Stochastic fusion simulations and experiments suggest passive and active roles of hemagglutinin during membrane fusion

Abstract

Influenza enters the host cell cytoplasm by fusing the viral and host membrane together. Fusion is mediated by hemagglutinin (HA) trimers that undergo conformational change when acidified in the endosome. It is currently debated how many HA trimers, w, and how many conformationally changed HA trimers, q, are minimally required for fusion. Conclusions vary because there are three common approaches for determining w and q from fusion data. One approach correlates the fusion rate with the fraction of fusogenic HA trimers and leads to the conclusion that one HA trimer is required for fusion. A second approach correlates the fusion rate with the total concentration of fusogenic HA trimers and indicates that more than one HA trimer is required. A third approach applies statistical models to fusion rate data obtained at a single HA density to establish w or q and suggests that more than one HA trimer is required. In this work, all three approaches are investigated through stochastic fusion simulations and experiments to elucidate the roles of HA and its ability to bend the target membrane during fusion. We find that the apparent discrepancies among the results from the various approaches may be resolved if nonfusogenic HA participates in fusion through interactions with a fusogenic HA. Our results, based on H3 and H1 serotypes, suggest that three adjacent HA trimers and one conformationally changed HA trimer are minimally required to induce membrane fusion (w = 3 and q = 1).

Figure 1

Virus-cell interaction represented in the simulation space. (a) A close-up, three-dimensional view of half of the virus bound to the host membrane. (b) Two-dimensional representation of the three-dimensional picture. The simulation space represents the viral membrane overlapping the target membrane. The HA and R species move within a hexagonal lattice domain. Any interaction between the virus and host membrane occurs within the contact area (yellow). An example of using a w = 3, q = 1 criterion for forming a fusible unit is outlined (blue perimeter). (c) Catalog of simulated events in the fusion model. The simulation species are shown on the left-hand side as a top-down view; the corresponding physical interpretations of the species are shown in the middle through cross-sectional side views. The viral and target membranes are labeled V and T, respectively. (Yellow ovals) HA₁ binding domain; (red objects) HA₂ fusion domain of an HA_1,2 trimer. (Brighter red) Portions of the HA₂ domain representing the hydrophobic fusion peptides that inserts into the target membrane. To see this figure in color, go online.

Figure 2

Determining possible solutions for w, q, and k_bend using the Constant FB approach on the fusion data of Imai et al. (13) at ρ_HA,200 and using rate values in Table 1 for pH 5.2. The ratio D_test/D_crit is used to determine if simulations match with the data of Imai et al. If D_test/D_crit is <1, simulations are accepted. An example of an invalid solution is shown when w = 3 and q = 3 (olive green line), as noted by D_test/D_crit being >1. To see this figure in color, go online.

Figure 3

Sample simulation log-log plot of V_max versus HA_1,2 density for (a) Variable F approach for the condition that w = 3 while q is varied. The slopes of the best-fit lines are 0.65 and 1.6 for q = 1 and 2, respectively. (b) Variable FB approach for q = 1 while w is varied. The slopes of the best-fit lines are 0.62, 1.59, 2.19, and 3.03 for w values of 1, 2, 3, and 4, respectively. The r² values for all regression lines are at least 0.99. The unit of HA density, [HA_1,2], has been converted to its corresponding mass ratio of HA to lipid to be consistent with the results of Imai et al. (13).

Figure 4

Slope values of log V_max versus log [HA_1,2] for various combinations of w and q values. Some simulations were unnecessary due to the inability to yield results that are consistent with the fusion data of Imai et al. (13). (Shaded bar) 95% confidence interval of the data of Imai et al. (13). The numerical values of the slopes are provided in parentheses.

Figure 5

X31 fusion results at various pH conditions for SLB A at ρ_HA,200. (a) Simulations are able to fit experimental data by adjusting only k_bend while keeping w = 3 and q = 1. (b, right axis and circles) The mean k_bend values for pH 3.0, 3.5, 4.0, and 4.5 are 0.2, 0.05, 0.018, and 0.01 s⁻¹, respectively, with standard deviation shown in error bars. (b, left axis and triangles) The mean R18 diffusivity values for pH 3.0, 3.5, 4.0, and 4.5 are 0.82, 0.66, 0.55, and 0.51 μm²/s, respectively, with standard deviation shown in error bars. Mobile fractions of R18 were close to 1 for all cases. Both R18 diffusivity and k_bend decrease with increasing pH over this range.

Figure 6

X31 virus fusion results at pH 4.0 using SLB A and SLB B. Simulations were able to match experimental data for two different target membranes membrane compositions by adjusting k_bend only. The values of k_bend from the fits are 0.018 (±0.001) s⁻¹ for SLB A and 0.0035 (±0.0002) s⁻¹ for SLB B.

Figure 7

Sensitivity index values for rate parameters at (a) pH 4.5, where both k_bend and k_merge have large sensitivity index values relative to other parameters and (b) pH 3.0, where k_merge is the most sensitive parameter. The parameter values are provided in the legend of each plot.