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

Similar humoral responses but distinct CD4+ T cell transcriptomic profiles in older adults elicited by MF59 adjuvanted and high dose influenza vaccines

Older age (≥ 65 years) is associated with impaired responses to influenza vaccination, leading to the preferential recommendation of MF59-adjuvanted (MF59Flu) or high-dose (HDFlu) influenza vaccines for this age group in the United States. Herein, we characterized transcriptomic profiles of CD4+ T cells isolated from 234 recipients (≥ 65 years) of either MF59Flu or HDFlu vaccine, prior to vaccination and 28 days thereafter. We identified 412 and 645 differentially expressed genes (DEGs) in CD4+ T cells of older adults after receiving MF59Flu and HDFlu, respectively. DEGs in CD4+ T cells of MF59Flu recipients were enriched in 14 KEGG pathways, all of which were downregulated. DEGs in CD4+ T cells of HDFlu recipients were enriched in 11 upregulated pathways and 20 downregulated pathways. CD4+ T cells in both vaccine groups shared 50 upregulated genes and 75 downregulated genes, all of which were enriched in 7 KEGG pathways. The remaining 287 and 520 DEGs were specifically associated with MF59Flu and HDFlu, respectively. Unexpectedly, none of these DEGs was significantly correlated with influenza A/H3N2-specific HAI titers, suggesting these DEGs at the individual level may have a limited role in protection against influenza. Our findings emphasize the need for further investigation into other factors influencing immunity against influenza in older adults.

Baseline immune states (BIS) associated with vaccine responsiveness and factors that shape the BIS

Vaccines are among the greatest inventions in medicine, leading to the elimination or control of numerous diseases, including smallpox, polio, measles, rubella, and, most recently, COVID-19. Yet, the effectiveness of vaccines varies among individuals. In fact, while some recipients mount a robust response to vaccination that protects them from the disease, others fail to respond. Multiple clinical and epidemiological factors contribute to this heterogeneity in responsiveness. Systems immunology studies fueled by advances in single-cell biology have been instrumental in uncovering pre-vaccination immune cell types and genomic features (i.e., the baseline immune state, BIS) that have been associated with vaccine responsiveness. Here, we review clinical factors that shape the BIS, and the characteristics of the BIS associated with responsiveness to frequently studied vaccines (i.e., influenza, COVID-19, bacterial pneumonia, malaria). Finally, we discuss potential strategies to enhance vaccine responsiveness in high-risk groups, focusing specifically on older adults.

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.

T Cell Transcriptional Signatures of Influenza A/H3N2 Antibody Response to High Dose Influenza and Adjuvanted Influenza Vaccine in Older Adults

Older adults experience declining influenza vaccine-induced immunity and are at higher risk of influenza and its complications. For this reason, high dose (e.g., Fluzone) and adjuvanted (e.g., Fluad) vaccines are preferentially recommended for people age 65 years and older. However, T cell transcriptional activity shaping the humoral immune responses to Fluzone and Fluad vaccines in older adults is still poorly understood. We designed a study of 234 older adults (≥65 years old) who were randomly allocated to receive Fluzone or Fluad vaccine and provided blood samples at baseline and at Day 28 after immunization. We measured the humoral immune responses (hemagglutination inhibition/HAI antibody titer) to influenza A/H3N2 and performed mRNA-Seq transcriptional profiling in purified CD4+ T cells, in order to identify T cell signatures that might explain differences in humoral immune response by vaccine type. Given the large differences in formulation (higher antigen dose vs adjuvant), our hypothesis was that each vaccine elicited a distinct transcriptomic response after vaccination. Thus, the main focus of our study was to identify the differential gene expression influencing the antibody titer in the two vaccine groups. Our analyses identified three differentially expressed, functionally linked genes/proteins in CD4+ T cells: the calcium/calmodulin dependent serine/threonine kinase IV (CaMKIV); its regulator the TMEM38B/transmembrane protein 38B, involved in maintenance of intracellular Ca2+ release; and the transcriptional coactivator CBP/CREB binding protein, as regulators of transcriptional activity/function in CD4+ T cells that impact differences in immune response by vaccine type. Significantly enriched T cell-specific pathways/biological processes were also identified that point to the importance of genes/proteins involved in Th1/Th2 cell differentiation, IL-17 signaling, calcium signaling, Notch signaling, MAPK signaling, and regulation of TRP cation Ca2+ channels in humoral immunity after influenza vaccination. In summary, we identified the genes/proteins and pathways essential for cell activation and function in CD4+ T cells that are associated with differences in influenza vaccine-induced humoral immunity by vaccine type. These findings provide an additional mechanistic perspective for achieving protective immunity in older adults.

