RNA-binding proteins regulate immune-related alternative splicing in inherited salt-losing tubulopathies

Abstract

Background: Inherited salt-losing tubulopathies (SLT) are rare disorders caused by gene mutations that disrupt renal tubular ion transport. However, the molecular mechanisms underlying SLT pathogenesis remain unclear. This study aims to elucidate the functional genes and potential regulatory mechanisms associated with SLT.

Methods: We established a study cohort comprising inherited SLT patients, age-matched patients with acquired hypokalemia, and healthy volunteers. Clinical characteristics were compared among the groups. RNA sequencing (RNA-seq) was performed to obtain transcriptomic profiles, followed by analysis of gene expression patterns and alternative splicing events (ASEs). Key findings were validated using RT-qPCR.

Results: SLT patients exhibited a higher prevalence of recurrent viral infections (65%, P = 0.004) and autoimmune thyroid disorders (30%, P = 0.022) compared to healthy controls. RNA-seq analysis identified 2,611 differentially expressed genes (DEGs) in SLT patients, including 1,236 upregulated and 1,375 downregulated genes. These DEGs were primarily enriched in innate immune responses and adaptive immunity pathways. Additionally, significant alterations in gene expression related to viral defense and stress responses were observed. Notably, we identified several RNA-binding proteins (RBPs) that may contribute to SLT pathogenesis by regulating ASEs of immune-related genes.

Conclusion: Our findings highlight the critical role of RBPs in SLT pathogenesis and provide novel insights into the immune profiles and gene expression dynamics in SLT. This study lays the foundation for future research into targeted therapies and personalized treatment strategies for SLT management.

Keywords: Alternative splicing events; Hypokalemia; Immune; Inherited salt-losing tubulopathies; RNA-binding proteins; Transcriptomic.

Fig. 1

Transcriptome differences between iSLTs, C_hk and HC populations. A PCA plot of samples after normalizing all genes expression levels. The ellipse for each group is the confidence ellipse. B Volcano plot showing all DEGs between C_hk vs HC (left) and iSLTs vs HC (right) comparison group. Red indicates genes with upregulated expression. Blue indicates genes with downregulated expression. C–F Bar plot showing the most enriched GO results of up/down regulated genes between the different comparison groups

Fig. 2

Dynamic analysis of DEGs. A DEGs of two comparison group (iSLT vs HC, C_hk vs HC) divided in nine clusters based on expression pattern. The expression trends of each cluster genes shown in line chart. B Hierarchical clustering heat map showing expression levels of all clustered DEGs. C The top 5 most enriched GO terms (biological process) were illustrated for DEGs in each cluster

Fig. 3

Identification of RBPs associated with salt-losing tubulopathies, hypokalemia disease controls and healthy population. A Venn diagram showing the overlapped genes between human RNA binding protein (RBP) genes and clustered gene set of cluster1, 2, 3, 5, 7 and 8. B Bar plot showing the most enriched GO results of overlapped RBPs in Fig. 3A. C Bar plot showing the expression pattern and statistical difference of selected DERBPs. Error bars represent mean ± SEM. ***P-value < 0.001, **P < 0.01. D Venn diagram showing the overlapped genes between human RBP genes and clustered gene set of cluster6. E Bar plot showing the most enriched GO results of overlapped RBPs in Fig. 3D. F Bar plot showing the expression pattern and statistical difference of selected DERBPs from RNA-seq and RT-qPCR. Error bars represent mean ± SEM. ***P-value < 0.001, **P < 0.01

Fig. 4

RBP-ASG regulation associated with hypokalemia or salt-losing tubulopathies. A The bar plot showing the percentage of all NIR alternative splicing events. B The pie plot showing the number of NIR significant ASEs in two comparison group C_hk vs HC (left) and iSLTs vs HC (right). C Bar plot showing the number of ASE per gene in two comparison group, median number marked with dashed line. Y axis: numbers of genes. X axis: numbers of ASE per gene. D Venn diagram showing the overlap of immune associated ASGs. E Co-expression network plot showing the regulatory network consisting of RBPs and immune-related ASGs and the top 10 most enriched GO terms. Co-expression relationship pairs were identified using a threshold of P value ≤ 0.01 and Pearson coefficient ≥ 0.6 or ≤ -0.6

Fig. 5

Identification of pathogenesis-related ASGs. A Regulatory networks between RBPs and pathogenic ASGs. B–G Bar plot showing the AS ratio and statistical difference of immune associated genes from RNA-seq and RT-qPCR. Error bars represent mean ± SEM. ***P < 0.001, **P < 0.01, *P < 0.05