# Unraveling the impact of VHL exon 2 mutations in erythrocytosis or von Hippel-Lindau disease identified RNA-binding proteins involved in VHL splicing

Valéna Karaghiannis, Loïc Schmitt, Franck Chesnel, Emilie-Fleur Gautier, Marjorie Leduc, Morgane Le Gall, Salam Idriss, Sophie Couvé, Anne Barlier, Guillaume Sarrabay, et al.

► To cite this version:

Valéna Karaghiannis, Loïc Schmitt, Franck Chesnel, Emilie-Fleur Gautier, Marjorie Leduc, et al. Unraveling the impact of VHL exon 2 mutations in erythrocytosis or von Hippel-Lindau disease identified RNA-binding proteins involved in VHL splicing. American Journal of Human Genetics, 2025, 112 (11), pp.2591-2604. 10.1016/j.ajhg.2025.09.002. hal-05284377

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# Unraveling the impact of VHL exon 2 mutations in erythrocytosis or von Hippel-Lindau disease identified RNA-binding proteins involved in VHL splicing

Valéna Karaghiannis¹, Loïc Schmitt², Franck Chesnel³, Emilie-Fleur Gautier⁴, Marjorie Leduc⁵, Morgane Le Gall⁵, Salam Idriss⁶, Sophie Couvé⁷, Anne Barlier⁸, Guillaume Sarrabay⁸, Nada Maaziz⁹, Bruno Cassinat¹⁰, Laurence Legros¹¹, Vincent Thibaud¹², Stéphane Richard¹³, François Girodon¹⁴, Julie Miro¹⁵, Sylvie Tuffery-Giraud¹⁵, Yannick Arlot³, Betty Gardie¹⁶

¹ EPHE, PSL Université, Paris, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, 44000 Nantes, France.

² Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, 44000 Nantes, France; CNRS UMR6290 (IGDR), Université Rennes 1, 2 av du Pr L Bernard, 35043 Rennes Cedex, France.

³ CNRS UMR6290 (IGDR), Université Rennes 1, 2 av du Pr L Bernard, 35043 Rennes Cedex, France.

⁴ Proteom'IC facility, Université Paris Cité, CNRS, INSERM, Institut Cochin, 75014 Paris, France; Laboratoire d'Excellence GR-Ex, 75010 Paris, France.

⁵ Proteom'IC facility, Université Paris Cité, CNRS, INSERM, Institut Cochin, 75014 Paris, France.

⁶ Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, 44000 Nantes, France.

⁷ EPHE, PSL Université, Paris, France; CNRS UMR 9019, Gustave Roussy, Université Paris-Saclay, UMR 9019 CNRS, 114 rue Edouard Vaillant, 94800 Villejuif, France.

⁸ Aix Marseille University, APHM, INSERM, MMG, U1251, GEnOPé Département, M2GM, Timone Hospital, Marseille, France.

⁹ Service d'Hématologie Biologique, Pôle Biologie, CHU de Dijon, Dijon, France.

¹⁰ Université Paris Cité, APHP, Hôpital Saint-Louis, Laboratoire de Biologie Cellulaire, 75010 Paris, France.

¹¹ Service d'hématologie clinique, Hôpital Bicêtre, 94270 Kremlin-Bicêtre, France.

¹² Service d'hématologie clinique, Hôpital Saint Vincent de Paul, Université catholique de Lille, 59000 Lille, France.

¹³ EPHE, PSL Université, Paris, France; Réseau national de référence pour cancers rares de l'adulte PREDIR labellisé par l'Institut national du cancer (INCa), AP-HP, Hôpital de Bicêtre, 94270 Le Kremlin-Bicêtre, France.
**Keywords:**

ACMG; ESE; ESS; RBPs; RNA-binding proteins; VHL; Von Hippel-Lindau disease; hnRNPAB; hnRNPF; protein stability; splicing.

