Biology subjects

Kleinstein, S. H.

Publications and source records attributed to Kleinstein, S. H..

At least 19 recordsLinked to original sources

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2025

Single cell immunophenotyping identifies CD8+ GZMK+ IFNG+ T cells as a key immune population in cutaneous Lyme disease

The skin lesion erythema migrans (EM) is the first clinical sign of Lyme disease, an infection due to the tick-transmitted bacterium Borrelia burgdorferi (Bb). Previously, we used scRNA-Seq to characterize the cutaneous immune response in the EM lesion, focusing on B cells. Here, with an expanded sample size, we profiled T cell responses in EM lesions compared to autologous uninvolved skin. In addition to CD4+ T cell subsets known to be abundant in the EM, we identified clonal expansion of CD8+ GZMK+ IFNG+ T cells that exhibited significant differential expression of interferon-regulated genes. This subset included IFNG+ cells with low cytotoxic gene expression, which may promote inflammation. While FOXP3+ regulatory T cells were also increased in EM, they exhibited little IL10 expression. In contrast, a CD4+ FOXP3- tissue-resident T cell subset contained the largest population of cells with IL10 expression. Fibroblasts, endothelial cells, and pericytes were the principal cells that significantly differentially expressed key T cell-recruiting chemokines. These studies represent the first comprehensive interrogation of the cutaneous T cell response to Bb infection using single cell transcriptomics with adaptive immune receptor sequencing, providing insight into the skin barrier defense and orchestration of the immune response to this vector-borne pathogen.

immunology↗

bioRxiv2024

Innate immune epigenomic landscape following controlled human influenza virus infection

Viral infections can induce changes in innate immunity that persist after virus clearance. Here, we used blood samples from a human influenza H3N2 challenge study to perform comprehensive multi-omic analyses. We detected remodeling of immune programs in innate immune cells after resolution of the infection that was proportional in magnitude to the level of prior viral load. We found changes associated with suppressed inflammation including decreased cytokine and AP-1 gene expression as well as decreased accessibility at AP-1 targets and interleukin-related gene promoter regions. We also found decreased histone deacetylase gene expression, increased MAP kinase gene expression, and increased accessibility at interferon-related gene promoter regions. Genes involved in inflammation and epigenetic-remodeling showed modulation of gene-chromatin site regulatory circuit activity. These results reveal a coordinated rewiring of the epigenetic landscape in innate immune cells induced by mild influenza virus infection.

immunology↗

bioRxiv2024

nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Nonlinear Iterative Partial Least Squares

MotivationWith the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data. ResultsWe introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors. AvailabilitynipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.

bioinformatics↗

bioRxiv2024

Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction

Antibodies play a crucial role in adaptive immune responses by determining B cell specificity to antigens and focusing immune function on target pathogens. Accurate prediction of antibody-antigen specificity directly from antibody sequencing data would be a great aid in understanding immune responses, guiding vaccine design, and developing antibody-based therapeutics. In this study, we present a method of supervised fine-tuning for antibody language models, which improves on previous results in binding specificity prediction to SARS-CoV-2 spike protein and influenza hemagglutinin. We perform supervised fine-tuning on four pre-trained antibody language models to predict specificity to these antigens and demonstrate that fine-tuned language model classifiers exhibit enhanced predictive accuracy compared to classifiers trained on pretrained model embeddings. The change of model attention activations after supervised fine-tuning suggested that this performance was driven by an increased model focus on the complementarity determining regions (CDRs). Application of the supervised fine-tuned models to BCR repertoire data demonstrated that these models could recognize the specific responses elicited by influenza and SARS-CoV-2 vaccination. Overall, our study highlights the benefits of supervised fine-tuning on pre-trained antibody language models as a mechanism to improve antigen specificity prediction. Author SummaryAntibodies are vigilant sentinels of our adaptive immune system that recognize and bind to targets on foreign pathogens, known as antigens. This interaction between antibody and antigen is highly specific, akin to a fitting lock and key mechanism, to ensure each antibody precisely targets its intended antigen. Recent advancements in language modeling have led to the development of antibody language model to decode specificity information in the sequences of antibodies. We introduce a method based on supervised fine-tuning, which enhances the accuracy of antibody language models in predicting antibody-antigen interactions. By training these models on large datasets of antibody sequences, we can better predict which antibodies will bind to important antigens such as those found on the surface of viruses like SARS-CoV-2 and influenza. Moreover, our study demonstrates the potential of the models to "read" B cell repertoire data and predict ongoing responses, offering new insights into how our bodies respond to vaccination. These findings have significant implications for vaccine design, as accurate prediction of antibody specificity can guide the development of more effective vaccines.

