Multiscale modeling of influenza A virus infection supports the development of direct-acting antivirals

Abstract

Influenza A viruses are respiratory pathogens that cause seasonal epidemics with up to 500,000 deaths each year. Yet there are currently only two classes of antivirals licensed for treatment and drug-resistant strains are on the rise. A major challenge for the discovery of new anti-influenza agents is the identification of drug targets that efficiently interfere with viral replication. To support this step, we developed a multiscale model of influenza A virus infection which comprises both the intracellular level where the virus synthesizes its proteins, replicates its genome, and assembles new virions and the extracellular level where it spreads to new host cells. This integrated modeling approach recapitulates a wide range of experimental data across both scales including the time course of all three viral RNA species inside an infected cell and the infection dynamics in a cell population. It also allowed us to systematically study how interfering with specific steps of the viral life cycle affects virus production. We find that inhibitors of viral transcription, replication, protein synthesis, nuclear export, and assembly/release are most effective in decreasing virus titers whereas targeting virus entry primarily delays infection. In addition, our results suggest that for some antivirals therapy success strongly depends on the lifespan of infected cells and, thus, on the dynamics of virus-induced apoptosis or the host's immune response. Hence, the proposed model provides a systems-level understanding of influenza A virus infection and therapy as well as an ideal platform to include further levels of complexity toward a comprehensive description of infectious diseases.

Figure 1. Schematic depiction of the multiscale model.

(A) The extracellular level of infection comprises the growth and death of uninfected cells, their infection by free virions, the production of virus by infected cells, viral clearance/degradation, virus-induced apoptosis, and the lysis of apoptotic cells. (B) Infected cells are further segregated according to their infection age, i.e., the time that has elapsed since their infection. (C) The intracellular state of an infected cell is simulated using the model of influenza virus replication by Heldt et al. (see text and reference for details). (D) Both levels are coupled via the age-dependent virus production rate, which depends directly on the internal state of a cell and determines the number of virions released into the extracellular space.

Figure 2. The multiscale model captures the intracellular and extracellular level of infection.

Curves represent model fits to experimental infections of MDCK cells with influenza A/PR/8/34 (H1N1) depicted by symbols. (A) Levels of vRNA, cRNA (dashed, □) and mRNA (solid, ○) of segment 5 (encoding NP) and the amount of virus particles produced by an average infected cell in a synchronous, single round infection experiment (MOI = 6). Particle numbers correspond to the amount of hemagglutinating virus particles and were calculated from virus titer measurements by HA assay using Equation (12). Bars indicate the standard deviation of three independent experiments (two for the 9 and 10 hpi measurements). (B) Concentration of uninfected (solid, ○), infected (dashed, □) and apoptotic (dash-dotted, Δ) cells and infectious virus titer during multicycle infection (MOI = 0.1). Time courses were adopted from Isken et al. and are representative of three independent experiments .

Figure 3. Model predictions reproduce data for different infection conditions.

The model fit from Figure 2 (dashed, □) was used to predict the percentage of uninfected cells (A) and the infectious virus titer (B) for infections at an MOI of 10⁻⁴ (solid, ○) and of 3 (dash-dotted, Δ), respectively. These predictions were compared to data sets not used for model construction. Measurements were adopted from Isken et al. and are representative of three independent experiments .

Figure 4. Cell death constrains virus production.

Survival probability of an infected cell (solid) and virus production rate over the infected cell age neglecting cell death (dashed) and considering cell death (dash-dotted).

Figure 5. Inhibition of viral RNA synthesis, translation, or assembly/release efficiently impairs virus production.

(A) Simulated impact of drugs targeting the indicated steps of intracellular virus replication with varying efficacy. Colors indicate the fold change in the total number of virus particles an average infected cell produces over its lifetime compared to the drug-free regime. Numbers in circles correspond to the examples shown in B. (B) Time courses of selected viral components during drug treatment with 50% efficacy. Columns correspond to components depicted in the scheme. Dashed and solid lines are time courses in the absence and presence of drugs, respectively. All components were normalized to their maximum in the drug-free regime.

Figure 6. Cell death affects therapy success.

(A) Virus production rate over the age of an infected cell in the absence of drugs (solid) and during inhibition of cRNA synthesis with 50% efficacy (dashed). (B) Different survival probabilities for an infected cell assuming that virus-induced apoptosis occurs with the rate estimated from data in Figure 2B (1×, solid line) or at twofold (2×, dashed line) and fourfold (4×, dash-dotted line) its rate. (C) Total amount of virus particles released by an infected cell considering the combination of different production rates and survival probabilities.

Figure 7. Inhibition of virus entry delays infection.

(A) Simulated effect of drugs targeting the indicated steps of virus infection with an efficacy of 95%. Colors indicate the log₁₀ virus titer. Numbers in circles correspond to the examples shown in B. (B) Concentration of uninfected target cells and virus titer in the absence of drugs (solid line) and during treatment with inhibitors of virus fusion (dashed) and mRNA synthesis (dash-dotted) at 95% efficacy.