When the immune system encounters a disease-causing pathogen, it releases antibodies that can bind to specific regions of the bacterium or virus and help to clear the infection. These proteins are generated by B cells which, upon detecting the pathogen, can begin to mutate and alter the structure of the antibody they produce: the better the antibody is at binding to the pathogen, the more likely the B cell is to survive. This process of evolution produces B cells that make more effective antibodies. After the infection, some of these cells become ‘memory B cells’ which can be stimulated in to action when the pathogen invades again.

Many vaccines also depend on this process to trigger the production of memory B cells that can fight off a specific disease-causing agent. However, it is unclear to what extent memory B cells that already exist are able to continue to evolve and modify their antibodies. This is particularly important for the flu vaccine, as the virus that causes influenza rapidly mutates. To provide high levels of protection, the memory B cells formed following the vaccine may therefore need to evolve to make different antibodies that recognize mutated forms of the virus.

It is thought that the low effectiveness of the flu vaccine is partially because the response it triggers does not stimulate additional evolution of memory B cells. To test this theory, Hoehn et al. developed a computational method that can detect the evolution of B cells over time. The tool was applied to samples collected from the blood and lymph nodes (organ where immune cells reside) of people who recently received the flu vaccine. The results were then compared to B cells taken from people after different infections, vaccinations, and other conditions.

Hoehn et al. found the degree to which B cells evolve varies significantly between conditions. For example, B cells produced during chronic HIV infections frequently evolved over time, while such evolution was rarely observed during the autoimmune disease myasthenia gravis. The analysis also showed that memory B cells produced by the flu vaccine were able to evolve if recruited to the lymph nodes, but this was rarely detected in B cells in the blood.

These findings suggest the low efficacy of the flu vaccine is not due to a complete lack of B cell evolution, but likely due to other factors. For instance, it is possible the evolutionary process it stimulates is not as robust as in other conditions, or is less likely to produce long-lived B cells that release antibodies. More research is needed to explore these ideas and could lead to the development of more effective flu vaccines.

Measurably evolving populations are systems that undergo evolution rapidly enough for significant genetic differences to be detected in longitudinally sampled timepoints (Drummond et al., 2003). While this concept is frequently applied to viruses such as HIV (Rambaut et al., 2004) and SARS-CoV-2 (e.g., du Plessis et al., 2021), B cells experience similarly rapid evolution during affinity maturation. B cell affinity maturation is critical for developing high-affinity antibodies in response to infection and vaccination (Shlomchik and Weisel, 2012; Victora and Nussenzweig, 2012). During affinity maturation, somatic hypermutation (SHM) introduces mutations into the B cell receptor (BCR) loci at a rate orders of magnitude higher than the background rate of somatic mutations (McKean et al., 1984; Murphy et al., 2008). These modified BCRs are selected based on their binding affinity, and the process repeats cyclically within germinal centers (GCs Teng and Papavasiliou, 2007; Victora and Nussenzweig, 2012). Infection or vaccination can also stimulate pre-existing memory B cells that rapidly differentiate into antibody secreting plasmablasts or possibly re-enter GCs to undergo additional affinity maturation (Ellebedy, 2018; Mesin et al., 2020). A lack of vaccine-specific affinity maturation is thought to underlie the poor efficacy of seasonal influenza virus vaccination (Arevalo et al., 2020; Ellebedy, 2018). While recent work has shown that antigen-specific B cell lineages can be recruited into GCs following influenza vaccination (Turner et al., 2020), other work has been unable to detect significant increases in SHM frequency among circulating influenza-binding antibody lineages following vaccination (Ellebedy et al., 2016).

Whether seasonal influenza vaccination stimulates an increase in SHM frequency can be answered by determining whether influenza-binding B cell lineages found in GCs are measurably evolving following vaccination. This is distinct from simply quantifying SHM frequency. While influenza vaccination stimulates memory B cell lineages with high SHM frequency (Laserson et al., 2014; Wrammert et al., 2008), these lineages are only measurably evolving if their level of SHM detectably increases during the sampling interval surrounding vaccination. In this study, we show how a phylogenetic test of measurable evolution can be a powerful tool to detect increasing SHM frequency in longitudinally sampled BCR sequence datasets (Duchêne et al., 2015; Murray et al., 2016). We validate this approach through simulations and a survey of measurable evolution in B cell repertoires across a wide range of infections and vaccinations. We document significant heterogeneity among conditions, with some like HIV infection and primary hepatitis B vaccination enriched for measurably evolving lineages in the blood. We further show that while most circulating lineages following influenza virus vaccination are not measurably evolving, a subset of memory B cell lineages re-enter GCs and increase in SHM frequency.

