Dynamics of Influenza Virus Infection and Pathology

Saenz, Roberto A.; Quinlivan, Michelle; Elton, Debra; MacRae, Shona; Blunden, A. S.; Mumford, J. A.; Daly, Janet M.; Digard, Paul; Cullinane, Ann; Grenfell, Bryan T.; McCauley, John W.; Wood, James; Gog, Julia R.

doi:10.1128/jvi.02078-09

Cited by 188 publications

(311 citation statements)

References 48 publications

(63 reference statements)

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“…3A) for the process of within-host clearance of MeV RNA and fitted these models to the quantitative measurements acquired from the macaque experiments (28)(29)(30). We considered three nested models, which take different components of the immune system into account.…”

Section: Resultsmentioning

confidence: 99%

Prolonged persistence of measles virus RNA is characteristic of primary infection dynamics

Lin

Kouyos

Adams

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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109

136

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Measles virus (MeV) is the poster child for acute infection followed by lifelong immunity. However, recent work shows the presence of MeV RNA in multiple sites for up to 3 mo after infection in a proportion of infected children. Here, we use experimental infection of rhesus macaques to show that prolonged RNA presence is characteristic of primary infection. We found that viral RNA persisted in the blood, respiratory tract, or lymph nodes four to five times longer than the infectious virus and that the clearance of MeV RNA from blood happened in three phases: rapid decline coincident with clearance of infectious virus, a rebound phase with increases up to 10-fold, and a phase of slow decrease to undetectable levels. To examine the effect of individual host immune factors on MeV load dynamics further, we developed a mathematical model that expressed viral replication and elimination in terms of the strength of MeV-specific T-cell responses, antibody responses, target cell limitations, and immunosuppressive activity of regulatory T cells. Based on the model, we demonstrate that viral dynamics, although initially regulated by T cells, require antibody to eliminate viral RNA. These results have profound consequences for our view of acute viral infections, the development of prolonged immunity, and, potentially, viral evolution.immune responses | within-host modeling | virus clearance

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Section: Resultsmentioning

confidence: 99%

Prolonged persistence of measles virus RNA is characteristic of primary infection dynamics

Lin

Kouyos

Adams

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

109

136

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“…the limit of detection or 1 infectious particle. See the original studies and also Li & Handel [52] [83]. The latter was assumed to represent symptoms.…”

Section: Host Infectiousnessmentioning

confidence: 99%

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

Handel

Rohani

2015

Phil. Trans. R. Soc. B

110

181

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The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and—maybe most importantly—discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.

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“…Other than the model by Smith et al [29], we are not aware of any other experimentally confirmed model of the progression of S. pneumoniae. There are, however, several proposed models for influenza [31,51,52]. Of these, the model developed by Handel et al [28] is best suited for our purposes for three key reasons: (i) in contrast to models based on human [30] or equine [31] influenza, the model by Handel et al is based on a murine challenge system, with which all experiments on viral -bacterial interactions are studied; (ii) it explicitly considers innate immunity; and (iii) it is statistically fitted to experimental data.…”

Section: Discussionmentioning

confidence: 99%

“…There are, however, several proposed models for influenza [31,51,52]. Of these, the model developed by Handel et al [28] is best suited for our purposes for three key reasons: (i) in contrast to models based on human [30] or equine [31] influenza, the model by Handel et al is based on a murine challenge system, with which all experiments on viral -bacterial interactions are studied; (ii) it explicitly considers innate immunity; and (iii) it is statistically fitted to experimental data. That said, we have explored the robustness of our findings to alternative formulations of the Handel et al model, concerning the shape and strength of interference (electronic supplementary material, figure S6) and the potential dependence of immune response on viral load (electronic supplementary material, figure S7).…”

Section: Discussionmentioning

confidence: 99%

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Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza andStreptococcus pneumoniae

Shrestha

Foxman

Dawid

et al. 2013

J. R. Soc. Interface.

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A significant fraction of seasonal and in particular pandemic influenza deaths are attributed to secondary bacterial infections. In animal models, influenza virus predisposes hosts to severe infection with both Streptococcus pneumoniae and Staphylococcus aureus. Despite its importance, the mechanistic nature of the interaction between influenza and pneumococci, its dependence on the timing and sequence of infections as well as the clinical and epidemiological consequences remain unclear. We explore an immunemediated model of the viral -bacterial interaction that quantifies the timing and the intensity of the interaction. Taking advantage of the wealth of knowledge gained from animal models, and the quantitative understanding of the kinetics of pathogen-specific immunological dynamics, we formulate a mathematical model for immune-mediated interaction between influenza virus and S. pneumoniae in the lungs. We use the model to examine the pathogenic effect of inoculum size and timing of pneumococcal invasion relative to influenza infection, as well as the efficacy of antivirals in preventing severe pneumococcal disease. We find that our model is able to capture the key features of the interaction observed in animal experiments. The model predicts that introduction of pneumococcal bacteria during a 4-6 day window following influenza infection results in invasive pneumonia at significantly lower inoculum size than in hosts not infected with influenza. Furthermore, we find that antiviral treatment administered later than 4 days after influenza infection was not able to prevent invasive pneumococcal disease. This work provides a quantitative framework to study interactions between influenza and pneumococci and has the potential to accurately quantify the interactions. Such quantitative understanding can form a basis for effective clinical care, public health policies and pandemic preparedness.

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Dynamics of Influenza Virus Infection and Pathology

Cited by 188 publications

References 48 publications

Prolonged persistence of measles virus RNA is characteristic of primary infection dynamics

Prolonged persistence of measles virus RNA is characteristic of primary infection dynamics

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza andStreptococcus pneumoniae

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