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. 2025 May 19;16(5):601.
doi: 10.3390/genes16050601.

Transcriptomic Approach in Understanding Fabry Nephropathy: A Review of the Literature and Proof-of-Concept

Nika Breznik  1 , Tina Levstek  1   2 , Bojan Vujkovac  3 , Andreja Cokan Vujkovac  3 , Katarina Trebušak Podkrajšek  1   2
Affiliations

Transcriptomic Approach in Understanding Fabry Nephropathy: A Review of the Literature and Proof-of-Concept

Nika Breznik et al. Genes (Basel). .

Abstract

Background/objectives: Fabry nephropathy (FN) is a progressive complication of Fabry disease that significantly affects patient outcomes. However, the molecular mechanisms underlying FN are not yet fully understood. Recent advances in transcriptomics have opened new perspectives for the identification of early changes in gene expression associated with the development and progression of the disease.

Methods: This study includes a systematic review of transcriptomic findings in chronic kidney disease, with a particular focus on FN, and presents a proof-of-concept RNA sequencing analysis of peripheral blood samples from six Fabry patients with progressive nephropathy and six age- and sex-matched control subjects.

Results: The analysis identified 41 differentially expressed genes (DEGs), all of which were upregulated in Fabry patients. Enrichment analysis revealed significant involvement in immune-related pathways, including neutrophil degranulation, interferon, and cytokine signaling. Cell type enrichment revealed that neutrophils and other immune cells are key players in this process.

Conclusions: These results suggest that immune and inflammatory mechanisms play a central role in the pathogenesis of FN. The identified DEGs are involved in pro-fibrotic signaling and immune system activation and shed light on possible mechanisms underlying fibrosis, podocyte injury, and kidney damage. This study contributes to a deeper understanding of FN and may facilitate in the identification of early biomarkers for diagnosis and disease monitoring.

Keywords: Fabry disease; Fabry nephropathy; RNA sequencing; lysosomal storage disease; transcriptomic.

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Conflict of interest statement

N.B. and T.L. received funding from Chiesi to cover travel and accommodation expenses. B.V. has received speaker honoraria and consultancy fees from Sanofi Genzyme, Takeda Pharmaceuticals, Amicus, Chiesi, Swixx, and Eleva. A.C.V. received speaker fees and travel/accommodation support from Sanofi Genzyme and Takeda Pharmaceuticals. K.T.P. received travel and accommodation support from Sanofi Genzyme and speaker fees from Takeda Pharmaceuticals.

Figures

Figure 1
Figure 1
Volcano plot showing the results of the differential gene expression analysis between Fabry patients with progressive nephropathy and control subjects. A total of 41 significantly differentially expressed genes (DEGs) that met the criteria of |log2 fold change| > 1 and adjusted p-value < 0.05 are shown in red. Genes with adjusted p-value > 0.05 and |log2 fold change| < 1 are shown in gray. Genes with adjusted p-value < 0.05 but not meeting the fold-change criterion are shown in blue, while genes with |log2 fold change| > 1 but not statistically significant are marked in green.
Figure 2
Figure 2
The heatmap of the 41 differentially expressed genes (DEGs) identified between Fabry patients with progressive nephropathy and control subjects. Each column represents an individual sample, and each row corresponds to DEG. Expression values are scaled across genes. Hierarchical clustering was performed using Euclidean distance and Ward’s method. The top annotation bar indicates a group: orange for Fabry patients with progressive nephropathy and blue for control subjects. One Fabry patient clusters more closely with control subjects than with the other five patients.
Figure 3
Figure 3
Expression levels of the top 20 differentially expressed genes (DEGs), ranked by adjusted p-value. Orange points represent Fabry patients with progressive nephropathy and dark blue points represent control subjects.
Figure 4
Figure 4
Bar plot showing the top 10 significantly enriched pathways among the differentially expressed genes (DEGs). Pathway enrichment analysis was performed using Enrichr with the Reactome Pathways 2024 gene set library [20]. Pathways are ranked by −log10 (p-value).
Figure 5
Figure 5
Bar plot showing the top 10 significantly enriched cell types among the differentially expressed genes (DEGs). Cell type enrichment analysis was performed using Enrichr with the CellMarker 2024 gene set library [20]. Cell types are ranked by −log10 (p-value).
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