The extent to which seasonal influenza vaccination stimulates affinity maturation against vaccine antigens is unclear, and poor efficacy of seasonal influenza virus vaccination is often attributed to stimulation of pre-existing memory B cells interfering with novel responses to vaccine antigens (Ellebedy, 2018). While a prior study has shown that influenza-binding B cell lineages are found in GCs following seasonal influenza vaccination (Turner et al., 2020), other work has suggested that circulating influenza-binding B cell lineages do not accumulate additional SHM following vaccination (Ellebedy et al., 2016). To determine whether seasonal influenza vaccination stimulates additional evolution in B cell lineages, we developed and validated a framework to detect measurable evolution using longitudinally sampled BCR sequencing data. This phylogenetic test can be a powerful tool to detect ongoing B cell evolution using longitudinally sampled BCR datasets across a wide array of immunological conditions, including influenza virus vaccine responses. Our results confirm prior findings that there is little evidence of B cell evolution among lineages sampled in the peripheral blood following seasonal influenza vaccination (Ellebedy et al., 2016). However, we also show that seasonal influenza vaccination is capable of stimulating measurable evolution in influenza-binding, GC-associated B cell lineages.

To place our analyses of the influenza vaccination response in a broader context, we surveyed measurable evolution across a broad range of infections and vaccinations. Prior work has shown that chronic HIV infection induces long-term affinity maturation of broadly neutralizing antibody lineages in response to viral escape mutants (Liao et al., 2013; Vieira et al., 2018; Wu et al., 2015). Our results show that HIV infection is associated with an exceptionally strong signature of B cell evolution over time. This signature is not limited to single lineages. Rather, a substantial fraction of longitudinally sampled B cell lineages within the repertoires of subjects with HIV are measurably evolving, consistent with clonal competition among B cell lineages during HIV infection (Nourmohammad et al., 2019). Early childhood development during the first 3 years of life (Nielsen et al., 2019) showed the second-highest enrichment of measurably evolving lineages among surveyed conditions. This possibly reflects continual exposure to novel antigens during childhood. Further, primary vaccinations (meningococcus, primary hepatitis B)(Galson et al., 2016; Galson et al., 2015a) showed stronger signatures of measurable evolution than secondary vaccinations (adult seasonal influenza, hepatitis B booster) (Ellebedy et al., 2016; Galson et al., 2015b; Laserson et al., 2014; Turner et al., 2020). Overall, our results are consistent with the hypothesis that GC responses are stronger in response to novel antigens.

In addition to detecting measurable evolution, we also characterized selection operating on measurably evolving B cell lineages. We found that measurably evolving lineages showed evidence of purifying selection (ω_CDR < 1) (Table 2). Though perhaps counterintuitive, a strong signal of purifying selection is a straightforward prediction of evolution toward an adaptive peak (Hoehn et al., 2019). Similar evidence of purifying selection during affinity maturation has been observed in other studies including influenza vaccination, HIV infection, and healthy controls (Cizmeci et al., 2021; Hoehn et al., 2019; Sheng et al., 2016; Yaari et al., 2015). Importantly, ω estimates are an average across all codons within CDRs or FWRs. It is possible that positive selection operated on a small number of codon sites, but that this signal was outweighed by the larger number of sites under purifying selection. Codon-specific models may be useful in future analyses to identify these sites under positive selection (e.g., Yang et al., 2000). When interpreting these results, it is also important to note that parameters were estimated using all mutations represented by each lineage tree, including those that potentially occurred before the first sampled timepoint. In all, these results are consistent with typical forces of selection having operated on measurably evolving lineages.

