Gene signatures related to HAI response following influenza A/H1N1 vaccine in older individuals

Leveraging baseline transcriptional features and information from single-cell data to power the prediction of influenza vaccine response

Introduction

Vaccination is still the primary means for preventing influenza virus infection, but the protective effects vary greatly among individuals. Identifying individuals at risk of low response to influenza vaccination is important. This study aimed to explore improved strategies for constructing predictive models of influenza vaccine response using gene expression data.

Methods

We first used gene expression and immune response data from the Immune Signatures Data Resource (IS2) to define influenza vaccine response-related transcriptional expression and alteration features at different time points across vaccination via differential expression analysis. Then, we mapped these features to single-cell resolution using additional published single-cell data to investigate the possible mechanism. Finally, we explored the potential of these identified transcriptional features in predicting influenza vaccine response. We used several modeling strategies and also attempted to leverage the information from single-cell RNA sequencing (scRNA-seq) data to optimize the predictive models.

Results

The results showed that models based on genes showing differential expression (DEGs) or fold change (DFGs) at day 7 post-vaccination performed the best in internal validation, while models based on DFGs had a better performance in external validation than those based on DEGs. In addition, incorporating baseline predictors could improve the performance of models based on days 1-3, while the model based on the expression profile of plasma cells deconvoluted from the model that used DEGs at day 7 as predictors showed an improved performance in external validation.

Conclusion

Our study emphasizes the value of using combination modeling strategy and leveraging information from single-cell levels in constructing influenza vaccine response predictive models.

Virus-specific and shared gene expression signatures in immune cells after vaccination in response to influenza and vaccinia stimulation

Background

In the vaccine era, individuals receive multiple vaccines in their lifetime. Host gene expression in response to antigenic stimulation is usually virus-specific; however, identifying shared pathways of host response across a wide spectrum of vaccine pathogens can shed light on the molecular mechanisms/components which can be targeted for the development of broad/universal therapeutics and vaccines.

Method

We isolated PBMCs, monocytes, B cells, and CD8+ T cells from the peripheral blood of healthy donors, who received both seasonal influenza vaccine (within <1 year) and smallpox vaccine (within 1 - 4 years). Each of the purified cell populations was stimulated with either influenza virus or vaccinia virus. Differentially expressed genes (DEGs) relative to unstimulated controls were identified for each in vitro viral infection, as well as for both viral infections (shared DEGs). Pathway enrichment analysis was performed to associate identified DEGs with KEGG/biological pathways.

Results

We identified 2,906, 3,888, 681, and 446 DEGs in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, in response to influenza stimulation. Meanwhile, 97, 120, 20, and 10 DEGs were identified as gene signatures in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, upon vaccinia stimulation. The majority of DEGs identified in PBMCs were also found in monocytes after either viral stimulation. Of the virus-specific DEGs, 55, 63, and 9 DEGs occurred in common in PBMCs, monocytes, and B cells, respectively, while no DEGs were shared in infected CD8+ T cells after influenza and vaccinia. Gene set enrichment analysis demonstrated that these shared DEGs were over-represented in innate signaling pathways, including cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling, RIG-I-like receptor signaling pathways, cytosolic DNA-sensing pathways, and natural killer cell mediated cytotoxicity.

Conclusion

Our results provide insights into virus-host interactions in different immune cells, as well as host defense mechanisms against viral stimulation. Our data also highlights the role of monocytes as a major cell population driving gene expression in ex vivo PBMCs in response to viral stimulation. The immune response signaling pathways identified in this study may provide specific targets for the development of novel virus-specific therapeutics and improved vaccines for vaccinia and influenza. Although influenza and vaccinia viruses have been selected in this study as pathogen models, this approach could be applicable to other pathogens.

Biological Sex and Pregnancy Affect Influenza Pathogenesis and Vaccination

Males and females differ in the outcome of influenza A virus (IAV) infections, which depends significantly on age. During seasonal influenza epidemics, young children (< 5 years of age) and aged adults (65+ years of age) are at greatest risk for severe disease, and among these age groups, males tend to suffer a worse outcome from IAV infection than females. Following infection with pandemic strains of IAVs, females of reproductive ages (i.e., 15-49 years of age) experience a worse outcome than their male counterparts. Although females of reproductive ages experience worse outcomes from IAV infection, females typically have greater immune responses to influenza vaccination as compared with males. Among females of reproductive ages, pregnancy is one factor linked to an increased risk of severe outcome of influenza. Small animal models of influenza virus infection and vaccination illustrate that immune responses and repair of damaged tissue following IAV infection also differ between the sexes and impact the outcome of infection. There is growing evidence that sex steroid hormones, including estrogens, progesterone, and testosterone, directly impact immune responses during IAV infection and vaccination. Greater consideration of the combined effects of sex and age as biological variables in epidemiological, clinical, and animal studies of influenza pathogenesis is needed.

