Influenza A virus surface proteins are organized to help penetrate host mucus.

Influenza A virus (IAV) enters cells by binding to sialic acid on the cell surface. To accomplish this while avoiding immobilization by sialic acid in host mucus, viruses rely on a balance between the receptor-binding protein hemagglutinin (HA) and the receptor-cleaving protein neuraminidase (NA). Although genetic aspects of this balance are well-characterized, little is known about how the spatial organization of these proteins in the viral envelope may contribute. Using site-specific fluorescent labeling and super-resolution microscopy, we show that HA and NA are asymmetrically distributed on the surface of filamentous viruses, creating a spatial organization of binding and cleaving activities that causes viruses to step consistently away from their NA-rich pole. This Brownian ratchet-like diffusion produces persistent directional mobility that resolves the virus's conflicting needs to both penetrate mucus and stably attach to the underlying cells, potentially contributing to the prevalence of the filamentous phenotype in clinical isolates of IAV.

Introduction

For a virus to infect a cell, it must reach receptors on the cell surface while avoiding neutralization or clearance by ‘decoy’ receptors in the surrounding environment. For viruses that bind to sialic acid, a receptor that is abundant both on the surface of cells and in the secreted extracellular mucosal environment, attachment and detachment is controlled by receptor-binding and receptor-destroying activities on the viral surface (Rosenthal et al., 1998; Zeng et al., 2008). Influenza A viruses (IAVs), respiratory pathogens that contribute to seasonal flu and have pandemic potential, achieve this balance with hemagglutinin (HA)-mediated receptor binding and neuraminidase (NA)-catalyzed receptor destruction (Air and Laver, 1989; Skehel and Wiley, 2000). Although the importance and genetic basis of the functional balance between HA and NA in IAV has been thoroughly characterized (Wagner et al., 2002; Xu et al., 2012; Yen et al., 2011), it is becoming clear that transmissibility of influenza viruses within and between hosts depends on factors beyond the sequence of these two genes (Chou et al., 2011; Herfst et al., 2012; Neumann and Kawaoka, 2015).

Mucosal barriers present the first line of defense against IAV infection. To infect the underlying epithelium, viral particles must first pass through a ~ 1–10 μm thick layer of mucus that is being steadily transported towards the pharynx where it can be swallowed, neutralizing any virus immobilized within it (Bustamante-Marin and Ostrowski, 2017; Wanner et al., 1996; Zanin et al., 2016). Viruses that bind too tightly to sialic acid will pass through the mucus barrier very slowly, and will thus be unable to reach the surface of an airway epithelial cell before mucociliary clearance. In contrast, viruses that bind only very weakly to sialic acid (or which rapidly destroy it through excessive NA activity [Cohen et al., 2013; Yang et al., 2014]) will quickly penetrate mucus, but may be unable to stably attach to the surface of the underlying epithelium once it is reached. Adaptations that help the virus to overcome both of these conflicting challenges, such as changes in virus morphology or a particular spatial organization of envelope proteins, could be evolutionarily favored during in vivo replication.

Interestingly, one feature of IAV that tends to diverge when clinical isolates are cultured in a laboratory environment, or when animals are infected with laboratory-grown strains, is particle morphology. While clinical isolates of IAV – samples adapted to transmission in a mucosal environment – form filamentous particles with a consistent diameter but widely varying length, laboratory-adapted strains tend to produce more uniform, spherical particles (Badham and Rossman, 2016; Chu, 1949; Dadonaite et al., 2016; Seladi-Schulman et al., 2013). Recent evidence from the 2009 pandemic suggests that filamentous morphology, conferred by the virus’s M segment, may play a role in transmission (Campbell et al., 2014; Lakdawala et al., 2011). However, whether or not virus morphology contributes directly to virus transmission – and if so, how – remains unclear. Similarly, although the two major envelope proteins of IAV, HA and NA, have been observed by electron microscopy to cluster non-uniformly on both the viral and pre-viral envelope (Calder et al., 2010; Harris et al., 2006; Leser and Lamb, 2017), whether and how the spatial organization of HA and NA affects virus transmission also remains unclear.

Motivated by these observations, we reasoned that virus shape, together with the packaging and organization of HA and NA in the viral membrane, could influence the balance of attachment and detachment in ways that promote efficient virus penetration through mucus. To test this idea, we sought to characterize the organization of proteins in filamentous IAV particles while simultaneously observing their engagement with sialic acid – a measurement that requires a non-destructive approach. To make this measurement possible, we recently developed strains of influenza A virus that are amenable to fluorescence microscopy through site-specific tags introduced into the viral genome (Vahey and Fletcher, 2019). Here we show that filamentous particles frequently contain asymmetric distributions of HA and NA in their membranes, and that this distinctive organization biases the diffusion of these particles in a persistent direction over distances of several microns. By enhancing the effective diffusion of a viral particle without reducing the stability of its attachment to the viral receptor, this mechanism could promote virus penetration across mucosal barriers.

Results

HA and NA are distributed asymmetrically on the surface of IAV particles

We first sought to characterize the organization and dynamics of proteins in the viral membrane. By labeling HA and NA, along with the viral nucleoprotein, NP, we are able to measure features of virus organization on intact, infectious particles that corroborate and extend previous observations made using electron microscopy (Calder et al., 2010; Chlanda et al., 2015; Harris et al., 2006; Leser and Lamb, 2017). For these experiments, we use a tagged variant of the strain A/WSN/1933 with M1 from A/Udorn/1972, which differs from WSN M1 at six residues and confers filamentous morphology (Elleman and Barclay, 2004). Consistent with our prior observations, this virus produces filamentous particles that vary widely in size, from sub-diffraction limited spots to particles > 10 μm in length (Vahey and Fletcher, 2019). In viruses with fluorescently-labeled HA and NA, we observe a pronounced tendency for NA to be enriched at one end of the virus (Figure 1A, inset). To quantify this enrichment, we measured the relative intensities of HA and NA within ~400 nm of each viral pole in filamentous particles > 1 µm in length. Across 20344 such particles, approximately one third have NA intensities at least 5-fold higher on one pole than on the other (Figure 1A, left). Similarly, viruses with labeled NP (the most abundant protein in vRNP complexes and a proxy for the virus genome) reveal individual foci that localize to one end of the virus. Aligning filamentous particles with polarized NP foci (4805 in total) reveals a tendency for NA to colocalize with NP (Figure 1A, right). For the subset of these particles > 4.5 µm in length (540 in total), we created a composite image showing the average densities of viral proteins within 2 µm of each viral pole (Figure 1—figure supplement 1). On average, NA is enriched ~2 x relative to HA at the NP-containing pole in these particles, as compared to the middle region of the particle and the opposite pole (Figure 1—figure supplement 1, bottom), suggesting that NA localization is linked (directly or indirectly) to the location of the viral genome. This protein distribution is stable, since photobleached portions of unfixed filamentous particles do not recover fluorescence in either HA or NA over tens of minutes (Figure 1B).

Figure 1.

Organization of the IAV envelope in filamentous virus particles.

(A) Abundance of viral proteins at one viral pole relative to the other, measured as the ratio of intensities within ~400 nm of either end of the virus. The plot to the left compares HA and NA intensities for 20344 filamentous particles of at least ~1 μm in length. Approximately one third of filamentous particles have NA intensities five-fold higher on one pole than the other, as indicated by the vertical dashed lines (distributions are symmetric in this case because poles are designated randomly). Inset images show a particle with NA enrichment of ~5.6 fold (~31st percentile of particles). The plot to the right shows the same comparison for particles aligned to their NP-foci, designated as pole 1 (distributions are determined from a total of 4805 particles with polarized NP distributions). The prevalence of particles with NA ratios > 1 indicates a tendency for NA to be enriched at the virus’s genome-containing pole. Inset images show a particle with NA enrichment of ~3.3 fold (~48th percentile of particles based on NA polarity). (See also Figure 1—figure supplement 1).(B) Photobleached fluorescent NA (top) and HA (bottom) on filamentous particles show no recovery after 20 min, indicating that NA and HA are immobilized in the viral membrane (scale bar = 500 nm). Data is representative of n = 8 viruses (NA) and n = 5 viruses (HA) from three biological replicates. (C) STORM reconstructions at ~30 nm resolution of a pair of viruses unresolveable by diffraction-limited microscopy, each showing the characteristic localization of NA at one end of the virus (scale bar = 200 nm). (D) STORM reconstructions of a filamentous virus at ~30 nm resolution reveal clusters of HA and NA which partly exclude each other, illustrated by the inverse correlation in HA and NA intensities along the axis of the virus (scale bar = 200 nm). (E) Distribution of HA-NA spatial correlation coefficients from regions of 24 filamentous particles following reconstruction at ~30 nm resolution. (F) To compare populations of virus that are able to detach from infected cell to those that remain bound to the cell surface with or without NAIs, we collect virus in two separate stages: first, we harvest virus from media at 16hpi, followed by the addition of media supplemented with an exogenous, oseltamivir-resistant bacterial neuraminidase. After one hour of treatment with exogenous NA, we harvest virus that had previously remained bound to the cell surface. (G) Plot showing the HA-NA polarity of viruses released in the presence or absence of NAI and exogenous sialidase. Boxes show median values of HA-NA polarity and 25th-75th percentile ranges for each of four biological replicates, with between 499 and 2067 filamentous viruses each (p-values determined using a paired-sample T-test). See also Figure 1—source data 1.

A composite image of the poles of 540 filamentous viruses ≥ 4.5 μm in length with fluorescently labeled HA (SrtA), NA (Sfp), and NP (FlAsH-EDT2). Particles are aligned by the location of the viral genome (NP). On average, NA intensity at the NP-containing (‘distal’) pole of the virus is enriched ~ 2 x relative to rest of the virus after normalizing by HA intensity (scale bar = 200 nm).

(A) Image of overlapping filamentous viruses acquired using diffraction-limited microscopy (shown with inverted contrast). (B) STORM reconstruction of the same image as in (A), with indicated cross sections i and ii. (C) To estimate the resolution of STORM reconstructions, we compare the collected STORM cross-sections of filamentous viruses (i and ii) to simulated cross-sections, where uniformly distributed molecules on the viral surface are each modeled as a gaussian whose standard deviation serves as a fit parameter. The standard deviation that yields the best fit with our data (~30 nm) provides an approximate resolution for our reconstructions.

Figure 1—figure supplement 1.

Distribution of HA, NA, and NP in filamentous particles.

A composite image of the poles of 540 filamentous viruses ≥ 4.5 μm in length with fluorescently labeled HA (SrtA), NA (Sfp), and NP (FlAsH-EDT2). Particles are aligned by the location of the viral genome (NP). On average, NA intensity at the NP-containing (‘distal’) pole of the virus is enriched ~ 2 x relative to rest of the virus after normalizing by HA intensity (scale bar = 200 nm).

To investigate the finer details of protein organization and to determine if NA in fluorescent viruses is clustered, as suggested by electron microscopy (Calder et al., 2010; Harris et al., 2006), we use two-color stochastic optical reconstruction microscopy (STORM) to reconstruct images of HA and NA with resolution ~10X better than the diffraction limit (~30 nm compared to ~300 nm; Figure 1—figure supplement 2). At this resolution, we find that the tendency of NA to concentrate at one of the viral poles is pronounced even in particles smaller than 300 nm in length (Figure 1C). We also find that the NA seen at low levels along the length of filamentous viruses without super-resolution imaging is actually organized into small NA clusters (Figure 1D). Additionally, HA and NA distributions in these particles are modestly anti-correlated (Figure 1E), suggesting that receptor-binding and receptor-destroying activities may be spatially segregated on some regions of the virus. Collectively, these measurements present a picture of a variegated IAV envelope whose spatial organization is stable and coupled to the presence and location of the viral genome, with ~70% of NP-containing viruses having NA biased to the proximal pole.

