H2020Индивидуална стипендия2018–2021

FluPRINT · Tracing the inFLUenza vaccine imPRINT on immune system to identify cellular signature of protection

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2018-06-01 → 2021-05-31
Финансиране от ЕС
269 858 €
Участници
2
Схема
MSCA-IF-GF

Линиите свързват координатора с партньорите.

Накратко на български

Имунните отговори след ваксинация срещу грип, като ролята на специфичните Т-клетки, се анализират чрез нови компютърни модели. Разбирането на тези механизми помага за подобряване на настоящите ваксини.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Tracing the inFLUenza vaccine imPRINT on immune system to identify cellular signature of protection

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.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Influenza virus causes a large socioeconomic burden on society, with 70 000 deaths every year in Europe. It is estimated that 1 in 1000 children and elderly every year are hospitalized due to influenza infection. Children, due to high susceptibility and high levels of shedding, are the main source of spread of the virus. Therefore, CDC in 2010 included children as a high priority group for influenza vaccination. Two influenza vaccines are licensed: inactivated (IIV) and live attenuated (LAIV) vaccine. The LAIV was introduced to provide broader protection by additional stimulation of T cell responses. At present the two major obstacles in the widespread use of LAIV are concerns raised over vaccine effectiveness and the lack of immunological correlates of protection. In 2016 the CDC in the US recommended against the use of LAIV due to its poor effectiveness in the 2015/2016 season. However, the same vaccine, in the same season had high effectiveness as assessed by UK and Finland public health authorities. Currently the reason for this discrepancy is not known. This project will take advantage of cohorts of children who have received LAIV provided by both US and UK sponsors, to investigate the immunological basis for the observed variability and to define the role of adaptive immunity by applying the systems biology tools and machine learning algorithms for predictive modelling. Progress in the clinical investigation of children has been hampered by limited methods that could be applied to the small blood volumes, but recent advances in systems biology have opened new opportunities that did not exist before. Tracing the influenza vaccine imprint on immune system, termed FluPRINT by the proposed project will help to identify cellular signatures of vaccine-induced protection in children which is of importance for the development of next generation of influenza vaccines that will be more effective in this target population.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз