Structural and immunological characterization of the H3 influenza hemagglutinin during antigenic drift

Domain: neuroscience
Published: 2025-12-11
Editorial outcome: major_revision

Abstract
The quest for a universal influenza vaccine holds great promise for mitigating the global burden of influenza-related morbidity and mortality. However, challenges persist in identifying conserved epitopes capable of eliciting robust and durable immune responses. In this study, we explore the influence of glycan evolution on H3 hemagglutinin from 1968 to present day and its impacts on protein structure, antigenicity and immunogenicity by using computational, biochemical and biophysical techniques. Structural characterization of HK/68 and Sing/16 by cryo-electron microscopy shows that while HK/68 is resistant to enzymatic deglycosylation, removal of glycans destabilizes the hyperglycosylated head and membrane-proximal region in Sing/16. Furthermore, the appearance of glycans in Sing/16 hemagglutinin head domain shifts the polyclonal immune response upon vaccination to target the esterase and stem. These insights expand our understanding of glycans beyond their role in protein folding and highlight the interplay among glycan integration and immune recognition to design a universal influenza vaccine.

Introduction
Hemagglutinin (HA), the most abundant glycoprotein on the surface of influenza viruses, serves as the primary antigenic target for neutralizing antibodies1. Consequently, genetic mutations in HA can drive antigenic drift, undermining pre-existing immunity and leading to vaccine mismatches2. In addition to genetic mutations, changes in the asparagine (N)-linked glycosylation pattern of HA contribute to immune evasion and have been shown to play a critical role in the emergence and spread of influenza A viruses (IAV) in humans. Notable examples include the antigenicity drop of the H3N2 strain in 2003 and the emergence of the 2009 H1N1 swine flu pandemic3,4.

N-linked glycosylation is a critical co-translational and post-translational modification that impacts the structure and immunogenicity of viral proteins, including influenza HA5. For instance, adding specific glycans during viral replication is essential for the proper folding and trafficking of HA6. Glycans also shield the protein from the host immune system by preventing antibody recognition and promoting viral infectivity and replication. Thus, the accumulation of glycans in the receptor binding site (RBS) or RBS-proximal region is a strategy that the virus uses for immune evasion and to tune sialic acid receptor recognition by different glycoforms7. At the same time, changes in the number and location of glycans can also expose new epitopes that can be targeted by the immune system8,9. Previous studies have shown that the absence of a recently acquired glycan in a well-matched H3N2 vaccine resulted in ferrets and humans being unable to produce antibodies that efficiently neutralized circulating glycosylated viruses3. Thus, the interplay between HA glycosylation and the host immune response is critical to IAV pathogenesis and strain emergence.

Influenza HA exhibits a varying number of PNGS in both the variable head and the conserved stem region, dependent upon the specific virus strain and subtype. A PNGS is defined by the presence of the canonical Asn-X-Ser/Thr sequon, where X is any residue except proline. Analysis of the peptide sequence of hemagglutinins has shown that H3 circulating human strains have doubled the number of PNGS since its zoonotic emergence in 1968, with a particular concentration around the RBS9–12. Importantly, the presence of a PNGS is not sufficient to guarantee glycan occupancy13. While sequence analysis can be useful in predicting glycan occupancy on the HA surface, mass spectrometry-based approaches are necessary to accurately quantify and characterize the glycoforms present14,15. Although these methods can provide valuable insights, they do not offer information on the structural changes that occur as the glycosylation on HA evolves. Furthermore, even though crystallographic studies have provided some insights into how glycans affect epitope accessibility and recognition by antibodies, these studies have not fully elucidated how glycans dynamically impact HA protein stability3,16.

Here, we used cryo-EM, polyclonal epitope mapping, computational modeling, and biochemical approaches, including glycoproteomics and immunoassays, to investigate the structural and functional consequences of glycan incorporation on H3 HA over the past five decades. Our analysis revealed that incorporation of PNGS, particularly in the globular head domain, shifts glycoform composition toward a higher mannose content and affects the stability of HA. Cryo-EM reconstructions demonstrated that enzymatic deglycosylation resulted in significant destabilization of the more contemporary H3 HA, Sing/16, whereas the 1968 H3 HA remained structurally intact following glycan removal. This difference is most pronounced in the hyper-glycosylated head domain, where glycan accumulation not only affects structural integrity but also has a substantial impact on epitope exposure and accessibility. These findings underscore the structural, functional, and stabilization impact of glycan addition on influenza HA and have direct implications for rational vaccine design and manufacturing processes.

Results
Emerging PNGS and substitutions in H3 viruses over the years
HA is a trimeric glycoprotein with each monomer composed of a globular head containing a vestigial esterase subdomain in its lower portion, and a stem domain17 (Fig. 1A). Mutations that introduce N-linked glycosylation sites in the HA glycoprotein play a critical role in protein folding and immune evasion for human H3N2 viruses. This process may result in the addition of N-linked glycans, which can lead to substantial antigenic changes. Notably, the addition of glycans in H3 HAs is estimated to occur every 5 to 7 years and has been shown to influence vaccine efficacy10. To investigate the trends in the accumulation of glycans on HA over the years, we analyzed the presence of PNGS within circulating strains spanning 1968–2022. We analyzed N-glycosidic linkages to the Asn residue of the glycosylation motif Asn-X-Ser/Thr accumulated on HA based on the analysis of ~11,000 sequences of naturally occurring H3 strains using the code previously described by Wu et al.3. Specifically, we aligned the sequences the of ~11,000 naturally occurring HA H3 strains and analyzed N-glycosidic linkages to the Asn residue of the glycosylation motif Asn-X-Ser/Thr accumulated through time.3

Figure 1B illustrates the calculated frequencies of PNGS of the total number of circulating strains each year. Raw values and percentages can be found in Supplementary Table 1. From 1968–2022, we observe the number of PNGS per protomer on HA went from 7 in 1968 (Fig. 1B, C) to 12 in 2022 (Fig. 1B, C). PNGS such as N6 and N7, found on HA’s stem region, occur at a very low frequency, and therefore were not always represented (Supplementary Table S1). PNGS such as N8, N22, N38, N165, N285 and N483 have been present on HA since 1968 and persist until 2022 (Fig. 1B and Supplementary Fig. S1). Interestingly, these PNGS all appear on the vestigial esterase or stem region of HA except for N165, which is in the globular head at the interface between protomers (Fig. 1C). Aside from the 5 conserved PNGS appearing on HA’s vestigial esterase or stem region (Fig. 1B, C and Supplementary Fig. S1), the number of additional PNGS in these regions has remained limited, never exceeding 2 per year, except in 1990–1991, when N45 briefly appeared in 20% of circulating strains alongside N81 and N63 (Fig. 1B and Supplementary Fig. S1).

By analyzing the glycans present on A/Hong Kong/1/1968 (HK/68) and comparing them to A/Singapore/INFIMH/16 (Sing/16), we observe that PNGS on HA’s globular head, such as N126, N133 and N246, appear in 1974, 1996, and 1982, respectively, and persist until 2022 (Fig. 1C and Supplementary Fig. S1). On the other hand, the appearance of PNGS such as N122 and N144 (Fig. 1B, C), tend to fluctuate throughout the years (Fig. 1C and Supplementary Fig. S1). Interestingly, N158, a key molecular determinant of antigenic distancing between influenza strains and a key residue impacting HA sialic acid binding specificity18–20, is found in <40% of the circulating strains from 2014 to 2020 and completely disappears in the following years. This is in accordance with previous studies suggesting that H3N2 circulating strains after 2021, are expected to eliminate a glycosylation site at N158, impacting viral cell entry and influencing escape from vaccine-elicited antibodies21.

Collectively, our data suggest PNGS appearing on the vestigial esterase and stem region tend to emerge less frequently and be more persistent when compared to PNGS appearing on the globular head (Fig. 1B, C and Supplementary Fig. S1). By analyzing the spatial distribution of PNGS in relation to residue conservation, we observed that PNGS predominantly emerge in non-conserved regions, specifically within the head domain (Fig. 1D).

In addition, we assessed whether the accumulation of PNGS in HA’s globular head coincides with the emergence of non-PNGS mutations. To understand where the non-PNGS mutations appeared on HA after the addition of each glycan, we examined the non-PNGS mutations in the year where the glycan was added (Supplementary Data 1). First, we observed that the globular head is the region with the highest frequency of amino acid mutations. The mutations are mostly located in loops, outside secondary structure elements (Fig. 1E, panel I). Second, we detected that the amino acid mutations in the RBS are residues that are not directly involved in receptor binding (Fig. 1E, panel II). This is consistent with the fact that 130-loop, 150-loop, and 190 alpha-helix, which are essential for receptor binding, are relatively conserved among HA subtypes7,10,22–24.

In summary, our analysis reveals a dynamic accumulation of PNGS on HA over the years, suggesting a discernible trend regarding the emergence and accumulation of PNGS in non-conserved regions of HA’s globular head.

Defining the structure and glycosylation profile of Sing/16
To better understand the complex relationship between glycan organization on the HA surface and its potential impact on emerging strains, we characterized the structures and glycosylation profile of both HK/68 and Sing/16 by cryo-EM (Fig. 2A–E and Supplementary Fig. S2) and mass-spectrometry (Fig. 2F, G). Notably, we obtained a structure of a fully glycosylated Sing/16 at 3.9 Å-resolution (Fig. 2A and Supplementary Fig. S2). Both the HK/68 as the Sing/16 H3 proteins were purified from HEK293T as well as HEK293S GnTI- cells, in which N-glycans remain in a high mannose form as they lack the ability to undergo further processing into complex-type glycans. The HA plasmids contain either one or two N-terminal superfolder green fluorescent protein (sfGFP) domains, for HK/68 and Sing/16, respectively. The addition of the sfGFP increases the yield of the protein, can improve angular distribution in cryo-EM, and serve as a tool for immune tracing while not affecting antigenicity25.