Figure 1—figure supplement 2.

Determining the resolution of virus images reconstructed using STORM.

(A) Image of overlapping filamentous viruses acquired using diffraction-limited microscopy (shown with inverted contrast). (B) STORM reconstruction of the same image as in (A), with indicated cross sections i and ii. (C) To estimate the resolution of STORM reconstructions, we compare the collected STORM cross-sections of filamentous viruses (i and ii) to simulated cross-sections, where uniformly distributed molecules on the viral surface are each modeled as a gaussian whose standard deviation serves as a fit parameter. The standard deviation that yields the best fit with our data (~30 nm) provides an approximate resolution for our reconstructions.

We next investigated whether spatial organization of the IAV envelope could have functional significance for virus binding and detachment. As a first test of this idea, we compared the spatial organization of viruses that were released from the cell surface with those that remained attached after challenging the virus with the neuraminidase inhibitor (NAI) oseltamivir carboxylate (He et al., 1999) (Materials and methods). To quantify virus spatial organization, we defined HA-NA polarity as the separation between the center of masses for HA and NA divided by the virus length. Interestingly, viruses that escaped the cell surface under NAI challenge showed higher HA-NA polarity (0.155; 95% confidence interval, CI = 0.139–0.171) than viruses released in the absence of NAI challenge, both before (0.123; 95% CI = 0.106–0.140) and after (0.100; 95% CI = 0.090–0.110) facilitating virus release by treating with exogenous sialidase (Figure 1F & G). In comparison, viruses treated with both NAI and exogenous sialidase have polarities similar to the untreated virus (0.124; 95% CI = 0.111–0.136). These results suggest that viruses with NA concentrated at one of the viral poles may be more effective at navigating environments rich in sialic acid.

Polarized viruses step persistently away from their NA-rich pole

To directly test how NA polarization might affect virus motion, we characterized interactions between fluorescently-labeled viruses and sialic acid coated coverslips, where the well-defined geometry and density of sialic acid allow straightforward analysis of virus diffusion (Materials and methods) (Figure 2A). Because our approach to fluorescently labeling NA preserves its activity (Figure 2—figure supplement 1) and viruses harboring fluorophores on both HA and NA preserve ~85% of their infectivity (Vahey and Fletcher, 2019), we could carry out functional assays with the virus and at the same time visualize HA and NA distributions on the viral membrane. Surprisingly, the motion we observed did not resemble randomly-oriented diffusion of the viral particle, but rather persistent, Brownian ratchet-like diffusion, in which filamentous particles with polarized distributions of NA exhibited directed mobility away from their NA-rich pole (Figure 2B, Video 1). Labeling coverslips with fluorescein-labeled Erythrina cristagalli lectin (ECL), which binds specifically to the terminal galactose exposed following sialic acid cleavage (Iglesias et al., 1982), revealed the history of virus trajectories and confirmed that virus motion is accompanied by receptor destruction (Figure 2B). Aligning the trajectories of mobile particles (defined as those with diffusion coefficients > 100 nm2/s, comprising ~68% of the population; Figure 2C) to the orientation of their HA-NA axis reveals that directional mobility can persist for several microns, many times the length of the particle itself (Figure 2D). Consistent with the observation that polarized distributions of HA and NA serve as a determinant for persistent motion and enhanced diffusion, we find that non-mobile viruses have, on average, less polarized distributions of HA and NA than mobile ones (Figure 2E).

Figure 2.

Filamentous IAV diffuses via a Brownian ratchet mechanism.

(A) Labeled viruses are placed on coverslips passivated with PEG2K and functionalized with biotinylated fetuin, which provides a high density of receptors for HA and substrates for NA that can be imaged using TIRF microscopy. (B) Time series of a virus migrating in the direction of its higher-HA pole (scale bar = 2 µm). Viruses exhibiting persistent motion on sialic acid-coated surfaces leave a trail of terminal galactose to which fluorescent ECL binds, indicating NA cleavage of receptors as the virus moves. (C) Distribution of virus diffusion coefficients determined by measuring the mean squared displacement versus time. Mobile particles are defined as those with diffusion coefficients > 100 nm2/s, corresponding to the more diffusive subset of the bimodally-distributed population. (D) Trajectories (measured from timelapse images) of n = 347 mobile viruses registered to their initial positions and aligned based on the orientation of the HA-NA axis. Blue and red dots at y = 0 show the median positions of HA and NA (with median separation of ~130 nm), respectively, across all viruses. Data is pooled from three biological replicates. (E) HA-NA organization correlates with virus mobility. Populations exhibiting little motion (‘non-mobile’) have significantly less polarized distributions of HA and NA than those that exhibit persistent directional mobility (quantification of n = 163 non-mobile and n = 347 mobile viruses combined from three biological replicates; boxes are centered on median values and span from 25th to 75th percentile; p-value calculated using a two-sample KS-test). See also Figure 2—source data 1.

The activity of viruses labeled with Alexa fluor 555 following the same protocol used for imaging experiments matches that of unlabeled samples. Data is from three biological replicates, normalized within each replicate to the MUNANA signal from unlabeled samples at the final timepoint. Under these labeling conditions, the efficiency of fluorophore attachment to NA is >50% (Vahey and Fletcher, 2019).

Figure 2—figure supplement 1.

Effect of fluorescent labeling on NA activity.

The activity of viruses labeled with Alexa fluor 555 following the same protocol used for imaging experiments matches that of unlabeled samples. Data is from three biological replicates, normalized within each replicate to the MUNANA signal from unlabeled samples at the final timepoint. Under these labeling conditions, the efficiency of fluorophore attachment to NA is >50% (Vahey and Fletcher, 2019).

Video 1.

Montage of IAV particles acquired using total internal reflectance microscopy at 30 s intervals.

Labeled HA is shown in blue and labeled NA is shown in red. Panels are shown at equivalent scales.

To determine if polarized distributions of NA were necessary for persistent directional mobility, we disrupted the spatial organization of viral surface proteins by removing the cytoplasmic tail of NA (residues 2–6; Figure 3—figure supplement 1A) and rescuing a tagged variant of the virus (NAΔCT). Although NA expression on the surface of infected cells is comparable to that seen for wildtype virus, deletion of the cytoplasmic tail reduces packaging of NA into virions (Figure 3—figure supplement 1B & C). Additionally, although NAΔCT virus imaged at ~30 nm resolution using STORM still exhibits NA clusters resembling those found in wildtype virus, the clusters are no longer immobilized on the surface of the virus, as revealed by photobleaching experiments (Figure 3—figure supplement 2A & B). Although both wildtype and NAΔCT viruses form filamentous particles (with wildtype virus producing a higher proportion of extremely large filaments >3 μm in length; Figure 3—figure supplement 2C), the polarity of NA distributions on the viral surface is significantly decreased in the NAΔCT strain relative to wildtype (Figure 3—figure supplement 2D). Using the same sialic acid coated surfaces, NAΔCT viruses no longer exhibited persistent directional motion (Figure 3A), indicating that spatial organization of NA at the poles is necessary for persistent mobility of IAV.

Figure 3—figure supplement 1.

Characterization of influenza A virus lacking the NA cytoplasmic tail (NAΔCT).

(A) Construction of an NA segment lacking the N-terminal cytoplasmic tail. Mutating the initial methionine and inserting a new start codon following the coding regions important for vRNA packaging enables the rescue of recombinant virus. (B) NAΔCT virus is expressed on the cell surface but is incorporated into budding viruses (indicated with arrow) less efficiently than wildtype NA (lower panels) (scale bar = 5 µm). (C) Imaging released virions supports this observation, with NA incorporation per virion reduced ~ three fold with the deletion of the cytoplasmic tail (scale bar = 10 µm).

Figure 3—figure supplement 2.

Characterization of NAΔCT virus organization and dynamics in the viral membrane.

(A) STORM imaging of NAΔCT reveals a clustered distribution of HA and NA on the surface of filamentous viruses, similar to the nanometer-scale organization of wildtype virus (scale bar = 200 nm). (B) In contrast to viruses with wildtype NA, photobleaching of NAΔCT on filamentous viruses shows partial recovery on the timescale of several minutes, indicating that the enzyme is at least partially mobile. Results are representative of recovery for n = 8 bleached viruses. (C) Particle length distributions for viruses with wildtype NA and NAΔCT, showing similar proportions of particles for lengths below ~ 3 μm. Viruses with wildtype NA produce a significantly higher proportion of particles larger than 3 μm. Plots show the mean and standard deviation for four biological replicates (*: p<0.05; **: p<0.01; ***: p<0.001, determined from a two-sample t-test). (D) Distributions of HA-NA polarity in wildtype and NAΔCT viruses. Boxes show median values and 25th-75th percentile ranges for each of four biological replicates; P-value is determined from a two-sample t-test.

Figure 3.

Organization of the IAV envelope and diffusion of sialic acid receptors determine persistence of directional mobility.

(A) NAΔCT virus create ECL tracks that appear less persistent than those generated by virus with wildtype NA. Red arrows indicate tracks where viruses have dissociated (scale bar = 2 µm). (B) Mean squared displacements (MSD) for mobile viruses with (left) and without (right) the NA cytoplasmic tail for an observation window of 1500 s. Measured MSDs (in magenta and green) are compared to MSDs from simulated random walks (gray) using the same step size and frequency but with uncorrelated direction. Dark lines show median MSDs, and shaded regions indicate the 25th to 75th percentile range. Data is pooled from three biological replicates. (C) Histogram of virus stepping angle relative to virus orientation for virus with wildtype NA (magenta) and NAΔCT (green). While wildtype viruses step in the direction of increasing HA roughly twice as frequently as they step in the opposite direction, virus with NAΔCT do not exhibit any orientational preference. Data is pooled from three biological replicates. (D) Quantification for n = 10 simulations of 250 nm viruses with uniform (green) or polarized (magenta) distributions of NA, showing the tendency of viruses to step preferentially along the viral axis only when NA is polarized. The model of an idealized virus is in general agreement with experimental results shown in (C) that include viruses with a wide variety of sizes and HA-NA organizations. (E) The effects of receptor (i.e. sialic acid) diffusion on virus motion. Allowing surface diffusion of sialic acid during simulations can enhance or suppress directional motion of viruses, depending on the receptor mobility and the catalytic rate of NA. Up to a diffusion coefficient of ~0.01 µm2/s, typical of a slowly diffusing membrane protein, a directional bias in virus diffusion is preserved. Trajectories show the results of 10 simulations for each condition in black, with the average is red. See also Figure 3—source data 1.

(A) Construction of an NA segment lacking the N-terminal cytoplasmic tail. Mutating the initial methionine and inserting a new start codon following the coding regions important for vRNA packaging enables the rescue of recombinant virus. (B) NAΔCT virus is expressed on the cell surface but is incorporated into budding viruses (indicated with arrow) less efficiently than wildtype NA (lower panels) (scale bar = 5 µm). (C) Imaging released virions supports this observation, with NA incorporation per virion reduced ~ three fold with the deletion of the cytoplasmic tail (scale bar = 10 µm).

(A) STORM imaging of NAΔCT reveals a clustered distribution of HA and NA on the surface of filamentous viruses, similar to the nanometer-scale organization of wildtype virus (scale bar = 200 nm). (B) In contrast to viruses with wildtype NA, photobleaching of NAΔCT on filamentous viruses shows partial recovery on the timescale of several minutes, indicating that the enzyme is at least partially mobile. Results are representative of recovery for n = 8 bleached viruses. (C) Particle length distributions for viruses with wildtype NA and NAΔCT, showing similar proportions of particles for lengths below ~ 3 μm. Viruses with wildtype NA produce a significantly higher proportion of particles larger than 3 μm. Plots show the mean and standard deviation for four biological replicates (*: p<0.05; **: p<0.01; ***: p<0.001, determined from a two-sample t-test). (D) Distributions of HA-NA polarity in wildtype and NAΔCT viruses. Boxes show median values and 25th-75th percentile ranges for each of four biological replicates; P-value is determined from a two-sample t-test.

