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  1. Author Response:

    Reviewer #1 (Public Review):

    There is continued speculation on the extent of within-host adaptive evolution of acutely infecting pathogens, including SARS-CoV-2 and influenza. Previous studies have found little evidence of positive selection during influenza infections of healthy adults. Here the authors examine within-host influenza dynamics in two interesting populations: children experiencing likely their first infections with H3N2, and children and adults infected with the newly emerging H1N1pdm09. The authors extend previous observations of adults infected with H3N2 to children, showing that despite potentially higher viral population sizes and/or longer infections, H3N2 largely experiences purifying selection within hosts. H1N1pdm09 infections, in some contrast, show some evidence of positive selection. The authors analyze specific substitutions in different genes, finding some evidence of CTL escape/reversion and epistasis through stabilizing mutations. Using a simple model, the investigators contend that H3N2 reaches mutation-selection equilibrium late in infections.

    This is a generally accurate and interesting analysis that enriches our understanding of within-host influenza dynamics. It is valuable to see the dynamics of (mostly) primary infections, where little antibody pressure is expected, and also some impact of the cellular immune response.

    We thank the reviewer for their careful consideration of our manuscript.

    My primary reservations concern the analysis of H1N1pdm09:

    First, the authors describe a higher rate of nonsynonymous substitutions early in infection, but the statistics backing this claim are unclear. Figure 2B shows box plots suggesting this trend, but the caption describes typically only two samples per day. In that case, it's better to plot the data points directly. Is there really statistical power to claim a significant trend over time and meaningful difference from H3N2?

    We agree that there is a lack in statistical power in the A/H1N1pdm09 virus dataset to claim meaningful differences in temporal trends to A/H3N2 within-host dynamics. The only reasonable conclusion that can be made here is that there was a greater accumulation in nonsynonymous iSNVs relative to synonymous ones in A/H1N1pdm09 within-host virus populations. As per the reviewer’s suggestion, we have now removed the boxplots for the A/H1N1pdm09 virus panel in Figure 2B, replacing it with a scatter plot. We have also updated the manuscript to reflect our inability to characterise the within-host temporal trends for A/H1N1pdm09 viruses using this dataset:

    Line 210: “We observed higher nonsynonymous evolutionary rates relative to synonymous ones initially after symptom onset but were unable to determine if they were significantly different due to the low number of samples (i.e. median = 2 samples per day post-symptom onset). In turn, we also could not meaningfully characterise the temporal trends of within-host evolution for the pandemic virus with this dataset. Nonetheless, consolidating over all samples across all time points, there was significantly higher rates of accumulation of nonsynonymous variants in the polymerase basic 2 (PB2), polymerase acidic (PA), HA and matrix (M) gene segments (Figure 2B, Figure 2 – figure supplement 2 and Figure 3 – figure supplement 2). All gene segments also yielded NS/S ratios > 1 (Table S1).”

    Line 565: “Owing to the low number of A/H1N1pdm09 virus samples and different next-generation sequencing platforms used to sequence samples of the two virus subtypes and consequently differences in base calling error rates and depth of coverage (Figure 1 – figure supplement 1), we were unable to directly compare the observed levels of within-host genetic diversity and evolutionary dynamics between the two influenza subtypes here.”

    Second, the authors interpret individuals infected with H1N1pdm09 infections as being as naive to the virus as ~2 year olds experiencing their first H3N2 infection (ll. 352-354). Setting cellular immunity aside--- which maybe we shouldn't---at least two studies found substantial targeting of an epitope on H1N1pdm09 HA that was homologous to H1N1 HA epitopes from the late 1970s and early 1980s (Linderman et al., 2014, PNAS, and Huang et al., 2015, JCI). In other words, there likely is some adaptive immune pressure with these H1N1pdm09 infections.

    Linderman et al. (PNAS, 2014) and Huang et al. (JCI, 2015) found that individuals born prior to the early 1980s possessed antibodies that recognized HA-166K (H3 numbering) residing in the Sa antigenic site of A/H1N1pdm09 viruses. They attributed this to previous exposures to seasonal A/H1N1 viruses with the HA-166K Sa epitope. This adaptive immune response likely led to the fixation of HA-K166Q in A/H1N1pdm09 viruses, which abrogated antibody recognition of this epitope. However, this epitope was shielded by glycans in seasonal A/H1N1 viruses in 1986 due to the acquisition of a glycosylation site in HA-129. As such individuals born after the late 1980s did not possess the same antibodies and are therefore unlikely to exert the same adaptive immune pressure as their older counterparts.

