Fitness Estimation for Viral Variants in the Context of Cellular Coinfection Open Access

Zhu, Huisheng (Fall 2021)

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Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host’s infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets and SARS-CoV-2 sequence data from hamsters and ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. In addition, cellular coinfection could explain the leveling-off we observed in the advantageous variant’s increase. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.

Table of Contents


Table of Contents

Introduction -------------------------------------------------------------------------------------------------------1 - 2

Materials and Methods-----------------------------------------------------------------------------------------3 - 9

        Deterministic within-host evolution model------------------------------------------------------3

        Simulated data------------------------------------------------------------------------------------------5

        Empirical H5N1 data-----------------------------------------------------------------------------------5

        Empirical SARS-CoV-2 data---------------------------------------------------------------------------6

        Statistical inference------------------------------------------------------------------------------------6

        With-in host dynamics modeling for SARS-CoV-2 D614G-------------------------------------7

Results------------------------------------------------------------------------------------------------------------10 - 22

        The extent of cellular coinfection impacts variant frequency dynamics--------------------10

        Statistical estimation of variant fitness using the deterministic within-host model-----12

                  Statistical inference with simulated data----------------------------------------------12

                  Statistical inference with experimental H5N1 challenge study-------------------13

                  Statistical inference with SARS-CoV-2 competition experiment------------------19

        SARS-CoV-2 D614G With-in Host Dynamics--------------------------------------------------------21

Discussion--------------------------------------------------------------------------------------------------------22 - 26

References-------------------------------------------------------------------------------------------------------27 - 29

Supplementary materials -----------------------------------------------------------------------------------30 - 32

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