(A) Enzymatic activity (measured using MUNANA) for intact virus samples with wildtype and NAΔCT, normalized by relative NA content (determined by imaging fluorescently labeled virus). Rates of MUNANA turnover per molecule of NA do not change significantly when the cytoplasmic tail of NA is deleted. Quantification shows the mean and standard error for three biological replicates; p-value determined by a two-sample t-test. (B) Evaluating the NA-activity of individual viruses against immobilized substrates: viruses bound to fetuin-functionalized coverslips were incubated for 30 min before labeling with fluorescent ECL to quantify cleavage of sialic acid. Normalizing by NA content (measured by imaging fluorescently-labeled NA) shows a significant increase in the activity of NAΔCT relative to NA(wt) for immobilized substrates. Boxes indicate the median and 25th / 75th percentile measured from two biological replicates with a total of 198 viruses (NA wildtype) and 130 viruses (NAΔCT); p-value determined using a two-sample KS test. (C) and (D) show quantification of the same data, without normalizing for NA abundance for the entire sample (in C) or for individual viruses (in D).

(A) Orientation of cylindrical (left) and spherical (right) viral particles on flat surfaces. Of the three rotational degrees of freedom, only rotations in θ are considered in our simulations of cylindrical particles. Rotations in ψ (which reduce the number of possible HA-SA interactions) are not observed experimentally. Rotations in φ likely occur, but do not appreciably change the number and location of HA and NA molecules proximal to the surface. Unlike cylindrical particles, spherical particles would be expected to rotate about θ, φ, and ψ, leading to variations in the number of NAs proximal to the surface over time which would tend to randomize the virus’s direction of motion. As a result, spherical particles are not included in simulations. (B) Model for virus binding and diffusion accounting for multivalent adhesion of HA, turnover of proximal substrates by NA, and thermal fluctuations constrained by the position of bound HA-SA pairs. The virus is modeled as a lattice of HA and NA whose distributions are specified. Viruses interact with uniform distributions of sialic acid on a two-dimensional surface. HAs within a 7.5 nm radius of sialic acid can probabilistically bind, while NAs within the same radius can probabilistically cleave. Once bound, virus position and bearing can change only if it does not displace any bound HA-SA pairs beyond the specified binding radius (7.5 nm in these simulations).

(A) Polarized distributions of NA (top) result in sialic acid distributions (middle) that decrease abruptly within a few tens of nanometers of the NA cluster. While most HAs sample from uniform distributions of sialic acid, those close to the NA cluster have a higher probability of binding to sialic acid that is offset from the HA in the positive direction. This results in a persistent bias in the direction of virus diffusion (bottom). (B) and (C) Similar to (A), but with two different types of symmetric NA distributions. In both cases, symmetric distributions of sialic acid (middle) result in symmetric random walks (bottom). In the one-dimensional case shown here, although the probability of positive or negative displacements are initially equal, once a displacement direction is established, the virus will tend to continue in the established direction. (D) Results of two-dimensional simulations with varying organizations of NA on the virus’s surface. Although localizing NA to one of the virus’s poles (‘polar’ configuration) produces consistent oriented motion, introducing separation between HA-rich and NA-rich regions with the same NA localization (‘gapped’ configuration) eliminates this tendency, as does localizing NA to both viral poles (‘bipolar’ configuration). Having moderately polarized NA distributions also suppresses oriented motion, since this configuration (‘off-center’) creates gradients in sialic acid density that partially cancel out over the length of the virus. Finally, NA in a random, uniformly-distributed configuration results in diffusion in a random direction. Because this configuration leads to more efficient cleavage of sialic acid and thus weaker virus adhesion, these simulations were performed with a lower number of total NAs (6 vs. 16) to prevent the virus from detaching altogether.

Simulations of virus mobility for particles 185 nm, 285 nm, and 385 nm in length, corresponding to 48, 84 and 115 simulated HA trimers per particle, respectively. For a constant HA-SA binding affinity, the average number of bound SA increases proportional to the number of HAs proximal to the surface (top row; average number of bound HA-SA pairs (±S.D.) is given in the upper-right corner of each panel), resulting in substantially reduced mobility in larger particles. If binding affinity is varied (by varying the HA-SA off rate, k) to maintain a constant number of average bound HA-SA pairs for viruses of different size, larger particles exhibit higher mobilities with greater angular persistence (bottom row). Virus trajectories and orientations are plotted from seven simulations comprised of 106 steps for each of the six conditions shown. Traces plotted in red correspond to averages across all simulations.

Correlations in the direction of successive steps in a random walk will increase the rate at which the walker explores its environment, analogous to how more rigid polymer chains typically have larger end-to-end distances than those with equivalent contour length but lower bending rigidity. To quantitatively compare the rate at which the less persistent NAΔCT viruses explore their environment to that of the more persistent wildtype virus, we determined mean squared displacements from observed trajectories of mobile viruses (diffusion coefficients > 100 nm2/s over the observation period) and simulated random walks in which the number of steps, the size of each step, and the time interval between steps all match the observed data, but the direction of each step is uncorrelated with previous steps (Figure 3B). From this analysis, we estimate that the diffusion coefficients of mobile viruses are enhanced approximately five-fold as a result of directional correlations (Figure 3B, left). In contrast, the diffusion of mobile NAΔCT viruses is only modestly influenced by directional correlations (~1.5 fold increase; Figure 3B, right). To further quantify the difference between viruses with wildtype NA and NAΔCT, we tracked the displacement of particles between subsequent frames acquired 30 s apart and plotted the distribution of the stepping angle relative to the orientation of the virus’s HA-NA axis. Viruses with wildtype NA step in the direction of increasing HA (with the NA pole at the rear) roughly twice as frequently as the opposite direction, while NAΔCT viruses exhibit no correlation between orientation and stepping direction (Figure 3C). Additionally, despite having lower amounts of NA in the viral membrane, NAΔCT viruses left significantly larger trails of cleaved receptors than their wildtype counterparts (Figure 3—figure supplement 3). These results demonstrate that the spatial organization and mobility of NA on the viral surface both play an important role in determining where and when it cleaves substrate.

Figure 3—figure supplement 3.

Comparison of NA activities for soluble and immobilized substrates.

(A) Enzymatic activity (measured using MUNANA) for intact virus samples with wildtype and NAΔCT, normalized by relative NA content (determined by imaging fluorescently labeled virus). Rates of MUNANA turnover per molecule of NA do not change significantly when the cytoplasmic tail of NA is deleted. Quantification shows the mean and standard error for three biological replicates; p-value determined by a two-sample t-test. (B) Evaluating the NA-activity of individual viruses against immobilized substrates: viruses bound to fetuin-functionalized coverslips were incubated for 30 min before labeling with fluorescent ECL to quantify cleavage of sialic acid. Normalizing by NA content (measured by imaging fluorescently-labeled NA) shows a significant increase in the activity of NAΔCT relative to NA(wt) for immobilized substrates. Boxes indicate the median and 25th / 75th percentile measured from two biological replicates with a total of 198 viruses (NA wildtype) and 130 viruses (NAΔCT); p-value determined using a two-sample KS test. (C) and (D) show quantification of the same data, without normalizing for NA abundance for the entire sample (in C) or for individual viruses (in D).

Computational modeling suggests a mechanism for persistent directional mobility

To investigate the mechanism of this persistent directional mobility, we modeled the diffusion of filamentous viruses with defined spatial organization of HA and NA as the viruses bind to sialic acid on a two-dimensional surface (Appendix, Figure 3—figure supplement 4). In this model, virus diffusion is constrained by the tens of attachments the HAs form with sialic acid at any instant in time (English and Hammer, 2004; Xu and Shaw, 2016). At the interface between HA-rich and NA-rich regions of the viral membrane, like those seen in fluorescence images (Figure 1A–D), NA will periodically gain access to, and hydrolyze, sialic acid as the position of the virus thermally fluctuates. The binding partners available to HAs located close to this interface will therefore be asymmetrically distributed, with more potential binding partners located further away from areas of higher NA density. This concept is illustrated most clearly in the case where the virus is constrained to a one-dimensional path. As this case illustrates, the sialic acid density along the length of the virus is lowest in close proximity to NA (Figure 3—figure supplement 5A–C). When NA is confined to a single pole, this results in a gradient in sialic acid density along the length of the particle. Since the number of available binding partners is higher in one direction than another, newly-formed HA-sialic acid bonds will tend to be offset from previous bonds in this direction. This leads to the persistent motion we observe experimentally as well as in our simulations (Video 2). Consistent with this mechanism, persistent motion of the virus is lost when NA is no longer localized to a single virus pole and the sialic acid distribution beneath the particle becomes symmetric (Figure 3—figure supplement 5B–D). We note that virions with symmetric distributions of NA also occur in the virus population and would not be expected to exhibit persistent motion (for example, those particles with NA polar ratios of ~1 in Figure 1A).

Figure 3—figure supplement 4.

simulation scheme.

(A) Orientation of cylindrical (left) and spherical (right) viral particles on flat surfaces. Of the three rotational degrees of freedom, only rotations in θ are considered in our simulations of cylindrical particles. Rotations in ψ (which reduce the number of possible HA-SA interactions) are not observed experimentally. Rotations in φ likely occur, but do not appreciably change the number and location of HA and NA molecules proximal to the surface. Unlike cylindrical particles, spherical particles would be expected to rotate about θ, φ, and ψ, leading to variations in the number of NAs proximal to the surface over time which would tend to randomize the virus’s direction of motion. As a result, spherical particles are not included in simulations. (B) Model for virus binding and diffusion accounting for multivalent adhesion of HA, turnover of proximal substrates by NA, and thermal fluctuations constrained by the position of bound HA-SA pairs. The virus is modeled as a lattice of HA and NA whose distributions are specified. Viruses interact with uniform distributions of sialic acid on a two-dimensional surface. HAs within a 7.5 nm radius of sialic acid can probabilistically bind, while NAs within the same radius can probabilistically cleave. Once bound, virus position and bearing can change only if it does not displace any bound HA-SA pairs beyond the specified binding radius (7.5 nm in these simulations).

Figure 3—figure supplement 5.

Virus organization, sialic acid distributions, and virus trajectories in one- and two-dimensional simulations.

(A) Polarized distributions of NA (top) result in sialic acid distributions (middle) that decrease abruptly within a few tens of nanometers of the NA cluster. While most HAs sample from uniform distributions of sialic acid, those close to the NA cluster have a higher probability of binding to sialic acid that is offset from the HA in the positive direction. This results in a persistent bias in the direction of virus diffusion (bottom). (B) and (C) Similar to (A), but with two different types of symmetric NA distributions. In both cases, symmetric distributions of sialic acid (middle) result in symmetric random walks (bottom). In the one-dimensional case shown here, although the probability of positive or negative displacements are initially equal, once a displacement direction is established, the virus will tend to continue in the established direction. (D) Results of two-dimensional simulations with varying organizations of NA on the virus’s surface. Although localizing NA to one of the virus’s poles (‘polar’ configuration) produces consistent oriented motion, introducing separation between HA-rich and NA-rich regions with the same NA localization (‘gapped’ configuration) eliminates this tendency, as does localizing NA to both viral poles (‘bipolar’ configuration). Having moderately polarized NA distributions also suppresses oriented motion, since this configuration (‘off-center’) creates gradients in sialic acid density that partially cancel out over the length of the virus. Finally, NA in a random, uniformly-distributed configuration results in diffusion in a random direction. Because this configuration leads to more efficient cleavage of sialic acid and thus weaker virus adhesion, these simulations were performed with a lower number of total NAs (6 vs. 16) to prevent the virus from detaching altogether.

