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DOI 10.3389/fcimb.2014.00177

Медицинская библиотека · Frontiers in cellular and infection microbiology · 2015

Mathematical modeling provides kinetic details of the human immune response to vaccination.

Le Dustin, Miller Joseph D, Ganusov Vitaly V

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

With major advances in experimental techniques to track antigen-specific immune responses many basic questions on the kinetics of virus-specific immunity in humans remain unanswered. To gain insights into kinetics of T and B cell responses in human volunteers we combined mathematical models and experimental data from recent studies employing vaccines against yellow fever and smallpox. Yellow fever virus-specific CD8 T cell population expanded slowly with the average doubling time of 2 days peaking 2.5 weeks post immunization. Interestingly, we found that the peak of the yellow fever-specific CD8 T cell response was determined by the rate of T cell proliferation and not by the precursor frequency of antigen-specific cells as has been suggested in several studies in mice. We also found that while the frequency of virus-specific T cells increased slowly, the slow increase could still accurately explain clearance of yellow fever virus in the blood. Our additional mathematical model described well the kinetics of virus-specific antibody-secreting cell and antibody response to vaccinia virus in vaccinated individuals suggesting that most of antibodies in 3 months post immunization were derived from the population of circulating antibody-secreting cells. Taken together, our analysis provided novel insights into mechanisms by which live vaccines induce immunity to viral infections and highlighted challenges of applying methods of mathematical modeling to the current, state-of-the-art yet limited immunological data.

Для цитирования: Le Dustin, Miller Joseph D, Ganusov Vitaly V. Mathematical modeling provides kinetic details of the human immune response to vaccination.. Frontiers in cellular and infection microbiology. 2015. DOI: 10.3389/fcimb.2014.00177.

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