Dear Editor,

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic disease leading to end-stage kidney disease [1]. ADPKD mainly arises from genetic alternations in the PKD1 and PKD2 genes, accounting for approximately 75% and 15% of cases, respectively [2]. In comparison to PKD1-related ADPKD, PKD2-related ADPKD presents with milder symptoms and is often diagnosed incidentally through imaging or urologic events at a later age, typically after the fourth decade of life [2].

We report the case of a family of patients with ADPKD caused by aberrant splicing in PKD2, from which a 56-yr-old woman presented with multiple renal and hepatic cysts incidentally found on an abdominal ultrasonography performed for a health check-up in April 2019. The institutional review board- of Kyung Hee University Medical Center, Seoul, Korea, approved this study (approval No. 2023-07-078). The patient had a height of 153 cm, weight of 55 kg, and body mass index of 23.5 kg/m², and had been taking medications for hypertension and hyperlipidemia for 6 yrs. Laboratory investigations showed a normal serum creatinine level (0.82 mg/dL, reference range: 0.34–1.08 mg/dL) and an estimated glomerular filtration rate of 83.4 mL/min/1.73m². The liver function test was in the normal range, and no specific findings were observed in the urine test. Abdominal computed tomography showed numerous bilateral renal cysts and hepatic cysts of varying sizes (Fig. 1A). The kidneys were enlarged, with a total kidney volume of 563.2 cm³. The mother, brother, and son of the patient had a history of multiple renal and hepatic cysts (Fig. 1B). Her mother also had a history of cerebral hemorrhage (Fig. 1).

Figure 1. The CT findings, pedigree analysis, and next-generation sequencing results of the proband with ADPKD. (A) Multiple renal cysts were identified in the transverse plane of the CT scan. Multiple hepatic cysts were observed in the transverse plane of the CT scan. Multiple renal and hepatic cysts were visible in the coronal plane of the CT scan. (B) Pedigree of the proband (III-2) with ADPKD. The mother (II-7), younger brother (III-4), and son (IV-2) of the proband were diagnosed as having multiple renal or hepatic cysts. The same PKD2 variant was identified in both the proband and her son. (C) Results of next-generation sequencing testing related to ADPKD for the proband and son. The same heterozygous variant in PKD2 (NM_000297.4:c.2020-5A>G) was found in the proband and son.
Abbreviations: CT, computed tomography; ADPKD, autosomal dominant polycystic kidney disease.

To make a definitive diagnosis of ADPKD, the patient and her son underwent clinical exome sequencing (Fig. 1C). Both individuals had the same heterozygous intronic variant at the acceptor motif, NM_000297.4:c.2020-5A>G, in PKD2. This variant has not been reported in the general population (https://gnomad.broadinstitute.org/) or other patients with ADPKD. Although reported once in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/, last accessed on April 18, 2024), it was classified as uncertain significance (review status: one star) because of insufficient evidence. We predicted the pathogenicity of this variant using SpliceAI [3] and observed a significant change in the acceptor score at position c.2020, decreasing from 0.99 to 0.24 (Delta score: 0.76) (Fig. 2A). In addition, at position c.2020-4, we observed a substantial change in the acceptor score from 0 to 1 (Delta score: 1.00) (Fig. 2A). In summary, this intronic variant is predicted to shift the acceptor site by +1 base pair (bp) instead of causing the typical exon skipping seen with intronic variants near exon boundaries. This shift may potentially induce a premature termination codon (PTC) (Fig. 2).

