Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing
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Evaluation Summary:
This paper performs a systematic analysis of the fitness landscape of the influenza virus protein neuraminidase (NA). The paper analyzes 864 different combinations of mutations, over six genetic backgrounds. The main findings are that the fitness landscape correlates well across genetic backgrounds, and that natural evolution of neuraminidase seems to select for neutrally charged variants.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)
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Abstract
As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.
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Author Response:
Reviewer #3 (Public Review):
The paper contains a substantial amount of novel experimental work, the experiments appear well done, and the analysis of the data makes sense. Raw data and analysis scripts have been made fully available.
I have two specific comments:
- While the paper talks extensively about deep mutational scanning, I don't think this is a deep mutational scanning study. In deep mutational scanning, we usually make every possible single-point mutation in a protein. This is not what was done here, as far as I can tell.
In the revised manuscript, we have avoided using deep mutational scanning to describe our experimental design. Instead, we described our approach as “a high-throughput experimental approach that coupled combinatorial mutagenesis and next-generation sequencing”
- For the analysis of …
Author Response:
Reviewer #3 (Public Review):
The paper contains a substantial amount of novel experimental work, the experiments appear well done, and the analysis of the data makes sense. Raw data and analysis scripts have been made fully available.
I have two specific comments:
- While the paper talks extensively about deep mutational scanning, I don't think this is a deep mutational scanning study. In deep mutational scanning, we usually make every possible single-point mutation in a protein. This is not what was done here, as far as I can tell.
In the revised manuscript, we have avoided using deep mutational scanning to describe our experimental design. Instead, we described our approach as “a high-throughput experimental approach that coupled combinatorial mutagenesis and next-generation sequencing”
- For the analysis of epistasis vs distance (Fig 4d, e, f), it would be better to look at side-chain distances rather than C_alpha distances. In covariation analyses, it can be seen that C_alpha distances are not a good predictor of pairwise interactions. Similar patterns may be observable here.
See e.g.: A. J. Hockenberry, C. O. Wilke (2019). Evolutionary couplings detect side-chain interactions. PeerJ 7:e7280.
Thank you for the suggestion. In the revised manuscript, we replaced the Cα analysis by a side-chain analysis according to Hockenberry and Wilke (see response to Essential Revisions above).
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Evaluation Summary:
This paper performs a systematic analysis of the fitness landscape of the influenza virus protein neuraminidase (NA). The paper analyzes 864 different combinations of mutations, over six genetic backgrounds. The main findings are that the fitness landscape correlates well across genetic backgrounds, and that natural evolution of neuraminidase seems to select for neutrally charged variants.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
Wang and colleagues investigate epistatic interaction between a group of seven positions in the influenza H3N2 neuraminidase protein by constructing all possible combinations of amino acids that were observed at these seven sites in the past 50 years. This exhaustive characterization in done in 6 different genetic backgrounds spanning the documented evolution of the virus since 1968. The study is impressive in its exhaustiveness and the systematic approach. The paper is well written and clear. The main findings of the paper are:
• Epistatic interactions between this set of seven residues in proximity do not depend much of the genetic background.
• Later genetic backgrounds are much more permissive to changes in these 7 positions.
• Changes in local charge result in reduced fitness.
Measuring all possible …
Reviewer #1 (Public Review):
Wang and colleagues investigate epistatic interaction between a group of seven positions in the influenza H3N2 neuraminidase protein by constructing all possible combinations of amino acids that were observed at these seven sites in the past 50 years. This exhaustive characterization in done in 6 different genetic backgrounds spanning the documented evolution of the virus since 1968. The study is impressive in its exhaustiveness and the systematic approach. The paper is well written and clear. The main findings of the paper are:
• Epistatic interactions between this set of seven residues in proximity do not depend much of the genetic background.
• Later genetic backgrounds are much more permissive to changes in these 7 positions.
• Changes in local charge result in reduced fitness.
Measuring all possible combinations of the residues in 6 different backgrounds is a very powerful and elegant way to explore how the evolutionary constraints on the protein changed over time and this paper stands out in presenting clean and well controlled data that supports the main claims.
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Reviewer #2 (Public Review):
This is a nice study. The authors use experimental and statistical methods to evaluate the fitness landscape of mutations within a region of the neuraminidase (NA) segment of the A/H3N2 influenza strain. Results from this segment contrast from those obtained from haemagglutinin, and contribute towards a long-term aim in research of predicting the evolution of seasonal influenza strains. This is a question with broad impact as a scientific matter, and with immediate application, touching on the need to regularly update the strains used in influenza vaccination.
The collection of mutagenesis data via deep mutational scanning creates a useful dataset. Using these data the authors successfully demonstrate that epistatic fitness effects in this region of the NA protein are largely conserved across a range of …
Reviewer #2 (Public Review):
This is a nice study. The authors use experimental and statistical methods to evaluate the fitness landscape of mutations within a region of the neuraminidase (NA) segment of the A/H3N2 influenza strain. Results from this segment contrast from those obtained from haemagglutinin, and contribute towards a long-term aim in research of predicting the evolution of seasonal influenza strains. This is a question with broad impact as a scientific matter, and with immediate application, touching on the need to regularly update the strains used in influenza vaccination.
The collection of mutagenesis data via deep mutational scanning creates a useful dataset. Using these data the authors successfully demonstrate that epistatic fitness effects in this region of the NA protein are largely conserved across a range of historical genetic backgrounds.
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Reviewer #3 (Public Review):
The paper contains a substantial amount of novel experimental work, the experiments appear well done, and the analysis of the data makes sense. Raw data and analysis scripts have been made fully available.
I have two specific comments:
- While the paper talks extensively about deep mutational scanning, I don't think this is a deep mutational scanning study. In deep mutational scanning, we usually make every possible single-point mutation in a protein. This is not what was done here, as far as I can tell.
- For the analysis of epistasis vs distance (Fig 4d, e, f), it would be better to look at side-chain distances rather than C_alpha distances. In covariation analyses, it can be seen that C_alpha distances are not a good predictor of pairwise interactions. Similar patterns may be observable here.
See e.g.: A. …
Reviewer #3 (Public Review):
The paper contains a substantial amount of novel experimental work, the experiments appear well done, and the analysis of the data makes sense. Raw data and analysis scripts have been made fully available.
I have two specific comments:
- While the paper talks extensively about deep mutational scanning, I don't think this is a deep mutational scanning study. In deep mutational scanning, we usually make every possible single-point mutation in a protein. This is not what was done here, as far as I can tell.
- For the analysis of epistasis vs distance (Fig 4d, e, f), it would be better to look at side-chain distances rather than C_alpha distances. In covariation analyses, it can be seen that C_alpha distances are not a good predictor of pairwise interactions. Similar patterns may be observable here.
See e.g.: A. J. Hockenberry, C. O. Wilke (2019). Evolutionary couplings detect side-chain interactions. PeerJ 7:e7280.
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