Integrative transcriptomic analysis reveals key drivers of acute peanut allergic reactions. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Watson, C.; Cohain, A.; Griffin, R.; Chun, Y.; Grishin, A.; Hacyznska, H.; Hoffman, G.; Beckmann, N.; Shah, H.; Dawson, P.; Henning, A.; Wood, R.; Burks, A.; Jones, S.; Leung, D.; Sicherer, S.; Sampson, H.; Sharp, A.; Schadt, E.; Bunyavanich, S.
• Is Part Of: Nature communications. Volume 8:Issue 1(2017)
• Page Start: 1
• Page End: 13
• Publication Date: 2017-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-017-02188-7
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Mechanisms driving acute food allergic reactions have not been fully characterized. We profile the dynamic transcriptome of acute peanut allergic reactions using serial peripheral blood samples obtained from 19 children before, during, and after randomized, double-blind, placebo-controlled oral challenges to peanut. We identify genes with changes in expression triggered by peanut, but not placebo, during acute peanut allergic reactions. Network analysis reveals that these genes comprise coexpression networks for acute-phase response and pro-inflammatory processes. Key driver analysis identifies six genes (LTB4R, PADI4, IL1R2, PPP1R3D, KLHL2, andECHDC3 ) predicted to causally modulate the state of coregulated networks in response to peanut. Leukocyte deconvolution analysis identifies changes in neutrophil, naive CD4+ T cell, and macrophage populations during peanut challenge. Analyses in 21 additional peanut allergic subjects replicate major findings. These results highlight key genes, biological processes, and cell types that can be targeted for mechanistic study and therapeutic targeting of peanut allergy. Rising rates of peanut allergy pose a public health problem. Here, the authors profile blood transcriptomes during double-blind, placebo-controlled oral challenge in peanut-allergic children to identify gene and cell composition changes, and construct causal networks to detect key allergic reaction drivers.