The structure of Sing/16 revealed the prototypic trimeric structure of HA (Fig. 2A and Supplementary Fig. S2). Each subunit consists of a membrane-distal polypeptide (HA1) and a membrane-proximal polypeptide (HA2). The HA1 subunit is comprised of a globular domain (residues 116–261) formed by an eight-stranded antiparallel β-sheet motif that includes a distal shallow pocket corresponding to the receptor-binding site, surrounded by the five immunodominant epitopes (A-E)26. The stem domain consists of a descending β-sheet motif from the HA1 subunit and is mainly formed by a helical coiled-coil structure corresponding to the HA2 subunit26. We also solved the cryo-EM structure of Sing/16 produced in GnTI- cells, at 3.7 Å-resolution and Sing/16 produced in HEK293T, at 3.9 Å-resolution (Supplementary Fig. S2). The two structures are highly similar with a Cα-root-mean-square deviation (rmsd) of 0.77 Å (Supplementary Fig. S3), suggesting the distribution of glycan types does not have a significant impact on the folded structure. The cryo-EM structure of HK/68 was resolved to 3.4 Å-resolution (Supplementary Fig. S2) and is highly similar to Sing/16 (Ca-rmsd of 0.68 Å), demonstrating that the overall structure was maintained over time despite a relatively large number of mutations and the addition of PNGS (Fig. 2B–E).

We observed density for core N-acetylglucosamine (GlcNac) moieties in our cryo-EM reconstructions (Fig. 2B–E) at all PNGS on both HK/68 and Sing/16 except at N122, which is within a well-resolved region of the protein, and N8, which is near the N-terminus and is part of a poorly ordered region of the protein. Although we can observe density for individual glycans, we cannot identify the specific glycoforms and levels of occupancy from cryo-EM alone. We therefore employed glycoproteomics-based liquid chromatography–tandem mass spectrometry (LC-MS/MS) to characterize the site-specific distribution of glycoforms at each PNGS for both strains (Fig. 2F, G). While we identified glycopeptides covering most PNGS, we lack coverage at N22 and N81 on HK/68, and N63 on Sing/16, either due to low peptide detection or poor fragmentation. Of the glycans detected, however, we found that HK/68 possessed on average 67% complex, 15% hybrid, and 16% high-mannose type glycans, while Sing/16 possessed 49% complex, 9% hybrid, and 28% high-mannose. Of the PNGS that are shared between the two strains, the largest change occurred at N165, which went from 20% high-mannose on HK/68 to 70% on Sing/16, in line with previous results describing a progressive increase in glycan-mediated occlusion of the HA surface, particularly within the immunogenic globular head associated with the emergency of high-mannose glycans positions N165 and N24627.

Glycan accumulation on the HA head affects epitope exposure
To investigate how changes in HA glycosylation affect epitope exposure we first utilized a modified version (see “Methods” and Supplementary Fig. S9) of our previously developed high-throughput atomistic modeling pipeline to generate a large ensemble of fully glycosylated HA models of both HK/68 and Sing/16 (Fig. 3A)28,29. From these ensembles, we calculated the glycan encounter factor (GEF) across the solvent-exposed surface of HA, which is a measure of the number of glycan heavy (non-hydrogen) atoms encountered by an external probe approaching the protein surface (see “Methods”). We found that the accumulation of glycans on HA’s head domain has a dramatic effect on the epitope accessibility in the region (Fig. 3B), with an average 4.7-fold increase in GEF for Sing/16 over HK/68 for head domain residues (Fig. 3B and Supplementary Fig. S4A, B). Normalized GEF values per residue, ranging from CYS52 to CYS277, were used for this calculation. In addition to the changes in the shielded surface area, we also observed an increase in average inter-glycan and glycan-protein contacts of glycans on the head domain of Sing/16 compared to the same glycans present on HK/68, which is a result of increased local crowding and glycan-glycan interactions (Supplementary Fig. S4B). This is important because the increased local contacts indicate reduced temporal dynamics of glycans within or around antibody epitopes can have an additional impact on epitope accessibility and antibody binding beyond just steric shielding effects16.

Next, we measured binding of six broadly neutralizing antibodies (bnAbs) against the stem (CR9114)30 and the RBS (S139/131, K03.2832, TJ5-533, C0534 and F045-9235) regions of HK/68 and Sing/16 (Fig. 4A and Supplementary Fig. S5A, B). HAs were expressed in HEK 293T, HEK 293S GnTI- cells or HEK 293S GnTI- followed by treatment with endo H and antibody binding was measured by ELISA. The glycosylation state of HK/68 and Sing/16 from differential expression and endo H treatment did not significantly affect the EC50 or AUCs of the stem or RBS antibodies (Fig. 4A and Supplementary Fig. S5A, B). Accordingly, after calculating the glycan encounter factor between HK/68, Sing/16 and C05, we observe that although the C05 epitope is surrounded by several acquired PNGS on Sing/16, including N133, N158, N165, and N246, it does not directly clash with any of them (Supplementary Fig. S5C).

Finally, to evaluate how glycans impact the natural antibody response against HA, we analyzed reference sera from the Erasmus Surveillance Institute from ferrets immunized against A/Bilthoven/16190/68, A/NL/109/03 (NL/03) and Sing/16 HA by HA inhibition (HAI) assay. Briefly, ferrets were inoculated with the virus intranasally, and serum was collected two weeks later. Antibody titers were measured by HAI assay, which determines antibody titers against HA by measuring the dilution factor of serum required to inhibit the interaction between the viral HA and the sialic acid receptor present on red blood cells, measured by the ability of serum antibodies to inhibit hemagglutination36. NL/03 was included to understand antibody responses from a time point between 1968 and 2016.

The results indicate a higher inhibition titer of the ferret antisera raised against Sing/16 (10,240) compared to HK/68 (2560) and NL03 (160) (Supplementary Table S2). Similarly, our biolayer Interferometry (BLI) analysis demonstrated a greater magnitude of binding between Sing/16 and the corresponding sera compared to HK/68 and NL/03, suggesting that ferret inoculation with Sing/16 elicited a more robust immune response compared to inoculations with HK/68 and NL/03 (Supplementary Fig. S6A). These results may not necessarily reflect intrinsic differences between the HAs themselves but could instead be influenced by differences in the infections. Thus, we conducted a competition ELISA and an electron microscopy-based polyclonal epitope mapping (EMPEM)37–40 assay to better understand how the polyclonal immune response elicited after immunization with the different strains differed from one another.

Our EMPEM analyses revealed that the fragment antigen binding domains (Fabs) of polyclonal antibodies complexed with matching protein targeted the HA head in HK/68, head and vestigial esterase domain in NL/03, and vestigial esterase and stem domains in Sing/16 (Fig. 4B–D and Supplementary Fig. S6). Similarly, we observed that while the presence of RBS bNAbs competed with polyclonal IgGs and Fabs isolated from sera in HK/68 and NL/03, only the stem bNAb CR9114 competed polyclonal IgGs and Fabs in Sing/16 (Fig. 4B, C and Supplementary Fig. S6). These data suggest that the increased number of glycans present on HA Sing/16 potentially favored the production of stem and esterase over head-directed antibodies against Sing/16.

De-glycosylation destabilizes the HA head domain of Sing/16 but not HK/68
N-linked glycans can stabilize proteins through a variety of mechanisms41,42, yet the biophysical consequences of HA glycosylation have not been thoroughly investigated. We previously showed that enzymatic deglycosylation of the Human Immunodeficiency Virus type-1 (HIV-1) Envelope glycoprotein leads to progressive destabilization and unfolding of the gp120 subunit, with the instability nucleating within the hypervariable loops 1-3 that compose the trimer apex28. Considering this, we sought to determine if HA was also being destabilized by endo H treatment. Therefore, we collected cryo-EM data of both endo H treated HK/68 and Sing/16 (Fig. 5 and Supplementary Fig. S7). Indeed, we observe significant destabilization of Sing/16 but not HK/68 following endo H treatment as seen from the cryo-EM 3-D classification results (Fig. 5A). Specifically, we observed that ~57% of the trimeric Sing/16 HA particles retained after 2-D classification exhibited some level of protein unfolding/destabilization primarily in the hyper-glycosylated head domain and to a lesser extent in the stem/membrane proximal regions (Fig. 5A). By pooling the particles from the well-folded 3-D classes (100% of the HK/68 particles) we obtained 2.3 Å and 3.2 Å-resolution reconstructions of endo H treated HK/68 and Sing/16, respectively (Fig. 5B). Both structures were nearly identical to their natively glycosylated counterparts in the protein backbone (Supplementary Fig. S3). endo H treatment of HK/68 resulted in a similar uniform distribution of views compared to endo H treated Sing/16 (Supplementary Fig. S2D). It is noteworthy that the resolution of 2.3 Å achieved for HK/68 represents the highest cryo-EM resolution reconstruction of HA to date, perhaps owing to the lack of glycans and isotropic tumbling in the vitreous ice on the cryo-EM grid. Difference maps calculated by subtracting the aligned endo H treated maps from the GnT1- maps reveal the full extent of the glycosylation on both structures (Fig. 5C), highlighting the nearly complete shielding of the head domain on Sing/16. The extent of glycan density observed in the difference maps closely matched the glycan density observed in simulated density maps generated from the ensemble of glycosylated atomistic models shown in Fig. 3 (Supplementary Fig. S8), further validating the accuracy of the modeling procedure and confirming that the density features observed in the difference maps indeed arise from glycan signal. Interestingly, by comparing the local map intensity around individual glycans between the GnTI- and stable endo H treated 3-D reconstructions we found that several glycans were incompletely digested by endo H (Fig. 5D and Supplementary Fig. S7), most prominently the glycan at N165, which retained ~10% and ~50% occupancy on HK/68 and Sing/16, respectively (Fig. 5E). In addition, 6 other glycans on Sing/16 also showed incomplete de-glycosylation, specifically, N8, N45, N63, N133, N246, and N285 (Fig. 5E). Finally, we measured the thermal stability of both HAs before and after endo H treatment via differential scanning fluorimetry (DSF) and observed a marked reduction in melting temperature of Sing/16 relative to the fully glycosylated sample along with the appearance of a second apparent melting transition (Fig. 5F and Supplementary Fig. S6).