There are several limitations to this study. Data from different studies were sampled according to different schedules and time intervals. Because the power to detect measurable evolution should increase over time (Figure 1G), this could confound comparisons among datasets. We note however that multiple influenza and HIV datasets were surveyed, and enrichment of measurable evolution within these conditions was not strongly related to the sampling range (Figure 2A). This suggests immunological condition, rather than sample range, was the primary determinant of observed differences. By including monoclonal antibody (mAb) sequences with experimentally validated binding in several datasets, we were able to show that measurably evolving lineages are more likely than nonmeasurably evolving lineages to contain mAb sequences. This is consistent with the idea that measurably evolving lineages are actively responding to antigen. These results should be interpreted cautiously, however. With the exception of Turner et al., 2020 only a small number mAb sequences were found in the lineages analyzed (mean = 3.6 per study). Further, lineages containing GC sequences were preferentially selected for mAb generation in Turner et al., 2020, which may artificially increase the likelihood that measurably evolving lineages contain mAbs. While intriguing and biologically plausible, conclusively determining whether measurable evolution predicts antigen binding is beyond the scope of this study. Another limitation is that maximum parsimony was used to estimate lineage tree topologies and branch lengths. While more sophisticated methods are available for inferring B cell lineage trees (e.g., Hoehn et al., 2019), maximum parsimony often has competitive performance for topology estimation (Davidsen and Matsen, 2018) and is faster than more complex maximum likelihood models designed for B cell lineages. Computational efficiency was particularly important as our analyses required constructing more than 20,000 lineages trees spanning approximately 1,100,000 BCR sequences. Finally, our analysis of GC-associated B cell lineages was limited to data sampled from a single subject. Thus, while the results here demonstrate that influenza vaccination is capable of inducing measurable evolution, it remains unclear whether this is a general feature of influenza vaccination.

Our analyses of measurable evolution involved a series of hypothesis tests, and the definition for ‘enrichment’ of measurable evolution (>5% of lineages) was chosen based on the expected false positive rate under the null hypothesis. This enrichment measure was chosen to compare the relative frequency of measurably evolving lineages among datasets. A lack of enrichment does not indicate a complete lack of measurably evolving lineages. Conversely, slight enrichment of measurably evolving lineages (~5%) should not be interpreted as proof of ongoing affinity maturation in a set of lineages. The analysis in Figure 2A was not intended to test the null hypothesis that no lineages are measurably evolving in a particular dataset. Because multiple studies were surveyed (Table 1), it is possible our results contain false positives. Indeed, while some conditions such as HIV have a strong signal of measurable evolution across multiple studies, others such as naive hepatitis B vaccination and allergen-specific immunotherapy are just above the significance threshold (≤7.2% lineages with p < 0.05) and are each represented by a single study (Figure 2A, Table 1). These latter datasets should be interpreted cautiously and with the understanding that the vast majority of their lineages were not measurably evolving. To limit the influence of false positives, a multiple testing correction (false discovery rate < 0.1) was performed in analyses investigating the properties of measurably evolving lineages (Figure 2B, Table 2). We note, however, that repeating our analyses of initial germline divergence using all measurably evolving lineages (unadjusted p < 0.05) or a more strict p value cutoff (adjusted p < 0.05) yielded similar results (Figure 2—figure supplement 4), indicating our results are robust to the thresholds used. Finally, we validated the specificity of the date randomization test in empirical data (Figure 1—figure supplement 2). While a significant decrease in divergence over time is biologically unlikely, false positives due to multiple testing or sequencing error should produce a similar number of lineages with a significant correlation in either direction. We quantified correlation in either direction using a two-tailed version of the clustered, resolved date randomization test with a critical value of 0.025 (see Methods). Under a null hypothesis of no ongoing evolution, 2.5% of lineages were expected to have a significant negative correlation between divergence and time (all false positives). However, we found such ‘negatively evolving’ lineages at a mean frequency of only 1.2% (median = 1.3%, range = 0–2.8%) across all datasets when using clustered permutations and resolved polytomies (Figure 1—figure supplement 2). This indicates the date randomization test we used is conservative, and that our chosen thresholds are likely more strict than necessary.

Beyond multiple testing, there are some biological scenarios that could plausibly give rise to a signal of measurable evolution without additional SHM occurring during the sampling interval. For instance, it is possible that all SHM within a lineage occurred before the first sampled timepoint in a study, but that more mutated, higher-affinity BCRs were preferentially stimulated and sampled in later timepoints. Such a scenario would likely result in a positive correlation between divergence and sample time. However, our results are easier to explain if measurable B cell evolution results at least in part from ongoing SHM. For instance, our analysis of measurably evolving lineages following influenza vaccination showed (1) many later-sampled GC B cells had higher divergence than any sampled day 5 plasmablast sequence in the same lineage and (2) continued association between GC cells (rather than the blood) and measurable evolution even among influenza-binding lineages. Nonetheless, it is still theoretically possible that more mutated B cells were not sampled at early timepoints, and were preferentially expanded in GCs compared to the peripheral blood. Thus, while our interpretation is that measurable B cell evolution most likely represents an ongoing SHM process, we cannot conclusively rule out biased selection of more mutated sequences that were generated before the sampling interval.