The impact of physical frailty on the response to inactivated influenza vaccine in older adults

Physical frailty's impact on hemagglutination inhibition antibody titers (HAI) and peripheral blood mononuclear cell (PBMC) transcriptional responses after influenza vaccination is unclear. Physical frailty was assessed using the 5-item Fried frailty phenotype in 168 community- and assisted-living adults ≥55 years of age during an observational study. Blood was drawn before, 3, 7, and 28 days post-vaccination with the 2017-2018 inactivated influenza vaccine. HAI response to the A/H1N1 strain was measured at Days 0 and 28 using seropositivity, seroconversion, log2 HAI titers, and fold-rise in log2 HAI titers. RNA sequencing of PBMCs from Days 0, 3 and 7 was measured in 28 participants and compared using pathway analyses. Frailty was not significantly associated with any HAI outcome in multivariable models. Compared with non-frail participants, frail participants expressed decreased cell proliferation, metabolism, antibody production, and interferon signaling genes. Conversely, frail participants showed elevated gene expression in IL-8 signaling, T-cell exhaustion, and oxidative stress pathways compared with non-frail participants. These results suggest that reduced effectiveness of influenza vaccine among older, frail individuals may be attributed to immunosenescence-related changes in PBMCs that are not reflected in antibody levels.

Microarray Gene Expression Dataset Re-analysis Reveals Variability in Influenza Infection and Vaccination

Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

Transcriptomic analysis of human IL-7 receptor alpha low and high effector memory CD8+ T cells reveals an age-associated signature linked to influenza vaccine response in older adults

Here, we investigated the relationship of the age‐associated expansion of IL‐7 receptor alpha low (IL‐7Rα^low) effector memory (EM) CD8⁺ T cells with the global transcriptomic profile of peripheral blood cells in humans. We found 231 aging signature genes of IL‐7Rα^low EM CD8⁺ T cells that corresponded to 15% of the age‐associated genes (231/1,497) reported by a meta‐analysis study on human peripheral whole blood from approximately 15,000 individuals, having high correlation with chronological age. These aging signature genes were the target genes of several transcription factors including MYC, SATB1, and BATF, which also belonged to the 231 genes, supporting the upstream regulatory role of these transcription factors in altering the gene expression profile of peripheral blood cells with aging. We validated the differential expression of these transcription factors between IL‐7Rα^low and ^high EM CD8⁺ T cells as well as in peripheral blood mononuclear cells (PBMCs) of young and older adults. Finally, we found a significant association with influenza vaccine responses in older adults, suggesting the possible biological significance of the aging signature genes of IL‐7Rα^low EM CD8⁺ T cells. The results of our study support the relationship of the expansion of IL‐7Rα^low EM CD8⁺ T cells with the age‐associated changes in the gene expression profile of peripheral blood cells and its possible biological implications.

Sex Differences in Older Adults' Immune Responses to Seasonal Influenza Vaccination

Background: Sex differences in immune responses to influenza vaccine may impact efficacy across populations. Methods: In a cohort of 138 older adults (50-74 years old), we measured influenza A/H1N1 antibody titers, B-cell ELISPOT response, PBMC transcriptomics, and PBMC cell compositions at 0, 3, and 28 days post-immunization with the 2010/11 seasonal inactivated influenza vaccine. Results: We identified higher B-cell ELISPOT responses in females than males. Potential mechanisms for sex effects were identified in four gene clusters related to T, NK, and B cells. Mediation analysis indicated that sex-dependent expression in T and NK cell genes can be partially attributed to higher CD4+ T cell and lower NK cell fractions in females. We identified strong sex effects in 135 B cell genes whose expression correlates with ELISPOT measures, and found that cell subset differences did not explain the effect of sex on these genes' expression. Post-vaccination expression of these genes, however, mediated 41% of the sex effect on ELISPOT responses. Conclusions: These results improve our understanding of sexual dimorphism in immunity and influenza vaccine response.