Methods
Code glycan prevalence over time
11,187 HA amino acid sequences were pairwise aligned to the reference sequence CAA24209 A/Aichi/2/1968 and analyzed using the Anchor tool package50. Alignments were grouped by year, and for each year each glycan position was queried with the pattern NXT/S, representing an asparagine followed by a threonine or serine residue at position N+2. Positions 22, 23, 24, 38, 54, 61, 79, 97, 138, 142, 149, 160, 174, 181, 262, 292, 301, and 499 counted from the CAA24209 reference were queried for the year range 1968–2022 for all sequence data. Counts and totals for each matching sequence at each position were collated and analyzed to determine prevalence.

Protein Expression and purification
pCD5-HA-GCN4-Fluorescent probe expression vectors were transfected into both HEK293T and HEK293S GnTI(-) cells (which are modified HEK293S cells lacking glucosaminyl transferase I activity (ATCC CRL-3022) with polyethyleneimine I (PEI) in a 1:8 ratio (µg DNA:µg PEI) as previously described25. The transfection mix was replaced after 6 hours with 293 SFM II suspension medium (Invitrogen, 11686029, supplemented with glucose 2.0g/L, sodium bicarbonate 3.6g/L, primatone 3.0g/L (Kerry), 1% glutaMAX (Gibco), 1.5% DMSO and 2mM valproic acid). Culture supernatants were harvested 5 days post-transfection. The HA expression was analyzed with SDS-PAGE followed by Western blot on PVDF membrane (Biorad) using α-strep-tag mouse antibodies 1:3000 (IBA Life Sciences). Additionally, fluorescence intensities were measured using a filter based PolarStar Omega plate reader. Subsequently, HA proteins were purified with Sepharose Strep-Tactin beads (IBA Life Sciences).

ELISA and competition ELISA
HK/68 and Sing/16 were HEK293T and GnTI-derived. GnTI-derived protein was further treated with Endo H (1000 units) for 5 h for the removal of high-mannose structures. Proteins were coated on maxisorp plates (Invitrogen) at a concentration of 2.5 μg/mL in PBS, overnight at 4°C. Blocking was performed for 2 h using 3% BSA, followed by incubation with primary antibody at 40 μg/mL for 2 h at room temperature. Following the primary antibody, a secondary antibody with an HRP conjugate (1:2000) was added, with an incubation of 45 min at room temperature. Finally, the plates were developed with TMB substrate solution (34028, Thermo Scientific) and the reaction was stopped after 3 min using 2.5M H2SO4. The competition ELISA was performed similarly, but after the blocking step and before adding the primary antibody, we incubated polyclonal Fab or IgG isolated from ferret sera at a concentration of 20 μg/μL in all wells.

Endoglycosidase H digestion of HK/68 and Sing/16 for cryo-EM experiments
0.5 mg of purified HK/68 or Sing/16 from HEK293S GnTI- cells was mixed with 20,000 units of Endoglycosidase H (Endo H; New England Biolabs) in non-denaturing reaction buffer to a final volume of 0.5 ml and incubated at 37°C for 5 h. To quench the reaction and purify the sample for cryo-EM experiments, the HAs were run over a size exclusion column Superdex 200 increase (Cytiva Life Sciences), and fractions were collected and concentrated.

Ferret samples used in the study
For the EMPEM assays used in this paper, serum samples for ferrets F10051, F0166 and F1042 obtained from Erasmus Surveillance Institute were used to test HK/68, NL/03 and Sing/16, respectively.

BLI/OCTET
Biolayer interferometry assays were performed using the Octet Red96 instrument (Sartorius, ForteBio). IgG from serum was immobilized onto Protein A Biosensors for 120 s followed by a 60 s baseline measurement in Kinetics buffer (PBS pH 7.2 with 0.01% w/v bovine serum albumin and 0.002% v/v tween-20). The biosensors were then dipped for 300 s into wells containing HK/68, NL03 or Sing/16 diluted in Kinetics buffer at a final concentration of 200 nM. The sensors were dipped into wells containing Kinetics buffer for 600 s to assess dissociation.

Serum IgG isolation and antibody digestion
IgG from ferret sera was isolated using CaptureSelect IgG-Fc Affinity Matrix (Thermo Scientific) as previously described40,51 In summary, 0.5 ml of ferret sera was mixed with 0.5 ml of washed CaptureSelect resin and 4 ml of PBS. For IgG digestion, the unbound IgG was discarded, and the resin was kept. Papain was activated for 15 min at 37°C in digestion buffer (100 mM Tris, 2 mM EDTA, 10 mM L-Cysteine, 1 mg/ml papain) and was added to the resin containing the IgG. Subsequently, digestion buffer (20 mM sodium phosphate, 10 mM EDTA, 20 mM cysteine, 0.1 mg/ml papain, pH 7.4) was added to the resin up to a total volume of 5 ml and the mixture was incubated for 4-5 h at 37°C. Iodacetamide was used to quench the reaction at a final concentration of 0.03 M, and the Fab/Fc from digested IgG was purified and concentrated using the size exclusion chromatography (Superdex 200 increase column, Cytiva Life Sciences). The fractions containing purified Fabs/Fc were concentrated using 10 kDa Amicon ultrafiltration units.

Purification of antigen-Fab complexes
Antigen-Fab complexes were generated by two methods: first, 15 μg of antigen were incubated with 1 mg of Fab overnight at RT. The complexes were purified using a Superdex 200 increase column on Akta Pure System (GE Healthcare) running in TBS buffer. The fractions containing the complexes were concentrated using 10 kDa Amicon ultrafiltration units and immediately added to a negative-stain grid. Second, immune complexes were prepared in a molar proportion of ~1:10 (antigen:polyclonal Fab), using 2 μg of antigen and 60 μg of polyclonal fab incubated overnight at room temperature. The unbound Fab was washed using TBS in a 100 kDa Amicon Ultra 0.5 mL centrifugal filter (Merck Millipore). Fab-antigen complexes were concentrated in the Amicon Ultra until they reached a concentration of 20 μg/ml.

Electron microscopy
Negative-stain sample preparation and imaging. Fab-antigen complexes were diluted to 20 μg/ml and applied for 10 s to 400 mesh Cu grids that were carbon coated and glow discharged at 15 mA for 25 s. The Fab-antigen complex was negatively stained with 2% uranyl-formate for 50 s. Data was collected using a Tecnai Spirit electron microscope at a nominal magnification of 52,000 X with a pixel size of 2.06 Å. The defocus range was set between −1.5 and −2 mm and the electron dose was 25 e-/Å2. Micrographs were recorded using a Tietz (4k) TemCam-F416 CMOS, and data was acquired using the automated imaging interface from Leginon52.

Negative-stain data processing. For antigen-Fab complexes, ~150,000 particles were picked using the Appion image processing package. Particles were transferred to Relion/3.0, and 2-D classification was performed53. Particles that contained trimer only or trimer-Fab complexes were selected for 3-D analysis. The 3-D reference for 3-D classifications and refinements was a low-resolution model of a non-liganded HA. Initial 3-D refinement was performed using a minimum of 100,000 particles to align all the particles prior to 3-D classification. Particles were then classified into 20–40 classes, and classes with similar features were combined and refined. Particles were transferred to Relion/3.,0 and 2-D classification was performed53.

Cryo-EM sample preparation. HK/68 and Sing/16 were diluted to a final concentration of 1 mg/ml and mixed with 0.5% Lauryl maltose neopentyl glycol (LMNG). Sing/16 was diluted to a final concentration of 7 mg/ml, 1 mg/ml and 0.2 mg/ml and mixed with 4 mM CHAPSO, 0.5% LMNG and 0.3% octyl betaglucoside (OBG), when expressed in HEK293T, HEK293S GnTI- and Endo H-treated, respectively. UltrAufoil R 1.2/1.3 300 mesh gold grids were plasma cleaned for 20 s at 15 mA using the Solarus advanced plasma cleaning system (Gatan) before loading the sample. Further, 3 μL of the sample was loaded onto the grid and plunge-frozen in nitrogen-cooled liquid ethane using the Vitrobot Mark IV (Thermo Fisher Scientific). The settings for the Vitrobot were as follows: Temperature: 4°C, humidity: 100%, blotting time: 5.5 s, blotting force: 1 and wait time: 3.5 s.

Cryo-EM data collection and image processing. Grids were loaded into a Titan Krios Cryo-Transmission Electron Microscope (FEI) operating at 300 kV and equipped with a K2 Summit direct electron detector camera (Gatan). The data was collected at a total cumulative dose of 50 e-/Å2. Magnification was set at 36,000x with a resulting pixel size of 1.15 Å/pix at the specimen plane. Automated data collection was performed using Leginon software52. The data collection details are presented in Supplementary Table S1.

The micrograph movie frames were aligned and dose-weighted using MotionCor254. In cryoSPARC v3.1.055, patch CTF was applied, and micrographs were manually curated. Further, particles were picked from micrographs using blob picker and template picker. The particles were then extracted, and a few rounds of 2-D classification were performed. Particle picks were subjected to a round of 3D classification and Non-Uniform 3-D Refinement. The highest resolution classes were selected and refined in 3-D refined while imposing C3 symmetry. In Chimera, the HA trimer A/Hong Kong/1/1968 (PDB entry 4FNK) crystal structure was aligned with HK/68 or Sing/16 and used as the initial model.

Model building and refinement. The final sharpened maps were used for subsequent model building. Multiple rounds of Rosetta relax refinement56 and manual Coot refinement57 were performed. To validate the analysis, EMRinger58 and MolProbity59 were used. The final refined model was submitted to the Protein Data Bank (PDB). Structural figures were generated using UCSF ChimeraX60 or Pymol61.

Computational modeling
Screening and elimination of glycan burial artifacts in models. Often, a molecular dynamics (MD) simulation produces a trajectory showing a glycan position that is, while thermodynamically stable, not realistically observed in experimentally derived models of the same glycoprotein29,62,63. This can cause glycans to be improperly buried inside the protein scaffold. Since recalculating the dynamics of a glycoprotein can be time consuming, a method of quickly determining the possible burial of a glycan within the protein scaffold was needed to help discern which glycosylation site on the scaffold would, in the case of the glycan being buried, need to be structurally exposed, via homology modeling, to obtain a realistic (unburied) spatial glycan position. Two separate computational methods were then developed28,29 and tested on 3 different viral spike glycoproteins belonging to three different viruses (HIV: BG505 SOSIP SIV: gp120/gp41 complex and Human Influenza: Hemagglutinin A (HA)). Here, HIV SOSIP Envs have been used as a test system for influenza since this is an update to our previous modeling method initially designed on SOSIPs28,29.

