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.

Beginning in December of 2019, a novel coronavirus, SARS-CoV-2, emerged in China and is now a global pandemic with extensive morbidity and mortality. With the emergence of this threat, an unprecedented effort to develop vaccines against this virus began. As vaccines are now being introduced globally, we face the prospect of millions of people being vaccinated with multiple types of vaccines many of which use new vaccine platforms. Since medical events happen without vaccines, it will be important to know at what rate events occur in the background so that when adverse events are identified one has a frame of reference with which to compare the rates of these events so as to make an initial assessment as to whether there is a potential safety concern or not. Background rates vary over time, by geography, by sex, socioeconomic status and by age group. Here we describe two key steps for post-introduction safety evaluation of COVID-19 vaccines: Defining a dynamic list of Adverse Events of Special Interest (AESI) and establishing background rates for these AESI. We use multiple examples to illustrate use of rates and caveats for their use. In addition we discuss tools available from the Brighton Collaboration that facilitate case evaluation and understanding of AESI.

Little is known about the relationship between human leukocyte antigen (HLA) class II genes and family history of asthma or atopy in relation to the incidence of childhood asthma. The objective of the study was to determine whether specific HLA class II genes (e.g., DRB1*03) are associated with asthma and whether such association explains the influences of family history of asthma or atopy on asthma incidence. A stratified random sample of 340 children who had HLA data available from the Rochester Family Measles Study cohort (n ¼ 876) and a convenience sample of healthy children aged 5-12 years were the participants. We conducted comprehensive medical record reviews to determine asthma status of these children. The associations between the presence of specific HLA alleles and development of asthma and the role of family history of asthma or atopy in the association were evaluated by fitting Cox models. The cumulative incidence of asthma by 12 years of age among children who carry HLA DRB1*03 was 33%, compared to 24.2% among those who did not carry this allele. Adjusting for family history of asthma or atopy, gender, low birth weight, season of birth, HLA DRB1*04, and HLA DQB1*0302, the hazards ratio for HLA DRB1*03 carriers was 1.8 (95% confidence interval: 1.1-2.9, P ¼ 0.020). We concluded that the HLA DRB1*03 allele is associated with asthma. However, the HLA class II gene does not explain the influences of family history of asthma or atopy on development of asthma. The mechanism underlying the association between asthma and HLA genes needs to be elucidated.

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.