Video 2.

Simulation of a filamentous virus ~ 250 nm in length with a polarized distribution of HA (blue) and NA (red) on its surface bound to a surface coated with sialic acid (green).

Since virus motion is dependent on the virus’s ability to establish distinct regions with and without sialic acid, we expect that dynamic distributions of either NA or sialic acid will alter virus mobility. Consistent with our experimental observations with the NAΔCT virus, allowing NA to freely diffuse eliminates directional bias in virus motion in both experiments (Figure 3C) and simulations (Figure 3D). However, allowing surface-bound sialic acid to diffuse can either enhance or suppress directed motion, depending on the kinetic parameters of HA and NA, the diffusion coefficient of sialic acid (D), and the size of the virus (Appendix). Although our experimental data corresponds to the case where sialic acid is immobilized (D ~0), our simulations predict that polarized IAV bound to slowly-diffusing transmembrane glycoproteins or gel-forming mucins (D <0.01 µm2/s) will exhibit enhanced directed mobility, likely due to the biased spatial distributions of sialic acid binding partners that are available to HAs distal to the virus’s NA-rich pole. In contrast, increasing the rate of sialic acid diffusion further (D >0.01 µm2/s) is expected to suppress persistent motion, by flattening the sialic acid gradient that is created beneath the virus (Figure 3E, Video 3). Finally, we find that increasing the length of the virus in simulations increases the number of HA-sialic acid interactions, reducing virus mobility but increasing directional persistence (Figure 3—figure supplement 6). Thus, the organization of proteins on the viral surface, the size of the virus, and the nature of the receptor to which the virus is bound can each influence the persistence of a virus’s motion.

Video 3.

Simulations of polarized filamentous viruses on surfaces with freely-diffusing sialic acid (green).

Simulations correspond to D = 10−7 μm2/s, D = 10−5 μm2/s, and D = 10−1 μm2/s, as plotted in Figure 3E.

Figure 3—figure supplement 6.

Contributions of virus morphology and binding affinity to persistent mobility.

Simulations of virus mobility for particles 185 nm, 285 nm, and 385 nm in length, corresponding to 48, 84 and 115 simulated HA trimers per particle, respectively. For a constant HA-SA binding affinity, the average number of bound SA increases proportional to the number of HAs proximal to the surface (top row; average number of bound HA-SA pairs (±S.D.) is given in the upper-right corner of each panel), resulting in substantially reduced mobility in larger particles. If binding affinity is varied (by varying the HA-SA off rate, k) to maintain a constant number of average bound HA-SA pairs for viruses of different size, larger particles exhibit higher mobilities with greater angular persistence (bottom row). Virus trajectories and orientations are plotted from seven simulations comprised of 106 steps for each of the six conditions shown. Traces plotted in red correspond to averages across all simulations.

Although our results focus on a two-dimensional geometry in which a virus adheres to a flat surface decorated with sialic acid (a model for the cell surface), we reasoned that they should generalize to three-dimensional systems, such as the secreted mucus gel through which IAV must penetrate to reach naïve cells to infect. To test this prediction, we cultured Calu-3 cells at an air-liquid interface, resulting in a ~ 1–10 µm thick gel of secreted mucus overlaying the apical surface of the cells (Figure 4A). Adding virus with labeled HA and NA to the mucus, followed by fixation and labeling with ECL, revealed tracks similar to those we observed on two-dimensional surfaces (Figure 4B). This suggests that the asymmetry of NA and HA biases the direction of virus diffusion in three dimensions, producing trails of cleaved sialic acid that can reach several microns in length as the virus moves (Figure 4C). This directional mobility is less prevalent in viruses with more uniformly distributed NA, though they nonetheless are capable of creating swaths of ECL-staining within the mucus that lack clear directionality (Figure 4D). Similar to our observations on idealized two-dimensional surfaces (Figure 3C), we find that viruses in mucus exhibit a slight tendency to align their HA-NA axis (captured at the moment of fixation) with the displacement of the virus relative to the center of ECL labeling (Figure 4E). These results suggest that the spatial organization of HA and NA on the virus surface may also promote penetration of polarized IAV particles through mucus barriers in vivo.

Figure 4.

IAV exhibits persistent directional mobility in native mucus gels.

(A) Calu-3 cells cultured at an air-liquid interface for ~10 days partially differentiate, secreting gel-forming mucins (visualized with an antibody against Muc5AC). (B) Labeled virus (fluorescent HA and NA) added to the mucosal surface bind and diffuse. Labeling with ECL reveals distinct tracks trailing up to several microns behind viruses (indicated with white arrows). The angle θ characterizes the alignment between the HA-NA axis of the virus and the displacement from the centroid of ECL labeling. (C) Panels (i-viii) of registered viruses showing ECL trails. Where polarized distributions of NA on the virus are visible (i, v, vi, viii), ECL labeling is most prominent trailing from the NA-rich pole. Calculated values of the angle θ are given in the lower left of each panel. (D) Larger filamentous virions with NA broadly distributed across their surfaces clear large patches of sialic acid on the mucosal surface. Images in B, C, and D are a representative sampling from three biological replicates. (E) Orientation of virus HA-NA axes with ECL displacement vectors (the angle θ defined schematically in B). The distribution shows alignments for 2242 virus particles with non-overlapping ECL tracks, pooled from three biological replicates. Dashed line indicates the expected level for a uniform random distribution of alignments. See also Figure 4—source data 1.

(A) Viruses entering the trachea (e.g. as aerosolized respiratory droplets) bind to the mucosal barrier. The probability of a particle infecting the underlying epithelium depends on the relative rates of virus diffusion (with a characteristic time of ~Δ2/D) and mucociliary clearance (with a characteristic time of ~ L/U). (B) Solutions to the first passage problem illustrated in panel (A) for different virus diffusion coefficients. Each curve plots the solution to Equations (6) and (7) with a different virus diffusion coefficient, starting from 80 nm2/s (black curve) and increasing by intervals of 80 nm2/s (gray or magenta curves). Black and magenta curves correspond to diffusion coefficients measured from Figure 3B with correlated (magenta) or uncorrelated (black) steps. The inset box shows physiological estimates of other parameters used to solve the first-passage problem.

Discussion

Advances in electron microscopy over the past several decades have presented an increasingly detailed picture of the morphology and organization of influenza A virus. This has revealed organizational features of filamentous IAV whose origins and functional significance remain unclear. By using site-specific fluorescent labeling to measure the organization of a virus while preserving its function, we are able to corroborate key observations of envelope protein non-uniformity obtained from electron microscopy and extend them to dynamic observations of both protein motion on the surface of the virus, as well as the directionally-persistent motion of influenza A virus particles as they engage with the virus receptor, sialic acid. These observations demonstrate that the morphology of a virus, the spatial organization of proteins in its membrane, and constraints on the diffusion of these proteins collectively confer the tendency for viral particles to exhibit persistent directional mobility, enhancing the virus’s rate of diffusion without diminishing its strength of adhesion. Importantly, our simulations show that this feature of filamentous morphology would not be limited to the extremely large particles whose length is easily measured using diffraction limited microscopy, but rather extends to capsule-shaped particles < 200 nm in length (Figure 3—figure supplement 6), which are known to be produced by filamentous strains of IAV (Calder et al., 2010). In contrast, this effect may be suppressed in spherical particles, due both to the absence of a clear axis for polarization and their ability to roll on two-dimensional surfaces. However, super-resolution live imaging will be required to confirm these predictions experimentally.

Although more work is needed to understand if these characteristics of IAV organization enhance infectivity and are adaptive during in vivo replication, a simple model of viral transport suggests how the directional viral mobility we observe could contribute during host-to-host transmission. On first entering the respiratory tract, viruses must diffuse through the mucosal barrier to infect the underlying airway epithelial cells. Competing with this process is mucocilliary clearance, which carries particles bound to mucus towards the pharynx where they are neutralized (Figure 4—figure supplement 1A). If the transport of a virus through a mucosal barrier is modeled as a first passage problem with a time limit imposed by the rate of mucociliary clearance, a five-fold increase in the diffusion coefficient (as predicted in Figure 3B) would lead to a proportional reduction in the first passage time. More dramatically, it would also increase the fraction of particles that reach the cell surface when the rate of mucociliary clearance is high and would normally prevent most particles from reaching the cell surface. Our experiments on two-dimensional sialic acid-coated surfaces suggest a diffusion coefficient of ~800 nm2/s for polarized viruses; adapting this value for a one-dimensional first passage model suggests that persistent motion increases the number of particles that reach the epithelium before being cleared by several orders of magnitude (Appendix, Figure 4—figure supplement 1B). Although there are multiple ways that a virus’s passage through mucus can be accelerated (reducing its size, decreasing the number of HAs on its surface, increasing the number of NAs, or making the spatial distribution of NA more uniform), each of these changes would likely reduce binding stability once the virus has managed to reach the cell surface. In contrast, the polarized distributions of NA on the viral surface that we observe increase virus diffusion without compromising binding stability.

Figure 4—figure supplement 1.

A first passage model for virus transport in mucus.

(A) Viruses entering the trachea (e.g. as aerosolized respiratory droplets) bind to the mucosal barrier. The probability of a particle infecting the underlying epithelium depends on the relative rates of virus diffusion (with a characteristic time of ~Δ2/D) and mucociliary clearance (with a characteristic time of ~ L/U). (B) Solutions to the first passage problem illustrated in panel (A) for different virus diffusion coefficients. Each curve plots the solution to Equations (6) and (7) with a different virus diffusion coefficient, starting from 80 nm2/s (black curve) and increasing by intervals of 80 nm2/s (gray or magenta curves). Black and magenta curves correspond to diffusion coefficients measured from Figure 3B with correlated (magenta) or uncorrelated (black) steps. The inset box shows physiological estimates of other parameters used to solve the first-passage problem.

Balancing the need to robustly attach to the cell surface with the need to escape immobilization in host mucus is a fundamental challenge for influenza and other viruses that use sialic acid to enter cells. Previous work has shown that influenza particles are not immobilized during their interactions with sialic acid (Guo et al., 2018; Sakai et al., 2017). Additionally, work characterizing the diffusion of influenza C virus, in which receptor-binding and receptor-destroying activities are combined in a single protein (HEF), has shown that the destruction of receptors on a 2D surface prevents virions from retracing their steps (Sakai et al., 2018). Our results show that influenza A virus accomplishes a similar feat through a different mechanism – asymmetric distribution of receptor-binding and receptor-destroying activities on the viral surface that results in persistent motion while maintaining stable attachment. The persistent directional mobility shown here orients filamentous virus motion parallel to its major axis, where it could further enhance the anomalous diffusion coefficients observed for cylindrical nanoparticles in mucus (Yu et al., 2016). Analogous to the combined importance of active motility and cell shape in mucosal bacteria (Bartlett et al., 2017; Sycuro et al., 2010), the directed mobility and cylindrical shape of IAV could help to explain the virus’s ability to penetrate host mucus and contribute to the prevalence of filamentous morphology in clinical isolates of IAV.

