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. 2024 Dec;79(12):3385-3400.
doi: 10.1111/all.16388. Epub 2024 Nov 8.

Epigenetic training of human bronchial epithelium cells by repeated rhinovirus infections

Marua Abu Risha  1   2 , Karosham D Reddy  2   3   4 , Sai Sneha Priya Nemani  2   3 , Constanze Jakwerth  5   6 , Carsten Schmidt-Weber  5   6 , Thomas Bahmer  2   7   8 , Gesine Hansen  9   10 , Erika von Mutius  6   11   12 , Klaus F Rabe  5   6 , Anna-Maria Dittrich  9   10 , Ruth Grychtol  9   10 , Nicole Maison  6   11   12 , Bianca Schaub  6   12   13 , Matthias V Kopp  14 , Folke Brinkmann  2   3 , Silke Meiners  2   15 , Uta Jappe  1   2   16 , Markus Weckmann  2   3   4 ; ALLIANCE Study Group as part of the German Centre for Lung Research (DZL)
Affiliations

Epigenetic training of human bronchial epithelium cells by repeated rhinovirus infections

Marua Abu Risha et al. Allergy. 2024 Dec.

Abstract

Background: Humans are subjected to various environmental stressors (bacteria, viruses, pollution) throughout life. As such, an inherent relationship exists between the effect of these exposures with age. The impact of these environmental stressors can manifest through DNA methylation (DNAm). However, whether these epigenetic effects selectively target genes, pathways, and biological regulatory mechanisms remains unclear. Due to the frequency of human rhinovirus (HRV) infections throughout life (particularly in early development), we propose the use of HRV under controlled conditions can model the effect of multiple exposures to environmental stressors.

Methods: We generated a prediction model by combining transcriptome and DNAm datasets from human epithelial cells after repeated HRV infections. We applied a novel experimental statistical design and method to systematically explore the multifaceted experimental space (number of infections, multiplicity of infections and duration). Our model included 35 samples, each characterized by the three parameters defining their infection status.

Results: Trainable genes were defined by a consistent linear directionality in DNAm and gene expression changes with successive infections. We identified 77 trainable genes which could be further explored in future studies. The identified methylation sites were tracked within a pediatric cohort to determine the relative changes in candidate-trained sites with disease status and age.

Conclusions: Repeated viral infections induce an immune training response in bronchial epithelial cells. Training-sensitive DNAm sites indicate alternate divergent associations in asthma compared to healthy individuals. Our novel model presents a robust tool for identifying trainable genes, providing a foundation for future studies.

Keywords: airway epithelium; asthma; human rhinovirus; trainable genes; trained immunity.

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Conflict of interest statement

MAR, KDR, SSPN, CJ, CSW, TB, GH, EVM, AMD, RG, NM, FB, SM, and UJ declare no conflict of interest regarding the content of this manuscript. MW reports grants from the COVID‐19 Research Initiative Schleswig‐Holstein, the Follow‐Up of Respiratory Infections in Schleswig‐Holstein (FRISH), the German Center of Lung Research (DZL, Funding No. 82DZL001B6), intramural funding of the Christian‐Albrechts‐University Kiel, the University of Lübeck, and the Leibniz Lung Center, Research Center Borstel. Funding institutions did not participate in the design and conduct of this study. MVK reports grant from the BMBF for the Deutsches Zentrum für Lungenforschung (DZL) and received consultant fees from Sanofi Aventis GmbH, Chiesi GmbH, Allergopharma GmbH along with payments or honoraria for lectures, presentations, speakers' bureaus, manuscript writing, or educational events from Sanofi Aventis GmbH, Infectopharm GmbH, Allergopharma GmbH. KFR received personal payments or honoraria from AstraZeneca, Boehringer Ingelheim, Chiesi Pharmaceuticals, CSL Behring, Sanofi & Regeneron, GlaxoSmithKlfvine, Berlin Chemie, and Menarini; K.F. Rabe also discloses participation on data safety monitoring boards/advisory boards for AstraZeneca, Boehringer Ingelheim, and Sanofi Regeneron, and leadership or fiduciary role in the German Center for Lung Research (DZL), German Chest Society (DGP), and American Thoracic Society (ATS). BS reports grants from the BMBF (German center for lung research, CPC‐Munich, DZL 82DZL033C2, Combat Lung diseases FP4), the German Center for Child and Adolescent Health (DZKJ; LMU/LMU Klinikum: 01GL2406A), from DFG (DFG‐SCHA 997/8–1 (BS); DFG‐SCHA 997/9–1, DFG‐SCHA‐997/10–1, DFG‐SCHA‐997/11–1). BS reports consulting fees from GlaxoSmithKline, Novartis, and Sanofi; payment/honoraria and participation on a Data Safety Monitoring Board or Advisory Board from Sanofi.

Figures

FIGURE 1
FIGURE 1
Chart outlining the DoE design for the full (A) and three‐level (B) factorial model. The three‐level factorial design used in this study includes 35 different samples with three independent variables (MOI, DOI, and NOI) with three replicates. Each NOI (noi1, noi3, and noi5) includes five points that create two regression lines for analysis.
FIGURE 2
FIGURE 2
Flow chart of the method to obtain trainable genes. (Right) The method used to obtain the trainable genes (TGs). (Left) The method used to obtain the trainable CpGs related to non‐coding regions (T.N.CpGs: Trainable CpGs in non‐coding regions).
FIGURE 3
FIGURE 3
Results of expression profiles of human bronchial epithelium after repeated infections. (A) Schematic diagram of the comparison groups and Venn diagram showing the number of significant overlapping DEGs and DMPs between N3v1 and N5v3. (B) Volcano plot showing expression differences between repeated infection groups (n = 35). Red indicates DEGs that differ at FDR <0.05 and have a positive foldchange; blue indicates DEGs that differ at FDR ≤0.05 and have a negative foldchange. (C) top gene ontology terms.
FIGURE 4
FIGURE 4
Expression response patterns for trainable genes. (A) The TGs are in four divided groups. These groups are based on expression linearity. (B) Shared pathways between TGs, genes shown in blue, indicate a downregulation, but genes shown in red indicate upregulation among repeated infections. Pathways are shown as grey circles.
FIGURE 5
FIGURE 5
S100A8 and cg20335425. (A) Regional plot and co‐methylation patterns at the S100A8 gene in BEAS‐2B. (B) Gene expression of S100A8 and (C) its correlation with the cg20335425. (D) In vitro results of cg20335425. (E) In vitro results of global methylation level of all S100A8 CpGs. (F) In vitro results of promoter methylation of S100A8‐related CpGs. (G) In vivo results of cg20335425. (H) In vivo results of global methylation level of all S100A8 CpGs. (I) In vivo results of promoter methylation of S100A8‐related CpGs. Asterisks (* or **) denote significant differences between groups, respectively, at p < 5 × 10−2 and 1 × 10−3.
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