Initially, it was hypothesized that measuring the Solvent Accessible Surface Area, or SASA, as a metric of the degree of glycosylation site (NGS) exposure would be a quick and computationally efficient method of predicting the possibility of glycan burial, given that this site is later glycosylated without the need for the glycan to be present on the structure at the time of analysis. In theory, performing this for every NGS of each of the five viral glycoproteins would give two approximate ranges of SASA values separated by a central cutoff methods value over which an NGS’s position would or would not result in a buried glycan. A script was written in Python-364 in conjunction with the Biopython65 module for Python-3 to parse each unglycosylated protein structure in Protein Data Bank (PDB) format. A Shrake-Rupley algorithm66 for calculating SASA is then performed on the structure, and the SASA value of each protein residue with respect to its adjacent residues is appended to its metadata in the structure object. Examples of the resulting SASA of each potential NGS and control non-glycosylation site asparagine residues of HA and BG505 are plotted as shown in Supplemental Fig. 7 panels A and B.

Although cutoff values could be approximated as the threshold between prospective buried and unburied glycans somewhat clearly in the case of HA, no correlation could be determined between SASA and glycan burial for more heavily glycosylated proteins like BG505 and gp120/gp41, as shown in Supplemental Fig. 7 panel C. For this reason, a new method was developed to better classify glycan burial in a more robust, yet computationally taxing, manner.

As it was observed that most instances of glycan burial occurred at the tips of the structure and not the glycan ‘stalk’ to which the NGS is bound, a new method was developed in python to iterate over every residue in every glycan of a structure and calculate the distances from each glycan residue to the center of the protein scaffold and the carbohydrate residue’s closest surface point to the center of the protein scaffold, comparing these two values, and using this comparison to determine glycan burial; the logical details of this method are described further via the pictorial algorithmic flowchart provided in Supplemental Fig. 7 panel J. The burial depth in angstroms of each sugar residue of each glycan is then plotted on a heatmap for each structure to quickly determine each residue’s relative degree of burial, examples of glycan positions and their corresponding heatmaps can be seen for BG505 in Supplemental Fig. 7 panels D–I. Finally, an overall comparison between the entirety of the glycan shields of human influenza and HIV spike proteins can be seen in Supplemental Fig. 7 panels K and L, highlighting the much larger degree of glycan burial on HIV BG505. Our updated method has helped ensure that no glycans have been buried within folds of the underlying protein for the HA models used in this study.

Modeling the glycan ensemble. Based on PNGS sequence and cryo-EM data, the glycosylation sites present on the HK/68 and SING/16 variants were determined. MS was used to select the most probable glycoform at each PNGS when possible. For glycosylation sites where the structure could not be experimentally determined, the most common glycan type for these variants was assumed (fucosylated 2-antennae, FA2). Following the methods described earlier28,29, an ensemble of 3D conformations of the glycosylated hemagglutinins was generated in atomistic detail. For each hemagglutinin variant, 10 protein structures determined via cryo-EM were used as templates for glycosylation. Glycans were added to the known glycosylation sites with ideal geometries as determined by the CHARMM36 forcefield then randomized by 1Å across atomic coordinates. Following this randomization step, the glycan structures were relaxed via 1000 steps of conjugate gradient minimization. At this stage, the structures were equilibrated with a 500 ps molecular dynamics simulation. To ensure the conformational space was adequately sampled, five rounds of simulated annealing were performed in which the PNGS asparagine residues, loops, and glycans were unrestrained. For each of the 10 template structures, 100 glycosylated conformations were sampled. The protein backbones of these conformations were aligned to yield an ensemble of 1000 possible glycan orientations, which displays the glycan surface coverage.

Glycan root mean square fluctuations. To demonstrate the dynamics of each glycan, the root mean square fluctuations (RMSF) at three different positions were determined. These positions were the tip (C5 of the sugar furthest from the base), the core (C5 of the β-mannose), and the base (Cα of the glycosylated asparagine). The RMSF measures the conformational fluctuations across the 1000 poses and is averaged over the three protomers present in each hemagglutinin. Calculating the RMSF of each of these positions allowed for the comparison of the fluctuations of the glycan branches and stems, as well as the isolation of any backbone fluctuations.

Glycan encounter factor. The per-residue glycan encounter factor (GEF)28,29 was calculated for each variant to quantify the extent of which the glycans effectively shield the surface of the underlying protein from external probes, such as antibodies. To do so, the geometric mean of the number of heavy (non-hydrogen) atoms encountered by a cylindrical probe with a 6Å diameter approaching each residue of the proteins from the three Euclidean axes was averaged over the three protomers. A 6Å probe was used to approximate the size of a hairpin loop – mimicking the initial encounter contact by a typical antibody. A geometric mean was performed over the x, y, z directions to ensure a residue that is accessible from one direction, was considered unshielded. The GEF was normalized by a factor of 1.5 (approximately p=0.05), and any values greater than one were set equal to one to indicate shielding of that residue. A GEF of zero indicates no shielding of that residue from external probes. A surface representation of the two variants was colored by the GEF to show regions of glycan coverage.

Simulated Cryo-EM Volumes from Atomistic Ensembles. The simulated cryo-EM volumes (Supplementary Fig. S8) were generated with a custom Python script available at: https://github.com/ZTBioPhysics/Synthetic_Cryo-EM_Volume_Generator.git. This script generates synthetic cryo-EM volumes from a set of PDB files. Each PDB file is read to generate a 3D density volume by depositing a precomputed Gaussian kernel at the location of every atom. The script can optionally filter out non-protein atoms (e.g., include only standard amino acids), and simulate experimental imperfections such as alignment errors (random translations and rotations) and additive Gaussian noise. Finally, the script sums the individual volumes and computes an average volume, which is then saved in MRC format for further visualization (e.g., in ChimeraX). For coherent results, the PDB files need to be pre-aligned to a common reference.

Glycoproteomics analysis
Glycoproteomics sample preparation. For N-linked glycan analysis, the samples were denatured at 95°C in a final concentration of 2% sodium deoxycholate (SDC), 200 mM Tris/HCl, 10 mM tris(2-carboxyethyl)phosphine, pH 8.0 for 10 min followed with 30 min reduction at 37°C. Samples were next alkylated by adding 40 mM iodoacetamide and incubated in the dark at room temperature for 45 min. Samples were split in four for parallel digestion with trypsin (Promega), chymotrypsin (Roche), lys-C (Sigma), gluC (Sigma), and trypsin (Sigma). 2 μg sample was used for each protease digestion. For each protease digestion, the denatured, reduced, and alkylated samples was diluted in a total volume of 100 μL 50 mM ammonium bicarbonate, adding proteases in a 1:50 ratio (w:w) for incubation overnight at 37°C. After overnight digestion, SDC was removed through precipitation by adding 2 μL trifluoroacetic acid (TFA) and centrifugation at 14,000 × g for 20 min. Following centrifugation, the supernatant containing the peptides was collected for desalting on a 30 µm Oasis HLB 96-well plate (Waters). The Oasis HLB sorbent was activated with 100% acetonitrile and subsequently equilibrated with 0.1% TFA in water. Next, peptides were bound to the sorbent, washed twice with 0.1% TFA in water and eluted with 40 µL of 50% acetonitrile/0.1% TFA in water (v/v). The eluted peptides were vacuum-dried and resuspended in 100 μL of 0.1% TFA in water.

Glycoproteomics LC-MS/MS measurements. For each sample and protease digestion, approximately 200 ng of peptides were analyzed by online reversed-phase chromatography using an Agilent 1290 UHPLC system coupled to a Thermo Scientific Orbitrap Eclipse Tribrid mass spectrometer. Peptide separation was performed using a Poroshell 120 EC C18 (50 cm × 75 μm, 2.7 μm, Agilent Technologies) analytical column and a ReproSil-Pur C18 (2 cm × 100 μm, 3 μm, Dr. Maisch) trap column. Samples were eluted over a 90 min gradient ranging from 0 to 55% acetonitrile. Peptides were analyzed at a resolution of 120,000 in MS1. MS1 scans were acquired with an AGC target set to 4e5, a maximum injection time of 50 ms, and a scan range of m/z 350–2000. Precursors were selected using a 1.6 m/z isolation window and fragmented by higher-energy collisional dissociation (HCD) with a normalized collision energy of 28%. HCD MS2 spectra were recorded at a resolution of 30,000, using centroid data mode and a scan range of m/z 120–4000. Oxonium ion detection was used to trigger additional fragmentation of glycopeptides. Upon detection of diagnostic glycan fragment ions, an additional scan of the same precursor was acquired using electron-transfer/higher-energy collisional dissociation (EThcD) with calibrated, charge-dependent ETD parameters and 27% normalized collision energy for supplemental activation. EThcD MS2 spectra were acquired in the Orbitrap at a resolution of 60,000 with a scan range of m/z 120–4000.