Materials and methods

Culturing and labeling virus

Strains of influenza A virus amenable to site specific labeling on HA, NA, and NP were designed and characterized as described previously (Vahey and Fletcher, 2019). Briefly, viruses expressing HA with five consecutive glycine residues following the signal sequence (for labeling via Sortase A [Theile et al., 2013]), NA with a c-terminal ybbR tag (for labeling via Sfp [Yin et al., 2006]), and NP with a c-terminal tetracysteine motif (for labeling via direct binding of the biarsenical dye FlAsH [Griffin et al., 1998]) were rescued using reverse genetics (Hoffmann et al., 2000) by transfecting co-cultures of HEK293T and MDCK-II cells with plasmids encoding each of the eight genomic segments under the control of bidirectional promoters. Cells used in this work were obtained and authenticated by the UC Berkeley Cell Culture Facility and tested negative for mycoplasma. All viruses used in this work are derived from A/WSN/1933, with the WSN M1 gene replaced by that of A/Udorn/1972, to establish the filamentous phenotype. MDCK-II cells used to propagate virus were maintained in DMEM supplemented with 10% fetal bovine serum (Thermo Fisher, 10438026) and 1x penicillin/streptomycin (Thermo Fisher, 15140122). Prior to infection, confluent monolayers of cells were washed once with PBS, and serum-containing growth media was replaced with virus growth media (MEM, 0.25% BSA, 1 μg/ml TPCK-treated trypsin, and penicillin/streptomycin). Viruses used for experiments were collected from cells following infection at MOI ~ 1 and 16 hr of growth at 37°C in virus growth media. Media containing virus was collected, centrifuged at 2000 × g for five minutes to remove cell debris, and treated with 10mU/ml soluble sialidase (from C. Perfringens, Sigma N2876) to ensure that viruses were well dispersed. Viruses were labeled in solution for 90 min at room temperature using NTC buffer (100 nM NaCl, 20 mM Tris pH 7.6, 5 mM CaCl2) supplemented with 5 mM MgCl2, Sortase A (180 µM enzyme, 50 µM CLPETGG peptide) and SFP synthase (5 µM enzyme, 5 µM CoA probe). Following labeling, Capto Core 700 beads (GE Healthcare;~1:1 resin volume to sample volume) were used to remove residual dyes and enzymes from the solution of labeled virus. For labeling NP with the biarsenical dye FlAsH, viruses were immobilized on coverslips, washed in NTC buffer, and incubated with 2 µM FlAsH for 30 min at room temperature.

Virus photobleaching

To qualitatively evaluate the mobility of HA and NA on the virion surface (Figure 1B, Figure 3—figure supplement 2B), unfixed, immobilized viruses were imaged using total internal reflectance (TIRF) microscopy. Filamentous virus of sufficient length (>5 μm) were positioned within the field of view such that when the field stop was closed, only approximately half of the virus was exposed to illumination. This half of the virus was then bleached using maximum laser power and then imaged at lower power at 30 s intervals as the field stop was opened to monitor recovery. Representative results are shown in Figure 1B and Figure 3—figure supplement 2B.

STORM imaging and analysis

Samples for STORM imaging were prepared by binding labeled virus to antibody or sialic acid (i.e. fetuin) functionalized coverslips for one hour at 4°C, followed by the incubation of Dragon Green-labeled 220nm-diameter streptavidin coated beads. Viruses and beads were then fixed to the surfaces with 4% paraformaldehyde in PBS and washed 3x with buffer containing 1M Tris pH 8.0, 5% glucose, and 140 mM β-mercaptoethanol. Following these washes, the buffer was supplemented with glucose oxidase and catalase to final concentrations of 0.6 mg/ml and 0.035 mg/ml, respectively, and mounted on the microscope for imaging.

STORM data was acquired in the following sequence. First, an image of the Dragon Green beads (serving as fiducial marks), HA (labeled with CF568-conjugated peptide and SrtA), and NA (labeled with AF647-CoA and SFP) was acquired, to enable registration. Next, a sequence of STORM images was acquired using a 640 nm laser at full power, with acquisition of the HA channel via a 560 nm laser at low power every 50 frames to correct for drift. After collecting 15000–35000 frames in this way, we performed STORM imaging on CF568-HA using a 560 nm laser. To correct for drift, we acquire an image every 50 frames using a 405 nm laser and 575/20 nm emission filter; these settings allow us to image the Dragon Green beads, while simultaneously accelerating blinking of the CF568 dye. For reconstructions of HA, we acquire 25000–35000 frames.

For quantification and localization of blinking events, we use the ImageJ plugin Thunderstorm (Ovesný et al., 2014), combined with custom Matlab scripts for additional drift correction and removal of fluorophores that remain in the ‘on’ state for more than one frame. This analysis results in a list of coordinates for each localization that we then use to reconstruct images of virus at varying resolutions. Reconstructed STORM images (e.g. Figure 1C & D) are displayed by representing each localization as a gaussian with a standard deviation of 30 nm.

NAI challenge assay

Challenge experiments with the neuraminidase inhibitor oseltamivir are performed as described previously (Vahey and Fletcher, 2019). The analysis from Figure 1E uses an image dataset from Vahey and Fletcher (2019), reanalyzed to measure the spatial organization of HA and NA on the surface of released virus particles. We infect a polarized monolayer of MDCK cells grown on a collagen gel at MOI of 1–3. After incubating cells with virus for one hour at 37°C, we wash to remove excess virus, replacing media with virus growth media supplemented with or without a specified concentration of oseltamivir carboxylate (Toronto Research Chemicals O700980), but without TPCK-treated trypsin. At 16 hr post infection, we remove the virus containing media for labeling and imaging, and replace with media supplemented with 1 U/ml NanI from C. perfringens (Sigma N2876). After treating with this exogenous sialidase for one hour at 37°C, we again collect cell culture media for virus labeling and imaging.

To measure the HA-NA polarization on viruses released in these experiments, we segment filamentous viruses with lengths > 1 μm and measure the intensity-weighted centroid (i.e. ‘center of mass’) for both the HA and NA channels. The vector connecting the NA centroid to the HA centroid defines the orientation of HA-NA polarity. Dividing the magnitude of this vector by the total particle length gives the HA-NA polarity metric plotted throughout this work.

Virus motility assay

Coverslips presenting sialic acid for virus attachment were prepared as described previously (Vahey and Fletcher, 2019). Briefly, NH2-PEG-OH (Rapp Polymere, 122000–2) supplemented with 2.5 mole-percent NH2-PEG-Biotin (Rapp Polymere, 133000-25-20) was conjugated to silanized coverslips for subsequent attachment of sialylated proteins. Following PEGylation, custom PDMS chambers were attached to coverslips, and chambers were incubated for 10 min at room temperature with streptavidin at 50 µg/ml in 150 mM NaCl, 25 mM HEPEs, pH 7.2, and washed 5X in the same buffer. Fetuin (Sigma F3004) labeled with NHS-biotin was then added at 100 nM and incubated ~30 min at room temperature. Coverslips were then washed 5X in NTC buffer and equilibrated to 4°C in preparation for virus binding. For surfaces functionalized in this way (Piehler et al., 2000), we expect a PEG density of ~0.75 molecules/nm2, corresponding to roughly one PEG-Biotin per 7 nm x 7 nm area on the surface. If each biotin is bound by one streptavidin tetramer and subsequently one molecule of biotinylated fetuin (with ~10 sialic acid residues per molecule), the surface density of sialic acid will be ~0.2 SA/nm2.

Viruses with HA and NA enzymatically labeled as described previously were bound to coverslips equilibrated to 4°C for one hour on ice. Immediately before imaging, excess virus was washed with pre-chilled NTC, and the sample was mounted on the microscope stage. After allowing the chamber to equilibrate to room temperature, the buffer in the chamber was exchanged to NA buffer (100 mM NaCl, 50 mM MES pH 6.5, 5 mM CaCl2), and timelapse recordings were collected using TIRF microscopy at 30 s intervals. To visualize trails of cleaved sialic acid, fluorescein-labeled Erythrina cristagalli lectin (ECL; Vector Labs FL-1141) was added to each well at a concentration of 5 µg/ml in NTC buffer with 10 mg/ml BSA and incubated for 30 min at room temperature. During this incubation period, rapid multivalent binding of ECL to cleaved sialic acid on the viral surface effectively blocks further motion of the virus. Images of ECL trails were acquired using TIRF microscopy without washing unbound ECL from the chamber.

To analyze images of viruses, we separately segment each image according to the intensity in both the HA and NA channels. Merging the two sets of segmented images allows us to determine the position of each virus (i.e. the centroid of the combined HA and NA masks) as well as their morphological features. To determine the HA-NA polarization of each virus, we calculate the distance between the centroid of HA and NA intensity for a particular virus, divided by that virus’s length. The data in Figure 2 and Figure 3 is compiled by extracting these features for each virus in each frame of a timelapse recording.

To analyze the trails of cleaved sialic acid left by mobile viruses, we segment images of samples labeled with fluorescent ECL according to the intensity of lectin staining. Because the virus itself contains high densities of sialic acid on its surface, we use a bandpass threshold to specifically quantify ECL bound to processed glycans on the coverslip (which produces a signal brighter than the background, but dimmer than the virus). Although viruses dissociate from some of the tracks, those that remain bound to the surface allow us to connect intensity of ECL labeling on the surface to intensities of HA and NA on the virus that generated the track. This data is plotted in Figure 3—figure supplement 3, with and without normalization to NA intensity.

Quantification of NA activity using MUNANA

For assays using the fluorogenic neuraminidase substrate MUNANA, 10 µl of solution containing labeled virus was diluted into 40 µl of NA buffer (100 mM NaCl, 50 mM MES pH 6.5, 5 mM CaCl2) and incubated in a test tube at 37°C. At time points of 0, 30, 60, 120, and 180 min, aliquots were collected and NA was inactivated by adding sodium carbonate to a final concentration of 100 mM, and fluorescence was measured by imaging a fixed volume of sample on a confocal microscope using excitation at 405 nm. The rate of turnover was then determined from the slope of the intensity versus time plot with the signal at 0 min subtracted from each timepoint. Because strains with wildtype NA and NAΔCT produce different titers of virus that also differ in their morphology and NA composition, we normalized samples to total NA content (Figure 3—figure supplement 3A), measured by immobilizing viruses on coverslips, imaging them, and integrating the total NA intensity associated with the two samples. This yields an estimated 6-fold difference in total NA content between virus with wildtype NA and viruses with NAΔCT.

Virus imaging at air-liquid interface of Calu-3 cell cultures

Calu-3 cells grown on plastic dishes for fewer than 10 passages (DMEM, 10% FBS, 1x penicillin/streptomycin, supplemented with 1 mM sodium pyruvate (Thermo Fisher, 11360070) and 1x non-essential amino acids (Thermo Fisher, 11140076)) were split at 80% confluence and seeded onto 6 mm transwell supports at 50000 cells per insert. Approximately 3 days after seeding, media from the apical compartment was removed (the ‘airlift’) and cells were provided with fresh media in the basal compartment every other day until being collected for experiments, 8–12 days following the airlift. Cells cultured in this way differentiated into a secretory phenotype, producing a layer of mucus ~1–10 µm thick over the apical surface of the monolayer (Figure 4A). To bind virus without washing away secreted mucus, 5–10 µl of labeled virus was added to the apical side of each transwell insert (enough to just coat the surface), and immediately removed, leaving ~1 µl of residual virus-containing solution that is approximately evenly distributed on the surface of the monolayer. The cells were then returned to the incubator for 3–6 hr, allowing virus to bind and diffuse, and allowing some of the residual moisture to dry before the cells are collected and fixed on ice for 20 min using 4% paraformaldehyde in PBS supplemented with 1 mM CaCl2. Following fixation, cells and mucus were labeled with MUC5AC monoclonal antibody (45M1; MA5-12178 ThermoFisher) and Erythrina cristagalli lectin labeled with FITC (5 μg/ml; Vector Laboratories, FL-1141). Following labeling, the transwell insert was carefully excised with a razor blade and inverted onto a coverslip for imaging.

To quantify alignment between the HA-NA axis of a virus particle and the direction of its displacement from the associated ECL track (Figure 4E), fluorescent images of HA, NA, and ECL were segmented in both HA (to identify virus particles) and ECL (to identify regions of cleaved sialic acid) channels. For each segmented virus particle, we calculate the HA-NA axis (defined as the vector displacement between the center of masses for HA and NA) and the ECL displacement (defined as the vector displacement between the center of masses for HA and ECL) (Figure 4B, right). Cases where multiple particles contact the same ECL track are excluded from analysis.