**Correspondence**

betty.gardie@inserm.fr

<footnote n="14">¹⁴ Service d'Hématologie Biologique, Pôle Biologie, CHU de Dijon, Dijon, France; Université de Bourgogne, INSERM U1231, 21078 Dijon, France; Laboratoire d'Excellence GR-Ex, 75010 Paris, France.</footnote>

<footnote n="15">¹⁵ PhyMedExp, University Montpellier, CNRS, INSERM, Montpellier, France.</footnote>

<footnote n="16">¹⁶ EPHE, PSL Université, Paris, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, 44000 Nantes, France; Laboratoire d'Excellence GR-Ex, 75010 Paris, France. Electronic address: betty.gardie@inserm.fr.</footnote>
Abstract

This study explores the impact of mutations in the exon 2 of the von Hippel-Lindau (VHL) gene, associated with erythrocytosis or von Hippel-Lindau disease. We analyzed 15 missense and synonymous genetic variants to assess their effects on splicing and VHL protein stability. Using in silico predictions and functional assays, we found that some specific mutations impact splicing and reduce protein stability, allowing their clinical classification as pathogenic. This study revealed exonic-splicing regulatory regions. Notably, by performing RNA-protein pull-down, we identified two RNA-binding proteins, hnRNPF and hnRNPAB, as key regulators of VHL splicing. Our findings reveal the limitations of current splicing-prediction tools in recognizing exonic-splicing enhancer (ESE) or silencer (ESS) sequences and suggest that mutations can differentially affect disease phenotypes by influencing both splicing and protein stability. These insights enhance our understanding of the molecular mechanisms underlying VHL-associated disorders and expand the landscape of regulatory elements and protein factors involved in VHL splicing regulation.
<figure><img src="image_2.png" /></figure>

Mutations in *VHL* exon 2 associated with erythrocytosis or VHL disease affect both splicing and protein stability. This study uncovers regulatory splicing elements and implicates RNA-binding proteins hnRNPF and hnRNPAB in *VHL* exon 2 regulation, highlighting limitations of current prediction tools and providing insights into *VHL*-related molecular pathogenesis.

Introduction

The von Hippel-Lindau (VHL) gene (MIM: 608537) is a tumor- suppressor gene essential for
various cellular processes due to its role in mediating interactions between different proteins,
despite lacking intrinsic enzymatic activity. One of the primary functions of the VHL protein
(pVHL) is the regulation of the hypoxia-inducible factor (HIF) (Figure S1), a key factor in
pathways involving metabolism, cell survival, apoptosis, and stress responses. 1 The HIF
complex consists of two subunits: a constitutive subunit (HIF-1β/ARNT) (ARNT [MIM:
126110]) and an oxygen- regulated subunit (HIF-1α) (HIF1A [MIM: 603348]), HIF-2α
(EPAS1 [MIM: 603349]), or HIF-3α (HIF3A [MIM: 609976]), with HIF-2α being particularly
implicated in VHL-related diseases. Under normal oxygen conditions, the stability of HIF-α
subunits is tightly regulated by the hydroxylation of two proline residues (Pro402/Pro564 and
Pro405/Pro531 of HIF-1α and HIF-2α, respectively) through the action of dioxygenases
prolyl hydroxylase domain (PHD), (EGLN2 [MIM: 606424]; EGLN1 [MIM: 606425];
EGLN3 [MIM: 606425]), which require oxygen and α-ketoglutarate.² This hydroxylation
promotes the binding of pVHL, which is part of an E3 ubiquitin ligase complex, to the
oxygen-dependent degradation domain (ODDD) of HIF-α, leading to its ubiquitination and
degradation via the proteasome.³ Under low-oxygen conditions, the hydroxylation is
inhibited, resulting in HIF-α stabilization and transcriptional activation in target genes via the
binding of the HIF-α/β heterodimer on hypoxiaresponsive elements (HREs), promoting
processes like erythropoiesis, angiogenesis, and metabolism shift.⁴

Mutations in the *VHL* gene can lead to two major phenotypes: the development of hypervascularized tumors (VHL disease [MIM: 193300]) or erythrocytosis (ECYT2 [MIM: 263400]), characterized by excessive red blood cell production and severe thrombotic events. VHL disease results from heterozygous germline mutations with a subsequent somatic loss of
the second allele in tumors, affecting 1 in 36,000 individuals worldwide and a penetrance of 90% by age 65.5 This disease is associated with the development of multiple benign or malignant tumors and cysts in numerous organs: retinal and central nervous system (CNS) hemangioblastomas, pheochromocytomas in the adrenal glands, paragangliomas in the sympathetic and parasympathetic ganglia, neuroendocrine tumors of the pancreas, and clear cell renal cell carcinomas (ccRCC). On the other hand, homozygous or compound heterozygotes mutations lead to the development of erythrocytosis, first described in the Chuvash region of Russia. These patients (and their heterozygous parents) never develop tumors (except two cases of hemangioblastoma), and erythrocytosis is always transmitted in a recessive pattern. To date, numerous other VHL mutations associated with this phenotype have been discovered and are always different from mutations associated with the VHL disease (with very rare exceptions). Functional studies showed that erythrocytosis-related mutations disturb the degradation of HIF but to a lesser extent than the mutations involved in VHL disease (hypomorphic mutations). The VHL gene has various functions, and genotype-phenotype correlations in VHL-related diseases remain complex.

