estimated levels of superspreading found by modeling studies that incorporate data on actual contact statistics in England. We discuss how even in the absence of superspreading, high levels of genetic drift can be generated via community structure in the host contact network. Our results suggest that further investigations of heterogeneous host contact structure may be important for understanding the high levels of genetic drift observed for SARS-CoV-2 in England.

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“…S and L diverged from each other early in the LTEE evolution, around 6.5k generations, where S emerged as an ecologically-distinct, but closely related strain that partially invaded the initially L-dominated population 41, 42 . Ascensao et al (2023) 34 previously created random barcoded transposon knockout libraries of S and L, allowing them to track the frequency dynamics of many subclones of each strain within populations via amplicon sequencing. When they co-cultured the S and L libraries together, they saw that the total frequency of S relative to L fluctuated strongly (Figure 1A).…”

Section: Introductionmentioning

confidence: 99%

Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations

Ascensao,

Lok,

Hallatschek

2024

Preprint

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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 abundance fluctuations do not strongly impact evolution, 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 relatedE. colistrains, 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-genotypeS. cerevisiaepopulations. 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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Quantifying the local adaptive landscape of a nascent bacterial community

Cited by 255 publications

References 74 publications

Bayesian inference of relative fitness on high-throughput pooled competition assays

Bayesian inference of relative fitness on high-throughput pooled competition assays

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations

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