bioinformatics↗

bioRxiv2024

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. Availability and implementationnf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow). Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/576147v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@e39504org.highwire.dtl.DTLVardef@14c52aforg.highwire.dtl.DTLVardef@1c87664org.highwire.dtl.DTLVardef@1d4d78b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser

Antibodies are vital to human immune responses and are composed of genetically variable heavy and light chains. These structures are initially expressed as B cell receptors (BCRs). BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated heavy and light chains, but advancements in single cell sequencing now pair heavy and light chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired heavy and light chain sequences to build phylogenetic trees. We found incorporating light chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree building methods and persisted even when mixing bulk and single cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some light chains were missing, such as when mixing single cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for heavy and light chain gene partitions. Thus, we recommend using maximum likelihood methods with separate heavy and light chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

immunology↗

bioRxiv2023

A systems vaccinology resource to develop and test computational models of immunity

Computational models that predict an individuals response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such dry run for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.

systems biology↗

bioRxiv2023

Early cellular and molecular signatures correlate with severity of West Nile Virus infection

Infection with West Nile Virus (WNV) can drive a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence at 3 months and 1 year employing multiplexed single cell protein and transcriptional profiling (CyTOF and Seq-Well) complemented with matched serum proteomics and metabolomics. At the acute time point, we detected both an elevated proportion of pro-inflammatory markers in innate immune cell types and reduced frequency of regulatory T cell activity in participants with severe infection compared to those with asymptomatic infection. Single-cell transcriptomics of paired samples revealed that asymptomatic donors had higher expression of genes associated with innate immune pathways, in particular anti-inflammatory CD16+ monocytes at the acute time point. A multi-omics analysis identified factors--beyond those from individual analyses--that distinguished immune state trajectory between severity groups. Here we highlighted the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

immunology↗

bioRxiv2023

SPEAR: a Sparse Supervised Bayesian Factor Model for Multi-omic Integration

MotivationPredictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. ResultsWe developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of COVID-19 severity and breast cancer tumor subtypes. AvailabilitySPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR. Contactjeremy.gygi@yale.edu; leying.guan@yale.edu

bioinformatics↗

bioRxiv2022

IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles as well as a novel method to infer individual genotypes. We demonstrate the strength of the two by comparing their outcomes to other genotype inference methods and validated the genotype approach with independent genomic long read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET). To allow researchers to explore further the approach on real data and to adapt it for their future uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).

immunology↗

bioRxiv2022

Platelet response to influenza vaccination reflects effects of aging

Platelets are uniquely positioned as mediators of not only hemostasis but also innate immunity, but how age and alterations in functional status such as frailty influence platelet function during an immune response remains unclear. We assessed the platelet transcriptome in younger (age 21-35) and older (age [≥] 65) adults (including frail and non-frail individuals) following influenza vaccination. Prior to vaccination, we identified an age- and frailty-associated increase in expression of platelet activation and mitochondrial RNAs and decrease in RNAs encoding proteins mediating translation. Using tensor decomposition analysis, we also elucidated dynamic post-vaccination platelet activation and translation signatures associated with age and frailty. At the protein level, enhanced platelet activation was found in non-frail older adults, compared to young individuals both prior to and post-vaccine; but frail adults showed decreased platelet activation compared to non-frail that could reflect the influence of decreased translation RNA expression. Our results reveal an age-dependent alteration in platelet function prior to and post-vaccination that may contribute to age-associated chronic inflammation.

immunology↗

bioRxiv2021

Pipeline for retrieval of COVID-19 immune signatures

ObjectiveThe accelerating pace of biomedical publication has made retrieving papers and extracting specific comprehensive scientific information a key challenge. A timely example of such a challenge is to retrieve the subset of papers that report on immune signatures (coherent sets of biomarkers) to understand the immune response mechanisms which drive differential SARS-CoV-2 infection outcomes. A systematic and scalable approach is needed to identify and extract COVID-19 immune signatures in a structured and machine-readable format. Materials and MethodsWe used SPECTER embeddings with SVM classifiers to automatically identify papers containing immune signatures. A generic web platform was used to manually screen papers and allow anonymous submission. ResultsWe demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. This demonstrates the efficacy of using a SVM classifier with document embeddings of the abstract and title, to retrieve papers with scientifically salient information, even when that information is rarely present in the abstract. Additionally, classification based on the embeddings identified the type of immune signature (e.g., gene expression vs. other types of profiling) with a positive predictive value of 74%. ConclusionCoupling a classifier based on document embeddings with direct author engagement offers a promising pathway to build a semistructured representation of scientifically relevant information. Through this approach, partially automated literature mining can help rapidly create semistructured knowledge repositories for automatic analysis of emerging health threats.

immunology↗

bioRxiv2021

An innate immune activation state prior to vaccination predicts responsiveness to multiple vaccines