The VHL gene, spanning 12 kb, is located on chromosome 3p25-2612 and contains three exons that are transcribed into two main splicing isoforms. The first includes all three exons, producing two proteins, depending on the initiation translation codon: pVHL21313 and pVHL160.14 The second splice variant does not contain the exon 2 and encodes the pVHL172, which is not able to downregulate HIF.15 Recent studies discovered a cryptic exon (E1') present in both transcript isoforms, adding complexity to the splicing of the VHL gene.9 Previous work from our laboratory has shown that the regulation of VHL splicing is complex. Few mutations in the VHL gene have been described to impact splicing; however, the precise mechanisms controlling VHL splicing remain unclear.

Alternative splicing is a key contributor to protein diversity and functional complexity in mammals. This process is influenced by RNA-binding proteins (RBPs) that interact with specific sequences in the pre-messenger RNA, modulating spliceosome assembly and splicing outcomes.16,17 The role of RBPs, such as heterogeneous nuclear ribonucleoproteins (hnRNPs), is particularly important in mediating these interactions, either promoting or inhibiting exon inclusion.18 These proteins bind to RNA with varying affinities based on a core recognition sequence of 4-6 nt. Within this context, exonic-splicing enhancer (ESE) sequences on the gene promote exon inclusion, while exonic-splicing silencer (ESS) sequences repress exon recognition, acting as negative regulators of splicing.18-20 The misrecognition of these splicing regulatory sequences can generate aberrant mRNAs, leading to protein isoforms that can be dysfunctional. Previous studies have primarily focused on the role of RBPs in hypoxia using the *Caenorhabditis elegans* model organism but have not thoroughly explored their role in human VHL gene splicing.21

This study investigates the impact of variants located in exon 2 on VHL splicing and protein stability by analyzing 15 variants that target identical codons but that, depending on the nucleotide substitution, result in distinct phenotypes. Through a detailed investigation, we attempted to classify these variants according to the American College of Medical Genetics and Genomics (ACMG) guidelines. 22 These investigations allowed the delineation of particular regions prone to splicing defects when mutated. RNA-protein pull-down using specific VHL-RNA probes identified RBPs partners. We notably studied the roles of hnRNPF and hnRNPAB in the splicing process. Our findings provide new insights into the molecular mechanisms underlying VHL-associated disorders, revealing the binding of specific RBPs to VHL RNA and the critical role of hnRNPF and hnRNPAB in VHL splicing.
# Methods

## Patient samples and sequencing

Samples were obtained after receiving approval from the ethics committee and written informed consent obtained from each patient. DNA was extracted from whole blood, and genetic screening was performed using high-throughput sequencing technologies with a dedicated panel of genes known to be related to erythrocytosis and VHL disease.

## In silico analysis

The MobiDetails platform<sup>23</sup> was used to access the Splicing Prediction Pipeline (SPiP) and SpliceAI in order to predict the impact of some exon 2 variants in the *VHL* gene on mRNA splicing. To predict the interaction of RBPs with exon 2 of the *VHL* gene, we utilized the RBPmap database. To assess the importance of the target nucleotide in RBP binding-site recognition, we examined the conservation of the mutated nucleotide within the key recognition sequence for RBPs. Additional information is given in supplemental information.

## Cell culture

HEK293 (human embryonic kidney cells), HeLa (cervical cancer cells), HKC-8 (human proximal tubule epithelial cells), UT7 (erythroid progenitor leukemia cells), and lymphoblastoid cell line (LCL) cells, derived from patient lymphocytes and immortalized using Epstein-Barr virus (EBV), were cultured under specific conditions: HEK293, HeLa, and HKC-8 cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 4.5 g/L glucose, 10% fetal bovine serum (FBS), and penicillin-streptomycin (P/S, 100 UI/mL). UT7 cells were cultured in minimum essential medium (MEM) complemented with 10% FBS, P/S, L-glutamine (2 µmol/mL), and recombinant erythropoietin (EPO) (EPREX, 2 IU/mL). LCLs were grown in RPMI 1640, with 10% FBS and P/S.

