The antigenic landscape of N1 neuraminidase in human influenza A virus strains isolated between 2009 and 2020

Reviewer #1 (Public review):

Summary:

In this paper, the authors have performed an antigenic assay for human seasonal N1 neuraminidase using antigens and mouse sera from 2009-2020 (with one avian N1 antigen). This shows two distinct antigen groups. There is poorer reactivity with sera from 2009-2012 against antigens from 2015-2019, and poorer reactivity with sera from 2015-2020 against antigens from 2009-2013. There is a long branch separating these two groups. However, 321 and 423 are the only two positions that are consistently different between the two groups. Therefore these are the most likely cause of these antigenic differences.

Strengths:

(1) A sensible rationale was given for the choice of sera, in terms of the genetic diversity.

(2) There were two independent batches of one of the antigens used for generating sera, which demonstrated the level of heterogeneity in the experimental process.

(3) Replicate of the Wisconsin/588/2019 antigen (as H1 and H6) is another useful measure of heterogeneity.

(4) The presentation of the data, e.g. Figure 2, clearly shows two main antigenic groups.

(5) The most modern sera are more recent than other related papers, which demonstrates that has been no major antigenic change.

Weaknesses:

(1) Issues with experimental methods
As I am not an experimentalist, I cannot comment fully on the experimental methods. However, I note that BALB/c mice sera were used, whereas outbred ferret sera are typically used in influenza antigenic characterisation, so the antigenic difference observed may not be relevant in humans. Similarly, the mice were immunised with an artificial NA immunogen where the typical approach would be to infect the ferret with live virus intra-nasally.

(2) Five mice sera were generated per immunogen and then pooled, but data was not presented that demonstrated these sera were sufficiently homogenous that this approach is valid.

(3) There were no homologous antigens for most of the sera. This makes the responses difficult to interpret as the homologous titre is often used to assess the overall reactivity of a serum. The sequence of the antigens used is not described, which again makes it difficult to interpret the results.

(4) To be able to untangle the effects of the individual substitutions at 321, 386, and 432, it would have been useful to have included the naturally occurring variants at these positions, or to have generated mutants at these positions. Gao et al clearly show an antigenic difference with ferret sera correlated separately with N386K and I321V/K432E.

(5) The challenge experiments in Gao et al showed that NI titre was not a good correlate of protection, so that limits the interpretation of these results.

Issues with the computational methods

(6) The NAI titres were normalised using the ELISA results, and the motivation for this is not explained. It would be nice to see the raw values.

(7) It is not clear what value the random forest analysis adds here, given that positions 321 and 432 are the only two that consistently differ between the two groups.

(8) As with the previous N2 paper, the metric for antigenic distance (the root mean square of the difference between the titres for two sera) is not one that would be consistent when different sera are included. More usual metrics of distance are Archetti-Horsfall, fold down from homologous, or fold down from maximum.

(9) Antigenic cartography of these data is fraught. I wonder whether 2 dimensions are required for what seems like a 1-dimensional antigenic difference - certainly, the antigens, excluding the H5N1, are in a line. The map may be skewed by the high reactivity Brisbane/18 antigen. It is not clear if the column bases (normalisation factors for calculating antigenic distance) have been adjusted to account for the lack of homologous antigens. It is typical to present antigenic maps with a 1:1 x:y ratio.

Issues with interpretation

(10) Figure 2 shows the NAI titres split into two groups for the antigens, however, A/Brisbane is an outlier in the second antigenic group with high reactivity.

(11) Following Gao et al, I think you can claim that it is more likely that the antigenic change is due to K432E than I321V, based on a comparison of the amino acid change.

Appraisal:

Taking into account the limitations of the experimental techniques (which I appreciate are due to resource constraints), this paper meets its aim of measuring the antigenic relationships between 2009-2020 seasonal N1s, showing that there were two main groups. The authors discovered that the difference between the two antigenic groups was likely attributable to positions 321 and 432, as these were the only two positions that were consistently different between the two groups. They came to this finding by using a random forest model, but other simpler methods could have been used.

Impact:

This paper contributes to the growing literature on the potential benefit of NA in the influenza vaccine.

Reviewer #2 (Public review):

Summary:

In this study, Catani et al. have immunized mice with 17 recombinant N1 neuraminidases (NAs) from human isolates circulating between 2009-2020 to investigate antigenic diversity. NA inhibition (NAI) titers revealed two groups that were antigenically and phylogenetically distinct. Machine learning was used to estimate the antigenic distances between the N1 NAs and mutations at residues K432E and I321V were identified as key determinants of N1 NA antigenicity.

Strengths:

Observation of mutations associated with N1 antigenic drift.