Figure 2. Additional confirmatory results for interpreting the genetic variant identified in the proband. (A) The pathogenicity of NM_000297.4:c.2020-5A>G was predicted using SpliceAI [³]. It was predicted that, because of this intronic variant, acceptor loss would occur at the +5 bp position (with a change from 0.99 to 0.24, delta score of 0.76), and acceptor gain would occur at the +1 bp position (with a change from 0 to 1, delta score of 1). The SpliceAI score was visualized with some modifications using MobiDetails (https://mobidetails.iurc.montp.inserm.fr/MD/) (B) Sanger sequencing was conducted to reassess the presence of the intronic variant in the proband, and the results confirmed its presence. (C) Agarose gel electrophoresis of a targeted reverse transcription PCR product, showing a single band of 299 bp in the control and two bands of different sizes (299 bp and 303 bp) in the proband. (D) Targeted RNA sequencing revealed a change in the 299-bp RNA sequence (NM_000297.4:r.2019_2020insacag), which is anticipated to induce a PTC (p.(Asn674Thrfs*10)). The boundary between exons 9 and 10 in the RNA sequence is marked with a vertical dashed line on the chromatogram. (E) PKD2 structure (yellow) predicted using AlphaFold2 [⁵] based on the mutant sequence compared with the canonical PKD2 structure (purple) using ChimeraX Matchmaker [⁶]. Regions predicted to have the same structure based on RMSD in MatchMaker are highlighted in green. (F) The MatchMaker Multalign viewer displays the RMSD values (Cα RMSD) of the pairwise sequence alignment between the canonical and mutant sequences as the height of the histogram bars. Matched residues are highlighted with green boxes.
Abbreviations: bp, base pair; PTC, premature termination codon; RMSD, root mean square deviation.

Therefore, we extracted DNA and RNA from the proband to determine whether there were changes in the RNA sequence as predicted using SpliceAI [3]. We confirmed the presence of the intronic variant using Sanger sequencing (Fig. 2B). Additionally, reverse transcription PCR revealed that, unlike the single band observed in the control (299 bp), our patient had two distinct bands of different sizes (299 bp and 303 bp) (Fig. 2C). Through targeted RNA sequencing, we confirmed that, as predicted using SpliceAI [3], the acceptor site was shifted by +1 bp (NM_000297.4:r.2019_2020insacag), leading to the eventual induction of PTC (p.(Asn674Thrfs*10)) (Fig. 2D) and likely triggering nonsense-mediated decay (NMD) [4].

However, because of the lack of research on NMD efficiency for PKD2, we predicted the structural changes in the mutant PKD2 protein, assuming that the mRNA resulting from this intronic variant might escape NMD and undergo translation. To this end, we compared and analyzed the PKD2 protein structure (yellow) predicted using AlphaFold2 [5] based on the mutant sequence with the canonical PKD2 structure (purple) using ChimeraX Matchmaker (Fig. 2E–2F) [6]. Comparing the canonical and mutant protein structures suggested that some regions would have different structures despite having the same sequence (e.g., residues 1–200). Notably, the region spanning residues 750–785 is crucial for Ca²⁺ binding (https://www.ebi.ac.uk/interpro/protein/UniProt/Q13563/) and PKD2 channel activity regulation. However, in the mutant protein, this region is not translated, which is predicted to significantly affect protein function [7].

According to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines [8] and ClinGen recommendations (https://clinicalgenome.org/) [9, 10], this intronic variant was classified as likely pathogenic based on the evidence PVS1, PM2_Supporting, and PP1_Supporting. This case, where an intronic variant located outside the ±1 or 2 dinucleotide positions of the donor/acceptor sites induces a PTC owing to intron retention rather than exon skipping [11], highlights the significance of splicing prediction in interpreting intronic variants.

We thank Eun Chong Byun, RN (Rare Disease Center, Kyung Hee University Medical Center) and Haeran Jin, MT (Department of Laboratory Medicine, Kyung Hee University Medical Center) for their dedicated efforts in conducting the patient’s family analysis and additional confirmatory tests.

Park KS and Kim JS had full access to all the data in the study and take responsibility for data integrity and the accuracy of data analysis. Conceptualization: Yoon SY, Park KS, and Kim JS; Methodology: Yoon SY, Park KS, and Kim JS; Investigation: Yoon SY, Park KS, and Kim JS; Visualization: Yoon SY, Park KS, and Kim JS; Supervision: Yoon SY, Park KS, and Kim JS; Writing – original draft: Yoon SY, Park KS, and Kim JS; Writing – review & editing: Yoon SY, Park KS, and Kim JS. All authors have read and approved the final manuscript. The corresponding authors attest that all listed authors meet the authorship criteria and that no other author meeting the criteria has been omitted.

None declared.

This work was supported by a grant from Kyung Hee University in 2023 (KHU-20233269).