## Cycloheximide chase

Twenty-four hours following transfection with the relevant pCMV-HA-VHL plasmids using jet PRIME transfection reagent (Polyplus), HEK293 cells were treated with cycloheximide (Sigma, 100 µg/mL) to inhibit new protein synthesis. Cells were harvested at 0, 3, 6, and 9 h after treatment. At each time point, cells were lysed in 1× passive lysis buffer to extract total proteins (Promega). The lysates were then subjected to western blot analysis for the detection and quantification of VHL proteins over the chase period.<sup>9,24</sup>
## Western blotting

Western blot analyses were performed using specific protocols. For cycloheximide chase and RBP small interfering RNA (siRNA) experiments, proteins were separated on NuPage 4%-12% Bis-Tris gels (Life Technologies) and were transferred to nitrocellulose membranes (Bio-Rad). Membranes were first incubated with primary antibodies anti-HA (BioLegend, 1/1,000), anti-hnRNPF (3H4 Santa Cruz Biotechnology, 1/100), and anti-hnRNPAB (G-10 Santa Cruz Biotechnology, 1/400) and then with horseradish peroxidase (HRP)-conjugated secondary antibodies (Cell Signaling Technology, 1/10,000). Ponceau staining and/or β-actin (1/5,000) was used for normalization. Chemiluminescence detection was performed using Clarity Western ECL Substrate kit (Bio-Rad).

For endogenous VHL protein detection, proteins were extracted with Laemmli sample buffer. Proteins were separated by SDS-PAGE on 12.5% Bis-Tris gels and transferred to nitrocellulose membranes. Membranes were incubated with anti-pVHL JD-1956 (1/200) (patent #14305925.1-1402-2014), followed by HRP-conjugated anti-mouse IgG (1/25,000) and normalized to actin. Detection was done using ECL select substrate (Bio-Rad).

## Minigene experiments

Minigene constructs were generated using the pCas2 plasmid as a backbone.25 These constructs were designed to incorporate exon 2, flanked by adjacent intronic sequence of the *VHL* gene as described.9 Transfection procedures varied based on cell-line requirements: standard transfections for most cell lines were performed using the jet PRIME transfection reagent (Ozyme), while UT7 and LCL cells underwent nucleofection with Cell Line Nucleofector Kit V and C respectively (Lonza). Twenty-four hours post transfection, total RNA was harvested from the cells.

## ACMG classification

The classification of genetic variants was guided by the ACMG criteria.22 Our approach integrated clinical data, family history, criteria from the publication,26 *in silico* predictions, and *in vitro* assay results, which facilitated a thorough evaluation and precise categorization of each variant. The ACMG guidelines classify variants into five distinct categories for Mendelian disorders: class 1 (benign), class 2 (likely benign), class 3 (variant of uncertain significance [VUS]), class 4 (likely pathogenic), and class 5 (pathogenic). The classification of these variants was also based on the expertise of the group dedicated to the *VHL* gene (consortium ClinGen VHL: https://clinicalgenome.org/affiliation/50036). Criteria are detailed in the supplemental information.

## RNA probe synthesis

The probe design incorporated a 39-nt sequence from exon 2 of the *VHL* gene wild-type (WT) or mutant, with an EcoRI restriction site at the 3' end and HindIII/PstI sites at the 5' end (sequences detailed in supplemental information).
These 55-bp probes containing the coding sequence of the *VHL* gene from nucleotides 403–441 were subcloned into the pGEM-3z vector. The resultant pGEM-3z *VHL* E2 WT or mutant plasmids were linearized using the HindIII enzyme. RNA transcription was subsequently carried out using the RiboMAX Large Scale RNA Production System T7 (Promega). The synthesized RNA was then purified according to the RNeasy Plus Micro Kit protocol (Qiagen), which includes steps for the purification of small RNAs. The purity of the extracted RNA was assessed by 1% agarose gel electrophoresis, ensuring high-quality samples for further analysis.<sup>27</sup>