Memory CD4+ T cells enhance B-cell responses to drifting influenza immunization

Influenza A annually infects 5-10% of the world's human population resulting in one million deaths. Influenza causes annual epidemics and reinfects previously exposed individuals because of antigenic drift in the glycoprotein hemagglutinin. Due to antigenic drift, the immune system is simultaneously exposed to novel and conserved parts of the influenza virus via vaccination and/or infection throughout life. Preexisting immunity has long been known to augment subsequent hemagglutination inhibitory antibody (hAb) responses. However, the preexisting immunological contributors that influence hAb responses are not understood. Therefore, we adapted and developed sequential infection and immunization mouse models using drifted influenza strains to show that MHC Class II haplotype and T-cell reactivity influences subsequent hAb responses. We found that CB6F1 mice infected with A/CA followed by immunization with A/PR8 have increased hAb responses to A/PR8 compared to C57BL/6 mice. Increased hAb responses in CB6F1 mice were CD4⁺  T-cell and B-cell dependent and corresponded to increased germinal center A/PR8-specific B and T-follicular helper cells. These results suggest conserved MHC Class II restricted epitopes within HA are essential for B cells to respond to drifting influenza and could be leveraged to boost hAb responses.

Transcriptomic signatures of cellular and humoral immune responses in older adults after seasonal influenza vaccination identified by data-driven clustering

PBMC transcriptomes after influenza vaccination contain valuable information about factors affecting vaccine responses. However, distilling meaningful knowledge out of these complex datasets is often difficult and requires advanced data mining algorithms. We investigated the use of the data-driven Weighted Gene Correlation Network Analysis (WGCNA) gene clustering method to identify vaccine response-related genes in PBMC transcriptomic datasets collected from 138 healthy older adults (ages 50-74) before and after 2010-2011 seasonal trivalent influenza vaccination. WGCNA separated the 14,197 gene dataset into 15 gene clusters based on observed gene expression patterns across subjects. Eight clusters were strongly enriched for genes involved in specific immune cell types and processes, including B cells, T cells, monocytes, platelets, NK cells, cytotoxic T cells, and antiviral signaling. Examination of gene cluster membership identified signatures of cellular and humoral responses to seasonal influenza vaccination, as well as pre-existing cellular immunity. The results of this study illustrate the utility of this publically available analysis methodology and highlight genes previously associated with influenza vaccine responses (e.g., CAMK4, CD19), genes with functions not previously identified in vaccine responses (e.g., SPON2, MATK, CST7), and previously uncharacterized genes (e.g. CORO1C, C8orf83) likely related to influenza vaccine-induced immunity due to their expression patterns.

Detection and Quantification of Influenza A/H1N1 Virus-Specific Memory B Cells in Human PBMCs Using ELISpot Assay

Immune response following subsequent encounter of viruses (and vaccines) relies largely on the pool and frequencies of antigen-specific memory B cells. In addition to antibody titers, the reliable measurement of these cells in human blood (peripheral blood mononuclear cells/PBMCs or purified B cells) provides valuable information on the preparedness of the adaptive immune system to respond to infection or vaccines, and potentially supports the discovery of new quantitative correlates of protection. The Mayo Clinic Vaccine Research Group has developed and optimized a high-throughput ELISPOT-based assay for the quantification of influenza A/H1N1 virus-specific memory B cells in human PBMCs. Here, we present the materials and detailed methodology for using this assay on cryopreserved cells for the measurement of recall humoral immunity readiness (antigen-specific memory B cell frequencies) after influenza vaccination. This assay could be readily adapted to other influenza virus strains and other respiratory viruses and vaccines for use in systems biology and larger population-based studies.

Redefining priorities towards graded travel-related infectious disease research

Our knowledge of the health problems and infections encountered by international travellers has evolved considerably in the past decades. The growth of global networks such as the GeoSentinel Surveillance network, TropNet Europe, EuroTravNet and networks based in North America have provided valuable information on the frequency of a wide array of travel-related diseases and accidents, including details on the destination of travel and trends over time. The information gained from these network studies has provided important data for the practice of travel medicine and in some instances for the development of practice guidelines. However, network data due to a lack of denominators usually cannot serve as a basis for a GRADE approach to guideline development. Although epidemiological network studies will continue to serve an important role in travel medicine we encourage an additional strong focus towards translational scientific research questions and towards the broader use of novel techniques to obtain more accurate epidemiological analyses to address the many unanswered questions in our field.

Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses

Annual influenza vaccinations are currently recommended for all individuals 6 months and older. Antibodies induced by vaccination are an important mechanism of protection against infection. Despite the overall public health success of influenza vaccination, many individuals fail to induce a substantial antibody response. Systems-level immune profiling studies have discerned associations between transcriptional and cell subset signatures with the success of antibody responses. However, existing signatures have relied on small cohorts and have not been validated in large independent studies. We leveraged multiple influenza vaccination cohorts spanning distinct geographical locations and seasons from the Human Immunology Project Consortium (HIPC) and the Center for Human Immunology (CHI) to identify baseline (i.e., before vaccination) predictive transcriptional signatures of influenza vaccination responses. Our multicohort analysis of HIPC data identified nine genes (RAB24, GRB2, DPP3, ACTB, MVP, DPP7, ARPC4, PLEKHB2, and ARRB1) and three gene modules that were significantly associated with the magnitude of the antibody response, and these associations were validated in the independent CHI cohort. These signatures were specific to young individuals, suggesting that distinct mechanisms underlie the lower vaccine response in older individuals. We found an inverse correlation between the effect size of signatures in young and older individuals. Although the presence of an inflammatory gene signature, for example, was associated with better antibody responses in young individuals, it was associated with worse responses in older individuals. These results point to the prospect of predicting antibody responses before vaccination and provide insights into the biological mechanisms underlying successful vaccination responses.

Factors affecting immune responses to the influenza vaccine

Annual administration of the seasonal influenza vaccine is strongly recommended to reduce the burden of disease, particularly for persons at the highest risk for the viral infection. Even during years when there is a good match between the vaccine and circulating strains, host-related factors such as age, preexisting immunity, genetic polymorphisms, and the presence of chronic underlying conditions may compromise influenza vaccine responsiveness. The application of new methodologies and large-scale profiling technologies are improving the ability to measure vaccine immunogenicity and our understanding of the immune mechanisms by which vaccines induce protective immunity. This review attempts to summarize the general concepts of how host factors can contribute to the heterogeneity of immune responses induced by influenza vaccines.

Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations

The development of a humoral immune response to influenza vaccines occurs on a multisystems level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to interindividual variation in response to influenza vaccination. We first hypothesized that many of the differentially expressed (DE) genes observed after influenza vaccination result from changes in the composition of participants' peripheral blood mononuclear cells (PBMCs), which were assessed using flow cytometry. We demonstrated that DE genes in our study are correlated with changes in PBMC composition. We gathered DE genes from 128 other publically available PBMC-based vaccine studies and identified that an average of 57% correlated with specific cell subset levels in our study (permutation used to control false discovery), suggesting that the associations we have identified are likely general features of PBMC-based transcriptomics. Second, we hypothesized that more robust models of vaccine response could be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. We employed machine learning to generate predictive models of B-cell ELISPOT response outcomes and hemagglutination inhibition (HAI) antibody titers. The top HAI and B-cell ELISPOT model achieved an area under the receiver operating curve (AUC) of 0.64 and 0.79, respectively, with linear model coefficients of determination of 0.08 and 0.28. For the B-cell ELISPOT outcomes, CpG methylation had the greatest predictive ability, highlighting potentially novel regulatory features important for immune response. B-cell ELISOT models using only PBMC composition had lower performance (AUC = 0.67), but highlighted well-known mechanisms. Our analysis demonstrated that each of the three data sets (cell composition, mRNA-Seq, and DNA methylation) may provide distinct information for the prediction of humoral immune response outcomes. We believe that these findings are important for the interpretation of current omics-based studies and set the stage for a more thorough understanding of interindividual immune responses to influenza vaccination.

