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. 2024 Aug 30;25(17):9425.
doi: 10.3390/ijms25179425.

Specific microRNA Profile Associated with Inflammation and Lipid Metabolism for Stratifying Allergic Asthma Severity

Andrea Escolar-Peña  1 , María Isabel Delgado-Dolset  1 , Carmela Pablo-Torres  1 , Carlos Tarin  2 , Leticia Mera-Berriatua  1 , María Del Pilar Cuesta Apausa  3 , Heleia González Cuervo  3 , Rinku Sharma  4 , Alvin T Kho  5 , Kelan G Tantisira  6 , Michael J McGeachie  4 , Rocio Rebollido-Rios  7   8   9 , Domingo Barber  1 , Teresa Carrillo  3 , Elena Izquierdo  1 , María M Escribese  1
Affiliations

Specific microRNA Profile Associated with Inflammation and Lipid Metabolism for Stratifying Allergic Asthma Severity

Andrea Escolar-Peña et al. Int J Mol Sci. .

Abstract

The mechanisms underlying severe allergic asthma are complex and unknown, meaning it is a challenge to provide the most appropriate treatment. This study aimed to identify novel biomarkers for stratifying allergic asthmatic patients according to severity, and to uncover the biological mechanisms that lead to the development of the severe uncontrolled phenotype. By using miRNA PCR panels, we analyzed the expression of 752 miRNAs in serum samples from control subjects (n = 15) and mild (n = 11) and severe uncontrolled (n = 10) allergic asthmatic patients. We identified 40 differentially expressed miRNAs between severe uncontrolled and mild allergic asthmatic patients. Functional enrichment analysis revealed signatures related to inflammation, angiogenesis, lipid metabolism and mRNA regulation. A random forest classifier trained with DE miRNAs achieved a high accuracy of 97% for severe uncontrolled patient stratification. Validation of the identified biomarkers was performed on a subset of allergic asthmatic patients from the CAMP cohort at Brigham and Women's Hospital, Harvard Medical School. Four of these miRNAs (hsa-miR-99b-5p, hsa-miR-451a, hsa-miR-326 and hsa-miR-505-3p) were validated, pointing towards their potential as biomarkers for stratifying allergic asthmatic patients by severity and providing insights into severe uncontrolled asthma molecular pathways.

Keywords: allergic asthma; biomarkers; miRNAs; severity.

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Conflict of interest statement

CT reports conflicts of interest in terms of consulting fees by the GLG group and payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events by San Pablo CEU University and Genotipia. MME reports conflicts of interest in terms of the funding for the present manuscript by RICORS Red de Enfermedades Inflamatorias (REI) RD21 0002 0008 and by Fundación Mutua Madrileña (AP177712021) and a leadership or fiduciary role in EAACI EXCOM. MJM reports conflicts of interest in terms of the funding for the present manuscript by NIH NHLBI Grants. MIDD reports conflicts of interest in terms of grants or contracts from ALK. KGT reports conflicts of interest in terms of the funding for the present manuscript by the National Institutes of Health. DB reports conflicts of interest in terms of funding for the present manuscript by ISCIII (PI19/00044) (co-founded by FERDER); grants or contracts from ALK A/S; and payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events by Diater Labs. The rest of the authors have no conflicts of interest.

Figures

Figure 1
Figure 1
Analysis of DE miRNAs in severe uncontrolled and mild allergic asthmatic patients. Hierarchical clustering of Z-score normalized expression values of the 40 DE miRNAs between severe uncontrolled and mild allergic asthmatic patients. Yellow: mild group; purple: severe uncontrolled group.
Figure 2
Figure 2
Enrichment analysis of biological processes from gene ontology terms using differentially expressed miRNAs between severe uncontrolled and mild allergic asthmatic patients. (A) Top 50 enriched processes are depicted (FDR < 0.05), where dot colors represent the p-adjusted values and sizes represent the number of miRNAs enriched per term. (B) Network plot showing those enriched terms with more than 10 miRNAs. miRNA expression (Log2(FC), severe uncontrolled vs. mild) is represented in color scale.
Figure 3
Figure 3
Correlations between differentially expressed miRNAs in severe uncontrolled and mild allergic asthmatic patients and several inflammatory-related metabolites, and predicted miRNA targets. (A) Correlation plot of 40 DE miRNAs and inflammatory-related metabolites. MiRNAs with no correlations are not included in the plot. Circles in blank are non-significant correlations (p-value > 0.05). (B) Predicted miRNAs’ targets (miRDB score > 80) linked to their correlated asthma-inflammatory-related metabolites. For both panels, color boxes: green, sphingolipids; yellow: fatty acids, pink: glicerophospholipids and blue, aminoacids.
Figure 4
Figure 4
Random forest-based classifier. ROC curves of the generated models. Table showing accuracies, area under the curve (AUC) values and confidence intervals (CIs) of the trained models with different number of features. The best model is marked.
Figure 5
Figure 5
Differentially expressed miRNAs in validation cohort. (A) Workflow for biomarkers identification. (B) Bar plot of the relative miRNA expression values indicated by Log2(FC) of DE miRNAs in study cohort (severe uncontrolled (SU) vs. mild (M) comparative, black) and validation cohort (not controlled vs. well controlled comparative, striped; not controlled vs. partially controlled comparative, white). Validated miRNAs are written in green. * Childhood Asthma Management Program (CAMP) cohort.
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