Identification of potential biomarkers and immune infiltration characteristics in severe asthma

OBJECTIVES

We hope to identify key molecules that can be used as markers of asthma severity and investigate their correlation with immune cell infiltration in severe asthma.

METHODS

An asthma dataset was downloaded from the Gene Expression Omnibus database and then processed by R software to obtain differentially expressed genes (DEGs). First, multiple enrichment platforms were applied to analyze crucial biological processes and pathways and protein-protein interaction networks related to the DEGs. We next combined least absolute shrinkage and selection operator logistic regression and the support vector machine-recursive feature elimination algorithms to screen diagnostic markers of severe asthma. Then, a local cohort consisting of 40 asthmatic subjects (24 with moderate asthma and 16 with severe asthma) was used for biomarker validation. Finally, infiltration of immune cells in asthma bronchoalveolar lavage fluid and their correlation with the screened markers was evaluated by CIBERSORT.

RESULTS

A total of 97 DEGs were identified in this study. Most of these genes are enriched in T cell activation and immune response in the asthma biological process. CC-chemokine receptor 7 (CCR7) and natural killer cell protein 7(NKG7) were identified as markers of severe asthma. The highest area under the ROC curve (AUC) was from a new indicator combining CCR7 and NKG7 (AUC = 0.851, adj. p < 0.05). Resting and activated memory CD4 T cells, activated NK cells, and CD8 T cells were found to be significantly higher in the severe asthma group (adj. p < 0.01). CCR7 and NKG7 were significantly correlated with these infiltrated cells that showed differences between the two groups. In addition, CCR7 was found to be significantly positively correlated with eosinophils (r = 0.38, adj. p < 0.05) infiltrated in bronchoalveolar lavage fluid.

CONCLUSION

CCR7 and NKG7 might be used as potential markers for asthma severity, and their expression may be associated with differences in immune cell infiltration in the moderate and severe asthma groups.

Transcriptional signatures associated with rubella virus-specific humoral immunity after a third dose of MMR vaccine in women of childbearing age

Multiple factors linked to host genetics/inherent biology play a role in interindividual variability in immune response outcomes after rubella vaccination. In order to identify these factors, we conducted a study of rubella-specific humoral immunity before (Baseline) and after (Day 28) a third dose of MMR-II vaccine in a cohort of 109 women of childbearing age. We performed mRNA-Seq profiling of PBMCs after rubella virus in vitro stimulation to delineate genes associated with post-vaccination rubella humoral immunity and to define genes mediating the association between prior immune response status (high or low antibody) and subsequent immune response outcome. Our study identified novel genes that mediated the association between prior immune response and neutralizing antibody titer after a third MMR vaccine dose. These genes included the following: CDC34; CSNK1D; APOBEC3F; RAD18; AAAS; SLC37A1; FAS; and JAK2. The encoded proteins are involved in innate antiviral response, IFN/cytokine signaling, B cell repertoire generation, the clonal selection of B lymphocytes in germinal centers, and somatic hypermutation/antibody affinity maturation to promote optimal antigen-specific B cell immune function. These data advance our understanding of how subjects' prior immune status and/or genetic propensity to respond to rubella/MMR vaccination ultimately affects innate immunity and humoral immune outcomes after vaccination.

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines

Vaccines represent one of the major advances of modern medicine. Despite the many successes of vaccination, continuous efforts to design new vaccines are needed to fight "old" pandemics, such as tuberculosis and malaria, as well as emerging pathogens, such as Zika virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination strategies. A systems biology approach is increasingly being adopted to address these major challenges using multiple high-dimensional technologies combined with in silico models. Although the goal is to develop predictive models of vaccine efficacy in humans, applying this approach to animal models empowers basic and translational vaccine research. In this review, we provide an overview of vaccine immune signatures in preclinical models, as well as in target human populations. We also discuss high-throughput technologies used to probe vaccine-induced responses, along with data analysis and computational methodologies applied to the predictive modeling of vaccine efficacy.