Glycoproteomics data analysis. One set of four parallel protease digestions (trypsin, gluC, chymotrypsin, lysC) was defined as a replicate analysis. These digestions were performed in duplicate, and each analyzed in technical triplicate for a total of six replicate analyses for HK/68 and Sing/16. The acquired data was analyzed using Byonic (v5.9.125) against a custom database of recombinant influenza HA protein sequences and the proteases used in the experiment, searching for glycan modifications with 10/20 ppm search windows for MS1/MS2, respectively. Up to 4 missed cleavages were permitted using C-terminal cleavage at R/K for trypsin, 6 at E/D for gluC, 6 at WFLYM for chymotrypsin or 4 at K for lysC. For N-linked analysis, carbamidomethylation of cysteine was set as fixed modification, oxidation of methionine/tryptophan as variable rare 2. N-glycan modifications were set as variable common 2, allowing up to max. 2 variable common and 2 rare modifications per peptide. All N-linked glycan databases from Byonic were merged into a single non-redundant list to be included in the database search. The Byonic result files were filtered for glycopeptides with scores ≥200, and additional peptide-spectrum matches (PSMs) containing non-glycosylated asparagines at PNGS were included at the same score cutoff. Glycans were classified based on HexNAc content as truncated (≤2 HexNAc; <3 Hex), paucimannose (2 HexNAc, 3 Hex), high mannose (2 HexNAc; >3 Hex), hybrid (3 HexNAc) or complex (>3 HexNAc). Reported peptide spectral matches (PSMs) were pooled across the four parallel protease digestions, from which the fractions of unglycosylated, high-mannose, hybrid, and complex glycans were calculated and averaged over the six replicates. The Python script used for the analysis is provided in the PRIDE deposition.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript the authors describe structural and functional perturbations that differential N-linked glycosylation engenders on the influenza hemagglutinin (HA) protein.\nParticularly, the authors show that (1) influenza HAs have been accumulation the number of glycosylations from 1968 when H3N2 viruses began to circulate within the human host and today (2) that increased glycosylation of the HA head domain reduces the binding on head-directed bnAbs and shifts polyclonal antibody responses and (3) that enzymatic removal of the glycans results in a structural destabilization of the more glycosylated HA trimer.\nIn general, the manuscript presents sound data and is relatively well-written. However, outstanding questions remain:\nMajor:\nI don't quite agree with the statement about the interpretation of EMPEP and BLI data in lines 279-281. Indeed, simply because EMPEM with Fab does not show RBS-directed Ab classes, it does not mean those mAbs do not exist. EMPEM is performed at low protein concentrations and with Fab, therefore favoring only high-affinity interactions. Lower affinity Fab or IgG responses certainly exist; indeed the HAI data show higher values for the highly glycosylated HA. Therefore, RBS-directed responses should also be higher. The authors should address this experimentally and conceptually.\nMinor:\n1 - The summary statement mentions challenges in identifying conserved epitopes. This is untrue - stem and RBS epitopes are conserved and protective - the challenges posed are how to best elicit them with a suitable vaccine formulation and regimen.\n2 - The term vestigial esterase and esterase are used interchangeably throughout the manuscript in the main body and in the figures/legends. It should be 'vestigial esterase' throughout.\n3 - What are 'glycan heavy atoms'? Presumably non-hydrogen atoms?\n4 - In Figure 3, if the N22 glycan was not detected in mass spec, how was it modeled? i.e. which type of glycan was used for modeling?\n5 - In Figure 4, what is the N of technical replicates and independent experiments performed for the data in panel A? Is the difference observed for Sing/16 bnAb binding significant? In the same vein, there is a discrepancy about data interpretation in the main text and the discussion about the reactivity of bnAbs to HK/68 versus Sing/16.\n6 - In Figure 5D, what are the red versus bkue bars? In general, the figure lenends lack detail. Likewise, the methods section should be proof-read as there are typos (e.g., 20 mg/ml protein concentration for EMPEM) and visible track-changes in a couple of instances.", "reviewer_2": "This is an outstanding paper that will be of wide interest to virologists, viral immunologists, and glycobiologists.\nJust a few suggestions for the authors to consider.\n1. Expand on the differences in immunogenicity in ferrets of the three H3N2 viruses. I would mention that this is not necessarily intrinsic to the HA immunogens but might be related to differences in the infections themselves. To establish the former, it would be necessary to immunize with recombinant HAs.\n2. The effect of HA N-linked glycans on antibody binding has been reported to be temperature-sensitive in some cases (https://doi.org/10.1371/journal.ppat.1004204). Would be interesting to examine the effects of temperature (at least 37 degrees, higher as well if enthusiastic) on glycan blockading of Ab...easily examined by HI.", "reviewer_3": "Ward and co-workers analyzed the ~11,000 sequences of influenza HA protein variants over the decades to demonstrate the preferential increase of N-glycosylation sites within the head region of the HA compared to the stem region. They mapped the changes in N-glycosylation onto the structures of two representative variants, HK/68 and Sing/16, to highlight the effects of shielding by these glycans. The changes in high-mannose contents of the N-glycosylation sites are quantified by glycoproteomics analyses (without technical or biological replicates). They further used a previously developed modelling procedure to estimate the shielding effects of the N-glycans, which are more prominent in the head region. The increased glycan shielding effects may be responsible for shifting the antibody recognition from the head region to the stem region, as demonstrated by the EM-based polyclonal epitope mapping (EMPEM). Finally, the authors compared the high-resolution cryo-EM maps derived from HK/68 and SIng/16 with and without EndoH treatments to show that the removal of N-glycans leads to loss of well-defined EM density within the head region, an observation described as destabilization. The thermal dynamic effect of the EndoH treatment was investigated by differential scanning fluorimetry: there were no significant changes in the melting temperature for HK/68, while some differences were observed for Sing/16. Overall, the study confirmed existing literature about the periodic increase of N-glycosylation sites within the HA protein, and the effect of clustering that results in increased high-mannose contents of the crowded N-glycans. The increased protein surface shielding effects were illustrated by computational modelling as well as cryo-EM data processing to highlight the difference maps corresponding to the N-glycans. Nonetheless, most of the data are confirmative with limited new findings. Therefore, this work would be suited for a more specialized journal.\nMajor comments\n1. The domain definitions shown in Figure 1D should be moved to the very beginning of Figure 1 with 1D domain schematics to highlight the N-glycosylation sites as shown in the current Figure 1A/B to aid visual comparison of the pretty complex figures throughout the manuscript.\n2. Lines 132-137: It is not easy to link the stacked graph in Figure 1A with the long descriptions from line 102 to line 131 and to draw the conclusion about the less frequent and more stable glycosylation patterns within the esterase and stem regions.\n3. Figure 1C: It is unclear how the structural mapping of the same set of the ~11,000 sequences would lead to very different colouring patterns for HK/68 and Sing/16. Sing/16 also exhibits more N-glycans in the stem region than HK/68, i.e., the increased N-glycosylation sites are not limited to the head region. There are also regions coloured in grey/black within the cavities; it is hard to distinguish between the two structures. This is presumably due to the shadows, but in some places, the dark regions coincide with the N-glycan models, and the glycan models are not labelled. It would be good to improve the presentation to highlight the increased number and density of the N-glycans in the HA of Sing/16.\n4. Line 203: The authors should elaborate on how the change in high-mannose content observed for N165 in HA is in line with the previous results reported in ref. 24.\n5. Lines 245-248: The authors stated that the different glycosylation profiles of HK/68 and Sing/16 did not impact the stem-specific antibody CR8020 binding, but they do have an impact on the binding of the head-specific antibody, C05. When referring to Figure 5B, there are indeed changes in the OD485 readouts at high antibody concentrations of C05, but it is not apparent how much difference there is in the EC50 values. The corresponding EC50 should be presented and compared to demonstrate their differences are statistically significant.\n6. The BLI data presented in Figure 4Ci are not good. They are not quantitatively fit a specific binding model (typically a one-site binding model) to deduce the on- and off-rates to determine the binding affinity in dissociation constants. The binding phase (on-rate) of all three traces is not what is expected for a simple one-site binding. More importantly, the colours of the three traces are not defined, making it difficult to relate to the descriptions made in lines 267-271.\n7. The EMPEM analysis indeed showed a stem-binding Fab in Figure 4Civ for the sera against Sing/16 (blue volume) but in the same dataset, a head-binding Fab (purple volume) is also observed. The head-binding Fab binds to HA in a very similar pose as the one observed in the sera against NL03 (Figure 4Ciii). It is, therefore, not a clear cut as stated by the authors. More importantly, the EMPEM results themselves are not sufficient to support the conclusion drawn by the authors in lines 278-281: “Further, our EMPEM data along with our BLI analysis indicate that the polyclonal stem and esterase directed antibodies against Sing/16 not only effectively neutralize the virus but also, are potentially more efficient than the head directed antibodies against HK/68.”\n8. Supplementary Figure S3 is oversimplified. The data collection and the cascades of particle selections should be described in detail.\n9. The thermal denaturation data derived from differential scanning fluorimetry in Figure 5F shows very insignificant transitions for Sing/16 without EndoH treatment, raising the concern of whether the sample was intact and properly folded when subject to the thermal denaturation analysis. Additionally, it is not apparent that multiple transitions can be assigned to specific folding events if the system exhibits multiphasic unfolding processes. Even so, the authors highlighted the second transition peak for the EndoH-treated Sing/16 (red curve) to compare with the very small peak of the untreated Sing/16 (green curve), and the transition temperature for the untreated is, in fact, lower than the treated one. This is contrary to the statement made by the authors.\n10. Last but not least, can the authors use the modelling procedure presented in Figure 3A to correlate with the difference maps shown in Figure 5C to demonstrate the reliability of the modelling results?\nMinor comments\n1. Lines 76-78: Some references about the essential role of mass spectrometry in defining the N-glycosylations at specific PNGS should be included.\n2. Ref. 3 does not mention the glycan addition every 5- years as cross-references in line 97, but ref. 10 does.