Human B cell lineages associated with germinal centers following influenza vaccination are measurably evolving
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Evaluation Summary:
The manuscript by Hoehn et al use a novel approach to quantify the somatic evolution in B cells. It brings together existing datasets to investigate the evidence for detectable evolution across longitudinal samples of BCR repertoires. This work provides significant new insight into which stimuli induce effective immune responses, and has the potential to improve vaccine design. Notably, these results are of interest for characterizing B cell responses, especially to vaccinations that induce a poor immune response, such as influenza.
(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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Abstract
The poor efficacy of seasonal influenza virus vaccines is often attributed to pre-existing immunity interfering with the persistence and maturation of vaccine-induced B cell responses. We previously showed that a subset of vaccine-induced B cell lineages are recruited into germinal centers (GCs) following vaccination, suggesting that affinity maturation of these lineages against vaccine antigens can occur. However, it remains to be determined whether seasonal influenza vaccination stimulates additional evolution of vaccine-specific lineages, and previous work has found no significant increase in somatic hypermutation among influenza-binding lineages sampled from the blood following seasonal vaccination in humans. Here, we investigate this issue using a phylogenetic test of measurable immunoglobulin sequence evolution. We first validate this test through simulations and survey measurable evolution across multiple conditions. We find significant heterogeneity in measurable B cell evolution across conditions, with enrichment in primary response conditions such as HIV infection and early childhood development. We then show that measurable evolution following influenza vaccination is highly compartmentalized: while lineages in the blood are rarely measurably evolving following influenza vaccination, lineages containing GC B cells are frequently measurably evolving. Many of these lineages appear to derive from memory B cells. We conclude from these findings that seasonal influenza virus vaccination can stimulate additional evolution of responding B cell lineages, and imply that the poor efficacy of seasonal influenza vaccination is not due to a complete inhibition of vaccine-specific B cell evolution.
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Author response
Reviewer #3 (Public Review)
Major concerns:
- The most substantive concern is that there is an alternative explanation for the data which must be ruled out in order to conclude that mutations are occurring during the study period. Consider the following scenario. Suppose that B cell clones expanded and diversified through somatic hypermutation prior to the study period (that is, prior to the secondary vaccination event which is the focus of the study). It seems that preferential expansion of highly mutated subclones during the study period could bias detected sequences towards more divergent sequences, even without ongoing somatic mutation during the study period. Preferential expansion of divergent sequences would give rise to higher average divergence as the study period goes on, giving the appearance of …
Author response
Reviewer #3 (Public Review)
Major concerns:
- The most substantive concern is that there is an alternative explanation for the data which must be ruled out in order to conclude that mutations are occurring during the study period. Consider the following scenario. Suppose that B cell clones expanded and diversified through somatic hypermutation prior to the study period (that is, prior to the secondary vaccination event which is the focus of the study). It seems that preferential expansion of highly mutated subclones during the study period could bias detected sequences towards more divergent sequences, even without ongoing somatic mutation during the study period. Preferential expansion of divergent sequences would give rise to higher average divergence as the study period goes on, giving the appearance of accumulation of additional mutations, but in fact these mutations had occurred prior to the study period and are simply more readily detected in the sparsely sampled repertoire sequencing data after their expansion. Far from being simply a pathological counter-example, this scenario seems biologically plausible, given that B cells harboring more divergent, affinity-matured sequences should generally have higher affinity antibodies that allow them to better compete for limited antigen and thus provide stronger division stimulus. This model predicts that some highly divergent sequences exist at early timepoints and would occasionally be detected. An example of this is seen in Fig 3C, where a divergent tip from an early sample time is present (labeled PB and colored blue, in the middle of the diagram), indicating that this divergent sequence was present early. While the authors' model of ongoing evolution is supported, this alternative model also appears to be consistent with the data and must be ruled out in order to conclude that clones are accumulating mutations during the study period, which is the central claim and most interesting and impactful finding of the work. The authors must provide evidence that their approach can distinguish between these scenarios. This could potentially be accomplished using simulations of the two scenarios to determine the power of the approach to distinguish between them.
