NIH Scientists Model Immune Variation and Responses to Flu Vaccination

NIH Scientists Model Immune Variation and Responses to Flu Vaccination

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NIH Scientists Model Immune Variation and Responses to Flu Vaccination

In a new study, NIH scientists describe an approach to modeling and predicting human immune responses to influenza, or flu, vaccination based on the state of the immune system. The researchers found that the frequency of a few immune cell types present before vaccination was sufficient to predict the level of the flu-specific antibodies made after vaccination.

More work is needed to assess whether this predictor holds true in more people, across different seasons, and in vaccines against other diseases. The study appears in the April 10, 2014, online issue of Cell.

Read more about the study. 

NIH Scientists Model Immune Variation and Responses to Flu Vaccination
Study Offers Potential Framework for Predicting a Person’s Responsiveness to Vaccination

In a new study, NIH scientists describe an approach to modeling and predicting human immune responses to influenza, or flu, vaccination based on the state of the immune system before immunization. These findings provide a conceptual framework for identifying factors that influence immune responses in people, which potentially may be used to optimize treatment. The study appears in the April 10, 2014, online issue of Cell.

Background

“Systems biology” refers to an integrated, broad approach to studying biology by examining how diverse components work together to achieve a biological outcome, rather than scrutinizing a single factor and its effects. Systems biology combines computational modeling and laboratory experiments to generate hypotheses about how complex systems function and determine whether or not they hold true in living organisms.

The frequency of a few cell populations (colored circles, left) present before vaccination may be used to predict the level of antibodies (green, right) made after vaccination. Some people (red, top) respond much better to vaccination compared to other people (blue, bottom). Scientists are trying to understand what determines these differences.
Credit: Yuri Kotliarov (CHI), Pam Schwartzberg (NHGRI), and John Tsang (NIAID).

A major goal of systems biology is creating models that can reliably predict an outcome, such as identifying which patients are likely to benefit from a specific therapy. While such clinical applications are in the distant future, researchers continue to lay the foundation for this work. Given the breadth of expertise, data, and tools needed for systems biology research, a multi-institute NIH initiative called theCenter for Human Immunology, Autoimmunity and Inflammation (CHI)and NIAID’s Human Immunology Project Consortium (HIPC) offer resources to help researchers conduct these types of studies.

Results of Study

In this CHI study led by NIH scientists John Tsang, Ph.D., from NIAID’s Laboratory of Systems Biology, and Pamela Schwartzberg, M.D., Ph.D., from NHGRI’s Genetic Disease Research Branch, researchers assessed and modeled the state of the immune system before and after administration of the 2009 seasonal and pandemic H1N1 flu vaccines. More than 60 healthy volunteers participated, and blood samples were taken multiple times before and after vaccination.

These samples provided many types of data for modeling, including the frequencies of different immune cell types, expression of genes, the levels of flu-specific antibodies, and the activity of antibody-producing cells, called B cells. Such data sets contain tens of thousands of measurements per person, so one of the biggest challenges is developing ways to meaningfully integrate the data to gain new insights on the human immune system, particularly the response to flu vaccination.

After accounting for factors such as age, gender, and pre-existing immunity to flu, the researchers found that the frequency of a few immune cell types present before vaccination was sufficient to predict the level of the flu-specific antibodies made after vaccination. More work is needed to assess whether this predictor holds true in more people, across different seasons, and in vaccines against other diseases.

Significance

The study is one of the first to use a systems biology approach to identify predictive factors that influence immune responses to flu vaccination. Importantly, the predictive factors identified (the frequencies of immune cells) were relatively stable within individuals over time. Therefore, cell populations may be reliable and promising biomarkers for assessing how a person may respond to vaccination. The analytical approaches developed in this study also may be applicable in future large-scale studies.

By examining pre-vaccination data, this study also contributes to our understanding of what constitutes a normal, or healthy, human immune system. A measurable definition of normal immune parameters, especially given the genetic variation among people, is not well defined, but it is necessary to create reliable, predictive models and effective therapies.

Next Steps

Currently, CHI researchers are modeling the innate immune response to flu vaccination, which occurs early on before antibodies are made. They also will use systems biology modeling to compare how immune responses differ when vaccines are given with or without adjuvants and to evaluate vaccines against other diseases. Adjuvants are substances used to boost the effectiveness of vaccines.

Researchers are using systems biology to model immune responses to commonly prescribed drugs like steroids and statins. Finally, CHI aims to address whether or not there are genetic contributions to the predictive factors uncovered in this study.

Reference

Tsang JS, Schwartzberg PL, Kotliarov Y, Biancotto A, Xie Z, Germain RN, Wang E, Olnes MJ, Narayanan M, Golding H, Moir S, Dickler HB, Perl S, Cheung F, Baylor HIPC Center, CHI Consortium. Global analyses of human immune variation reveal baseline predictors of the post-vaccination response. Cell (2014)

This work is funded by the Intramural Programs of NIAID, NCI, NHLBI, NIAMS, NICHD, NIDDK, NINDS, NIEHS, NEI and NHGRI.

Dr. Tsang’s Lab

Dr. Schwartzberg's Lab