\n3. The figures are not made to the publication standards. Many labels are too small (Figure 1A, Figure 2F/G, Figure 5F, Supporting Figure S2, Supplementary Figure S7A/B). The FSC curve of Sing16/HEK293F is not plotted in the same way as the other four. They should be plotted consistently with high resolutions.\n4. Supplementary Figure S4 lists the r.m.s.d. values without units (presumably in Å).\n5. Lines 184 and 187: A Greek alpha should replace the “a” of Ca-root-mean-square deviation.\n6. Line 310: The authors stated that the cryo-EM maps of EndoH-treated HK/68 and Sing/16 are “nearly identical” to their natively glycosylated counterparts with reference to Figure S3. This should be Figure S4 instead.\n7. Lines 311-313: The statement about the more uniform spatial distribution is not adequately supported by the data presented in Figure S3. Quantitative angular distributions of the observed particle images should be given to support this statement.", "reviewer_4": "1. In the Introduction, the authors correctly pointed out that “the presence of a PNGS is not sufficient to guarantee glycan occupancy”. Therefore PNGS and glycans are not interchangeable terms. Yet, in the first section of the Results, prior to the second section on “Defining the structure and glycosylation profile of Sing/16”, the word “glycan” was used instead of PNGS in several places. For example,\nLn 96: glycan addition in H3 HAs is estimated to occur every 5 to 7 years - it is unclear if the authors refer only to PNGS, or that the actual additional glycan occupancy has been experimentally verified.\nLn 99: “We analyzed N-glycosidic linkages to the Asn residue of the glycosylation motif Asn-X-Ser/Thr accumulated on HA based on the analysis of ~11,000 sequences of naturally occurring H3 strains using the code previously described by Wu et al.” - It is unclear if the authors refer only to PNGS or have actually analyzed the glycans.\nLn 108: “Interestingly, these glycans all appear on esterase or stem regions of HA except for N165, which is in the globular head at the interface between protomers (Figure 1B). Other glycans that also appear on esterase or stem regions on HA such as N45, N63, N81 and N276, appear transiently (Figure 1A-B).” - it would seem that the authors actually meant only the PNGS and NOT glycans.\nLn 120: “By analyzing the glycans present on A/Hong Kong/1/1968 (HK/68) …….. Again, here the authors described and refer to Fig 1 when it appears to be referring to PNGS only.\n2. Ln 197: “We lack coverage at N22 and N81 on HK/68, and N45 and N63 on Sing/16, either due to low peptide detection or poor fragmentation.” - the authors could, in principle, look at the MS dataset and verify if any of the non-glycosylated peptides were detected versus any potential glycosylated versions. If it is due to poor MS2 fragmentation, the authors could verify if putative glycopeptide peaks were present at MS1 level. In fact, the authors did not address the degree of site occupancy. Analytically, this is not trivial. The authors should, however, be more precise in their choice of words. What does it mean by “low peptide detection” or “poor fragmentation”? In contrast, the authors appear to have detected non-glycosylated N122 peptide from the Sing/16 sample but neither the glycosylated nor the unglycosylated N122 peptide was detected in the HK/68 sample. Is it not detected or low peptide detection, or detected but poor fragmentation?\n3. No Suppl data on the glycopeptide identification by Byonic search results was provided and hence the quality cannot be assessed. The authors stated that “The raw LC-MS/MS files and analyses have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD051492” A quick look up using the identifier cannot find any matching dataset.\nThe statement in the Methods section: All reported glycopeptides in the Byonic result files were manually inspected for quality of fragment assignments (with scores ≥ 200). This is not a very convincing criterion although commonly adopted in the field. For current purposes (Fig 2F, G), the number of mismatches or misassignments would perhaps have no significant consequence on the big picture. It should be noted that to classify glycosyl composition with HexNAc=3 as hybrid type can be misleading. HexNAc3 can also be contributed by complex type biantennary glycans with diLacNAc or lacdiNAc on one antenna, or a triantennary glycan.\n4. It will be good to do similar LC-MS/MS analysis of endoH-treated sample and see if those sites classified as carrying the highest proportion of high mannose structures are indeed those most affected."}, "author_response": "Response to Reviewers for Manuscript NCOMMS-24-73049\n(Original reviewers’ comments in black, our response in green)\nReviewer #1 (Remarks to the Author):\nIn this manuscript the authors describe structural and functional perturbations that\ndifferential N-linked glycosylation engenders on the influenza hemagglutinin (HA) protein.\nParticularly, the authors show that (1) influenza HAs have been accumulation the number of\nglycosylations from 1968 when H3N2 viruses began to circulate within the human host and\ntoday (2) that increased glycosylation of the HA head domain reduces the binding on head-\ndirected bnAbs and shifts polyclonal antibody responses and (3) that enzymatic removal of\nthe glycans results in a structural destabilization of the more glycosylated HA trimer.\nIn general, the manuscript presents sound data and is relatively well-written.\nWe sincerely appreciate the positive feedback from the reviewer on the manuscript.\nHowever, outstanding questions remain:\nMajor:\nI don't quite agree with the statement about the interpretation of EMPEP and BLI data in lines\n279-281. Indeed, simply because EMPEM with Fab does not show RBS-directed Ab classes,\nit does not mean those mAbs do not exist. EMPEM is performed at low protein concentrations\nand with Fab, therefore favoring only high-affinity interactions. Lower affinity Fab or IgG\nresponses certainly exist; indeed, the HAI data show higher values for the highly glycosylated\nHA. Therefore, RBS-directed responses should also be higher. The authors should address\nthis experimentally and conceptually.\nWe thank the reviewer for the helpful comments. In response, we have added competition\nELISA data using both Fabs and polyclonal IgG, to further clarify this point. As described in\nline 269-279 and Figure 4B-D, competition for the head domain is increased in HK/68 and\nNL03 compared to Sing/16, highlighting the impact of glycosylation on antibody binding. As\nsuggested by the reviewer, we attempted to perform EMPEM using polyclonal IgG. However,\nwe observed substantial aggregation and crosslinking between IgGs, which prevented\nsuccessful image processing. Therefore, we optimized our EMPEM protocol to improve Fab-\nantigen complex recovery. Specifically, after complexing the antigen with Fabs, samples\nwere not passed through a SEC column and instead we removed unbound Fabs using 100\nkDa Amicon filters and centrifuging at slow speed (Methods line 521-526). This adjustment\nenabled us to confirm Fab binding to the head of HK/68 and to detect head-binding Fabs to\nNL03 that were not previously observed (Figure 4B-C). However, we still did not detect Fab\nbinding to the head of Sing/16. Additionally, we performed a competition ELISA using a panel\nof bNAbs against the RBS (S139/1, K03.28, TJ5-5, C05 and F045-92) and against the stem\n(CR9114) and using polyclonal IgG and Fabs isolated from reference ferret sera (Figure 4A).\nSimilarly to the EMPEM data, while we observed competition of RBS antibodies and HK/68\nand NL/03 sera, we only observed competition of stem antibodies in Sing/16 sera. Thus, while\nFabs targeting the head domain may be present in the Sing/16 response, our EMPEM and\ncompetition ELISA data suggest they are of low abundance (Figure 4).\nMinor:\n1 - The summary statement mentions challenges in identifying conserved epitopes. This is\nuntrue - stem and RBS epitopes are conserved and protective - the challenges posed are how\nto best elicit them with a suitable vaccine formulation and regimen.\nWe thank the reviewer for pointing this out, the revised manuscript includes a sentence in the\nsummary section with the correct statement (line 33-34).\n2 - The term vestigial esterase and esterase are used interchangeably throughout the\nmanuscript in the main body and in the figures/legends. It should be 'vestigial esterase'\nthroughout.\nWe thank the reviewer for this suggestion. The correct name is vestigial esterase and this has\nnow been changed through the text and figures.\n3 - What are 'glycan heavy atoms'? Presumably non-hydrogen atoms?\nWe thank the reviewer for raising this question. Indeed, in modeling, ‘heavy atoms’ refers to\nall non-hydrogen atoms. This has now been clarified (line 223 and lines 662-663).\n4 - In Figure 3, if the N22 glycan was not detected in mass spec, how was it modeled? i.e.\nwhich type of glycan was used for modeling?\nThe reviewer raises an important point on how the modelling was done without mass\nspectrometry data. In that case, the most common glycan type for these structures (FA2) was\nused (Line 724-743).\n5 - In Figure 4, what is the N of technical replicates and independent experiments performed\nfor the data in panel A? Is the difference observed for Sing/16 bnAb binding significant? In the\nsame vein, there is a discrepancy about data interpretation in the main text and the\ndiscussion about the reactivity of bnAbs to HK/68 versus Sing/16.\nWe thank the reviewer for pointing this out. Experiments were done in duplicate and this is\nnow stated in the legend of Figure 4 and S5 and S6. To test the significance of antibody binding\nbetween recombinantly expressed hemagglutinins in 293T, GnTI- and endo-H treated in a\npanel of five head antibodies and one stem antibody, we did an ELISA and found that there\nare minimal, insignificant differences regarding the affinity of the head and stem antibodies\nbetween differently glycosylated HAs. We have added a panel in Figure 4 (panel A) and\nSupplementary Figure 5 with the broader panel of head and stem antibodies and wrote the\nresults and discussion accordingly (lines 243-249). Finally, we have revised the text to clarify\nthe interpretation of bnAb reactivity to HK/68 versus Sing/16 and have also updated the\ncorresponding figure (Figure 4A and S5) to ensure consistency with the revised interpretation\n(Page 11-14)\n6 - In Figure 5D, what are the red versus blue bars? In general, the figure legends lack detail.\nLikewise, the methods section should be proof-read as there are typos (e.g., 20 mg/ml\nprotein concentration for EMPEM) and visible track-changes in a couple of instances.\nWe thank the reviewer for pointing this out. We have added more detail in Figure 5 panel D\nand in the figure legend. Additionally, we incorporated a supplementary figure to have more\ncontext on panel C (Supplementary Figure 8). The supplementary figure shows the simulated\ncryo-EM volumes from atomistic ensembles (Figure S8). Specifically, it shows synthetic cryo-\nEM density maps generated from the 1000 atomistic models of fully glycosylated HK/68\n(blue) and Sing/16 (green) HAs are shown at high and low contour levels from the top and side\n(Figure S8 and Figure 3). Also, we have proof-read the methods sections and polished it\naccordingly.\nReviewer #2 (Remarks to the Author):\nThis is an outstanding paper that will be of wide interest to virologists, viral immunologists,\nand glycobiologists.\nWe sincerely appreciate the positive feedback and enthusiasm from the reviewer on the\nmanuscript.\nJust a few suggestions for the authors to consider.\n1. Expand on the differences in immunogenicity in ferrets of the three H3N2 viruses. I would\nmention that this is not necessarily intrinsic to the HA immunogens but might be related to\ndifferences in the infections themselves. To establish the former, it would be necessary to\nimmunize with recombinant HAs.