Causes and Consequences of Spatial Within-Host Viral Spread

Gallagher, Molly; Brooke, Christopher B.; Ke, Ruian; Koelle, Katia

doi:10.20944/preprints201810.0232.v1

Cited by 17 publications

(14 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding is also consistent with data from chest CT scans of infected individuals showing that infection spreads progressively to larger areas in the lungs (51, 52). More broadly, it has been suggested that infection is highly spatial for other viruses such as influenza (59), and thus, the spatial nature of within-host virus spread may be a general feature of respiratory viruses. However, more research (similar to what has been done in influenza, e.g.…”

Section: Discussionmentioning

confidence: 99%

Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness

Zitzmann

Ribeiro

et al. 2020

Preprint

Self Cite

161

View full text Add to dashboard Cite

SARS-CoV-2 is a human pathogen that causes infection in both the upper respiratory tract (URT) and the lower respiratory tract (LRT). The viral kinetics of SARS-CoV-2 infection and how they relate to infectiousness and disease progression are not well understood. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection in both the URT and LRT. We fit the models to viral load data from patients with likely infection dates known, we estimated that infected individuals with a longer incubation period had lower rates of viral growth, took longer to reach peak viremia in the URT, and had higher chances of presymptomatic transmission. We then developed a model linking viral load to infectiousness. We found that to explain the substantial fraction of transmissions occurring presymptomatically, the infectiousness of a person should depend on a saturating function of the viral load, making the logarithm of the URT viral load a better surrogate of infectiousness than the viral load itself. Comparing the roles of target-cell limitation, the innate immune response, proliferation of target cells and spatial infection in the LRT, we found that spatial dissemination in the lungs is likely to be an important process in sustaining the prolonged high viral loads. Overall, our models provide a quantitative framework for predicting how SARS-CoV-2 within-host dynamics determine infectiousness and represent a step towards quantifying how viral load dynamics and the immune responses determine disease severity.

show abstract

Section: Discussionmentioning

confidence: 99%

Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness

Zitzmann

Ribeiro

et al. 2020

Preprint

Self Cite

161

View full text Add to dashboard Cite

show abstract

“…IAV genetic drift playing a strong role in these human challenge studies is not unanticipated, given that the challenge reference strain was a seasonal influenza strain that was relatively well adapted to human hosts and that egg- and cell culture-adapted variants were quickly excluded from the in vivo viral populations (31). The effect of spatial structure within the host respiratory system (54) may further augment the effects of genetic drift on DVG populations.…”

Section: Discussionmentioning

confidence: 99%

The Dynamics of Influenza A H3N2 Defective Viral Genomes from a Human Challenge Study

Martin

Kaul

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

248 words 17 Importance: 135 words 18 Main text: 4,747 words 19 Importance 39The evolution of influenza virus, in terms of single nucleotide variants and the reassortment of 40 gene segments, has been studied in detail. However, influenza is known to generate defective 41 viral genomes (DVGs) during replication, and little is known about how these genomes evolve 42 both within hosts and at the population level. Studies in animal models have indicated that 43 prophylactically or therapeutically administered DVGs can impact patterns of disease 44 progression. However, the formation of naturally-occurring DVGs, their evolutionary dynamics, 45and their contribution to disease severity in human hosts is not well understood. Here, we 46 identify the formation of de novo DVGs in samples from human challenge studies throughout the 47 course of infection. We analyze their evolutionary trajectories, revealing the important role of 48 genetic drift in shaping DVG populations during acute infections with well-adapted viral strains. 49 of the samples analyzed and were most common in the PB2, PB1, and PA gene segments. A 73 limitation of this study, however, is that it offered only a cross-sectional view of IAV DVG 74 populations. While it has been shown that Sendai virus DVG populations expand during the first 75 12 hours of infection in a mouse model (25), the evolution of IAV DVG populations within a 76 human host over the course of an infection has not been well characterized. 77Here, we report an analysis of IAV DVG populations identified from deep sequencing 78 data taken over the course of infection during two longitudinal human challenge studies with 79 different treatment cohorts. We observe the generation of de novo DVGs in nearly all subjects, 80 primarily in the polymerase gene segments (PB2, PB1, and PA). DVG populations were highly 81 variable over time in DVG species composition as well as in DVG species relative abundance. 82Over the course of infection, individual DVG species were observed to arise, fluctuate in 83 abundance, as well as disappear from the DVG population. Overall, we found no trend towards 84 decreasing diversity of DVG populations or towards shorter DVG species during the five days 85 post challenge, likely due to the dominance of stochastic effects. Furthermore, we were unable to 86 detect an association between DVG levels and peak viral titers, potentially due to the negative 87 feedback between DVG and wild-type virus. Similarly, higher DVG levels were not associated 88 with more severe symptoms. This study helps to illustrate the stochastic dynamics of DVG 89 populations within a host during acute infection with a well-adapted viral strain, a scenario under 90 which fitness variation in the wild-type virus population is expected to be relatively small. 91 Materials and Methods 92Ethics statement. The procedures followed in the human challenge studies were in accordance 93 with the Declaration of Helsinki. The studies were approved by the institutional review boards 94 (IRBs) of Duke Univer...

show abstract

“…Despite the majority of modeling work focusing on well-mixed models, an important aspect of acute respiratory infections is the spatial nature of the infection process in respiratory tract epithelium 67 . As discussed above, well-mixed models in general predict that the IFN response affects peak viral load and viral dynamics afterwards 24,40 .…”

Section: Accepted Articlementioning

confidence: 99%

Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

Perelson

2021

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS‐CoV‐2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID‐19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, “Everything should be made as simple as possible, but not simpler,” and this idea underlies the modeling we describe below.

show abstract

Causes and Consequences of Spatial Within-Host Viral Spread

Cited by 17 publications

References 90 publications

Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness

Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness

The Dynamics of Influenza A H3N2 Defective Viral Genomes from a Human Challenge Study

Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

Contact Info

Product

Resources

About