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Review
. 2018 Sep;285(1):97-112.
doi: 10.1111/imr.12692.

Host-pathogen Kinetics During Influenza Infection and Coinfection: Insights From Predictive Modeling

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Free PMC article
Review

Host-pathogen Kinetics During Influenza Infection and Coinfection: Insights From Predictive Modeling

Amber M Smith. Immunol Rev. .
Free PMC article

Abstract

Influenza virus infections are a leading cause of morbidity and mortality worldwide. This is due in part to the continual emergence of new viral variants and to synergistic interactions with other viruses and bacteria. There is a lack of understanding about how host responses work to control the infection and how other pathogens capitalize on the altered immune state. The complexity of multi-pathogen infections makes dissecting contributing mechanisms, which may be non-linear and occur on different time scales, challenging. Fortunately, mathematical models have been able to uncover infection control mechanisms, establish regulatory feedbacks, connect mechanisms across time scales, and determine the processes that dictate different disease outcomes. These models have tested existing hypotheses and generated new hypotheses, some of which have been subsequently tested and validated in the laboratory. They have been particularly a key in studying influenza-bacteria coinfections and will be undoubtedly be useful in examining the interplay between influenza virus and other viruses. Here, I review recent advances in modeling influenza-related infections, the novel biological insight that has been gained through modeling, the importance of model-driven experimental design, and future directions of the field.

Keywords: Bacterial infection; T Cells; coinfection; infectious diseases; lung; mathematical model; model validation; monocytes/macrophages; virus infection.

Figures

Figure 1
Figure 1
Data‐Driven Mathematical Modeling and Model‐Driven Experimental Design. Data‐driven mathematical modeling studies are iterative and entail developing a model to describe the underlying biology, calibrating the model to experimental or clinical data, analyzing the model with mathematical techniques, using the model to make predictions and design experiments, and validating the predictions in the laboratory or clinic
Figure 2
Figure 2
Viral Kinetic Model and Dynamics. A schematic of the standard viral kinetic model,14 associated equations, fit to data from mice infected with influenza A/Puerto Rico/34/8 (PR8),24 and timeline of major host responses are shown. The model tracks susceptible “target” cells (T), two classes of infected cells (I1 and I2), and virus (V). Target cells are infected by virus at rate βV. Once infected, the cells undergo an eclipse phase, which accounts for the time between infection and virus production. To account for these dynamics, infected cells are split into two classes, where k is the transition rate from unproductive to productive. Infected cells are lost at rate δI2 per day. Virus is produced at rate p per infected cell and is cleared at rate c per day. The resulting model dynamics are shown for a saturating infected cell death rate, that is, δI2=δd/Kδ+I2, where δd/Kδ is the maximum rate of clearance and Kδ is the half‐saturation constant. Viral kinetics generally split into ~5 phases: initial infection of cells, exponential growth, peak, a slow decay, and a fast decay/clearance. Major host responses influencing these phases include, type I interferons (IFN), natural killer (NK) cells, T cells, and antibody (Ab)
Figure 3
Figure 3
Viral–Viral Coinfection Model. Model schematic, equations, and dynamics for a viral–viral coinfection where two viruses (virus‐a and virus‐b) compete for target cells.42 In this model, target cells can be infected by virus‐a (orange) with rate βa and by virus‐b (magenta) with rate βb. The model structure from the standard viral kinetic model is retained, but different rates of the eclipse phase (ka,b), infected cell clearance (δa,b), virus production (pa,b), and virus clearance (ca,b) are allowed. This interaction results in significantly reduced viral loads for the slower growing virus (magenta) and negligible declines in viral loads for the faster growing virus (orange). See Ref. 42 for fits to viral load data
Figure 4
Figure 4
Viral–Bacterial Coinfection Model. Model schematic, equations, and dynamics for a viral–bacterial coinfection model where influenza virus depletes aMΦ (MA) or renders them dysfunctional according to ϕ^(V), which reduces bacterial clearance.41 In addition, bacteria (P) enhances virus production according to the function â(P). Bacteria replicate logistically (r(1P/KP)) and are cleared at rate γMfP,MAMA. The remaining equations are given by the standard viral kinetic model. These interactions result in a rebound of virus and rapid bacterial growth (cyan). The bacterial growth trajectory is defined by a threshold (green),44 such that bacterial titers will decline when bacteria‐aMΦ pairs are below the threshold (black), remain constant when bacteria‐aMΦ pairs are at the threshold (green), and increase when bacteria‐aMΦ pairs are above the threshold (cyan). Because aMΦs decline throughout an influenza virus infection, the dose required to initiate an infection also declines. See Ref. 41 for fits to viral and bacterial load data, Refs. 113, 125, 126 for validation of the model predictions, and Ref. 44 for validation of the threshold

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