Many factors determine whether an individual responding to vaccination will generate an immune response that can lead to protection. Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccines. However, the generalizability and mechanisms that underlie this association remain poorly defined. Here, we sought to identify a common pre-vaccination signature and mechanisms that could predict the immune response across a wide variety of vaccines. We leveraged the "Immune Signatures Data Resource" created by the NIH Human Immunology Project Consortium (HIPC) to integrate data from 28 studies involving 13 different vaccines and associate the blood transcriptional status of 820 healthy young adults with their responses. An unsupervised analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination states, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations downstream of NF{kappa}B and IRF7. Innate and adaptive immune cell subset-specific genes were also associated with the three pre-vaccination states. Importantly, individuals whose pre-vaccination state was enriched in pro-inflammatory response genes known to be downstream of NF{kappa}B tended to have higher serum antibody responses one month after vaccination. A supervised analysis of the same data resulted in a single classifier, also enriched for NF{kappa}B regulated genes, that predicted the antibody response across most of the vaccines. Projection into single-cell RNA-sequencing data suggested that this pre-vaccination state was attributable to the signature of activation of non-classical monocytes and myeloid dendritic cells. Transcriptional signatures of acute responses to bacterial and not viral infections were enriched in the high pro-inflammatory pre-vaccination state and also included NF{kappa}B regulated genes. The pro-inflammatory pre-vaccination state was highly reminiscent of the innate activation state triggered by TLR ligands or adjuvants. These results demonstrate that wide variations in the transcriptional state of the immune system in humans can be a key determinant of responsiveness to vaccination. They also define a transcriptional signature NF{kappa}B activation at baseline, that is associated with a greater magnitude of antibody response to multiple vaccines, and suggest that modulation of the innate immune system by next-generation adjuvants targeting NF{kappa}B before vaccine administration may improve vaccine responsiveness.

systems biology↗

bioRxiv2021

Comparing host module activation patterns and temporal dynamics in infection by influenza H1N1 viruses

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

bioinformatics↗

bioRxiv2021

A curated collection of human vaccination response signatures

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying "immune signatures". Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard (www.hipc-dashboard.org), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.

bioinformatics↗

bioRxiv2021

Elevated N-glycosylation of immunoglobulin G variable regions in myasthenia gravis highlights a commonality across autoantibody-associated diseases

Elevated N-linked glycosylation of immunoglobulin G variable regions (IgG-VN-Glyc) is an emerging molecular phenotype associated with autoimmune disorders. To test the broader specificity of elevated IgG-VN-Glyc, we studied patients with distinct subtypes of myasthenia gravis (MG), a B cell-mediated autoimmune disease. Our experimental design included adaptive immune receptor repertoire sequencing to quantify and characterize N-glycosylation sites in the global B cell receptor repertoire, proteomics to examine glycosylation patterns of the circulating IgG, and production of human-derived recombinant autoantibodies, which were studied with mass spectrometry and antigen binding assays to confirm occupation of glycosylation sites and determine whether they alter binding. We found that the frequency of IgG-VN-Glyc motifs was increased in the B cell repertoire of MG patients when compared to healthy donors. Motifs were introduced by both biased V gene segment usage and somatic hypermutation. IgG-VN-Glyc could be observed in the circulating IgG in a subset of MG patients. Autoantigen binding, by patient-derived MG autoantigen-specific monoclonal antibodies with experimentally confirmed presence of IgG-VN-Glyc, was not altered by the glycosylation. Our findings extend prior work on patterns of variable region N-linked glycosylation in autoimmunity to MG subtypes. Although occupied IgG-VN-Glyc motifs are found on MG autoantigen-specific monoclonal antibodies, they are not required for binding to the autoantigen in this disease.

immunology↗

bioRxiv2021

Human B cell lineages engaged by germinal centers following influenza vaccination are measurably evolving

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.1 Consistent with this notion, no significant increase in somatic hypermutation (SHM) among circulating influenza-binding lineages was detected following seasonal vaccination in humans.2 A more recent study showed that at least a subset of vaccine-induced B cell lineages are recruited into germinal centers (GCs) following vaccination, suggesting that affinity maturation of these lineages can occur.3 Crucially, however, it has not been demonstrated whether these GC-engaged lineages actually accumulate additional SHM. Here, we address this point using a phylogenetic test of measurable evolution. We first validate this test through simulations and demonstrate measurable B cell evolution in known examples of affinity maturation such as the response to HIV infection. We then show that lineages in the blood are rarely measurably evolving following influenza vaccination, but that GC-engaged lineages - likely derived from memory B cells - are frequently measurably evolving. These findings confirm that seasonal influenza virus vaccination can stimulate additional SHM among responding B cell lineages.

immunology↗