Differences in microRNA levels across metabo-endotypes reveal novel insights into asthma heterogeneity

Abstract Rationale We previously validated five clinically distinct asthma metabo-endotypes (mechanistically derived asthma subgroups). We hypothesize that metabo-endotype membership may be partially driven by differences in serum microRNA profiles and their influence on metabolite levels. Objectives To determine whether serum miRNA levels can help understand the underlying drivers of metabolic dysregulation across metabo-endotypes. Method We compared expression levels of serum microRNAs across 1121 children grouped into five asthma metabo-endotypes using ANCOVA. A LASSO model was leveraged to determine the most important miRNAs for discriminating metabo-endotype membership. Finally, multiple linear regression models and two-sample t-tests were employed to determine whether serum microRNA ~ plasma metabolite relationships differed between individuals within different metabo-endotypes. Measurements and main results Of 317 serum miRNAs, 132 (41.6%) demonstrated significantly different expression across metabo-endotypes (FDR < 0.05), with miR-143-3p showing the greatest variation (FDR p = 5.7 × 10− 19). Most differences were driven by metabo-endotypes 2 and 3, the most and least severe. Enrichment analysis of microRNAs’ predicted target genes revealed critical asthma pathways, including Th17 and Th1/Th2 cell differentiation. A model based on 17 miRNAs was able to discriminate membership of metabo-endotype 2 versus 3 (AUC:81%, CI: 73%-88%). There was some evidence that relationships between specific miRNAs and metabolites differed between individuals in metabo-endotypes 2 and 3, which may suggest differential posttranscriptional regulation of pathways including eicosanoid and arginine metabolism. Conclusions The results provide some evidence to suggest differential miRNA regulated gene expression between biologically and clinically distinct asthma metabo-endotypes, with a potentially important role for miR-143-3p. Understanding these relationships may uncover novel therapeutic targets and guide more personalized treatment strategies.