Current Challenges in Vaccinology

The development of vaccines, which prime the immune system to respond to future infections, has led to global declines in morbidity and mortality from dreadful infectious communicable diseases. However, many pathogens of public health importance are highly complex and/or rapidly evolving, posing unique challenges to vaccine development. Several of these challenges include an incomplete understanding of how immunity develops, host and pathogen genetic variability, and an increased societal skepticism regarding vaccine safety. In particular, new high-dimensional omics technologies, aided by bioinformatics, are driving new vaccine development (vaccinomics). Informed by recent insights into pathogen biology, host genetic diversity, and immunology, the increasing use of genomic approaches is leading to new models and understanding of host immune system responses that may provide solutions in the rapid development of novel vaccine candidates.

Association of DNA-Methylation Profiles With Immune Responses Elicited in Breast Cancer Patients Immunized With a Carbohydrate-Mimicking Peptide: A Pilot Study

Immune response to a given antigen, particularly in cancer patients, is complex and is controlled by various genetic and environmental factors. Identifying biomarkers that can predict robust response to immunization is an urgent need in clinical cancer vaccine development. Given the involvement of DNA methylation in the development of lymphocytes, tumorigenicity and tumor progression, we aimed to analyze pre-vaccination DNA methylation profiles of peripheral blood mononuclear cells (PBMCs) from breast cancer subjects vaccinated with a novel peptide-based vaccine referred to as P10s-PADRE. This pilot study was performed to evaluate whether signatures of differentially methylated (DM) loci can be developed as potential predictive biomarkers for prescreening subjects with cancer who will most likely generate an immune response to the vaccine. Genomic DNA was isolated from PBMCs of eight vaccinated subjects, and their DNA methylation profiles were determined using Infinium^® MethylationEPIC BeadChip array from Illumina. A linear regression model was applied to identify loci that were differentially methylated with respect to anti-peptide antibody titers and with IFN-γ production. The data were summarized using unsupervised-learning methods: hierarchical clustering and principal-component analysis. Pathways and networks involved were predicted by Ingenuity Pathway Analysis. We observed that the profile of DM loci separated subjects in regards to the levels of immune responses. Canonical pathways and networks related to metabolic and immunological functions were found to be involved. The data suggest that it is feasible to correlate methylation signatures in pre-treatment PBMCs with immune responses post-treatment in cancer patients going through standard-of-care chemotherapy. Larger and prospective studies that focus on DM loci in PBMCs is warranted to develop pre-screening biomarkers before BC vaccination.

Clinical Trial Registration:www.ClinicalTrials.gov, Identifier: NCT02229084.

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.

Immunosenescence: A systems-level overview of immune cell biology and strategies for improving vaccine responses

Immunosenescence contributes to a decreased capacity of the immune system to respond effectively to infections or vaccines in the elderly. The full extent of the biological changes that lead to immunosenescence are unknown, but numerous cell types involved in innate and adaptive immunity exhibit altered phenotypes and function as a result of aging. These manifestations of immunosenescence at the cellular level are mediated by dysregulation at the genetic level, and changes throughout the immune system are, in turn, propagated by numerous cellular interactions. Environmental factors, such as nutrition, also exert significant influence on the immune system during aging. While the mechanisms that govern the onset of immunosenescence are complex, systems biology approaches allow for the identification of individual contributions from each component within the system as a whole. Although there is still much to learn regarding immunosenescence, systems-level studies of vaccine responses have been highly informative and will guide the development of new vaccine candidates, novel adjuvant formulations, and immunotherapeutic drugs to improve vaccine responses among the aging population.

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.