We agree that this is a potential alternative explanation for a significant positive correlation between divergence and time, and have now addressed this as a possibility in the Results and Discussion sections. We have also removed explicit references to detecting “ongoing SHM” in the text, in favor terms that more directly reflect what our test detects such as “B cell evolution” or “increasing SHM frequency” which do not imply novel SHM over the sampling interval. Nevertheless, we believe our results are more easily explained as a result of ongoing SHM, and have added some text making that point. In the context of influenza vaccination, day 5 plasmablasts represent the breadth of the B cell memory pool. If measurable evolution were due solely from preferential recall, we would expect the divergences of sequences at later timepoints to fall within the range of day 5 plasmablasts. Instead, in the high-GC influenza binding lineages we identified (Fig 3B/C), many late-sampled GC sequences are clearly more diverged from the day 5 plasmablast response. Further, if measurable evolution from influenza vaccination were due simply to preferential re-stimulation of highly mutated B cells, we would expect influenza binding lineages without any GC sequences to be measurably evolving. To test this, we repeated the analysis in Fig 3A using only lineages containing influenza-binding monoclonal antibodies (mAbs). Results were highly consistent with Fig 3A: influenza-binding lineages without GC sequences were less likely to be evolving than those with high proportions of GC sequences (Figure 3 – figure supplement 3). Thus, significant GC involvement, rather than simply binding to influenza, is more predictive of measurable evolution. All of these points are more easily explained if measurable evolution is the result of additional SHM. Nonetheless, we cannot definitely rule out this alternative explanation, we have highlighted both possible mechanisms of B cell evolution. We have included descriptions of this new analysis in the Results (pp. 14-15) and Discussion (pp. 20-21).
- Statistical support for measurable evolution appears to be lacking in several key examples. The reported percentages of measurably evolving lineages in several scenarios (7.2% for primary hepatitis B vaccination; 6.5% for allergen-specific immunotherapy; 5.9% for HIV infection) are near the false positive rate of the test (5% of lineages measurably evolving). The authors have performed this test on datasets from ~21 studies, raising a concern that multiple hypothesis testing could give rise to false positives in some of the datasets. These results are interpreted as evidence of measurable evolution, even though they could seemingly be explained by the false discovery rate combined with multiple hypothesis testing. The authors should clarify how these results can be interpreted in light of the false positive rate of their test and multiple hypothesis testing, and must consider whether more conservative conclusions are warranted in these scenarios.
We appreciate the reviewer’s concern and have added a new section on multiple hypothesis testing to the Discussion detailing these caveats (pp. 19-20), as well as additional details to the relevant Results sections (p. 10). We also repeated our initial germline divergence analysis that used “adjusted” measurably evolving lineages without the multiple testing correction, and found similar results (Figure 2 – figure supplement 4). We also repeated these analyses using a more strict cutoff (adjusted p < 0.05), which also yielded similar results (Figure 2 – figure supplement 4). These are discussed in the main text
Minor comments
- The authors define measurably evolving populations as systems undergoing mutation and selection rapidly enough to be detected. Despite this definition, the test employed for measuring evolution appears to focus purely on accumulation of mutations without examining selection per se. Mutations accumulating neutrally could be detected as measurable evolution. For the sake of clarity, the authors should explain more clearly whether their test examines selection and ensure that initial definitions are consistent with later usage. It may be interesting to further examine whether the mutations detected as measurable evolution in antibody lineages are neutral or selected using classical tests for selection, such as the dN/dS statistic or summary statistics of the site frequency spectrum.
We have now made it clearer in the initial definition that measurable evolution does not necessarily require selection. This definition is more in line with the original definition of measurably evolving populations from Drummond et al 2003: “We define measurably evolving populations (MEPs) as populations from which molecular sequences can be taken at different points in time, among which there are a statistically significant number of genetic differences.
- Statistical support for the association between GC B cells and measurable evolution should be clarified. On p14 L7-8, it is reported that "6.5% of lineages containing sequences from GC B cells were measurably evolving, compared to only 3.7% of lineages with no identified GC sequences." However, this does not constitute convincing evidence for the association because this difference of proportions is not significant. The proportions are 3 lineages measurably evolving among 46 lineages containing sequences from GC B cells, and 4 lineages measurably evolving among 107 lineages not containing sequences from GC B cells. Applying Fisher's exact test for a difference of proportion yields P = 0.43. While the evidence based on the trend in Fig 3A (of fraction measurably evolving against GC sequence percentage) is compelling, the authors should clarify whether each difference of proportions is significant. Providing statistical support for the trend itself, such as through bootstrapping or simulation, would seem most direct.
We have now included a bootstrap analysis of this relationship to demonstrate its significance.
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Evaluation Summary:
The manuscript by Hoehn et al use a novel approach to quantify the somatic evolution in B cells. It brings together existing datasets to investigate the evidence for detectable evolution across longitudinal samples of BCR repertoires. This work provides significant new insight into which stimuli induce effective immune responses, and has the potential to improve vaccine design. Notably, these results are of interest for characterizing B cell responses, especially to vaccinations that induce a poor immune response, such as influenza.
