Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

Smith, Amber M.

doi:10.1111/imr.12692

Cited by 72 publications

(89 citation statements)

References 146 publications

(640 reference statements)

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“…Employing targeted model-driven experimental designs to examine and validate the predictions of models like the one presented here is pivotal to elucidating the mechanisms of infection spread and clearance [57,58]. Determining the factors that influence disease severity/weight loss is the first step to understand the disproportionate mortality in at-risk populations (e.g., elderly) and…”

Section: /35mentioning

confidence: 99%

Dynamically Linking Influenza Virus Infection Kinetics, Lung Injury, Inflammation, and Disease Severity

Myers

Smith

Lane

et al. 2019

Preprint

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Influenza A viruses cause a significant amount of morbidity and mortality. Understanding how the infection is controlled by host immune responses and how different factors influence severity are critical to combat the infection. During infection, viral loads increase exponentially, peak, then decline until resolution. The viral decline is often biphasic, which we previously determined is a consequence of density-dependent infected cell clearance. The second, rapid clearance phase corresponds with the infiltration of CD8 + T cells, but how the rate changes with infected cell density and T cell density is unclear. Further, the kinetics of virus, infected cells, and CD8 + T cells all contribute to disease severity but do not seem to be directly correlated. Thus, we investigated the relations between viral loads, infected cells, CD8 + T cells, lung pathology, and disease severity/symptoms by infecting mice with influenza A/PR8, simultaneously measuring virus and CD8 + T cells, and developing and calibrating a kinetic model. The model predicted that infection resolution is sensitive to CD8 + T cell expansion, that there is a critical T cell magnitude below which the infection is significantly prolonged, and that the efficiency of T cell-mediated clearance is dependent on infected cell density. To further examine the latter finding and validate the model's predicted dynamics, we quantified infected cell kinetics using lung histomorphometry. These data showed that the area of lung infected matches the predicted cumulative infected cell dynamics, and that the area of resolved infection parallels the relative CD8 + T cell magnitude. Our analysis further revealed a nonlinear relationship between disease severity (i.e., weight loss) and the percent of the lung damaged. Establishing the predictive capabilities of the model and the critical connections that map the kinetics of virus, infected cells, CD8 + T cells, lung pathology, and disease severity during influenza virus infection aids our ability to forecast the course of infection, disease progression, and potential complications, thereby providing insight for clinical decisions.

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Section: /35mentioning

confidence: 99%

Dynamically Linking Influenza Virus Infection Kinetics, Lung Injury, Inflammation, and Disease Severity

Myers

Smith

Lane

et al. 2019

Preprint

Self Cite

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“…To understand how the immune system evolves during the co-infection between 24 viruses and bacteria, and the role of specific cytokines as contributing factors for these 25 severe infections, we use Topological Data Analysis (TDA) along with an extensive 26 semi-unsupervised parameter value grid search, and k-nearest neighbour analysis. 27 We find persistent shapes of the data in the three infection scenarios, single viral and 28 bacterial infections and co-infection. The immune response is shown to be distinct for 29 each of the infections scenarios and we uncover that the immune response during the 30 August 2, 2019 1/15 co-infection has three phases and two transition points, a previously unknown property 31 regarding the dynamics of the immune response during co-infection.…”

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confidence: 79%

Topological data analysis to uncover the shape of immune responses during co-infection

Sasaki

Bruder

Hernandez‐Vargas

2019

Preprint

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1Co-infections by multiple pathogens have important implications in many aspects of 2 health, epidemiology and evolution. However, how to disentangle the contributing 3 factors of the immune response when two infections take place at the same time is 4 largely unexplored. Using data sets of the immune response during 5 influenza-pneumococcal co-infection in mice, we employ here topological data analysis 6 to simplify and visualise high dimensional data sets. 7 We identified persistent shapes of the simplicial complexes of the data in the three 8 infection scenarios: single viral infection, single bacterial infection, and co-infection. 9The immune response was found to be distinct for each of the infection scenarios and we 10 uncovered that the immune response during the co-infection has three phases and two 11 transition points. During the first phase, its dynamics is inherited from its response to 12 the primary (viral) infection. The immune response has an early (few hours post 13 co-infection) and then modulates its response to finally react against the secondary 14 (bacterial) infection. Between 18 to 26 hours post co-infection the nature of the immune 15 response changes again and does no longer resembles either of the single infection 16 scenarios. 17 Author summary 18The mapper algorithm is a topological data analysis technique used for the qualitative 19 analysis, simplification and visualisation of high dimensional data sets. It generates a 20 low-dimensional image that captures topological and geometric information of the data 21 set in high dimensional space, which can highlight groups of data points of interest and 22 can guide further analysis and quantification. 23To understand how the immune system evolves during the co-infection between 24 viruses and bacteria, and the role of specific cytokines as contributing factors for these 25 severe infections, we use Topological Data Analysis (TDA) along with an extensive 26 semi-unsupervised parameter value grid search, and k-nearest neighbour analysis. 27 We find persistent shapes of the data in the three infection scenarios, single viral and 28 bacterial infections and co-infection. The immune response is shown to be distinct for 29 each of the infections scenarios and we uncover that the immune response during the 30 August 2, 2019 1/15 co-infection has three phases and two transition points, a previously unknown property 31 regarding the dynamics of the immune response during co-infection. 32Introduction 33 Co-infection is the simultaneous infection of a host by two or more phathogens. We are 34 continuously exposed to multiple potential pathogens; many people are chronically (e.g. 35 HIV) or latently (e.g. herpes viruses) infected, and we all carry potential pathogens in 36 our colonising microbial flora. This means that nearly every new infection is some sort 37 of co-infection, and globally, co-infections are the norm rather than the exception [1]. 38There is an impressive number of combinations of pathogens that derive synergy 39 from contemporan...

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“…However, it is not clear how the virus-virus and virus-host interactions influence disease severity and lead to these varied outcomes. Two or more virus agents can interact in diverse ways which may arise from the consequences of their inoculation order, inter exposure time, initial inoculums, different combinations of viruses, number of coinfecting viruses and host immune state [17,18]. Thus, coinfections pose a combinatorial problem which can be challenging to study in a laboratory set up alone.…”

Section: Introductionmentioning

confidence: 99%

Effect of stochasticity on coinfection dynamics of respiratory viruses

2019

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Background Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality. Results In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection. Conclusions We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus. Electronic supplementary material The online version of this article (10.1186/s12859-019-2793-6) contains supplementary material, which is available to authorized users.

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Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

Cited by 72 publications

References 146 publications

Dynamically Linking Influenza Virus Infection Kinetics, Lung Injury, Inflammation, and Disease Severity

Dynamically Linking Influenza Virus Infection Kinetics, Lung Injury, Inflammation, and Disease Severity

Topological data analysis to uncover the shape of immune responses during co-infection

Effect of stochasticity on coinfection dynamics of respiratory viruses

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