## Desthiobiotinylation of the 3' end of RNA

To bind the *VHL* E2 WT or mutant RNA probes to streptavidin beads, the RNAs were initially labeled at the 3' end with desthiobiotin using the Pierce RNA 3' End Desthiobiotinylation Kit (Thermo Fisher Scientific). The labeling process required an overnight incubation with 1 nmol of biotinylated cytidine (bis)phosphate. After incubation, the RNA was separated from the reaction mixture by extraction with a chloroform/isoamyl alcohol mixture (24:1). Subsequently, the RNA was precipitated for 2 h at -20°C to ensure thorough recovery. Following precipitation, the labeled RNA probe was resuspended in RNase-free TE (Tris-EDTA) buffer and subsequently stored at -20°C for subsequent use.

## RNA-protein pull-down, mass spectrometry, and proteomic analysis

The RNA-protein pull-down assay was conducted using the Pierce Magnetic RNA-Protein Pull-Down Kit (Thermo Fisher). Initially, streptavidin magnetic beads were incubated with 3' desthiobiotin-labeled RNA probes. HeLa nuclear lysates (Ipracell) were added (200 µg) to the complexes of RNA beads. The complexes were incubated for 1 h at 4°C with shaking, after which they were washed to remove non-specifically bound materials, and both the probe and the bound proteins were eluted. The eluted samples were analyzed by mass spectrometry (MS), which allowed the identification and quantification of the proteins that interact with the RNA probe. Protocol of MS and RBPs analysis is detailed in the supplemental information.

## siRNA

HEK293 cells were transfected twice (24 h after plating and 48 h after the first transfection), using 100 nM RBP siRNA hnRNPF 5'-GCGACCGAGAACGACAUUU(rArC)-3'28 or hnRNPAB 5'-GGU AGUACAAACUACGGCA(rArC)-3'.29 The reverse sequences for all siRNAs included a dTdT at the 3' end. The negative control used was ON-TAR ET plus Non-targeting Pool (Horizon Discovery). Four hours post transfection, the culture medium was replaced with fresh medium. For RNA analysis, samples were extracted, while protein extraction was performed by lysing the dry cell pellets with RIPA buffer (50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 1% NP-40, 0.1% SDS).
## RT-PCR and RT-qPCR assays

RNA isolation (Macherey-Nagel kit) was followed by reverse transcription to synthesize cDNA. For the minigene assay, reverse transcription was followed by PCR using specifically designed primers that anneal to exons A and B within the minigene constructs. Silencing of the main transcript isoforms of targeted RBPs was quantified by RT-qPCR using SYBR Green (Thermo Fisher Scientific). To quantify *VHL* isoforms, RT-qPCR was conducted using TaqMan Universal PCR Master Mix (Thermo Fisher Scientific) with primers spanning exons E1/E2 and E1/E3. The quantitative results were analyzed using StepOne software.

## Statistical analysis

Data from the cycloheximide chase, minigene assay quantification focusing on exon 2 skipping, and MS intensities are presented as mean 5 standard error of mean (SEM). To assess the statistical differences between multiple groups, we employed two-way ANOVA with Dunnett's *post hoc* test for all the assays. All statistical analyses were performed using GraphPad Prism software

(version 8.01.1). Values of *p* ≤ 0.05 were considered statistically significant.

# Results

## Identification of *VHL* variants in exon 2 associated with erythrocytosis

Genetic screening was performed using next-generation sequencing (NGS) on a collection of blood samples from patients presenting with erythrocytosis and associated diseases.<sup>24,30</sup> This sequencing was made after exclusion of common causes of erythrocytosis, including polycythemia vera and secondary forms related to renal, cardiac, or pulmonary disorders. Two *VHL* gene variants, c.398C>T (GenBank: NM_000551.4) (p.Thr133Ile) (GenBank: NP_000542.1) (T133I) and c.461C>G (p.Pro154Arg) (P154R), were detected in a homozygous state and associated with erythrocytosis. One additional *VHL* variant, c.460C>G (p.Pro154Ala) (P154A), was found in a heterozygous state, associated with chronic thrombosis. These newly identified variants have been compared to previously published cases to provide a comprehensive landscape of *VHL*-associated mutations (Tables 1 and S1).

