bioRxivImmunology
Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses
T. Hagan, B. Gerristen, L. E. Tomalin, S. Fourati, M. P. Mule, D. G. Chawla, D. Rychkov, E. Henrich, H. E. Miller, J. Diray-Arce, P. Dunn, A. Lee, The Human Immunology Project Consortium (HIPC), O. Levy, R. Gottardo, M. M. Sarwal, J. Tsang, M. Suarez-Farinas, R.-P. Sekaly, S. Kleinstein, B. Pulendran
Abstract
Systems biology approaches have been used to define molecular signatures and mechanisms of immunity to vaccination. However, most such studies have been done with single vaccines, and comparative analysis of the response to different vaccines is lacking. We integrated temporal transcriptional data of over 3,000 samples, obtained from 820 healthy adults across 28 studies of 13 different vaccines and analyzed vaccination-induced signatures associated with the antibody response. Most vaccines induced similar kinetics of shared transcriptional signatures, including signatures of innate immunity occurring 1-3 days post-vaccination, as well as the canonical plasmablast and cell cycle signatures appearing 7 days post-vaccination. However, the yellow fever vaccine YF-17D uniquely induced an early transient signature of T and B cell activation at Day 1, followed by delayed antiviral/interferon and plasmablast signatures that peaked at Days 7 and 14-21, respectively. Thus, despite the shared transcriptional response to most vaccines, at any given time point there was no evidence for a "universal signature" that could be used to predict the antibody response to all vaccines. However, accounting for the asynchronous nature of responses led to the identification of a time-adjusted signature that improved prediction antibody of responses across vaccines. These results provide a transcriptional atlas of the human immune response to vaccination and define a common, time-adjusted signature of antibody responses to vaccination.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
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