Mepolizumab Alters Regulation of Airway Type-2 Inflammation in Urban Children with Asthma by Disrupting Eosinophil Gene Expression but Enhancing Mast Cell and Epithelial Pathways

Abstract

Rationale: Mepolizumab (anti-IL5) reduces asthma exacerbations in urban children. We previously utilized nasal transcriptomics to identify inflammatory pathways (gene co-expression modules) associated with exacerbations despite this therapy. To understand mepolizumab’s precise impact on these pathways, we assess gene co-expression and loss of correlation, “decoherence,” using differential co-expression network analyses. Methods: 290 urban children (6-17 years) with exacerbation-prone asthma and blood eosinophils ≥150/microliter were randomized (1:1) to q4 week placebo or mepolizumab injections added to guideline-based care for 52 weeks. Nasal lavage samples were collected before and during treatment for RNA-sequencing. Differential co-expression of gene networks was evaluated to assess interactions and regulatory aspects of type-2 and eosinophilic airway inflammation. Results: Mepolizumab, but not placebo, significantly reduced the overall expression of an established type-2 inflammation gene co-expression module (fold change=0.77, p=0.002) enriched for eosinophil, mast cell, and epithelial IL-13 response genes (242 genes). Mepolizumab uncoupled co-expression of genes in this pathway. During mepolizumab, but not placebo treatment, there was significant loss of correlation among eosinophil-specific genes including RNASE2 (EDN), RNASE3 (ECP), CLC, SIGLEC8, and IL5RA contrasting a reciprocal increase in correlation among mast cell-specific genes (TPSAB1, CPA3, FCER1A), T2 cytokines (IL4, IL5, and IL13), and POSTN. Conclusions: These results suggest mepolizumab disrupts the regulatory interactions of gene co-expression among airway eosinophils, mast cells and epithelium by interrupting transcription regulation in eosinophils with enhancement in mast cell and epithelial inflammation. This paradoxical effect may contribute to an incomplete reduction of asthma exacerbations and demonstrates how differential co-expression network analyses can identify targets for more precise therapies