Statistics and replicates

Replicates referenced throughout this paper refer to biological replicates, defined as virus collected from separate infected cell cultures, labeled, and assayed as indicated. No statistical methods were used to predetermine sample size. Image data was excluded from analysis in rare cases if the sample drifted on the microscope stage, or if coverslip preparations showed non-specific virus binding. Statistical tests and the number of replicates used in specific cases are described in figure captions. All statistical tests were performed in Matlab R2017b using the Statistics and Machine Learning Toolbox. Confidence intervals for the data in Figure 1G were calculated using a critical value of the Student’s t distribution of 3.182 (95% confidence interval for n-1 = 3 degrees of freedom).

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Influenza A virus surface proteins are organized to help penetrate host mucus" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Richard A Neher as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Arup Chakraborty as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Mark Zanin (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In this manuscript, Vahey and Fletcher investigate the importance of the spatial organization of hemagglutinin and neuraminidase in facilitating the movement of influenza A viruses (IAVs) through mucus. They report that neuraminidase proteins cluster at one pole of the virus and this clustering asymmetrically frees the virion from tethering to sialic acid, thereby facilitating directed motion. This is an interesting novel finding that provides insights into outstanding questions in the field. However, a number of issues need to be addressed before we can recommend publication.

Essential revisions:

The issues raised during review and the ensuing discussion broadly fall into four categories concerning i) further quantification, ii) the theoretical model, iii) direct assays of infectivity, and (iv) availability of analysis code.

1) A number of statements would benefit from more quantitative analysis.

a) Figure 1 should show distributions of data rather than representative examples. Figure 1A could, for example, be backed up by two histograms showing NA/HA intensities in the first/last 20% of the virion. Or last/first 100nm or similar. Figure 1D needs a distribution of correlation coefficients of HA/NA. Showing one example is simply not enough. Figure 1E should show distributions as well. Bar graphs are not good.

b) Figure 3A-C: While the measurement of the persistence of orientation is useful and interesting, we would like to see a quantitative and direct assessment of diffusivity, for example by comparing MSD vs time for the deletion mutant and the WT NA. It should be quantified whether the morphology of the viruses change when the cytoplasmic tail of NA is deleted. If so, does this affect the ability to determine the orientation of the particles?

c) Figure 4C D: Again, you should try to find a meaningful quantitative comparison rather than just showing examples.

d) Other statements need more precision: E.g. Results section, paragraph one: How were polarized viruses selected? What fraction of particles were filamentous? etc.

2) The theoretical model should be explained more clearly and connected better to the experimental results.

a) We suggest moving some of the descriptions of the model into the main text and maybe include an illustration of the model, the sialic acid distribution along the particle, and the resulting directional motion. It would also help to discuss in more simple and explicit terms that how macroscopic diffusion increases with more directionally persistent microscopic motion.

b) When describing the different physical effects, you should be careful not to confuse the reader with diffusion constants of NA, receptors, and viruses all labeled D. Please differentiate them with different symbols or subscripts.

c) The model prediction that very slow sialic acid diffusion results in slow virus diffusion should be better explained. How does this prediction compare with the experiments in Figure 2 using coverslips functionalized with biotin-anchored (and presumably immobile) receptors?

d) Please point out that Figure 4—figure supplement 1 essentially only shows the first-passage probability calculated in the supplement (please number equations and refer to them directly). This figure could be improved by changing the x-scale seconds -> minutes and illustrating more generally how increasing virus diffusion increases the probability of reaching the epithelium. You could add several curves for different D, plot this on a log scale, and remove the 5000x.

e) The assumption of an absorbing boundary at the epithelium implies that binding is essentially instantaneous and irreversible. This should be discussed.

3) Additional infectivity and/or mobility assays.

a) Could infectivity be assessed directly using mucus-producing cells such as CaLu-3? Such data would considerably strengthen the claim that enhanced diffusion increased infectivity.

b) Enhanced diffusion in polarized filamentous virus compared to spherical viruses is counteracted by their larger size. Is there a head-to-head comparison?

c) The data in this paper was obtained using viruses containing fluorescent labels. Have the authors confirmed these observations using unlabeled viruses? Whilst obviously fluorescent microscopy could not be conducted with unlabeled viruses, the morphology of virions propagated in mucus-producing cells could be compared to those propagated in non-mucus producing cells and studies of their ECL tracks could possibly be conducted. This experiment could add further weight to their statement that filamentous morphologies are adaptations to replication in the presence of mucus.

4) Simulation code and analysis scripts need to be made available, preferably on a repository like GitHub with appropriate documentation.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Influenza A virus surface proteins are organized to help penetrate host mucus" for further consideration at eLife. Your revised article has been favorably evaluated by Arup Chakraborty (Senior Editor) and a Reviewing Editor.

By and large, the authors have presented a compelling revision. They have quantified a number of previously rather qualitative statements by showing distributions rather than representative examples, present additional data on virus mobility directly comparing NA and NA-DCT viruses, streamlined notation, and explained the theoretical model better. However, there are a number of remaining issues that we would like to have clarified/rectified.

Figure 1: the distributions are useful, but the alignment of the NA rich pole with NP seems rather imprecise. Reading the previous version of the manuscript, I was certainly expecting a more clear cut picture. The insets in Figure 1A also suggest a much more pronounced asymmetry. How closely is this more blurry picture accounted for in the simulation?

Figure 1E, Anticorrelation of HA and NA: This anticorrelation is pretty weak and the main take home message from the figure seems to be that HA and NA are clustered. Hence I think the statement "...NA clusters that appear to largely exclude HA" is too strong.

Figure 1G seems problematic. The caption states that p-values are calculated using a two-sample t-test. What enters as independent data point here? Strictly speaking, you have n=4 replicates and I doubt that this would support the conclusion that these cases are different. A paired test on +/- NAI samples from the same replicate would probably be more powerful. Generally, quantifying the difference (and confidence intervals) between two conditions is preferable to rejecting a null.

The addition to Figure 4E does not help. This figure shows that there is little evidence for variation of ECL intensity and HA-NA polarity. Making an arbitrary cut at 0.06 and fitting lines to points below and above is not appropriate. Furthermore, a correlation of ECL intensity and HA-NA polarity doesn't quantify the examples given in C or D. You could try to show ECL intensity distributions aligned with the NA polarity or similar. But as of now, panels C, D, and E are just examples.

The comparisons of diffusion with/without correlations make sense only when you specify the time interval over which the displacements are measured (paragraph three of subsection “Polarized viruses step persistently away from their NA-rich pole”). You do so later, but I'd suggest moving it forward.

Essential revisions:

The issues raised during review and the ensuing discussion broadly fall into four categories concerning i) further quantification, ii) the theoretical model, iii) direct assays of infectivity, and (iv) availability of analysis code.

1) A number of statements would benefit from more quantitative analysis.

a) Figure 1 should show distributions of data rather than representative examples. Figure 1A could, for example, be backed up by two histograms showing NA/HA intensities in the first/last 20% of the virion. Or last/first 100nm or similar. Figure 1D needs a distribution of correlation coefficients of HA/NA. Showing one example is simply not enough. Figure 1E should show distributions as well. Bar graphs are not good.

We have revised Figure 1 to further quantify our results, and to display data as distributions rather than isolated examples or bar graphs. Specifically, in Figure 1A we have added histograms showing relative HA and NA intensities in the outermost ~400nm of each virus particle (since virus lengths vary considerably, we believe that analyzing intensities within a fixed distance of the viral poles is preferable to analyzing within a fixed percentage of virus length). We have also performed this analysis for the subset of the population that contain NP at one end of the particle, by aligning each particle to the NP-containing pole. This analysis (shown in Figure 1A, right) replaces the composite image of a virus population that was previously presented in Figure 1B and which is now included as Figure 1—figure supplement 1.

Additionally, we have added a new panel, Figure 1E, which shows correlation coefficients for spatial distributions of HA and NA from STORM images of 24 viruses. This additional analysis supports the observation that HA and NA are spatially segregated on the surface of the virus. Finally, we have revised our presentation of data from Figure 1E (now Figure 1G in the revised manuscript) to show distributions of data from each of the four biological replicates. We have revised the initial paragraphs of the Results section to reflect these additions, and to streamline and clarify our description of the data.

b) Figure 3A-C: While the measurement of the persistence of orientation is useful and interesting, we would like to see a quantitative and direct assessment of diffusivity, for example by comparing MSD vs time for the deletion mutant and the WT NA. It should be quantified whether the morphology of the viruses change when the cytoplasmic tail of NA is deleted. If so, does this affect the ability to determine the orientation of the particles?

We agree with the reviewer’s suggestion than quantitative comparisons of diffusion coefficients are critical to understand the consequences of deleting the NA cytoplasmic tail. To facilitate this comparison, we have moved the plots showing mean squared displacement versus time for the wildtype virus from Figure 2D (in the previous manuscript) to Figure 3B (in the revised manuscript), where they are now plotted alongside the corresponding data for the cytoplasmic tail deletion mutant. These results show that wildtype virus has a higher diffusion coefficient than NAΔCT virus, but that this situation is reversed when directional correlations are computationally removed from the data. This result highlights the importance of directional persistence in enhancing the diffusion of wildtype viruses.

Separately, we have also measured the size distribution of viruses with wildtype NA or ΔCT NA. This data, which is presented in Figure 3—figure supplement 2C, shows that the length distributions are similar up to a length of ~3μm, beyond which the wildtype virus is more highly represented. This result is also conveyed qualitatively in Figure 3—figure supplement 1C, which shows images of the two types of virus. Because both types of virus produce filamentous particles with well-defined major and minor axes, this difference in the abundance of extremely large particles (which represent a minor proportion of our data) does not affect our ability to determine particle orientation. To clarify that wildtype and NAΔCT are similar – but not identical - in morphology, we have revised the text to include the statement “…both wildtype and NAΔCT viruses form filamentous particles (with wildtype virus producing a higher proportion of extremely large filaments, >3μm in length; Figure 3—figure supplement 2C).”

c) Figure 4C and D: Again, you should try to find a meaningful quantitative comparison rather than just showing examples.

To quantify ECL tracks left in mucus more comprehensively, we measured the HA-NA polarity of viral particles and the intensity of ECL labeling associated with each particle. The results of this analysis are now plotted as Figure 4E, which shows the relationship between HA-NA polarity, and the extent of ECL labeling in the surrounding mucus. Referring to this data in the text, we write: “Overall, we find that the amount of cleaved sialic acid in proximity to a viral particle tends to increase with HA-NA polarity beyond a threshold value of ~0.06 (Figure 4E).”

d) Other statements need more precision: E.g. Results section, paragraph one: How were polarized viruses selected? What fraction of particles were filamentous? etc.

We have revised the manuscript to make statements categorizing virus particles more quantitatively precise.

Regarding the virus used, we write: “For these experiments, we use a tagged variant of the strain A/WSN/1933 with M1 from A/Udorn/1972, which differs from WSN M1 at six residues and confers filamentous morphology (Elleman and Barclay, 2004). Consistent with our prior observations, this virus produces filamentous particles that vary widely in size, from sub-diffraction limited spots to particles >10μm in length (Vahey and Fletcher, 2019).” To address the fraction of filamentous particles, we have included a plot of particle length distributions in Figure 3—figure supplement 2C. By describing particle morphology in this way (rather than reporting the fraction of particles that are filamentous), we avoid the need to establish an arbitrary cutoff between filamentous and non-filamentous morphology.

