Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Koelle, Katia; Farrell, Alex; Brooke, Christopher B.; Ke, Ruian

doi:10.1101/359067

Cited by 6 publications

(8 citation statements)

References 34 publications

(57 reference statements)

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“…In both the HM and TS models, the viral decline after peak viremia is due to the depletion of target cells. When the doubly infected cells die at a higher rate than the singly infected cells, we would expect a bi-phasic viral decline, consistent with findings of our recent study [41]. This bi-phasic viral decline is consistent with data sets from human challenge studies and experimental studies on ponies [12,15].…”

Section: Viral Load Dynamics and Frequency Of Co-infectionsupporting

confidence: 91%

“…the multiplicity of infection, MOI) and viral output of an infected cell (the consequences of MOI) and appropriate mathematical models that incorporate these quantities (for example, Ref. [41]) will be crucial to correctly interpret drivers of viral load dynamics. Further, another consequence of frequent coinfection is that the viral growth rate becomes relatively insensitive to changes in the fraction of SIP production.…”

Section: Discussionmentioning

confidence: 99%

“…This assumption allows us to model cells with any number of co-infecting virions within them while only explicitly modeling the doubly infected cell populations (E Z and I Z ). Modeling this system without this assumption would require either a larger, more complicated model [18,19,48] or an alternate model formulation [41].…”

Section: Discussionmentioning

confidence: 99%

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Coinfection of semi-infectious particles can contribute substantially to influenza infection dynamics

Farrell

Brooke

Koelle

et al. 2019

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Author SummaryInfluenza A viruses (IAVs) represent a large public health burden across the world. Currently, our understanding of their infection dynamics is incomplete, which hinders the development of effective vaccines and treatment strategies. Recently, it was shown that a large fraction of virions, called semi-infectious particles, do not cause productive infection on their own; however, coinfection of these particles leads to productive infection. The extent that semi-infectious particles and, more broadly, coinfection contribute to overall influenza infection dynamics is not clear. To address this question, we constructed mathematical models explicitly keeping track of semiinfectious particles and coinfection. We show that coinfection can be frequent over the course of infection and that SIPs play an important role in regulating infection dynamics. Our results have implications towards developing effective therapeutics.

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Section: Viral Load Dynamics and Frequency Of Co-infectionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Coinfection of semi-infectious particles can contribute substantially to influenza infection dynamics

Farrell

Brooke

Koelle

et al. 2019

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“…Hopefully, the data generated here will help inform future mathematical modeling efforts focused on understanding IAV within-host infection dynamics. Our results to date certainly suggest that future model structures may better capture the underlying biology of IAV infection if they account for the phenotypic consequences of cellular co-infection (41,42). Altogether, our results clearly demonstrate that cellular MOI can have concrete effects on infection outcome, highlighting the functional importance of collective interactions during IAV infection (43).…”

Section: Cellular Co-infection Enhances Type III (But Not Type I) Ifnsupporting

confidence: 57%

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

Martin

Harris

Sun

et al. 2019

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15 During viral infection, the numbers of virions infecting individual cells can vary significantly over 16 time and space. The functional consequences of this variation in cellular multiplicity of infection 49 Cellular co-infection plays an important, yet poorly defined role in shaping the outcome of 50 influenza A virus (IAV) infection. By facilitating reassortment between incoming viral genomes, 51 cellular co-infection can give rise to new viral genotypes with increased fitness or emergence 52 potential (1). Cellular co-infection can also enhance the replicative potential of the virus by 53promoting the complementation and multiplicity reactivation of the semi-infectious particles that 54 constitute the bulk of influenza virus populations (2-4). Despite its clear importance, the 55 prevalence and the specific functional consequences of cellular co-infection during IAV infection 56 remain largely unknown. 57 58We and others have previously shown that cellular co-infection can be common in vivo (5,6). 59There is growing evidence that IAV replication and spread is focal and thus that the distribution 60 of individual virions across cells and tissues is highly spatially structured, resulting in foci of high 61 cellular multiplicity of infection (MOI) (7-10). Given the dynamic distribution of virions over time 62and space during infection, it appears likely that the MOIs of individual infected cells can vary 63 significantly. This raises the question whether this variation in the number of virions that infect a 64 cell has distinct phenotypic consequences. If so, it could have significant implications for 65understanding IAV infection dynamics as two viral populations of identical size and genome 66 sequence could give rise to divergent infection outcomes if the dispersal patterns of virions (and 67 thus the MOI distribution across cells) differs. 68 69Several previous studies have suggested that the number of virions that enter a given cell 70 (referred to throughout as "cellular MOI" or "viral input") may affect replication kinetics and 71 interferon (IFN) induction (11-14); however, the phenotypic consequences of cellular MOI during 72 IAV infection have not yet been rigorously or comprehensively quantified. Here, we combine 73 precise single-cycle infection experiments with statistical model fitting to reveal that cellular MOI 74 significantly alters virus production rates, the host response to infection, and the potential for 75 further superinfection. In doing so, we precisely define the functional forms that govern the 76 relationships between the amount of viral input and the phenotypes of infected cells, information 77 that will aid future efforts to quantitatively model IAV infection. Altogether, these results reveal 78and define an unappreciated role for cellular co-infection in shaping the outcome of IAV infection. 79 80 RESULTS 81To define how variation in cellular MOI affects viral replication dynamics and the host response 82to infection, we infected either MDCK or A549 cells with A/Puerto Rico/8...

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“…Instead, there are only a small number of target cells that are available to the virus to infect.Thus, the rate at which susceptible cells become infected can rapidly saturate even while many target cells remain susceptible. A final approach for modeling space implicitly is to allow for overdispersion of virus among target cells, by assuming, for example, a negative binomial distribution for viral particles across host cells rather than a Poisson distribution[59].With overdispersion, a small number of target cells are infected with a large number of virions, while a large number of target cells might still be uninfected. Overdispersion can thus capture expected viral distribution patterns under the assumption of spatial viral spread.…”

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confidence: 99%

Causes and Consequences of Spatial Within-Host Viral Spread

Gallagher¹,

Brooke²,

Ke³

et al. 2018

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The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Recent experimental studies, however, have started to shed more light on the mechanisms and spatial dynamics of viral spread within hosts. Here, we review these experimental studies as well as the limited number of computational modeling efforts that have begun to integrate spatial considerations for understanding within-host viral spread. We limit our review to influenza virus to highlight key mechanisms affecting spatial aspects of viral spread for pathogens of the respiratory tract. There is considerable empirical evidence for highly spatial within-host spread of influenza virus, yet few computational modeling studies that shed light on possible factors that structure the dynamics of this spatial spread. In existing modeling studies, there is also a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature to provide informed theoretical expectations for what viral and host factors may impact the spatial patterns of within-host viral dynamics and for how spatial spread will affect the genetic composition of within-host viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host influenza virus spread and the potential for within-host spatial considerations to inform the development of disease control strategies.

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Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Abstract: complementation 21 22 23 24 25 26 51 viral load dynamics and allows for a new class of questions to be addressed that consider the 52 effects of cellular coinfection, collective viral interactions, and viral complementation in within-53 host viral dynamics and evolution. 54 3 55

Cited by 6 publications

References 34 publications

Coinfection of semi-infectious particles can contribute substantially to influenza infection dynamics

Coinfection of semi-infectious particles can contribute substantially to influenza infection dynamics

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

Causes and Consequences of Spatial Within-Host Viral Spread

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