(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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Reviewer #1 (Public Review):
Understanding why some immune stimuli (e.g. influenza vaccination) induce worse immune responses than others is important both as a matter of fundamental biology, and in order to improve vaccine design. The authors provide significant new insight into this: they measure the prevalence of evolution in B cell lineages responding to a variety of stimuli, and compare these to similar measurements in influenza vaccine response. They show that evolution is more prevalent in cases where it is expected (e.g. HIV, prime vaccination) vs not (e.g. boost vaccination), and that while lineages in blood with detectable evolution are rare after influenza vaccination, they are likely present, and are more prevalent in GC samples. They apply an existing metric to B cell receptor repertoires for (I believe) the first time, …
Reviewer #1 (Public Review):
Understanding why some immune stimuli (e.g. influenza vaccination) induce worse immune responses than others is important both as a matter of fundamental biology, and in order to improve vaccine design. The authors provide significant new insight into this: they measure the prevalence of evolution in B cell lineages responding to a variety of stimuli, and compare these to similar measurements in influenza vaccine response. They show that evolution is more prevalent in cases where it is expected (e.g. HIV, prime vaccination) vs not (e.g. boost vaccination), and that while lineages in blood with detectable evolution are rare after influenza vaccination, they are likely present, and are more prevalent in GC samples. They apply an existing metric to B cell receptor repertoires for (I believe) the first time, while adding an improvement to it (polytomy resolution).
The basic idea – when you have longitudinal data, focusing on lineages whose SHM increases with time – is on the one hand quite obvious, but on the other, the authors demonstrate both that it's a more accurate technique than I would have expected, and that the particular method they use of quantifying "SHM increase" is a good one. Both of these things are of significant value. Those interested in understanding poor immune responses, as well as those looking to find evolving lineages in B cell receptor sequencing data more generally, will likely find the publicly available software useful (although note that I did not test the software).
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Reviewer #2 (Public Review):
The manuscript by Kenneth Hoehn et al. investigates the evidence for detectable evolution across longitudinal samples of BCR (B cell receptor) repertoires. The authors set out to quantify the degree to which such continued evolution can be observed in existing high-throughput BCR repertoire sequencing datasets. They focus in particular on the context of influenza vaccination with corresponding recent datasets (Turner et al. 2020) with blood and lymph node samples.
The authors applied a robust statistical framework that they tested extensively on synthetic data and proved to be a relevant tool to analyze immune repertoires. Over 20 distinct datasets were analyzed to cover a wide range of conditions of acute and chronic infections as well as healthy individuals and their response to vaccination. This …
Reviewer #2 (Public Review):
The manuscript by Kenneth Hoehn et al. investigates the evidence for detectable evolution across longitudinal samples of BCR (B cell receptor) repertoires. The authors set out to quantify the degree to which such continued evolution can be observed in existing high-throughput BCR repertoire sequencing datasets. They focus in particular on the context of influenza vaccination with corresponding recent datasets (Turner et al. 2020) with blood and lymph node samples.
The authors applied a robust statistical framework that they tested extensively on synthetic data and proved to be a relevant tool to analyze immune repertoires. Over 20 distinct datasets were analyzed to cover a wide range of conditions of acute and chronic infections as well as healthy individuals and their response to vaccination. This comprehensive analysis is valuable in the field where consistent analysis of different datasets is not found often. The key findings, that is an enrichment in certain conditions, notably HIV infections, and lack of thereof in others, support previously known findings.
The method of detecting measurable evolution between timepoints adapted to B cells sheds new light on long-standing questions about affinity maturation, particularly ones of formation of the memory repertoire and its reentry to germinal centers upon vaccination. Due to a limited number of datasets and donors in each cohort, as well as limited sequencing depth, this approach has a number of limitations. New experiments are necessary to fill in the gaps in our understanding of the evolution of B cell repertoires. The important contribution of this work consists not only in a useful review of existing datasets but also in suggesting new directions in studies based on high-throughput sequencing.
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Reviewer #3 (Public Review):
Hoehn et al. studied the somatic evolution of human BCRs after immune stimulation. Using a phylogenetic approach to analyze publically available longitudinal BCR repertoire sequencing data, they examined whether and how mutations accumulate in BCR clones over time after primary or secondary immune stimulus. They focused particularly on the important open question of whether seasonal influenza vaccination elicits de novo somatic mutation and clonal evolution. The main conclusion of the study is that germinal center-associated B cell clones undergo continued evolution after seasonal influenza vaccination.
