Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection

Authors: Emily E. Ackerman¹,², Jordan J.A. Weaver¹,² and Jason E. Shoemaker¹,³,⁴,⁵

Abstract

The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low pathogenic H1N1 or high pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the rela-tionship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high pathogenic viral strains.

Code Availability

This repository consists of example code to recreate the figures present in the original manuscript

Plot Replication

Figure 1 (model schematics), Figure 2 (Basinhopping screening), Figure 3 (parameter sensitivity), and Figure 6 (parameter distribution) are straightforward plotting of results from existing packages and are thus excluded.

Figure 4

Model 4 output for minimum energy parameter set (lines) and corresponding training data (markers) for H1N1 (top row) and H5N1 (bottom row). AD results (all parameters allowed to independently estimate across strains) are shown in black and NSSD results (all parameters shared between strains) are shown in blue. Intervals represent the standard deviation of the 1,000 lowest energy parameter sets. Data from Shoemaker et al ⁶ are shown with the standard deviation associated with triplicate data points per timepoint.

Figure 5

Model 4 output for the minimum energy parameter set (line) for OSSD parameterizations and corresponding training data (markers) for H1N1 (top row) and H5N1 (bottom row). Data from Shoemaker et al ⁶ are shown with the standard deviation associated with triplicate data points per timepoint.

Figure 7

Model 4 output for minimum energy parameter set (line) for virus related parameter independent (V) and corresponding training data (markers) for H1N1 (top row) and H5N1 (bottom row). Data from Shoemaker et al ⁶ are shown with the standard error associated with triplicate data points per timepoint.The code for this figure is the same as Figure 5, with different parameter values.

Footnotes

1. Department of Chemical & Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA ↩ ↩² ↩³

2. These authors contributed equally to this work ↩ ↩²

3. McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA ↩

4. Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA ↩

5. Correspondence: jason.shoemaker@pitt.edu ↩

6. doi:10.1371/journal.ppat.1004856 ↩ ↩² ↩³