Responders and non-responders to influenza vaccination: A DNA methylation approach on blood cells

Several evidences indicate that aging negatively affects the effectiveness of influenza vaccination. Although it is well established that immunosenescence has an important role in vaccination response, the molecular pathways underlying this process are largely unknown. Given the importance of epigenetic remodeling in aging, here we analyzed the relationship between responsiveness to influenza vaccination and DNA methylation profiles in healthy subjects of different ages. Peripheral blood mononuclear cells were collected from 44 subjects (age range: 19-90 years old) immediately before influenza vaccination. Subjects were subsequently classified as responders or non-responders according to hemagglutination inhibition assay 4-6 weeks after the vaccination. Baseline whole genome DNA methylation in peripheral blood mononuclear cells was analyzed using the Illumina® Infinium 450 k microarray. Differential methylation analysis between the two groups (responders and non-responders) was performed through an analysis of variance, correcting for age, sex and batch. We identified 83 CpG sites having a nominal p-value <.001 and absolute difference in DNA methylation of at least 0.05 between the two groups. For some CpG sites, we observed age-dependent decrease or increase in methylation, which in some cases was specific for the responders and non-responders groups. Finally, we divided the cohort in two subgroups including younger (age < 50) and older (age ≥ 50) subjects and compared DNA methylation between responders and non-responders, correcting for sex and batch in each subgroup. We identified 142 differentially methylated CpG sites in the young subgroup and 305 in the old subgroup, suggesting a larger epigenetic remodeling at older ages. Interestingly, some of the differentially methylated probes mapped in genes involved in immunosenescence (CD40) and in innate immunity responses (CXCL16, ULK1, BCL11B, BTC). In conclusion, the analysis of epigenetic landscape can shed light on the biological basis of vaccine responsiveness during aging, possibly providing new appropriate biomarkers of this process.

Fighting against a protean enemy: immunosenescence, vaccines, and healthy aging

The progressive increase of the aged population worldwide mandates new strategies to ensure sustained health and well-being with age. The development of better and/or new vaccines against pathogens that affect older adults is one pivotal intervention in approaching this goal. However, the functional decline of various physiological systems, including the immune system, requires novel approaches to counteract immunosenescence. Although important progress has been made in understanding the mechanisms underlying the age-related decline of the immune response to infections and vaccinations, knowledge gaps remain, both in the areas of basic and translational research. In particular, it will be important to better understand how environmental factors, such as diet, physical activity, co-morbidities, and pharmacological treatments, delay or contribute to the decline of the capability of the aging immune system to appropriately respond to infectious diseases and vaccination. Recent findings suggest that successful approaches specifically targeted to the older population can be developed, such as the high-dose and adjuvanted vaccines against seasonal influenza, the adjuvanted subunit vaccine against herpes zoster, as well as experimental interventions with immune-potentiators or immunostimulants. Learning from these first successes may pave the way to developing novel and improved vaccines for the older adults and immunocompromised. With an integrated, holistic vaccination strategy, society will offer the opportunity for an improved quality of life to the segment of the population that is going to increase most significantly in numbers and proportion over future decades.

Reevaluation of immune activation in the era of cART and an aging HIV-infected population

Biological aging is associated with immune activation (IA) and declining immunity due to systemic inflammation. It is widely accepted that HIV infection causes persistent IA and premature immune senescence despite effective antiretroviral therapy and virologic suppression; however, the effects of combined HIV infection and aging are not well defined. Here, we assessed the relationship between markers of IA and inflammation during biological aging in HIV-infected and -uninfected populations. Antibody response to seasonal influenza vaccination was implemented as a measure of immune competence and relationships between IA, inflammation, and antibody responses were explored using statistical modeling appropriate for integrating high-dimensional data sets. Our results show that markers of IA, such as coexpression of HLA antigen D related (HLA-DR) and CD38 on CD4+ T cells, exhibit strong associations with HIV infection but not with biological age. Certain variables that showed a strong relationship with aging, such as declining naive and CD38+ CD4 and CD8+ T cells, did so regardless of HIV infection. Interestingly, the variable of biological age was not identified in a predictive model as significantly impacting vaccine responses in either group, while distinct IA and inflammatory variables were closely associated with vaccine response in HIV-infected and -uninfected populations. These findings shed light on the most relevant and persistent immune defects during virological suppression with antiretroviral therapy.