## *In silico* analysis of genetic variants pathogenicity

*In silico* analysis of each genetic variant was performed by using a set of computational tools. The MetaDome database, which refers to data from gnomAD and ClinVar, was used to analyze the mutation tolerance at each position. However, the limitation of this tool was the prediction of the same impact on mutation tolerance, despite different amino acid (aa) changes for a targeted aa (Table 2). We leveraged the MobiDetails database,23 which combines a vast array of data sources, notably the SPiP scores used to predict splicing impacts. We considered a score of less than 30% to indicate no or minor impact, 30%-60% to suggest moderate impact, 60%-80% to denote a strong impact, and over 80% to represent a severe impact. Additionally, the SpliceAI splice-donor loss score provided further insights
into splicing-efficiency alterations.31 The analyses predicted an impact on splicing only for some variants close to the exon boundary (Pro154 being the last aa encoded by exon 2) and weak differences for those located in the center of exon 2. Nevertheless, differences observed in phenotypes associated with specific mutations suggested more subtle changes and raised the need for functional validation to better understand the potential consequences of these variants. We therefore next sought to experimentally assess two key aspects: their impact on protein stability and pre-mRNA splicing.

<table><caption>Table 1. Description of variants located in exon 2 of the VHL gene</caption><thead><tr><th>Nucleotide position NM_000551.4</th><th>Zygosity</th><th>Impact on protein NP_000542.1</th><th>Protein one letter code</th><th>Phenotype</th></tr></thead><tbody><tr><td>c.397A&gt;T</td><td>heterozygous</td><td>p.(Thr133Ser)</td><td>T133S</td><td>tumors</td></tr><tr><td>c.398C&gt;T<sup>a</sup></td><td>homozygous</td><td>p.(Thr133Ile)<sup>a</sup></td><td>T133I<sup>a</sup></td><td>erythrocytosis<sup>a</sup></td></tr><tr><td>c.413C&gt;G</td><td>heterozygous</td><td>p.(Pro138Arg)</td><td>P138R</td><td>tumors</td></tr><tr><td>c.413C&gt;T</td><td>homozygous</td><td>p.(Pro138Leu)</td><td>P138L</td><td>erythrocytosis</td></tr><tr><td>c.414A&gt;G</td><td>heterozygous</td><td>p.(Pro138-)</td><td>P138P</td><td>tumors</td></tr><tr><td>c.429C&gt;T</td><td>homozygous/comp<br/>heterozygous</td><td>p.(Asp143-)</td><td>D143D</td><td>erythrocytosis</td></tr><tr><td>c.429C&gt;G</td><td>heterozygous</td><td>p.(Asp143Glu)</td><td>D143E</td><td>tumors</td></tr><tr><td>c.430G&gt;A</td><td>comp. heterozygous</td><td>p.(Gly144Arg)</td><td>G144R</td><td>erythrocytosis</td></tr><tr><td>c.431G&gt;C</td><td>heterozygous</td><td>p.(Gly144Ala)</td><td>G144A</td><td>tumors</td></tr><tr><td>c.431G&gt;A</td><td>heterozygous</td><td>p.(Gly144Glu)</td><td>G144E</td><td>tumors</td></tr><tr><td>c.460C&gt;G<sup>a</sup></td><td>heterozygous</td><td>p.(Pro154Ala)<sup>a</sup></td><td>P154A<sup>a</sup></td><td>chronic thrombosis<sup>a</sup></td></tr><tr><td>c.460C&gt;T</td><td>heterozygous</td><td>p.(Pro154Ser)</td><td>P154S</td><td>tumors</td></tr><tr><td>c.461C&gt;G<sup>a</sup></td><td>homozygous</td><td>p.(Pro154Arg)<sup>a</sup></td><td>P154R<sup>a</sup></td><td>erythrocytosis<sup>a</sup></td></tr><tr><td>c.461C&gt;T</td><td>heterozygous</td><td>p.(Pro154Leu)</td><td>P154L</td><td>tumors</td></tr><tr><td>c.462A&gt;C</td><td>heterozygous</td><td>p.(Pro154-)</td><td>P154P</td><td>tumors</td></tr></tbody></table>

Column 1 specifies the nucleotide changes according to the current nomenclature (sequence encoded by VHL E1-E2-E3). Column 2 indicates their genetic status: homozygous, heterozygous, or compound heterozygous ("comp. heterozygous," meaning that this mutation has been transmitted by one parent. The mutation transmitted by the other parent is detailed in Table S1B). Columns 3 and 4 present the corresponding amino acid (aa) substitutions resulting from these mutations. Column 5 indicates the associated phenotype (either erythrocytosis or tumor formation).

