Metabolomic and transcriptomic signatures of influenza vaccine response in healthy young and older adults

BCG vaccination is associated with longitudinal changes in systemic eicosanoid levels in elderly individuals: A secondary outcome analysis

We investigated how BCG vaccination affects the levels of certain eicosanoids, namely Leukotriene B4, 15-epimer of LXA4, prostaglandin F2, Lipoxin A4, Prostaglandin E2 and Resolvin D1 in the plasma of healthy elderly individuals (aged 60-80) before vaccination, one month post-vaccination (M1), and six months post-vaccination (M6). This study is part of the clinical trial "BCG Vaccine Study: Reducing COVID-19 Impact on the Elderly in Indian Hotspots," registered in the clinical trial registry (NCT04475302). While some primary outcomes have been previously reported, this analysis delves into the immunological outcomes. Our findings indicate that BCG vaccination leads to reduced plasma levels of 15-epi-LXA4, LXA4, PGE2, and Resolvin D1 at both M1 and M6. In contrast, there is a notable increase in circulating levels of LTB4 at these time points following BCG vaccination. This underscores the immunomodulatory effects of BCG vaccination and hints at its potential to modulate immune responses by dampening inflammatory reactions.

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.

Metabolic Reprogramming of Immune Cells Following Vaccination: From Metabolites to Personalized Vaccinology

Identifying metabolic signatures induced by the immune response to vaccines allows one to discriminate vaccinated from non-vaccinated subjects and decipher the molecular mechanisms associated with the host immune response. This review illustrates and discusses the results of metabolomics-based studies on the innate and adaptive immune response to vaccines, long-term functional reprogramming (immune memory), and adverse reactions. Glycolysis is not overexpressed by vaccines, suggesting that the immune cell response to vaccinations does not require rapid energy availability as necessary during an infection. Vaccines strongly impact lipids metabolism, including saturated or unsaturated fatty acids, inositol phosphate, and cholesterol. Cholesterol is strategic for synthesizing 25-hydroxycholesterol in activated macrophages and dendritic cells and stimulates the conversion of macrophages and T cells in M2 macrophage and Treg, respectively. In conclusion, the large-scale application of metabolomics enables the identification of candidate predictive biomarkers of vaccine efficacy/tolerability.

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

Infection with West Nile virus (WNV) drives 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 employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated 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.

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.