Affinity maturation is a rapid evolutionary process. It is perhaps surprising that, while we identified conditions enriched for measurably evolving lineages, most lineages in circulation were not measurably evolving (Table 1). One explanation is that our analyses did not use sufficiently long sampling intervals to detect affinity maturation, though we believe this is unlikely. Studies in mice have estimated that SHM occurs at ~10⁻³ SHM/bp/division (Kleinstein et al., 2003; McKean et al., 1984), and that GC B cells cycle every 6–12 hr (Allen et al., 2007; Hauser et al., 2007; Victora and Nussenzweig, 2012). Simulations using conservative assumptions (strong selection, 24-hr cell cycle) and replicating the sample structure of our shortest-term influenza dataset (4 weeks), showed high power with >90% true positive rate (Figure 1H). Further, we found an enrichment of blood-derived measurably evolving lineages after only 4 weeks in one study (Galson et al., 2015a), and after 8 weeks corresponding to a known context of affinity maturation (GC entry, Figure 3). Overall, these results show that the sample times of our surveyed datasets should be sufficient to detect ongoing B cell evolution if it were occurring. However, it is possible that lineages may not remain in GCs continuously, which would slow the rate of evolution compared to our simulations. A more plausible explanation for the lack of measurable evolution is that most lineages in the blood are either nonspecific to the condition being studied, or derive from a GC-independent response (Mesin et al., 2020; Takemori et al., 2014; Taylor et al., 2012). It is also possible that lineages relevant to the condition being studied are inefficiently stimulated.

We find that seasonal influenza virus vaccination in young adults induces a GC reaction where maturation of vaccine-specific B cell lineages occurs, including those likely recruited from the pre-existing memory B cell compartment. These results imply that poor efficacy of seasonal influenza vaccination does not result from a complete lack of vaccine-induced B cell evolution. While we showed that B cells in these evolving lineages increased in amino acid replacement mutation frequency, it remains possible that this evolution is less able to select affinity-increasing mutations (Hoehn et al., 2019), that the overall number of evolving lineages is reduced, or that the products of this vaccine-induced evolution are not efficiently translated into memory and long-lived plasma cells. These latter two explanations are consistent with the results of our survey of longitudinally sampled peripheral blood datasets, which found an enrichment of measurably evolving lineages in some primary immune response conditions, but not influenza vaccination. Future studies will be needed to fully test these hypotheses about the causes of poor efficacy of seasonal influenza vaccination.

The manuscript is a computational study. All data used are publicaly available. Source code are available at https://bitbucket.org/kleinstein/projects (copy archived at https://archive.softwareheritage.org/swh:1:rev:1ca83cda5d1baac880c71c314b0adc359314f6fa). All of the OAS datasets are available at this URL: http://opig.stats.ox.ac.uk/webapps/oas.

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Author details

1. Kenneth B Hoehn

   Department of Pathology, Yale School of Medicine, New Haven, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review and editing

   Competing interests

   receives consulting fees from Prellis Biologics

   "This ORCID iD identifies the author of this article:" 0000-0003-0411-4307

2. Jackson S Turner

   Department of Pathology and Immunology, Washington University School of Medicine, St Louis, United States

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   is the recipient of a licensing agreement with Abbvie and has received consulting fees from Gerson Lehman Group

3. Frederick I Miller

   Worcester Polytechnic Institute, Worcester, United States

   Contribution

   Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

4. Ruoyi Jiang

   Department of Immunobiology, Yale School of Medicine, New Haven, United States

   Contribution

   Data curation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

5. Oliver G Pybus

   Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Ali H Ellebedy

   1. Department of Pathology and Immunology, Washington University School of Medicine, St Louis, United States
   2. The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St Louis, United States

   Contribution

   Resources, Supervision, Writing – review and editing

   Competing interests

   The Ellebedy laboratory received funding under sponsored research agreements from Emergent BioSolutions and AbbVie

7. Steven H Kleinstein

   1. Department of Pathology, Yale School of Medicine, New Haven, United States
   2. Department of Immunobiology, Yale School of Medicine, New Haven, United States
   3. Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, United States

   Contribution

   Conceptualization, Funding acquisition, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   steven.kleinstein@yale.edu

   Competing interests

   receives consulting fees from Northrop Grumman and Peraton

   "This ORCID iD identifies the author of this article:" 0000-0003-4957-1544

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