To make the distinction between mobile and non-mobile particles more quantitative and precise, we have revised our analysis to define these categories according to measured diffusion coefficients. Specifically, we first calculate a diffusion coefficient for each particle from its squared displacement versus time, as measured from time lapse videos. This results in a bimodal distribution of measured diffusion coefficients, which we plot in Figure 2C in the revised manuscript. Based on this distribution, we define mobile particles as those with diffusion coefficients >100nm2/s, corresponding to ~68% of the particles measured. The remaining particles are categorized as non-mobile. We use the same criteria in our analysis of NAΔCT virus in Figure 3B and C, taking the mobile population as those with diffusion coefficients >100nm2/s.

Regarding the reviewers’ question of how polarized viruses were selected, we note that, while HA-NA polarity is quantified in several figures (Figure 1G, 2E, 4E), we did not use this as a metric for selecting particles in any of our experiments. Rather, we characterized and reported the polarity of the viruses in the populations of each experiment.

2) The theoretical model should be explained more clearly and connected better to the experimental results.

a) We suggest moving some of the descriptions of the model into the main text and maybe include an illustration of the model, the sialic acid distribution along the particle, and the resulting directional motion. It would also help to discuss in more simple and explicit terms that how macroscopic diffusion increases with more directionally persistent microscopic motion.

To explain the theoretical model more clearly and intuitively, we have revised its discussion in the main text and provided an additional supplementary figure (Figure 3—figure supplement 5) which illustrates the distribution of cleaved sialic acid that is created by bound viruses that have different distributions of NA on their surface.

To provide a simple and intuitive analogy for how macroscopic diffusion increases with more directionally persistent microscopic motion, we have revised the text to include an analogy between our experiments and the persistence length of a polymer chain. Specifically, in the paragraph comparing the MSDs of wildtype and ΔCT virus, we have added the sentence: “Correlations in the direction of successive steps in a random walk will increase the rate at which the walker explores its environment, analogous to how more rigid polymer chains typically have larger end-to-end distances than those with equivalent contour length but lower bending rigidity.”

Finally, we appreciate the reviewer’s suggestion to move additional material from the supplement. However, we were unsatisfied by our attempts to combine this material with the main text of the manuscript and feel that it is best left as an appendix. We hope that in organizing the manuscript in this way we have made our key results easier to follow, while still providing a detailed description of all aspects of the work.

b) When describing the different physical effects, you should be careful not to confuse the reader with diffusion constants of NA, receptors, and viruses all labeled D. Please differentiate them with different symbols or subscripts.

We appreciate the possible confusion regarding the use of D to define the multiple distinct diffusion coefficients. We have revised the manuscript text to refer to sialic acid diffusion coefficients as DSA, and virus diffusion coefficients as D, and we have additionally attempted to make it clear from context what we are referring to in each case.

c) The model prediction that very slow sialic acid diffusion results in slow virus diffusion should be better explained. How does this prediction compare with the experiments in Figure 2 using coverslips functionalized with biotin-anchored (and presumably immobile) receptors?

As the reviewers correctly point out, our simulations predict that virus diffusion is reduced when sialic acid is immobilized, a scenario that models our experimental results using biotin-anchored fetuin. The origin for the enhancement in virus mobility that we predict when the sialic acid diffusion coefficient is low but non-zero is the ability of HAs along a greater portion of the virus to sample from spatially non-uniform distributions of sialic acid. In contrast, when the sialic acid is immobilized, only those HAs adjacent to the NA-rich pole sample from non-uniform distributions of binding partners.

We have added text to clarify this point. In the main article, we write: “Although our experimental data corresponds to the case where sialic acid is immobilized (DSA ~ 0), our simulations predict that IAV bound to slowly-diffusing transmembrane glycoproteins gel-forming mucins (DSA <0.01µm2/s) will exhibit enhanced directed mobility, likely due to the biased spatial distributions of sialic acid binding partners that are available to HAs away from the virus’s NA-rich pole.” This point is reinforced in the text, where we describe this result: “Although sialic acid diffusion can attenuate persistent virus motion if it is too rapid, it can also increase the rate of motion relative to immobile sialic acid by allowing HAs distal to the HA-NA interface to also sample from asymmetric distributions of receptors (Figure 3E).”

d) Please point out that Figure 4—figure supplement 1 essentially only shows the first-passage probability calculated in the supplement (please number equations and refer to them directly). This figure could be improved by changing the x-scale seconds -> minutes and illustrating more generally how increasing virus diffusion increases the probability of reaching the epithelium. You could add several curves for different D, plot this on a log scale, and remove the 5000x.

We have revised Figure 4—figure supplement 1 as suggested by the reviewers, changing the x-axis from seconds to minutes, changing the vertical axis to log scale, and plotting the solution for a range of virus diffusion coefficients (80nm2/s to 800nm2/s at intervals of 80nm2/s). We have also numbered all equations in the manuscript.

We have also attempted to make it clear that these plots show the results of the first-passage model discussed in the text; in both the original and revised manuscript, we note this in the sentence where we refer to the figure: “…adapting this value for a one-dimensional first passage model suggests that persistent motion increases the number of particles that reach the epithelium before being cleared by several orders of magnitude (Figure 4—figure supplement 1B).” This is also mentioned in the text: “The results of these calculations are plotted in Figure 4—figure supplement 1, where we compare the first passage probability of viruses with five-fold differences in their diffusion coefficients in a mucus layer that is 2 μm thick.” Finally, the title of the figure - “a first-passage model for virus transport in mucus” – also states that the results displayed correspond to a first-passage probability, and we have added this label directly to the axis of the plot in Figure 4—figure supplement 1B.

e) The assumption of an absorbing boundary at the epithelium implies that binding is essentially instantaneous and irreversible. This should be discussed.

The reviewers correctly point out an implicit assumption we made in using our first-passage model to estimate the percentage of “successful” virus particles – that the epithelium acts as an absorbing boundary. We have added the following discussion of this point to the text:

“For the parameters listed here, the timescale for mucociliary clearance (~20 min) is substantially shorter than the timescale for virus diffusion (~2h), and the percentage of successful particles (defined for simplicity as those that reach the epithelial surface) is extremely sensitive to their diffusion coefficient: the five-fold decrease in D that we estimate would result from the loss of persistent motion would lead to >1000-fold decrease in the number of successful particles (Figure 4—figure supplement 1B). This calculation assumes that binding of viruses to the cell surface is immediate and irreversible – assumptions that are reasonable if the cell surface is densely decorated with sialic acid and if viruses that come in contact with it are rapidly endocytosed. However, regardless of how interactions between viruses and the underlying epithelium are modeled, the rate at which viruses encounter the epithelium – our primary focus, and a prerequisite for productive infection - will always be inversely proportional to the virus’s diffusion coefficient.”

3) Additional infectivity and/or mobility assays.

a) Could infectivity be assessed directly using mucus-producing cells such as CaLu-3? Such data would considerably strengthen the claim that enhanced diffusion increased infectivity.

As suggested by the reviewers, one hypothesis suggested by this work is that virus particles that exhibit polarized distributions of HA and NA (and thus a tendency towards directionally-persistent diffusion) may have higher infectivity in mucosal environments. Testing this hypothesis definitively would require measuring the infectious potential of virus particles in mucus as a function of their shape and molecular organization, ideally with other features of the virus held constant. One way to accomplish this could be to sort virus particles from the same genetic background into polarized and non-polarized subsets, and to measure the particle-to-PFU ratio for each as a measure of infectious potential. Alternatively, another possibility would be to visualize infection of mucus-producing cells directly, allowing us to measure rates of infection for different virus phenotypes relative to their frequency within the overall population.

Although we agree that these experiments would be informative and worthwhile, they present technical challenges and are currently outside our experimental capabilities. Because of this limitation, our intention in both the original and revised manuscript has been to limit our claims to only the characteristics we are able to measure (i.e. diffusional characteristics of virus particles), without making the claim that these characteristics enhance infectivity. To clarify this point, we have revised the manuscript to use more neutral language when describing persistent diffusion, and to be more explicit about what our results do not allow us to conclude:

First paragraph of Discussion: “this advantage of filamentous morphology" revised to “this feature of filamentous morphology".

Second paragraph of Discussion: “Although more work is needed to understand if these characteristics of IAV organization are adaptive during in vivo replication” revised to “Although more work is needed to understand if these characteristics of IAV organization enhance infectivity and are adaptive during in vivo replication”.

b) Enhanced diffusion in polarized filamentous virus compared to spherical viruses is counteracted by their larger size. Is there a head-to-head comparison?

The reviewers correctly point out that, with everything else held constant, particle mobility will tend to decrease with increasing particle size. Although filamentous particles can be much larger than spherical ones (and would thus be expected to have lower mobility), this is not universally the case; filamentous strains of IAV produce pill-shaped particles as small as 120nm in length, with a diameter of ~80nm measured from the outermost glycoprotein layer (Calder et al., 2010). Although these viruses are smaller in terms of surface area than many spherical influenza particles, their lack of radial symmetry is shared by other filamentous particles and suggests that they could still support polarized distributions of HA and NA and exhibit the directional diffusion observed here in larger particles. However, this is challenging to confirm experimentally, as we are only able to characterize the features of particles with dimensions below the diffraction limit of our microscope (~300nm) using super-resolution methods that require fixation.

Although we do not have direct head-to-head comparison of spherical and filamentous IAV, we explored how particle size influences mobility computationally in Figure 3—figure supplement 6 of the revised manuscript (Figure 3—figure supplement 5 in the original submission). Here, our simulations show that particle mobility decreases for larger particles when the HA-sialic acid binding affinity is held constant, but that particle mobility increases for filamentous particle if instead the number of HA-sialic acid bonds is held constant. Similar simulations of spherical particles would also be interesting; however, as discussed in Figure 3—figure supplement 4 in the original and revised manuscript, the dynamics of spherical particles on two-dimensional surfaces would need to account for particle rolling, which we have neglected for simplicity.

We have revised the manuscript to clarify this point and the limits of our experimental results. Specifically, we have revised the first paragraph in the Discussion to read in part:

“Importantly, our simulations show that this feature of filamentous morphology would not be limited to the extremely large particles whose length is easily measured using diffraction limited microscopy, but rather extends to capsule-shaped particles <200nm in length (Figure 3—figure supplement 5), which are known to be produced by filamentous strains of IAV (Calder et al., 2010). In contrast, this effect may be suppressed in spherical particles, due both to the absence of a clear axis for polarization and their ability to roll on two-dimensional surfaces. However, more work is needed using higher-resolution live imaging to confirm these predictions experimentally.”

c) The data in this paper was obtained using viruses containing fluorescent labels. Have the authors confirmed these observations using unlabeled viruses? Whilst obviously fluorescent microscopy could not be conducted with unlabeled viruses, the morphology of virions propagated in mucus-producing cells could be compared to those propagated in non-mucus producing cells and studies of their ECL tracks could possibly be conducted. This experiment could add further weight to their statement that filamentous morphologies are adaptations to replication in the presence of mucus.

The reviewers here raise two important points. The first point regards the effect of labeling on virus mobility. We have sought to address this comment in three complementary ways. In our prior work, we measured the effect of virus labeling on infectivity, finding that virus with fluorophores on both HA and NA retain ~85% infectivity relative to unlabeled virus (Figure 1F from Vahey et al., 2019). The observation that labeled viruses bind to and enter cells with efficiency similar to unmodified viruses suggests that the labeling is not substantially perturbing the virus’s ability to engage with sialic acid at physiological densities on the cell surface. Additionally, in the present work we have characterized the effect of labeling on NA activity, using the fluorogenic substrate MUNANA (Figure 2—figure supplement 1 in both the original and revised manuscript). These results demonstrate that NA activity is not altered by the attachment of Alexafluor 555, the fluorescent dye used for functional assays throughout this work. Finally, ECL labeling of tracks left by unlabeled virus resemble those left by labeled virus (Author response image 1). Collectively, these experiments give us confidence that the labeling strategy employed throughout this work does not influence our results.