    Out of the 32 A/H1N1pdm09-infected individuals analysed in our study, only six of them were born before 1986. The median birth year of all individuals was 1999 (IQR = 1989, 2005). Hence, the same adaptive immune pressure on HA-166K was not present in these younger individuals during the first wave of the A/H1N1pdm09 pandemic then. We also did not detect the HA-166Q variant in any of the six older individuals born prior to 1986.

    Besides HA-166K, Li et al. (JEM, 2013) also found that individuals born between 1983 and 1996 have narrowly focused antibodies against the HA-133K epitope as a result of previous exposures to seasonal A/H1N1 viruses. HA-133K has, however, remained conserved in the global A/H1N1pdm09 virus population to date. We also did not find any variants above the calling threshold in any of the individuals investigated.

    The HA protein is the primary target of human adaptive immune response, which in turn drives its antigenic evolution (Petrova and Russell, Nat Rev Microbiol, 2018). In terms of cellular immunity, HA encodes few CTL epitopes (Woolthuis et al., Sci Rep, 2016). Most CTL epitopes are found in the nucleoprotein (NP), which we have considered here in our discussion observing recurrent NP-G384R variants independently found in multiple individuals.

    Finally, it is curious that mutation-selection balance is posited for H3N2 but not H1N1pdm09. Obviously there's not much real "balance" in infections that are so short, and the H1N1pdm09 infections appear shorter than H3N2. As there is likely some preexisting immunity shortening infections with H1, does this imply the mutation-selection balance story is unlikely to hold for H3N2 in older children and adults? What evolutionary dynamics can convincingly be ruled out after more careful consideration of the H1N1pdm09 temporal trends?

    As mentioned earlier, the A/H1N1pdm09 virus dataset lack statistical power. As such, we are unable to characterise temporal trends for the pandemic virus and have no longer discuss this in the updated manuscript (see response to reviewer #3 as well).

    However, the reviewer was right to point out one of our key conclusions that mutation-selection balance is only observed in naïve young children with longer A/H3N2 virus infections and would be less likely to hold for the typically shorter-lived infections of older children and adults. We have now put more emphasis on this conclusion in the abstract and discussion:

    Line 42: “For A/H3N2 viruses in young children, early infection was dominated by purifying selection. As these infections progressed, nonsynonymous variants typically increased in frequency even when within-host virus titres decreased. Unlike the short-lived infections of adults where de novo within-host variants are rare, longer infections in young children allow for the maintenance of virus diversity via mutation-selection balance creating potentially important opportunities for within-host virus evolution.”

    Line 530: “Through simulations of a within-host evolution model, we investigated the hypothesis that in the absence of any positive selection, the accumulation of nonsynonymous iSNVs was a result of their neutral or only weakly deleterious effects and the expanding within-host virion population size during later timepoints in longer infections of naïve young children such that mutation-selection balance was reached. In contrast, this balance was not detected in otherwise healthy older children or adults with short-lived influenza virus infections lasting no more than a week where de novo nonsynonymous iSNVs are rarely found 4,8–11,44.”

    Reviewer #2 (Public Review):

    At the global level, influenza evolution is characterized by positive selection and antigenic drift. While similar dynamics have been seen in chronically infected individuals, multiple studies of acute infections have been characterized by limited diversity and a lack of antigenic selection. Here the authors leverage a unique dataset of deeply sampled, longitudinal isolates from individuals whose infection lasted up to two weeks. The intermediate length of these infections helps bridge observations from studies of acute and chronically infected hosts. Additionally, the data set is comprised of endemic H3N2 isolates as well as H1N1pdm09 isolates from infections early during the 2009 pandemic. The dataset provides insight into host-level differences between emerging and endemic viruses. Although there is little evidence of within-host antigenic selection the authors do uncover a few mutations found in multiple samples at later time points. Their detailed analysis shows these may be the result of positive selection and epistatic interactions. Additionally, the study reveals increasing rates of nonsynonymous substitution over time and simulations show these trends would be expected under mutation-selection balance with most NS mutations being mildly deleterious. Nonsynonymous rates are also higher in H1N1pdm09 isolates as could be expected of a virus that is less adapted to its host.

