The mechanisms of how protective immunity against influenza is accomplished and which components of the immune system are necessary to mount an effective response to influenza are currently unclear. Uncovering these mechanisms would help to improve current vaccines. The main goal of this project was to characterize influenza vaccine-induced immune responses with the aim of defining cellular and molecular correlates of protection. This project covered an issue that has been poorly studied in humans and that is the role of influenza-specific T cells after vaccination. Correlating the cellular signature after vaccination with the vaccine efficacy is a novel approach to the current problem about the usage of influenza vaccines.
Recent advances in computational biology make it possible to extract knowledge and identify patterns in an unbiased manner from large clinical datasets and to integrate different data types collected across studies. In this work, we have developed a novel computational approach that automates data analysis, Sequential Iterative Modeling “OverNight” (SIMON). Our approach automatically builds state-of-the-art machine learning models testing more than 180 algorithms to find the ones which fit any given data distribution. Such an iterative process maximizes predictive accuracy of the generated models and is especially suited for clinical data collected across multiple cohorts. SIMON was applied to data from five clinical studies across eight influenza seasons and with over 3,000 parameters considered. SIMON identified several immune cell subsets, including CD4+ T helper cells, regulatory T cells, and cytotoxic CD8+ T cells that correlated with an effective antibody response to influenza vaccination. While T helper cells are in general thought to be the principal source of T cell “help” for antibody production and the generation of high-affinity memory B cells, the role of regulatory and cytotoxic T cells in the induction of antibodies is unknown.
Experimental data confirmed that these cell subsets were elevated in vaccine responders.
Altogether, our findings reveal the unexpected role of cytotoxic and regulatory T cells in the generation of protective antibody responses after influenza vaccination, as well as provide a novel tool for the integration of multi-omics data. The ‘systems immunology’ approach established in the FluPRINT research project can accelerate the understanding of how immune responses to influenza are generated and can help to improve future flu vaccine formulations, while SIMON as an open-source knowledge discovery software it can help researchers to identify biomarkers important for other vaccines, therapies and diseases, such as it was applied to understanding correlates of protection in COVID-19 patients and durability of protective immunity after SARS-CoV-2 infection.