<sup>a</sup>Variants identified in the current study. A version of this table with a color code for visual guidance is provided in Table S1A. Detailed information is given in Table S1B.

## Protein stability is affected by specific mutations in VHL exon 2

Initially, we attempted to establish a functional analysis of HIF-2α degradation by pVHL using a real-time luciferase reporter assay.30 Although this method is sensitive, our results indicate that it was insufficient for distinguishing the loss of function of pVHL variants, particularly those associated with erythrocytosis, which may be considered hypomorphic7,10 (data not shown). During testing, it became obvious that certain mutant proteins required higher amounts of transfected plasmids to reach the same expression levels as WT pVHL. This observation led to further investigation into the impact of mutations on protein stability. To explore this, a cycloheximide chase assay was employed, which inhibits new protein synthesis and allows for the dynamic measurement of the degradation rate of overexpressed pVHL proteins, whether they are WT or mutant forms. The unstable c.485G>T (p. Cys162Phe) (C162F) pVHL mutant was used as a control. We observed a significant decrease in the stability of the p.Pro138Arg pVHL protein (\*\*p ≤ 0.01), with an even more pronounced reduction in stability for the p.Thr133Ile pVHL variant and all Pro154 variants, comparable to the drastic decrease observed in the Cys162Phe mutant (\*\*\*p ≤ 0.0001) (Figures 1 and S2). No significant impact on protein levels was observed for the p.Thr133Ser, p.Pro138Leu, p.Asp143Glu, p.Gly144Arg, p.Gly144Ala, and p.Gly144- Glu pVHL variants. In addition, the loss of stability of the pVHL p.Pro154Arg mutant was assessed by measuring the expression
of endogenous pVHL proteins in LCLs derived from a patient carrying the homozygous mutation. This analysis revealed that the pVHL p.Pro154Arg protein shows a significant reduction in protein abundance over time compared to the WT protein (****$p \le 0.0001$) (Figure S3). These results demonstrate that some mutations in *VHL* exon 2 clearly destabilized the pVHL protein (particularly those targeting proline 154), and others did not despite being associated with clinical symptoms. This observation led us to investigate whether certain mutations could instead impact mRNA splicing, especially those located near exon-intron junctions.

## Study of the impact on splicing of variants located in the last codon of *VHL* exon 2

To establish a reliable tool for measuring the impact of *VHL* variants on splicing, we compared *in silico* computational predictions (SPiP) with *in cellulo* splicing reporter assays (Minigene). Our investigation began with the analysis of five mutations located at the terminal end of exon 2, within or near the consensus splicing donor site (Figure 2). Given that splicing can vary across cell types, we conducted minigene assays in multiple cell lines representing tissues relevant to VHL disease—kidney cell lines (HEK293 and HKC8), as renal cell carcinoma is one of the major manifestations of the syndrome, and erythropoietic or EPO-producing cells (UT7 and Hep3B), due to the frequent association with erythrocytosis— in order to ensure a comprehensive evaluation of splicing outcomes.

Regarding the *VHL* c.460C>T variant, the minigene assays confirmed the SPiP prediction (5%), with no detectable impact on splicing. In contrast, the c.461C>T mutation induced a high rate of exon 2 skipping as shown by the presence of a lower band on the gel in all cell lines (Figure 2B), as predicted by the SPiP tool (69.33%). Concerning the synonymous variant c.462A>C, SPiP predicted a 98% likelihood of splicing alteration due to significant weakening of the canonical donor site of exon 2 (Table 2), which was confirmed by minigene assays. This variant induced a complex splicing pattern combining exon 2 skipping and the use of a cryptic intronic donor splice site located downstream of exon 2, resulting in the inclusion of an additional 40 nt (Figure S4A). Due to the limited resolution of the agarose gel, the slight shift observed in the c.462A>C band required gel purification and sequencing, which confirmed the presence of the 40-nt intronic inclusion (Figure S4B). Both splicing events, exon skipping and activation of the cryptic donor site, were observed in all cell lines except HEK293 cells, in which only the larger band was detected, suggesting a possible cell-specific splicing outcome. The inclusion of 40 nt in the transcript led to the introduction of a premature termination codon (PTC) in *VHL* exon 3. This PTC might lead to the expression of a truncated protein, p.Pro154Profs*31, confirming the severe loss of function associated with this mutation. Regarding the c.460C>G and c.461C>G variants, SPiP predicted a moderate splicing impact, with likelihoods of 30.67% and 43.03%, respectively (Table 2; Figure 2B), but minigene assays showed no splicing deregulation in any of the tested cell lines. To explore this further, RT-PCR was carried out to quantify endogenous *VHL* splicing isoforms in patients' cells. This analysis confirmed that there was no impact on splicing in peripheral blood mononuclear cells (PBMCs) from the patient carrying the c.460C>G variant (data not shown). However, an increase in exon 2 skipping was observed in LCLs derived from the patient carrying the c.461C>G variant in a homozygous state, showing approximately 10% increase in the E1-E3 isoform compared to the WT (Figure 2C). This observation highlights potential limitations in the sensitivity of the minigene assay.
<figure><img src="image_3.png" /><figcaption>Figure 1. Impact of variants on the stability of VHL proteins</figcaption></figure>