    Disentangling biological phenomena from methodological artifacts is a challenge in any deep-sequencing, within-host study. The increase in nonsynonymous and nonsense mutations seen in later samples with high Ct is consistent with the author's conclusions, but it is also consistent with PCR errors which are common in low titer samples. Although the authors have applied quality and depth thresholds to help mitigate against these artifacts, figure 1 figure-supplement 2 appears to show that some variants used in the analysis were only found in 1 of the multiple overlapping amplicons. These variants are potentially PCR artifacts and may indicate other variants at similar frequencies are also artifacts. The same phenomena might also just be a consequence of imperfect variant detection at low frequencies. It would be interesting to see if the same general trends in the estimated rates are observed if the variant-calling stringency is increased to exclude these such variants. Longitudinal sampling is a key strength of this study. Observing the same mutation at different time points suggests they are unlikely to be random PCR artifacts. And the abundance of nonsynonymous mutations seen in H1N1pdm09 isolates is maintained across minor allele frequencies. In general, the major conclusions appear robust to random PCR error.

    This is a thorough study of a unique dataset, that combines a cross-sectional and longitudinal analysis to uncover general trends (NS/S rates over time) and specific events (parallel evolution at later time points) that shape within-host influenza evolution. The authors support their conclusions with a diverse array of quantitative analyses (e.g. transmission-bottlenecks, with-host evolutionary rates, haplotype reconstruction). This study helps unite previous observations from acute and chronic infections and is an important step in a fuller understanding of how evolutionary forces act across biological scales.

    We thank reviewer 2 for reviewing our manuscript.

    Reviewer #3 (Public Review):

    The authors analyze deep sequencing data from H3N2 and pandemic H1N1 infections, primarily from children and young adults. The pandemic H1N1 samples came from the first year of the pandemic, just after the virus's emergence into human hosts, and the authors often had access to longitudinal samples from the same infection. The authors used within-host variants detected to estimate evolutionary rates at different times throughout the infection. They identify several instances of seemingly recurrent mutations, and they perform simulations to determine how synonymous and nonsynonymous mutations would accumulate over time given different assumptions about the distribution of fitness effects. The manuscript's findings largely reinforce prior findings about influenza's evolutionary dynamics within hosts and at transmission, though the authors analyze longitudinal samples from longer infections than in previous studies.

    We thank reviewer 3 for their thoughtful consideration of our manuscript.

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  2. Evaluation Summary:

    This work offers an in-depth exploration of within-host influenza evolution that helps untangle the process by which global trends arise from host-level dynamics. The manuscript will be of interest to virologists and evolutionary biologists alike, and the dataset provides a unique opportunity to explore evolutionary dynamics late in acute infections of both endemic and emerging viruses. Technical concerns relating to data interpretation and modeling assumptions suggest that some results might change after further investigation.

    (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. The reviewers remained anonymous to the authors.)

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  3. Reviewer #1 (Public Review):

    There is continued speculation on the extent of within-host adaptive evolution of acutely infecting pathogens, including SARS-CoV-2 and influenza. Previous studies have found little evidence of positive selection during influenza infections of healthy adults. Here the authors examine within-host influenza dynamics in two interesting populations: children experiencing likely their first infections with H3N2, and children and adults infected with the newly emerging H1N1pdm09. The authors extend previous observations of adults infected with H3N2 to children, showing that despite potentially higher viral population sizes and/or longer infections, H3N2 largely experiences purifying selection within hosts. H1N1pdm09 infections, in some contrast, show some evidence of positive selection. The authors analyze specific substitutions in different genes, finding some evidence of CTL escape/reversion and epistasis through stabilizing mutations. Using a simple model, the investigators contend that H3N2 reaches mutation-selection equilibrium late in infections.

    This is a generally accurate and interesting analysis that enriches our understanding of within-host influenza dynamics. It is valuable to see the dynamics of (mostly) primary infections, where little antibody pressure is expected, and also some impact of the cellular immune response.

    My primary reservations concern the analysis of H1N1pdm09:

    First, the authors describe a higher rate of nonsynonymous substitutions early in infection, but the statistics backing this claim are unclear. Figure 2B shows box plots suggesting this trend, but the caption describes typically only two samples per day. In that case, it's better to plot the data points directly. Is there really statistical power to claim a significant trend over time and meaningful difference from H3N2?

