Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples

Terbot, John W; Johri, Parul; Liphardt, Schuyler; Soni, Vivak; Pfeifer, Susanne; Cooper, Brandon S.; Good, Jeffrey M.; Jensen, Jeffrey D.

doi:10.1371/journal.ppat.1011265

Cited by 17 publications

(11 citation statements)

References 138 publications

(179 reference statements)

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“…We acknowledge, however, the complexity of intrahost evolution of SARS-CoV-2. Whereas within-host population dynamics might partially explain the results observed in this study, more comprehensive understanding would require development of models accounting jointly for multiple evolutionary processes as discussed in [88] and as already available for instance for primary HIV infection [89].…”

Section: Discussionmentioning

confidence: 99%

Phylogenetic meta-analysis of chronic SARS-CoV-2 infections in immunocompromised patients shows no evidence of elevated evolutionary rates

Översti,

Gaul,

Jensen

et al. 2023

Preprint

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Genomic sequences from rapidly evolving pathogens, sampled over time, hold information on disease origin, transmission, and evolution. Together with their sampling times, sequences can be used to estimate the rates of molecular evolution and date evolutionary events through molecular tip-dating. The validity of this approach, however, depends on whether detectable levels of genetic variation have accumulated over the given sampling interval, generating temporal signal. Moreover, different molecular dating methods have demonstrated varying degrees of systematic biases under different biologically realistic scenarios, such as the presence of phylo-temporal clustering.Chronic SARS-CoV-2 infection in immunocompromised patients has been linked to remarkably higher intra-host molecular rates than those of global lineages, facilitating the emergence of novel viral lineages. Yet, most studies reporting accelerated rates lack the evaluation of temporal signal or comparison of multiple methods of inference, both required to reliably estimate molecular rates. In this study, we use 26 previously published longitudinally sampled sequence series obtained from chronically infected immunocompromised patients to re-evaluate the rate of SARS-CoV-2 intrahost evolution. Using a range of methods, we analyse the strength of temporal signal and infer evolutionary rates from tip-calibrated phylogenies. Regardless of heterogeneity in rate estimates between sample series and methods, we find within-host rates to be in good agreement with rates derived from host-to-host transmission chains.Our findings suggest that when certain limitations of the methodology are disregarded, such as the underlying assumption of phylogenetic independence or the method’s sensitivity to phylo- temporal grouping, evolutionary rates can be substantially overestimated. We demonstrate that estimating within-host rates is a challenging question necessitating careful interpretation of findings. While our results do not support faster evolution across the complete viral genome during chronic SARS-CoV-2 infection, prolonged viral shedding together with relapsing viral load dynamics may nevertheless promote the emergence of new viral variants in immunocompromised patients.AUTHOR SUMMARYThe evolutionary origin of SARS-CoV-2 variants of concern (VOC) is a longstanding point of controversy, with multiple proposed explanations. Observations of immunocompromised individuals being at a greater risk of developing a prolonged SARS-CoV-2 infection have led to the ‘Chronic infection hypothesis’, suggesting that these cases may contribute to the emergence of VOCs. Correspondingly, many studies have reported accelerated viral evolution of SARS-CoV-2 within immunocompromised individuals with respect to the viral background population. However, many of these findings have not been validated with appropriate analytical methods. In this study we re-evaluate the rate of intrahost viral evolution of SARS- CoV-2 within immunocompromised patients utilising a range of methods. We assess the performance of different methodologies and compare our results to published estimates of SARS-CoV-2 evolutionary rates. Our systematic comparison showed no evidence supporting the previous claims of elevated levels of intrahost evolution in immunocompromised patients with chronic SARS-CoV-2. Instead, our findings exemplify the complexity of within-host viral dynamics, suggesting that a more comprehensive understanding of SARS-CoV-2 evolutionary processes would be derived from concurrent evaluation of viral genomic data together with patients’ clinical information.

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Section: Discussionmentioning

confidence: 99%

Phylogenetic meta-analysis of chronic SARS-CoV-2 infections in immunocompromised patients shows no evidence of elevated evolutionary rates

Översti,

Gaul,

Jensen

et al. 2023

Preprint

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“…However, our results suggest that the linked effects of selection (notably BGS) in compact genomes such as D. melanogaster impact demographic inference when using sites in neutral regions (Figure 4). Thus, for populations with compact genomes experiencing selection such as D. melanogaster, or in more extreme cases such as viruses where neutral sites unlinked to selected loci do not exist (Howell et al 2023;Terbot et al 2023), population history may be best inferred using joint inference approaches (e.g. Johri et al 2020) that account for the linked effects of selection and demography simultaneously.…”

Section: Demographic Inference With Complex Genomic Architecturementioning

confidence: 99%

Biases in ARG-based inference of historical population size in populations experiencing selection

Marsh,

Johri

2024

Preprint

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Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ARG-based approaches to demographic inference in typical empirical analyses are susceptible to mis-inference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, though it could cause mis-inference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid mis-inference due to the linked effects of selection.

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“…By contrast, the Drosophila melanogaster genome contains significantly more functional content, and the effects of selection are likely greater owing to larger effective population sizes, suggesting that a neutral / unlinked class of sites may well not exist – at least in sufficient quantity to estimate a neutral population history – thereby necessitating joint inference (Johri et al 2020). In organisms that are characterized by almost entirely functional genomes such as many viruses, joint inference will similarly be essential ( e.g., Jensen 2021; Morales-Arce et al 2022; Howell et al 2023; Terbot et al 2023).…”

Section: Introductionmentioning

confidence: 99%

The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects

Soni,

Pfeifer,

Jensen

2023

Preprint

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Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 andδaδi). We demonstrate that, if not properly modelled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination prior to utilizing population genomic data to quantify the effects of genetic drift (i.e., as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modelled in downstream inference.Significance StatementDespite evidence from numerous species that mutation and recombination rates vary along the genome, both rates tend to be modelled as fixed when performing population genetic inference. The impact of failing to account for this rate heterogeneity on the estimation of demographic and selective parameters has yet to be well quantified; thus, we here study this effect by comparing inference under both fixed and variable rate scenarios. Our results demonstrate that unaccounted for rate heterogeneity can increase uncertainty and lead to mis-inference in certain scenarios. We highlight the importance of utilizing mutation and recombination rate maps where possible, and of modelling the uncertainty underlying these estimates.

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Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples

Cited by 17 publications

References 138 publications

Phylogenetic meta-analysis of chronic SARS-CoV-2 infections in immunocompromised patients shows no evidence of elevated evolutionary rates

Phylogenetic meta-analysis of chronic SARS-CoV-2 infections in immunocompromised patients shows no evidence of elevated evolutionary rates

Biases in ARG-based inference of historical population size in populations experiencing selection

The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects

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