\nThe reviewer raises a valid point, and we have changed the text accordingly (Line 257-271)\nWe have further addressed this point in the discussion (Lines 364–371).\n2. The effect of HA N-linked glycans on antibody binding has been reported to be\ntemperature-sensitive in some cases (https://doi.org/10.1371/journal.ppat.1004204).\nWould be interesting to examine the effects of temperature (at least 37 degrees, higher as\nwell if enthusiastic) on glycan blockading of Ab...easily examined by HI.\nWe agree with the reviewer that it would be interesting to examine the effects of temperature\non glycan blockading of antibodies, however, we believe it is beyond the scope of this paper.\nReviewer #3 (Remarks to the Author):\nWard and co-workers analyzed the ~11,000 sequences of influenza HA protein variants over\nthe decades to demonstrate the preferential increase of N-glycosylation sites within the head\nregion of the HA compared to the stem region. They mapped the changes in N-glycosylation\nonto the structures of two representative variants, HK/68 and Sing/16, to highlight the effects\nof shielding by these glycans. The changes in high-mannose contents of the N-glycosylation\nsites are quantified by glycoproteomics analyses (without technical or biological replicates).\nThe original glycoproteomics analyses are based on the combined findings of four parallel\nprotease digestions. While it is true that each was analyzed by a single LC-MS/MS run, the\nredundant coverage across the different protease digestions does make for a more robust\nestimate of the relative glycan profiles across the N-linked glycosylation sites of HA. That\nbeing said, we appreciate the reviewer’s concerns and opted to redo our glycoproteomics\nanalyses with additional replicates. The revised manuscript now reports glycoproteomics\nfindings of four parallel protease digestions performed in duplicate, each in technical\ntriplicate, for a total of six replicate analyses per sample. These new analyses replace the\noriginally reported glycoproteomics experiments as the original MS instrument used for the\nfirst experiments was recently decommissioned. The newly reported experiments are\nperformed on an improved OrbiTrap TriBid mass spectrometer (Orbitrap Eclipse). The new\nfindings are fully in line with the original experiments; if anything, we find a more pronounced\neffect of increased underprocessed high-mannose glycans in the Sing/16 sample (Figure 2F-\nG).\nThey further used a previously developed modelling procedure to estimate the shielding\neffects of the N-glycans, which are more prominent in the head region. The increased glycan\nshielding effects may be responsible for shifting the antibody recognition from the head\nregion to the stem region, as demonstrated by the EM-based polyclonal epitope mapping\n(EMPEM). Finally, the authors compared the high-resolution cryo-EM maps derived from\nHK/68 and SIng/16 with and without EndoH treatments to show that the removal of N-glycans\nleads to loss of well-defined EM density within the head region, an observation described as\ndestabilization. The thermal dynamic effect of the EndoH treatment was investigated by\ndifferential scanning fluorimetry: there were no significant changes in the melting\ntemperature for HK/68, while some differences were observed for Sing/16. Overall, the study\nconfirmed existing literature about the periodic increase of N-glycosylation sites within the\nHA protein, and the effect of clustering that results in increased high-mannose contents of\nthe crowded N-glycans. The increased protein surface shielding effects were illustrated by\ncomputational modelling as well as cryo-EM data processing to highlight the difference maps\ncorresponding to the N-glycans. Nonetheless, most of the data are confirmative with limited\nnew findings. Therefore, this work would be suited for a more specialized journal.\nWe thank the reviewer for the thoughtful feedback. While our results confirm general trends\npreviously reported in the literature, such as the progressive addition of N-glycosylation sites\nand the clustering of high-mannose glycans, our study goes further by integrating cryo-EM,\npolyclonal epitope mapping, computational modeling, glycoproteomics, and immunoassays\nto examine how these changes influence antibody binding and HA structure. This approach\ngoes beyond previous reports by linking glycosylation patterns to functional antibody binding\nand competition in a structurally resolved context. We hope the reviewer will agree that this\nmechanistic perspective advances our understanding of HA evolution and immunogenicity\nin a way that is relevant to virology, structural biology and influenza vaccine design.\nMajor comments\n1. The domain definitions shown in Figure 1D should be moved to the very beginning of Figure\n1 with 1D domain schematics to highlight the N-glycosylation sites as shown in the current\nFigure 1A/B to aid visual comparison of the pretty complex figures throughout the manuscript.\nWe agree with the reviewer and have rearranged Figure 1 accordingly. The domain definitions\noriginally shown in Figure 1D have been moved to the beginning of the figure to aid in visual\ncomparison and improve clarity in the presentation of N-glycosylation sites across the HA\nstructure.\n2. Lines 132-137: It is not easy to link the stacked graph in Figure 1A with the long descriptions\nfrom line 102 to line 131 and to draw the conclusion about the less frequent and more stable\nglycosylation patterns within the esterase and stem regions.\nWe appreciate the reviewers’ comments and the paragraph was rewritten and restructured\nfor better clarity (Lines 107-130).\n3. Figure 1C: It is unclear how the structural mapping of the same set of the ~11,000\nsequences would lead to very different colouring patterns for HK/68 and Sing/16. Sing/16\nalso exhibits more N-glycans in the stem region than HK/68, i.e., the increased N-\nglycosylation sites are not limited to the head region. There are also regions coloured in\ngrey/black within the cavities; it is hard to distinguish between the two structures. This is\npresumably due to the shadows, but in some places, the dark regions coincide with the N-\nglycan models, and the glycan models are not labelled. It would be good to improve the\npresentation to highlight the increased number and density of the N-glycans in the HA of\nSing/16.\nWe thank the reviewer for raising this point. We agree that the conservation patterns between\nHK/68 and Sing/16 are very similar because even if the amino acid sequence is different the\nconservation per residue will be similar. For clarity and because glycosylation differs\nbetween strains, we show both HK68 and Sing16 and we have changed the shadowing to\nmake sure all the glycans are visible and labelled (Figure 1D).\n4. Line 203: The authors should elaborate on how the change in high-mannose content\nobserved for N165 in HA is in line with the previous results reported in ref. 24.\nWe thank the reviewer for the comment and we have addressed the issue in the text (line 202-\n207).\n5. Lines 245-248: The authors stated that the different glycosylation profiles of HK/68 and\nSing/16 did not impact the stem-specific antibody CR8020 binding, but they do have an\nimpact on the binding of the head-specific antibody, C05. When referring to Figure 5B, there\nare indeed changes in the OD485 readouts at high antibody concentrations of C05, but it is\nnot apparent how much difference there is in the EC50 values. The corresponding EC50\nshould be presented and compared to demonstrate their differences are statistically\nsignificant.\nWe thank the reviewer for raising this point. To test the significance of antibody binding\nbetween recombinantly expressed hemagglutinins in 293T, GnTI- and endo-H treated in a\npanel of five head antibodies and one stem antibody, we did multiple ELISAs using a panel of\nRBS binding antibodies as well as one stem antibody. We calculated the EC50s and the AUC\nand observed minimal differences, not significant, regarding the affinity of the head and stem\nantibodies between differently glycosylated HAs. Thus, RBS antibodies bind similarly\nbetween strains and differently glycosylated HAs. We have added a panel in Figure 4 (Figure\n4A) with the panel of head and stem antibodies and wrote the results and discussion\naccordingly (243-249).\n6. The BLI data presented in Figure 4Ci are not good. They are not quantitatively fit a specific\nbinding model (typically a one-site binding model) to deduce the on- and off-rates to\ndetermine the binding affinity in dissociation constants. The binding phase (on-rate) of all\nthree traces is not what is expected for a simple one-site binding. More importantly, the\ncolours of the three traces are not defined, making it difficult to relate to the descriptions\nmade in lines 267-271.\nWe thank the reviewer for pointing this out. While we agree that when using monoclonal\nantibodies, we can calculate dissociation constants because each antibody binds one\nepitope of the antigen at a specific affinity and dissociation constant, here, we tested a\npolyclonal antibody sample against one antigen. A polyclonal antibody sample contains\nantibodies with several specificities, specifically, antibodies that bind different epitopes.\nBecause of that, we cannot calculate the association and dissociation constants. BLI was\nperformed using polyclonal antibody samples as a control and to assess binding of the\nantibodies to the match antigens.\n7. The EMPEM analysis indeed showed a stem-binding Fab in Figure 4Civ for the sera against\nSing/16 (blue volume) but in the same dataset, a head-binding Fab (purple volume) is also\nobserved. The head-binding Fab binds to HA in a very similar pose as the one observed in the\nsera against NL03 (Figure 4Ciii). It is, therefore, not a clear cut as stated by the authors. More\nimportantly, the EMPEM results themselves are not sufficient to support the conclusion\ndrawn by the authors in lines 278-281: “Further, our EMPEM data along with our BLI analysis\nindicate that the polyclonal stem and esterase directed antibodies against Sing/16 not only\neffectively neutralize the virus but also, are potentially more efficient than the head directed\nantibodies against HK/68.” The purple (now pink) volume depicts the vestigial esterase\nepitope (Figure 4B-D). This region is within the head domain but antibodies binding to that\nregion are a distinct class of antibodies compared to the stem epitope, as has been\npreviously defined in the literature (Lines 96-97) For clarity, we have included a schematic\nrepresentation of the epitopes targeted and the name of each epitope in a panel of Figure\nS6C.\nWe have changed this statement and added data from competition ELISA (see reviewer’s 1\nanswer to question 1) for clarity (Line 272-279).\n8. Supplementary Figure S3 is oversimplified. The data collection and the cascades of\nparticle selections should be described in detail.\nWe thank the reviewer for pointing this out. We have updated Figure S3 and expanded the\ndescription of data collection and processing to provide additional detail.\n9. The thermal denaturation data derived from differential scanning fluorimetry in Figure 5F\nshows very insignificant transitions for Sing/16 without EndoH treatment, raising the concern\nof whether the sample was intact and properly folded when subject to the thermal\ndenaturation analysis. Additionally, it is not apparent that multiple transitions can be\nassigned to specific folding events if the system exhibits multiphasic unfolding processes.