Author response image 1.

ECL tracks from unlabeled virus.

Filamentous viruses unmodified by fluorophores leave persistent trails of cleaved sialic acid (detected by labeling with ECL and displayed here with inverted contrast) similar to those left by fluorescently labeled viruses (Fig. 2B in the revised manuscript).

The second point the reviewers raise here regards the characteristics of virus raised in the presence or absence of mucus; specifically, whether we could compare the morphology and diffusional characteristics of two different virus populations grown under these two conditions. These experiments would provide new insight into the environmental pressures that contribute to virus morphology, and they could help to test the hypothesis discussed in point 3a of this response - that filamentous morphology combined with polarized distributions of HA and NA is adaptive during replication in the presence of mucus. Although we agree that this would be a valuable contribution, we feel that these experiments are outside the scope of this work, which focuses more narrowly on mechanisms of virus diffusion.

4) Simulation code and analysis scripts need to be made available, preferably on a repository like GitHub with appropriate documentation.

We have compiled and documented Matlab data and code for generating plots from the manuscript, and for running simulations of virus diffusion. These are found in five compressed supplemental folders: Figure 1—source data 1, Figure 2—source data 1, Figure 3—source data 1, Figure 4—source data 1, and Supplementary code 1.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

By and large, the authors have presented a compelling revision. They have quantified a number of previously rather qualitative statements by showing distributions rather than representative examples, present additional data on virus mobility directly comparing NA and NA-DCT viruses, streamlined notation, and explained the theoretical model better. However, there are a number of remaining issues that we would like to have clarified/rectified.

We thank the reviewers for their favorable response, and for their continued evaluation of our manuscript. Our efforts to address the comments on our revision are described below and highlighted in the revised manuscript.

Figure 1: the distributions are useful, but the alignment of the NA rich pole with NP seems rather imprecise. Reading the previous version of the manuscript, I was certainly expecting a more clear cut picture. The insets in Figure 1A also suggest a much more pronounced asymmetry. How closely is this more blurry picture accounted for in the simulation?

As the reviewer’s point out, the virus population is quite heterogeneous in a number of regards, including the polarity of the NA distribution and its alignment with NP. As we report in the manuscript, NA is enriched >5-fold at one pole relative to the other pole in ~34% of particles, and NA is more abundant at the NP-containing pole in ~70% of cases. We believe that this variability is a natural (and interesting) feature of influenza A virus assembly.

To better calibrate the reader to the examples shown in the inset images in Figure 1A, we have added values for the NA enrichment at one pole relative to the other for both cases (this information is in the caption of the most recent revision). For the inset to the left, NA is enriched 5.6-fold at one pole relative to the other, while in the inset to the right, the corresponding enrichment is 3.3-fold. These values are within the 31st and 48th percentile of the total population, respectively. Thus, while each of these cases show a pronounced asymmetry (as remarked by the reviewer), they fall reasonably within the observed distributions. Indeed, nearly half of the virus population has a more polarized distribution of NA than the example shown in the inset to the right.

Of the particles with less polarized distributions of NA (i.e. a ratio of NA abundance at the two poles of ~1), some have more uniform distributions of NA along the entire particle length while others have bipolar distributions, with NA enriched at both poles. For both of these cases, we have performed simulations (Figure 3—figure supplement 5D) that show that uniform or bipolar particles do not exhibit directional mobility. To highlight this connection between these simulation results and heterogeneity we observe in the virus population, we have revised our discussion of Figure 3—figure supplement 5D to read:

“Consistent with this mechanism, persistent motion of the virus is lost when NA is no longer localized to a single virus pole and the sialic acid distribution beneath the particle becomes symmetric (Figure 3—figure supplement 5B-D). We note that virions with symmetric distributions of NA also occur in the virus population and would not be expected to exhibit persistent motion (for example, those particles with NA polar ratios of ~1 in Figure 1A).”

Figure 1E, Anticorrelation of HA and NA: This anticorrelation is pretty weak and the main take home message from the figure seems to be that HA and NA are clustered. Hence I think the statement "...NA clusters that appear to largely exclude HA" is too strong.

We agree that our results more clearly show that HA and NA are clustered on the virus surface, and that conclusively demonstrating exclusion of HA from NA clusters (or vice versa) would be challenging without further improvements in spatial resolution. Accordingly, we have revised the text to read:

“We also find that the NA seen at low levels along the length of filamentous viruses without super-resolution imaging is actually organized into small NA clusters (Figure 1D). Additionally, HA and NA distributions in these particles are modestly anti-correlated (Figure 1E), suggesting that receptor-binding and receptor-destroying activities may be spatially segregated on some regions of the virus.”

At least two considerations could lead the correlation coefficients that we measure to be conservative estimates and contribute to what the reviewer notes is a “pretty weak” anticorrelation. First, the finite resolution achievable using fluorescence microscopy (both conventional and super-resolution) will tend to obscure differences in spatial distributions of molecules that are densely packed and closely apposed. This optical blurring will necessarily make spatial correlations less negative. Second, our STORM reconstructions are two-dimensional projections of a three-dimensional object. Thus, if the virus is not axially symmetric in its protein distributions, the spatial correlations will become less negative due to spurious colocalization of molecules that are actually on opposite sides of the particle.

Figure 1G seems problematic. The caption states that p-values are calculated using a two-sample t-test. What enters as independent data point here? Strictly speaking, you have n=4 replicates and I doubt that this would support the conclusion that these cases are different. A paired test on +/- NAI samples from the same replicate would probably be more powerful. Generally, quantifying the difference (and confidence intervals) between two conditions is preferable to rejecting a null.

We appreciate the reviewer’s feedback on the presentation and statistical analysis for Figure 1G. In our analysis for this figure, the median value of each biological replicate for a given condition represents an independent data point. In our previous submissions, the p-values displayed in the figure were determined from a two-sample t-test based on these data points (four per condition).

In the revised manuscript, we have updated Figure 1G to show the results of a paired t-test, and we have added confidence intervals to our discussion of this data in the main text. Both the paired t-test and the two-sample t-test support the claim that viruses that detach from the cell surface during NAI treatment are more polarized than those that remain sequestered on the cell surface. We have provided the source data for this figure so that further quantitative analysis of our data can be carried out by anyone interested.

The addition to Figure 4E does not help. This figure shows that there is little evidence for variation of ECL intensity and HA-NA polarity. Making an arbitrary cut at 0.06 and fitting lines to points below and above is not appropriate. Furthermore, a correlation of ECL intensity and HA-NA polarity doesn't quantify the examples given in C or D. You could try to show ECL intensity distributions aligned with the NA polarity or similar. But as of now, panels C, D, and E are just examples.

Based on the reviewer’s suggestion, we have re-analyzed data for Figure 4 to examine alignment of the virus’s HA-NA axis with the axis of ECL labeling (defined as the vector connecting the virus center to the center of ECL labeling). As plotted in Figure 4E of the current revision, we find that the number of particles aligned within 60o of parallel to the ECL axis are about 1.5-fold more abundant than the number of particles aligned within 60o of antiparallel. This result is qualitatively similar to the plots shown in Figure 3C and D.

While the relationships we measure in this figure are modest, it is worth considering technical aspects of the native mucus gel experiments that pose limits on our analysis. Unlike the time-resolved, two-dimensional particle trajectories of the idealized reconstitution experiments that serve as the basis for analysis in figures 2 and 3, the experiments in Figure 4 only provide the position and bearing of a virus at one point in time: the instant at which we fix the sample. In addition, viruses in these experiments are free to move in three dimensions and may sit relatively far from the coverslip, making the samples more challenging to image and quantify. Finally, we expect that the mucus itself will diffuse and flow during the experiment, altering distributions of cleaved sialic acid in ways that we are unable to predict or measure. Despite these experimental caveats, we believe that the results presented in this figure provide valuable physiological context to the more quantitative measurements described elsewhere in the manuscript. To avoid confusion about what these results do and do not show, we have revised our description of Figure 4C-E in the main text, writing:

“This suggests that the asymmetry of NA and HA biases the direction of virus diffusion in three dimensions, producing trails of cleaved sialic acid that can reach several microns in length as the virus moves (Figure 4C). This directional mobility is less prevalent in viruses with more uniformly distributed NA, though they nonetheless are capable of creating swaths of ECL-staining within the mucus that lack clear directionality (Figure 4D). Similar to our observations on idealized two-dimensional surfaces (Figure 3C), we find that viruses in mucus exhibit a slight tendency to align their HA-NA axis (captured at the moment of fixation) with the displacement of the virus relative to the center of ECL labeling (Figure 4E). These results suggest that the spatial organization of HA and NA on the virus surface may also promote penetration of polarized IAV particles through mucus barriers in vivo.”

Lastly, we agree with the reviewer that Figure 4C and D are just examples, and we have clarified our discussion of these panels in the text and caption to make this caveat clear. To place these examples in the context of Figure 4E (where we plot data from the entire population), we have added the angular alignment for each particle relative to its ECL trail in the lower left corner of each panel. Because the example images in Figure 4D contain multiple particles in contact with overlapping ECL trails, we have not reported angular alignments in this case; as described in the Materials and methods section, these cases are also excluded from the population analysis in Figure 4E. The data for this plot is also included as a Source Data file for further analysis by interested individuals.

The comparisons of diffusion with/without correlations make sense only when you specify the time interval over which the displacements are measured (paragraph three of subsection “Polarized viruses step persistently away from their NA-rich pole”). You do so later, but I'd suggest moving it forward.

We have revised this section of the manuscript, writing that we “simulated random walks in which the number of steps, the size of each step, and the time interval between steps all match the observed data, but the direction of each step is uncorrelated with previous steps (Figure 3B).”

Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Cell line (C. familiaris)	MDCK-II	UC Berkeley Cell Culture Facility		https://bds.berkeley.edu/facilities/cell-culture
Cell line (H. sapiens)	Calu-3	UC Berkeley Cell Culture Facility		https://bds.berkeley.edu/facilities/cell-culture
Cell line (H. sapiens)	HEK293T	UC Berkeley Cell Culture Facility		https://bds.berkeley.edu/facilities/cell-culture
Antibody	anti-Muc5AC	Thermo Fisher	RRID:AB_10978001, Cat. #: MA5-12178	IF (1:400)
Antibody (lectin)	FITC-labeled Erythrina cristagalli lectin (ECL)	Vector	RRID:AB_2336437, Cat. #: FL-1141	IF (1:1000)
Recombinant DNA reagent	pcDNA3.1+vHA(G5)	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vHA	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vNA(ybbR)	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vNAΔCT(ybbR)	This paper		Vector shown schematically in Figure 3—figure supplement 1A
Recombinant DNA reagent	pcDNA3.1+vNA	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vNP(FlAsH)	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vNP	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vM (Ud M1)	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vN	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vPA	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vPB1	(Vahey and Fletcher, 2019)
Recombinant DNA reagent	pcDNA3.1+vPB2	(Vahey and Fletcher, 2019)
Peptide, recombinant protein	Streptavidin	Thermo Fisher	Cat. #: 434302
Peptide, recombinant protein	Neuraminidase from Clostridium perfringens	Sigma	Cat. #: N2876
Peptide, recombinant protein	Fetuin from fetal bovine serum	Sigma	Cat. #: F3004
Peptide, recombinant protein	CLPETGG peptide	Genscript
Chemical compound, drug	Alexa Fluor 555 maleimide	Thermo Fisher	Cat. #: A20346
Chemical compound, drug	Alexa Fluor 647 maleimide	Thermo Fisher	Cat. #: A20347
Chemical compound, drug	CF568 maleimide	Biotium	Cat. #: 92024
Chemical compound, drug	FlAsH-EDT2	Toronto Research Chemicals	Cat. #: F335200
Software	Matlab	Mathworks