    Second, the authors interpret individuals infected with H1N1pdm09 infections as being as naive to the virus as ~2 year olds experiencing their first H3N2 infection (ll. 352-354). Setting cellular immunity aside--- which maybe we shouldn't---at least two studies found substantial targeting of an epitope on H1N1pdm09 HA that was homologous to H1N1 HA epitopes from the late 1970s and early 1980s (Linderman et al., 2014, PNAS, and Huang et al., 2015, JCI). In other words, there likely is some adaptive immune pressure with these H1N1pdm09 infections.

    Finally, it is curious that mutation-selection balance is posited for H3N2 but not H1N1pdm09. Obviously there's not much real "balance" in infections that are so short, and the H1N1pdm09 infections appear shorter than H3N2. As there is likely some preexisting immunity shortening infections with H1, does this imply the mutation-selection balance story is unlikely to hold for H3N2 in older children and adults? What evolutionary dynamics can convincingly be ruled out after more careful consideration of the H1N1pdm09 temporal trends?

    Read the original source
    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    At the global level, influenza evolution is characterized by positive selection and antigenic drift. While similar dynamics have been seen in chronically infected individuals, multiple studies of acute infections have been characterized by limited diversity and a lack of antigenic selection. Here the authors leverage a unique dataset of deeply sampled, longitudinal isolates from individuals whose infection lasted up to two weeks. The intermediate length of these infections helps bridge observations from studies of acute and chronically infected hosts. Additionally, the data set is comprised of endemic H3N2 isolates as well as H1N1pdm09 isolates from infections early during the 2009 pandemic. The dataset provides insight into host-level differences between emerging and endemic viruses. Although there is little evidence of within-host antigenic selection the authors do uncover a few mutations found in multiple samples at later time points. Their detailed analysis shows these may be the result of positive selection and epistatic interactions. Additionally, the study reveals increasing rates of nonsynonymous substitution over time and simulations show these trends would be expected under mutation-selection balance with most NS mutations being mildly deleterious. Nonsynonymous rates are also higher in H1N1pdm09 isolates as could be expected of a virus that is less adapted to its host.

    Disentangling biological phenomena from methodological artifacts is a challenge in any deep-sequencing, within-host study. The increase in nonsynonymous and nonsense mutations seen in later samples with high Ct is consistent with the author's conclusions, but it is also consistent with PCR errors which are common in low titer samples. Although the authors have applied quality and depth thresholds to help mitigate against these artifacts, figure 1 figure-supplement 2 appears to show that some variants used in the analysis were only found in 1 of the multiple overlapping amplicons. These variants are potentially PCR artifacts and may indicate other variants at similar frequencies are also artifacts. The same phenomena might also just be a consequence of imperfect variant detection at low frequencies. It would be interesting to see if the same general trends in the estimated rates are observed if the variant-calling stringency is increased to exclude these such variants. Longitudinal sampling is a key strength of this study. Observing the same mutation at different time points suggests they are unlikely to be random PCR artifacts. And the abundance of nonsynonymous mutations seen in H1N1pdm09 isolates is maintained across minor allele frequencies. In general, the major conclusions appear robust to random PCR error.

    This is a thorough study of a unique dataset, that combines a cross-sectional and longitudinal analysis to uncover general trends (NS/S rates over time) and specific events (parallel evolution at later time points) that shape within-host influenza evolution. The authors support their conclusions with a diverse array of quantitative analyses (e.g. transmission-bottlenecks, with-host evolutionary rates, haplotype reconstruction). This study helps unite previous observations from acute and chronic infections and is an important step in a fuller understanding of how evolutionary forces act across biological scales.

    Read the original source
    Was this evaluation helpful?
  5. Reviewer #3 (Public Review):

    The authors analyze deep sequencing data from H3N2 and pandemic H1N1 infections, primarily from children and young adults. The pandemic H1N1 samples came from the first year of the pandemic, just after the virus's emergence into human hosts, and the authors often had access to longitudinal samples from the same infection. The authors used within-host variants detected to estimate evolutionary rates at different times throughout the infection. They identify several instances of seemingly recurrent mutations, and they perform simulations to determine how synonymous and nonsynonymous mutations would accumulate over time given different assumptions about the distribution of fitness effects. The manuscript's findings largely reinforce prior findings about influenza's evolutionary dynamics within hosts and at transmission, though the authors analyze longitudinal samples from longer infections than in previous studies.

    Read the original source
    Was this evaluation helpful?