Weaknesses:

Validation that K432E and I321V are responsible for antigenic drift was not determined in a background strain with native K432 and I321 or the restitution of antibody binding by reversion to K432 and I321 in strains that evaded sera.

Author rsponse:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this paper, the authors have performed an antigenic assay for human seasonal N1 neuraminidase using antigens and mouse sera from 2009-2020 (with one avian N1 antigen). This shows two distinct antigen groups. There is poorer reactivity with sera from 2009-2012 against antigens from 2015-2019, and poorer reactivity with sera from 2015-2020 against antigens from 2009-2013. There is a long branch separating these two groups. However, 321 and 423 are the only two positions that are consistently different between the two groups. Therefore these are the most likely cause of these antigenic differences.

Strengths:

(1) A sensible rationale was given for the choice of sera, in terms of the genetic diversity.

(2) There were two independent batches of one of the antigens used for generating sera, which demonstrated the level of heterogeneity in the experimental process.

(3) Replicate of the Wisconsin/588/2019 antigen (as H1 and H6) is another useful measure of heterogeneity.

(4) The presentation of the data, e.g. Figure 2, clearly shows two main antigenic groups.

(5) The most modern sera are more recent than other related papers, which demonstrates that has been no major antigenic change.

Weaknesses:

(1) Issues with experimental methods

As I am not an experimentalist, I cannot comment fully on the experimental methods. However, I note that BALB/c mice sera were used, whereas outbred ferret sera are typically used in influenza antigenic characterisation, so the antigenic difference observed may not be relevant in humans. Similarly, the mice were immunised with an artificial NA immunogen where the typical approach would be to infect the ferret with live virus intra-nasally.

Indeed, ferrets are the gold standard model for the study of influenza. The main reason for this is the susceptibility of ferrets to infection with primary human influenza virus isolates and their ability to transmit human influenza A and B viruses. Although mouse models often require the use of mouse-adapted influenza virus strains, it is still the most used model to study new developments on influenza vaccine.

In our previous publication we performed a parallel analysis of sera of ferrets that were primed by infection and boosted by recombinant protein, as well as mice that, like in this study that focuses on N1 NA, were prime-boosted with purified recombinant NA proteins in the presence of an adjuvant. Our data indicate that the NAI responses in immune sera from infected ferrets after infection and after boost enables similar antigenic classification and correlated strongly with those induced in mice that had been prime-boosted with adjuvanted recombinant NA (Catani et al., eLife 2024). To a large extend, the immunogenicity of an antigen relies on epitope accessibility, which may dictate a universal rule of immunogenicity and antigenicity (Altman et al., 2015).

(2) Five mice sera were generated per immunogen and then pooled, but data was not presented that demonstrated these sera were sufficiently homogenous that this approach is valid.

Although individual sera was not tested here. Based on previous studies from our group we are confident that a prime-boost schedule with 1 µg of adjuvanted soluble tetrameric NA, induces a highly homogeneous response in mice (Catani et al., 2022).

(3) There were no homologous antigens for most of the sera. This makes the responses difficult to interpret as the homologous titre is often used to assess the overall reactivity of a serum. The sequence of the antigens used is not described, which again makes it difficult to interpret the results.

The absence of homologous antigens may indeed make interpretation more difficult. However, we have observed that homologous sera do not always coincide with the highest reactivity, although highest reactivity is always found within an antigenic cluster. A sequence comparison would be appropriate to improve interpretability of the data. Therefore, a sequence alignment and a pairwise comparison will be provided in the revised manuscript as supplement.

(4) To be able to untangle the effects of the individual substitutions at 321, 386, and 432, it would have been useful to have included the naturally occurring variants at these positions, or to have generated mutants at these positions. Gao et al clearly show an antigenic difference with ferret sera correlated separately with N386K and I321V/K432E.

The prevalence of single amino acid substitutions in N1 NA of clinical H1N1 virus strains isolated between 2009 and 2024 is minimal, which may indicate reduced fitness (see Author response image 1) in strains with these substitutions in NA. Nevertheless, we agree that the rescue of single mutants would provide important evidence to untangle those individual impacts on antigenicity. We plan to generate mutants with substitution at these positions in NA of A/Wisconsin/588/2019 H1N1 and determine the NAI against our panel of sera.

Author response image 1.

Prevalence of the indicated N1 NA substitutions in all clinical human H1N1 isolates with unique sequences deposited in the GISAID data bank since 2009.

(5) The challenge experiments in Gao et al showed that NI titre was not a good correlate of protection, so that limits the interpretation of these results.