Systems serology for evaluation of HIV vaccine trials

The scale and scope of the global epidemic, coupled to challenges with traditional vaccine development approaches, point toward a need for novel methodologies for HIV vaccine research. While the development of vaccines able to induce broadly neutralizing antibodies remains the ultimate goal, to date, vaccines continue to fail to induce these rare humoral immune responses. Conversely, growing evidence across vaccine platforms in both non-human primates and humans points to a role for polyclonal vaccine-induced antibody responses in protection from infection. These candidate vaccines, despite employing disparate viral vectors and immunization strategies, consistently identify a role for functional or non-traditional antibody activities as correlates of immunity. However, the precise mechanism(s) of action of these "binding" antibodies, their specific characteristics, and their ability to be selectively induced and/or potentiated to result in complete protection merits parallel investigation to neutralizing antibody-based vaccine design approaches. Ultimately, while neutralizing and functional antibody-based vaccine strategies need not be mutually exclusive, defining the specific characteristics of "protective" functional antibodies may provide a target immune profile to potentially induce more robust immunity against HIV. Specifically, one approach to guide the development of functional antibody-based vaccine strategies, termed "systems serology", offers an unbiased and comprehensive approach to systematically survey humoral immune responses, capturing the array of functions and humoral response characteristics that may be induced following vaccination with high resolution. Coupled to machine learning tools, large datasets that explore the "antibody-ome" offer a means to step back from anticipated correlates and mechanisms of protection and toward a more fundamental understanding of coordinated aspects of humoral immune responses, to more globally differentiate among vaccine candidates, and most critically, to identify the features of humoral immunity that distinguish protective from non-protective responses. Overall, the systematic serological approach described here aimed at broadly capturing the enormous biodiversity in antibody profiles that may emerge following vaccination, complements the existing cutting edge tools in the cellular immunology space that survey vaccine-induced polyfunctional cellular activity by flow cytometry, transcriptional profiling, epigenetic, and metabolomic analysis to offer a means to develop both a more nuanced and a more complete understanding of correlates of protection to support the design of functional vaccine strategies.

Immunosenescence-Related Transcriptomic and Immunologic Changes in Older Individuals Following Influenza Vaccination

The goal of annual influenza vaccination is to reduce mortality and morbidity associated with this disease through the generation of protective immune responses. The objective of the current study was to examine markers of immunosenescence and identify immunosenescence-related differences in gene expression, gene regulation, cytokine secretion, and immunologic changes in an older study population receiving seasonal influenza A/H1N1 vaccination. Surprisingly, prior studies in this cohort revealed weak correlations between immunosenescence markers and humoral immune response to vaccination. In this report, we further examined the relationship of each immunosenescence marker (age, T cell receptor excision circle frequency, telomerase expression, percentage of CD28⁻ CD4⁺ T cells, percentage of CD28⁻ CD8⁺ T cells, and the CD4/CD8 T cell ratio) with additional markers of immune response (serum cytokine and chemokine expression) and measures of gene expression and/or regulation. Many of the immunosenescence markers indeed correlated with distinct sets of individual DNA methylation sites, miRNA expression levels, mRNA expression levels, serum cytokines, and leukocyte subsets. However, when the individual immunosenescence markers were grouped by pathways or functional terms, several shared biological functions were identified: antigen processing and presentation pathways, MAPK, mTOR, TCR, BCR, and calcium signaling pathways, as well as key cellular metabolic, proliferation and survival activities. Furthermore, the percent of CD4⁺ and/or CD8⁺ T cells lacking CD28 expression also correlated with miRNAs regulating clusters of genes known to be involved in viral infection. Integrated (DNA methylation, mRNA, miRNA, and protein levels) network biology analysis of immunosenescence-related pathways and genesets identified both known pathways (e.g., chemokine signaling, CTL, and NK cell activity), as well as a gene expression module not previously annotated with a known function. These results may improve our ability to predict immune responses to influenza and aid in new vaccine development, and highlight the need for additional studies to better define and characterize immunosenescence.

Transcriptional signatures of influenza A/H1N1-specific IgG memory-like B cell response in older individuals

BACKGROUND

Studies suggest that the recall-based humoral immune responses to influenza A/H1N1 originates from activated memory B cells. The aim of this study was to identify baseline, early and late blood transcriptional signatures (in peripheral blood mononuclear cells/PBMCs) associated with memory B cell response following influenza vaccination.

METHODS

We used pre- and post-vaccination mRNA-Seq transcriptional profiling on samples from 159 subjects (50-74years old) following receipt of seasonal trivalent influenza vaccine containing the A/California/7/2009/H1N1-like virus, and penalized regression modeling to identify associations with influenza A/H1N1-specific memory B cell ELISPOT response after vaccination.

RESULTS

Genesets and genes (p-value range 7.92E(-08) to 0.00018, q-value range 0.00019-0.039) demonstrating significant associations (of gene expression levels) with memory B cell response suggest the importance of metabolic (cholesterol and lipid metabolism-related), cell migration/adhesion, MAP kinase, NF-kB cell signaling (chemokine/cytokine signaling) and transcriptional regulation gene signatures in the development of memory B cell response after influenza vaccination.

CONCLUSION

Through an unbiased transcriptome-wide profiling approach, our study identified signatures of memory B cell response following influenza vaccination, highlighting the underappreciated role of metabolic changes (among the other immune function-related events) in the regulation of influenza vaccine-induced immune memory.