MicroRNA dysregulation in ataxia telangiectasia

Abstract

Introduction: Ataxia telangiectasia (AT) is a rare disorder characterized by neurodegeneration, combined immunodeficiency, a predisposition to malignancies, and high clinical variability. Profiling of microRNAs (miRNAs) may offer insights into the underlying mechanisms of complex rare human diseases, as miRNAs play a role in various biological functions including proliferation, differentiation, and DNA repair. In this study, we investigate the differential expression of miRNAs in samples from AT patients to identify miRNA patterns and analyze how these patterns are related to the disease.

Methods: We enrolled 20 AT patients (mean age 17.7 ± 9.6 years old) and collected clinical and genetic data. We performed short non-coding RNA-seq analysis on peripheral blood mononuclear cells (PBMCs) and fibroblasts to compare the miRNA expression profile between AT patients and controls.

Results: We observed 42 differentially expressed (DE)-miRNAs in blood samples and 26 in fibroblast samples. Among these, three DE-miRNAs, miR-342-3p, miR-30a-5p, and miR-195-5p, were further validated in additional AT samples, confirming their dysregulation.

Discussion: We identified an AT-related miRNA signature in blood cells and fibroblast samples collected from a group of AT patients. We also predicted several dysregulated pathways, primarily related to cancer, immune system control, or inflammatory processes. The findings suggest that miRNAs may provide insights into the pathophysiology and tumorigenesis of AT and have the potential to serve as useful biomarkers in cancer research.

Keywords: DNA repair; ataxia telangiectasia; cancer; immunodeficiency; microRNA.

Figure 1

DE-miRNA in AT PBMCs. The volcano plot of DE-miRNAs (A). Hierarchical clustering analysis of DE-miRNAs (B). DE-miRNAs, differentially expressed miRNAs; FDR, false discovery rate; FC, fold change. Light blue: low expression, purple: high expression.

Figure 2

DE-miRNAs in AT fibroblasts. The volcano plot of DE-miRNAs (A). Hierarchical clustering analysis of DE-miRNAs (B). DE-miRNAs, differentially expressed miRNAs; FDR, false discovery rate; FC, fold change. Light blue: low expression, purple: high expression.

Figure 3

Cross-analysis of AT PBMCs and fibroblasts. Venn diagram (or map) of intersection DE-miRNAs in AT (A). Indicated in orange are PBMCs DE-miRNAs; indicated in green are fibroblast DE-miRNAs; indicated in yellow are common DE-miRNAs. Enrichment analysis for target genes of DE-miRNAs in PBMCs at the top and in fibroblasts at the bottom (B). Significant KEGG pathways with FDR ¾0.05. The significance increases from blue to red.

Figure 4

The expression level of three selected DE-miRNAs. Real-time qPCR of miR-30a-5p, miR-342-3p, and miR-195-5p in PBMCs (A) and WBCs (B). The entire cohort of patients analyzed (union of PBMCs and WBCs) (C). Student’s t-test was performed; ns, not significant, *P ¾ 0.05, **P ¾ 0.01, ***P ¾ 0.001.

Figure 5

Target genes and pathways of selected DE-miRNAs. Alluvial plot showing a list of miR-30a-5p, miR-342-3p, and miR-195-5p common target genes (A). Dot plot of functional enrichment analysis of all target genes of miR-30a-5p, miR-342-3p, and mi-195-5p. Pathway analyses by Reactome (B) and Wikipathways (C). Significant pathways are listed and represented by circles colored according to the significance of the enrichment, and their size is proportional to the number of target genes regulating the described signaling pathways.