Extracellular Vesicle and Particle miRNAs in Maternal Plasma
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ABSTRACT:
PROVIDER: PRJNA925502 | ENA |
REPOSITORIES: ENA
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Project description:Background: Maternal pre-pregnancy BMI is a critical factor influencing the composition of breast milk. Breast milk has abundant extracellular vesicles (EVs) containing various biological molecules (cargo), including miRNAs. EVs are not degraded in the gastrointestinal system and circulation; thus, breast milk EVs (bEVs) interact with other organs in breastfed infants and modify the gene expression of recipient cells using miRNAs. In maternal obesity, miRNAs in bEVs are deregulated, which might be associated with adverse health outcomes in infants. In this study, we examined 798 miRNAs to determine which miRNAs are altered in the bEVs of obese mothers and their potential impact on breastfed infants. Methods: We recruited healthy nursing mothers who were either obese (BMI≥30) or lean (BMI<25) based on their pre-pregnancy BMI, and delivered a singleton baby in the prior six months. EVs were isolated from breast milk with ultracentrifugation. bEV characteristics were examined by flow cytometry and fluorescence imaging of EV markers. A total of 798 miRNAs were screened using a NanoString human miRNA panel to find deregulated miRNAs in bEVs of obese mothers compared to lean mothers. Results: We included 65 nursing mothers: 47 lean and 18 obese mothers based on pre-pregnancy BMI. After bEV isolation, we confirmed the expression of general EV markers. Out of 37 EV markers, CD326 was the most highly expressed marker in bEVs. From miRNA analysis using NanoString, we found that the most abundant miRNAs include miR-30b-5p, miR-494-3p, and let-7 families, and the list of top 10 miRNAs was not different between lean and obese mothers. Target genes of the top 10 miRNAs were associated with the EGFR, ErbB, and FoxO signaling pathway. Nineteen miRNAs were deregulated in bEVs of obese mothers (adjusted p < 0.05 cut-off), including miR-575, miR-548g-3p, miR-582-3p, and miR-652-5p. The target genes of these miRNAs are associated with lipid metabolism, inflammatory diseases, and nervous/cardiovascular system development. Conclusion: In this study, we demonstrated altered miRNAs in bEVs of obese mothers and identified the pathways of their potential target genes. Our findings will provide insight for future studies investigating the role of bEVs in breastfed infants.
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Project description:Asthma is a chronic lung disease with various clinical phenotypes, complicating its diagnosis and treatment. The micro-RNA (miRNA) profile of plasma-derived extracellular vesicles (EVs) may serve as potential circulating biomarkers for differentiating asthma phenotypes/endotypes. This study aims to characterize and compare the miRNA profiles in plasma-derived EVs across healthy controls (HC), non-severe asthmatics (NS), and severe asthmatics (SA). EVs were isolated from plasma samples of HC, NS, and SA, followed by physiochemical characterization and RNA isolation. Small RNA sequencing was performed, and differentially expressed (DE) miRNAs were identified through DESeq2 analysis. DE miRNAs and their predicted mRNA targets were identified using Ingenuity Pathway Analysis (IPA), and pathway enrichment was conducted using STRING DB and Enrichr. EVs from all groups were predominantly ~150-200 nm in size, with significantly higher EV counts in SA compared to HC and NS. miRNA expression analysis revealed unique and shared DE miRNAs across the three comparisons (HC vs. NS; HC vs. SA; NS vs. SA). A total of 16 unique DE miRNAs among these comparisons, between which in the NS vs. SA comparison, miR-515-3p positively correlates with lung function, and exacerbation and miR-133a-3p and miR-9-5p with ACT score. Target and pathway analyses from the NS vs. SA comparison indicated the enrichment of key pathways, including IL-4/IL-13, Th1, Th2, and Th17 cell differentiation, MAPK, PI3K-Akt, and receptor tyrosine kinase signaling. This study identified distinct miRNAs in plasma-derived EVs from NS vs. SA which could serve as potential circulating biomarkers for differentiating asthma severity.
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