On the whole, the work addresses an important question, the analysis approach is innovative, and the conclusions are generally well supported. However, there is one major problem consisting of an alternative …
Reviewer #3 (Public Review):
Hoehn et al. studied the somatic evolution of human BCRs after immune stimulation. Using a phylogenetic approach to analyze publically available longitudinal BCR repertoire sequencing data, they examined whether and how mutations accumulate in BCR clones over time after primary or secondary immune stimulus. They focused particularly on the important open question of whether seasonal influenza vaccination elicits de novo somatic mutation and clonal evolution. The main conclusion of the study is that germinal center-associated B cell clones undergo continued evolution after seasonal influenza vaccination.
On the whole, the work addresses an important question, the analysis approach is innovative, and the conclusions are generally well supported. However, there is one major problem consisting of an alternative explanation for the central result, which must be ruled out in order for the result to be compelling.
Major concerns:
1. The most substantive concern is that there is an alternative explanation for the data which must be ruled out in order to conclude that mutations are occurring during the study period. Consider the following scenario. Suppose that B cell clones expanded and diversified through somatic hypermutation prior to the study period (that is, prior to the secondary vaccination event which is the focus of the study). It seems that preferential expansion of highly mutated subclones during the study period could bias detected sequences towards more divergent sequences, even without ongoing somatic mutation during the study period. Preferential expansion of divergent sequences would give rise to higher average divergence as the study period goes on, giving the appearance of accumulation of additional mutations, but in fact these mutations had occurred prior to the study period and are simply more readily detected in the sparsely sampled repertoire sequencing data after their expansion. Far from being simply a pathological counter-example, this scenario seems biologically plausible, given that B cells harboring more divergent, affinity-matured sequences should generally have higher affinity antibodies that allow them to better compete for limited antigen and thus provide stronger division stimulus. This model predicts that some highly divergent sequences exist at early timepoints and would occasionally be detected. An example of this is seen in Fig 3C, where a divergent tip from an early sample time is present (labeled PB and colored blue, in the middle of the diagram), indicating that this divergent sequence was present early. While the authors' model of ongoing evolution is supported, this alternative model also appears to be consistent with the data and must be ruled out in order to conclude that clones are accumulating mutations during the study period, which is the central claim and most interesting and impactful finding of the work. The authors must provide evidence that their approach can distinguish between these scenarios. This could potentially be accomplished using simulations of the two scenarios to determine the power of the approach to distinguish between them.
2. Statistical support for measurable evolution appears to be lacking in several key examples. The reported percentages of measurably evolving lineages in several scenarios (7.2% for primary hepatitis B vaccination; 6.5% for allergen-specific immunotherapy; 5.9% for HIV infection) are near the false positive rate of the test (5% of lineages measurably evolving). The authors have performed this test on datasets from ~21 studies, raising a concern that multiple hypothesis testing could give rise to false positives in some of the datasets. These results are interpreted as evidence of measurable evolution, even though they could seemingly be explained by the false discovery rate combined with multiple hypothesis testing. The authors should clarify how these results can be interpreted in light of the false positive rate of their test and multiple hypothesis testing, and must consider whether more conservative conclusions are warranted in these scenarios.
Minor comments:
1. The authors define measurably evolving populations as systems undergoing mutation and selection rapidly enough to be detected. Despite this definition, the test employed for measuring evolution appears to focus purely on accumulation of mutations without examining selection per se. Mutations accumulating neutrally could be detected as measurable evolution. For the sake of clarity, the authors should explain more clearly whether their test examines selection and ensure that initial definitions are consistent with later usage. It may be interesting to further examine whether the mutations detected as measurable evolution in antibody lineages are neutral or selected using classical tests for selection, such as the dN/dS statistic or summary statistics of the site frequency spectrum.
2. Statistical support for the association between GC B cells and measurable evolution should be clarified. On p14 L7-8, it is reported that "6.5% of lineages containing sequences from GC B cells were measurably evolving, compared to only 3.7% of lineages with no identified GC sequences." However, this does not constitute convincing evidence for the association because this difference of proportions is not significant. The proportions are 3 lineages measurably evolving among 46 lineages containing sequences from GC B cells, and 4 lineages measurably evolving among 107 lineages not containing sequences from GC B cells. Applying Fisher's exact test for a difference of proportion yields P = 0.43. While the evidence based on the trend in Fig 3A (of fraction measurably evolving against GC sequence percentage) is compelling, the authors should clarify whether each difference of proportions is significant. Providing statistical support for the trend itself, such as through bootstrapping or simulation, would seem most direct.
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