\nEven so, the authors highlighted the second transition peak for the EndoH-treated Sing/16\n(red curve) to compare with the very small peak of the untreated Sing/16 (green curve), and\nthe transition temperature for the untreated is, in fact, lower than the treated one. This is\ncontrary to the statement made by the authors.\nWe thank the reviewer for raising these points. We have assessed the conformation of the HA\ntrimers before nano-DSF by negative stain EM and have added it to the supplementary data\n(Figure S6B). Additionally, we have repeated the experiment with the same amount of protein\nand getting similar size peaks in nano-DSF and have adjusted the statement with the new\nresults, similar to the ones we had before (Figure 5F).\n10. Last but not least, can the authors use the modelling procedure presented in Figure 3A to\ncorrelate with the difference maps shown in Figure 5C to demonstrate the reliability of the\nmodelling results?\nWe thank the reviewer for raising this point. We incorporated a supplementary figure to have\nmore context on panel C. The supplementary figure shows the simulated cryo-EM volumes\nfrom atomistic ensembles, specifically, it shows synthetic cryo-EM density maps generated\nfrom the 1000 atomistic models (Figure 3) of fully glycosylated HK/68 (blue) and SING/16\n(green) HAs shown at high and low contour levels from the top and side (Figure S8) (Line 315-\n319)\nMinor comments\n1. Lines 76-78: Some references about the essential role of mass spectrometry in defining\nthe N-glycosylations at specific PNGS should be included.\nWe thank the reviewer for the helpful comment. We have added two additional references to\nhighlight the essential role of mass spectrometry in defining N-glycosylation at specific\nPNGSs (Page 3; Line 76; Ref 14,15).\n2. Ref. 3 does not mention the glycan addition every 5- years as cross-references in line 97,\nbut ref. 10 does. We thank the reviewer for the helpful comment and have fixed the issue.\n3. The figures are not made to the publication standards. Many labels are too small (Figure\n1A, Figure 2F/G, Figure 5F, Supporting Figure S2, Supplementary Figure S7A/B.The FSC curve\nof Sing16/HEK293F is not plotted in the same way as the other four. They should be plotted\nconsistently with high resolutions.\nWe thank the reviewer for the helpful comment and have corrected the references. Regarding\nthe figures, we agree with the reviewer that several elements were not initially up to\npublication standards. We have revised Figures 1A, 2F/G, 4, 5F, Supplementary Figure 2,\nwhich we added the excel graph as supplementary data 1, and S9A/B (old S7A/B) to improve\nlabel size and clarity.\nWe also acknowledge the inconsistency in the FSC plot for Sing/16 produced in HEK293T\ncells. This discrepancy arises because this map was processed using RELION 4.0, while the\nother reconstructions were processed in cryoSPARC. We have added this information in the\nfigure legend for clarity (Figure S2C).\n4. Supplementary Figure S4 lists the r.m.s.d. values without units (presumably in Å). We thank\nthe reviewer for pointing this out. We have added the units (Å) to the figure and the figure\nlegend.\n5. Lines 184 and 187: A Greek alpha should replace the “a” of Ca-root-mean-square\ndeviation. We have replaced the a in the text (Line 184 and 653).\n6. Line 310: The authors stated that the cryo-EM maps of EndoH-treated HK/68 and Sing/16\nare “nearly identical” to their natively glycosylated counterparts with reference to Figure S3.\nThis should be Figure S4 instead. We thank the reviewer for pointing this out. We have\nchanged that in the text. It is now S3 because we rearranged supplementary figures.\n7. Lines 311-313: The statement about the more uniform spatial distribution is not\nadequately supported by the data presented in Figure S3. Quantitative angular distributions\nof the observed particle images should be given to support this statement.\nWe thank the reviewer for this comment. To address this, we have added view distribution\nplots from CryoSPARC to Figure S2 (panel D), which quantitatively illustrate the spatial\ndistribution of particle orientations.\nReviewer#4 (Remarks to the Author):\n1. In the Introduction, the authors correctly pointed out that “the presence of a PNGS is not\nsufficient to guarantee glycan occupancy”. Therefore PNGS and glycans are not\ninterchangeable terms. Yet, in the first section of the Results, prior to the second section on\n“Defining the structure and glycosylation profile of Sing/16”, the word “glycan” was used\ninstead of PNGS in several places. For example,\nLn 96: glycan addition in H3 HAs is estimated to occur every 5 to 7 years - it is unclear if the\nauthors refer only to PNGS, or that the actual additional glycan occupancy has been\nexperimentally verified. We thank the reviewer for this important note. The statement is in\nreference to published work see ref Altman et al., mBio 2019 “Human Influenza A Virus\nHemagglutinin Glycan Evolution Follows a Temporal Pattern to a Glycan Limit”, which\ncombined sequence analysis with biochemical methods to estimate the periodic acquisition\nof glycan on HA (Line 101).\nLn 99: “We analyzed N-glycosidic linkages to the Asn residue of the glycosylation motif Asn-\nX-Ser/Thr accumulated on HA based on the analysis of ~11,000 sequences of naturally\noccurring H3 strains using the code previously described by Wu et al.” - It is unclear if the\nauthors refer only to PNGS or have actually analyzed the glycans. We thank the reviewer for\nthe comment. We have revised the text for clarity to specify that, in Figure 1, our analysis was\nlimited to the presence of predicted PNGSs.\nLn 108: “Interestingly, these glycans all appear on esterase or stem regions of HA except for\nN165, which is in the globular head at the interface between protomers (Figure 1B). Other\nglycans that also appear on esterase or stem regions on HA such as N45, N63, N81 and N276,\nappear transiently (Figure 1A-B).” - it would seem that the authors actually meant only the\nPNGS and NOT glycans. We thank the reviewer for the comment and have restructured the\ntext to improve clarity (lines 107-130).\nLn 120: “By analyzing the glycans present on A/Hong Kong/1/1968 (HK/68) …….. Again, here\nthe authors described and refer to Fig 1 when it appears to be referring to PNGS only.\nWe thank the reviewer for the comment and have restructured the text to improve clarity.\n2. Ln 197: “We lack coverage at N22 and N81 on HK/68, and N45 and N63 on Sing/16, either\ndue to low peptide detection or poor fragmentation.” - the authors could, in principle, look at\nthe MS dataset and verify if any of the non-glycosylated peptides were detected versus any\npotential glycosylated versions. If it is due to poor MS2 fragmentation, the authors could\nverify if putative glycopeptide peaks were present at MS1 level. In fact, the authors did not\naddress the degree of site occupancy. Analytically, this is not trivial. The authors should,\nhowever, be more precise in their choice of words. What does it mean by “low peptide detection”\nor “poor fragmentation”? In contrast, the authors appear to have detected non-\nglycosylated N122 peptide from the Sing/16 sample but neither the glycosylated nor the\nunglycosylated N122 peptide was detected in the HK/68 sample. Is it not detected or low\npeptide detection, or detected but poor fragmentation?\nThe database search included identification of the unmodified asparagine. When we\nidentified it, it is reported as such. The missing sites should not be interpreted as lacking a\nglycan, but rather as blind spots in the experiment. Occupancy is in fact addressed in the\noriginal figure like for N122 in Sing/16 sample. For the other sites no unoccupied asparagines\nwere identified; to the best of our knowledge they are completely occupied up to the\ndetection limit of our method. We are hesitant to infer anything from MS1 levels alone\nbecause it yields too many false positives without opportunities to validate IDs based on\nfragmentation patterns. Please note that the glycoproteomics analyses have been redone on\na new instrument platform with additional replicate analyses. This has improved our\ncoverage to now also include N45 in Sing/16 and we recovered additional unglycosylated\nasparagines at sites beyond N122 in both HK/68 and Sing/16 samples.\n3. No Suppl data on the glycopeptide identification by Byonic search results was provided\nand hence the quality cannot be assessed. The authors stated that “The raw LC-MS/MS files\nand analyses have been deposited to the ProteomeXchange Consortium via the PRIDE\npartner repository with the dataset identifier PXD051492” A quick look up using the identifier\ncannot find any matching dataset.\nWe regret that the PRIDE login details have not been made available earlier. The data can be\naccessed under identifier PXD051492 using the following login details:\nReviewer account details:\nUsername: reviewer_pxd051492@ebi.ac.uk\nPassword: 4o1VxgX7\nThe statement in the Methods section: All reported glycopeptides in the Byonic result files\nwere manually inspected for quality of fragment assignments (with scores ≥ 200). This is not\na very convincing criterion although commonly adopted in the field. For current purposes (Fig\n2F, G), the number of mismatches or misassignments would perhaps have no significant\nconsequence on the big picture. It should be noted that to classify glycosyl composition with\nHexNAc=3 as hybrid type can be misleading. HexNAc3 can also be contributed by complex\ntype biantennary glycans with diLacNAc or lacdiNAc on one antenna, or a triantennary\nglycan.\nWe thank the reviewer for this insightful comment. As stated by the reviewer the score cutoff\nis widely used. Moreover, it is a lower limit. The scores range from 200 to 1717, with a\nmean/median of 549/483 across the datasets for both HK/68 and Sing/16\nThere appears to be a misunderstanding about the hybrid classification. The counts of\nHexNAc residues include the core pentasaccharide, so we start counting at 2. Hybrids have\n+1 on one arm (exactly 3), complex +1 for every antenna bringing the total to >=4. The only\nrisk we have in this classification is if the processed arm of the hybrid glycans contain\ndiLacNAc or LacdiNAc to bring its total to 4 or higher, very rare and the converse of what the\nreviewer suggests. This glycan classification is widely adopted in the glycoproteomics field.\n4. It will be good to do similar LC-MS/MS analysis of endoH-treated sample and see if those\nsites classified as carrying the highest proportion of high mannose structures are indeed\nthose most affected.\nWe thank the reviewer for the suggestion. While LC-MS/MS analysis of Endo H-treated\nsamples could in principle provide further support for the high-mannose classification at\nspecific sites, we believe this additional validation is not essential for the current study. The\nassignment of high-mannose glycoforms was based on well-established MS-based\nworkflows and stringent manual curation, consistent with standard practices in the\nglycoproteomics field."}, {"round": 2, "reviewer_comments": {"reviewer_1": "No further comments.", "reviewer_3": "The authors have sufficiently addressed all my comments and I would recommend the acceptance of the manuscript in its current form.", "reviewer_4": "I have no further questions on the issues I raised. I do acknowledge that I made a mistake in writing that “HexNAc3 can also be contributed by complex type biantennary glycans with diLacNAc or lacdiNAc on one antenna, or a triantennary glycan.” What I had in mind and should actually say is that HexNAc>3, specifically HexNAc=4, may still be a hyrbid type in situation when the extra HexNAc is contributed by a diLacNAc or lacdiNAc on the single extended antenna, or a bisecting GlcNAc. In any case, it is true that this glycan classification is widely adopted although it may misclassify some of the hybrid type structures as complex type."}, "author_response": ""}]

Decision letter