On the contrary, challenges experiments confirmed that drift occurred in NA from H1N1 viruses isolated between 2009 (CA/09) and 2015 (MI/15). The dilution of transferred sera to equal inhibitory titers indicate that the homologous ferret sera (shown in figure 5e-f)(Gao et al., 2019) is still effective in protecting against infection while heterologous sera are not. This result emphasises that the nature of the homologous NAI response is well-suited for protection against a homologous challenge, although mechanistic data was not provided.

Issues with the computational methods

(6) The NAI titres were normalised using the ELISA results, and the motivation for this is not explained. It would be nice to see the raw values.

Mice were immunized with different batches of recombinant protein. Each of those batches may have distinct intrinsic immunogenicity, as observed in Figure 1d. For that reason, NAI values were normalized using homologous ELISA titers induced by each respective NA antigen. A table with the raw values will be included in the revised manuscript.

(7) It is not clear what value the random forest analysis adds here, given that positions 321 and 432 are the only two that consistently differ between the two groups.

The substitutions at position 321 and 432 are indeed the only 2 consistently differing amino acids among the tested N1s. Although their correlation with antigenic clustering may be obvious after analysis, a random forest analysis would enable to reveal less obvious substitutions that contribute to the antigenic diversity. In the future, we intend to expand this methodology to strains that are not currently included in the panel. A random forest model is a relatively simple and performant method to deal with a new dataset.

(8) As with the previous N2 paper, the metric for antigenic distance (the root mean square of the difference between the titres for two sera) is not one that would be consistent when different sera are included. More usual metrics of distance are Archetti-Horsfall, fold down from homologous, or fold down from maximum.

The antigenic distances calculated prior to our random forest does use fold-difference as metrics as log2(max(EC50) / EC50). After having obtained the fold-difference values, a pairwise dissimilarity matrix was calculated to obtain the average antigenic distance between pairs of sera. A more detailed description of the methodology will be included in the methods session, including the R-code.

(9) Antigenic cartography of these data is fraught. I wonder whether 2 dimensions are required for what seems like a 1-dimensional antigenic difference - certainly, the antigens, excluding the H5N1, are in a line. The map may be skewed by the high reactivity Brisbane/18 antigen. It is not clear if the column bases (normalisation factors for calculating antigenic distance) have been adjusted to account for the lack of homologous antigens. It is typical to present antigenic maps with a 1:1 x:y ratio.

Antigenic cartography will be repeated excluding H5N1 and/or Brisbane/18 antigen. Data will be provided in the final rebuttal letter.

Issues with interpretation

(10) Figure 2 shows the NAI titres split into two groups for the antigens, however, A/Brisbane is an outlier in the second antigenic group with high reactivity.

Indeed, A/Brisbane/02/2018 has overall higher IC50 values. However, it still falls into the same cluster that we called AG2. Highlighting A/Brisbane/02/2018 may lead to the misinterpretation of a non-existent antigenic group.

(11) Following Gao et al, I think you can claim that it is more likely that the antigenic change is due to K432E than I321V, based on a comparison of the amino acid change.

Indeed, we would expect that substitution of the basic arginine to an acidic glutamate is more likely to impact antigenicity than the isoleucine-to-valine apolar substitution. Testing of mutant reassortants with single mutations may provide the definitive answer for that question.

Appraisal:

Taking into account the limitations of the experimental techniques (which I appreciate are due to resource constraints), this paper meets its aim of measuring the antigenic relationships between 2009-2020 seasonal N1s, showing that there were two main groups. The authors discovered that the difference between the two antigenic groups was likely attributable to positions 321 and 432, as these were the only two positions that were consistently different between the two groups. They came to this finding by using a random forest model, but other simpler methods could have been used.

Impact:

This paper contributes to the growing literature on the potential benefit of NA in the influenza vaccine.

Reviewer #2 (Public review):

Summary:

In this study, Catani et al. have immunized mice with 17 recombinant N1 neuraminidases (NAs) from human isolates circulating between 2009-2020 to investigate antigenic diversity. NA inhibition (NAI) titers revealed two groups that were antigenically and phylogenetically distinct. Machine learning was used to estimate the antigenic distances between the N1 NAs and mutations at residues K432E and I321V were identified as key determinants of N1 NA antigenicity.

Strengths:

Observation of mutations associated with N1 antigenic drift.

Weaknesses:

Validation that K432E and I321V are responsible for antigenic drift was not determined in a background strain with native K432 and I321 or the restitution of antibody binding by reversion to K432 and I321 in strains that evaded sera.

Reassortant A/Wisconsin/588/2019 with E432K, V321I and also K386N single mutations will be rescued and tested against the panel of sera.