Kennedy, Richard

Abstract 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.

AbstractThis article provides a review of studies evaluating the role of host (and viral) genetics (including variation in HLA genes) in the immune response to coronaviruses, as well as the clinical outcome of coronavirus‐mediated disease. The initial sections focus on seasonal coronaviruses, SARS‐CoV, and MERS‐CoV. We then examine the state of the knowledge regarding genetic polymorphisms and SARS‐CoV‐2 and COVID‐19. The article concludes by discussing research areas with current knowledge gaps and proposes several avenues for future scientific exploration in order to develop new insights into the immunology of SARS‐CoV‐2.

Background Identifying the biologic functions of groups of genes identified in high-throughput studies currently requires considerable time and/or bioinformatics experience. This is due in part to each resource housed within separate databases, requiring users to know about them, and integrate across them. Time consuming and often repeated for each study, integrating across resources and merging with data under study is an increasingly common bioinformatics task. Methods We developed an open-source R software package for assisting researchers in annotating their genesets with functions, pathways, and their interconnectivity across a diversity of network resources. Results We present rapid integration of term annotation and network resources (RITAN) for the rapid and comprehensive annotation of a list of genes using functional term and pathway resources and their relationships among each other using multiple network biology resources. Currently, and to comply with data redistribution policies, RITAN allows rapid access to 16 term annotations spanning gene ontology, biologic pathways, and immunologic modules, and nine network biology resources, with support for user-supplied resources; we provide recommendations for additional resources and scripts to facilitate their addition to RITAN. Having the resources together in the same system allows users to derive novel combinations. RITAN has a growing set of tools to explore the relationships within resources themselves. These tools allow users to merge resources together such that the merged annotations have a minimal overlap with one another. Because we index both function annotation and network interactions, the combination allows users to expand small groups of genes using links from biologic networks—either by adding all neighboring genes or by identifying genes that efficiently connect among input genes—followed by term enrichment to identify functions. That is, users can start from a core set of genes, identify interacting genes from biologic networks, and then identify the functions to which the expanded list of genes contribute. Conclusion We believe RITAN fills the important niche of bridging the results of high-throughput experiments with the ever-growing corpus of functional annotations and network biology resources. Availability Rapid integration of term annotation and network resources is available as an R package at github.com/MTZimmer/RITAN and BioConductor.org .

Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre−vaccination antibody titers and network interactions between pre−vaccination gene expression levels. The first-level baseline−antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre−existing antibody titers. In the second level, we clustered individuals based on pre−vaccination antibody titers to focus gene−based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene−association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene−based modeling. We provide an interactive tool that may be extended to other vaccine studies.

AbstractGenetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that k+1 traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

SummaryInfluenza causes significant morbidity and mortality annually. Although vaccination offers a considerable amount of protection, it is far from perfect, especially in aging populations. This is due to age‐related defects in immune function, a process called immunosenescence. To date, there are no assays or methods to predict or explain variations in an individual's level of response to influenza vaccination. In this study, we measured levels of several immune cell subsets at baseline (Day 0) and at Days 3 and 28 post‐vaccination using flow cytometry. Statistical modelling was performed to assess correlations between levels of cell subsets and Day 28 immune responses – haemagglutination inhibition (HAI) assay, virus neutralizing antibody (VNA) assay, and memory B cell ELISPOT. Changes in several groups of cell types from Day 0 to Day 28 and Day 3 to Day 28 were found to be significantly associated with immune response. Baseline levels of several immune cell subsets, including B cells and regulatory T cells, were able to partially explain variation in memory B‐cell ELISPOT results. Increased expression of HLA‐DR on plasmacytoid dendritic cells after vaccination was correlated with increased HAI and VNA responses. Our data suggest that the expression of activation markers (HLA‐DR and CD86) on various immune cell subsets, as well as the relative distribution of cell subsets, both have value in predicting immune responses to influenza vaccination in older individuals.