HEK293 cells were transfected with different VHL mutants fused to an HA tag followed by a cycloheximide chase assay and subsequent quantitative analysis using western blot with anti-HA antibody. The numbers below the graph indicate the time points (in hours) at which samples were harvested after the addition of cycloheximide. The intensity of bands was quantified using ImageLab software with the results normalized against β-actin levels. The histogram bars represent distinct groups: the control pVHL WT is shown in white, patients with erythrocytosis are depicted in red with corresponding red text, and tumor-associated variants are displayed in gray with black text. Variants that induce a significant loss of stability are underlined in black. Statistical significance of the differences

observed is indicated with *p* values: *** *p* ≤ 0.0001, ** *p* ≤ 0.001, * *p* ≤ 0.01, and \* *p* ≤ 0.05, based on one-way ANOVA with Dunnett's post hoc test. Data are represented as the mean ± standard error of the mean (n = 3).

<table><caption>Table 2. Functional characterization and ACMG classification of variants studied in VHL exon 2</caption><thead><tr><th rowspan="3">Nucleotide position</th><th rowspan="3">Protein position</th><th rowspan="3">Phenotype</th><th colspan="4">Impact on protein</th><th colspan="4">Impact on splicing</th><th rowspan="3">ACMG before functional studies and according to Clingen criteria</th><th rowspan="3">ACMG final classification</th></tr><tr><th colspan="2">In silico</th><th colspan="2">In vitro data</th><th colspan="2">In silico</th><th colspan="2">In vitro data</th></tr><tr><th>gnomAD v3</th><th>MetaDome</th><th>CHX Chase</th><th>SPiP score</th><th>Donor loss</th><th>Splicing</th><th>Splicing results</th></tr></thead><tbody><tr><td>c.397A>T</td><td>T133S</td><td>p.Thr133Ser</td><td>RCC</td><td>0</td><td>0.52: slightly intolerant</td><td>no impact</td><td>7.81%</td><td>0.00</td><td>no impact</td><td>VUS</td><td>VUS</td><td></td></tr><tr><td>c.398C>T</td><td>T133I</td><td>p.Thr133Ile</td><td>erythrocytosis</td><td>0</td><td>0.52: slightly intolerant</td><td>severe impact</td><td>7.81%</td><td>0.00</td><td>no impact</td><td>VUS</td><td>likely pathogenic</td><td></td></tr><tr><td>c.413C>G</td><td>P138R</td><td>p.Pro138Arg</td><td>RCC</td><td>0</td><td>0.91: slightly tolerant</td><td>high impact</td><td>4.35%</td><td>0.00</td><td>no impact</td><td>VUS</td><td>VUS</td><td></td></tr><tr><td>c.413C>T</td><td>P138L</td><td>p.Pro138Leu</td><td>erythrocytosis</td><td>0</td><td>0.91: slightly tolerant</td><td>no impact</td><td>4.35%</td><td>0.09</td><td>moderate impact</td><td>pathogenic</td><td>pathogenic</td><td></td></tr><tr><td>c.414A>G</td><td>P138P</td><td>p.Pro138−</td><td>VHL type 2B</td><td>0</td><td>N/A</td><td>N/A</td><td>4.35%</td><td>0.00</td><td>high impact</td><td>pathogenic</td><td>pathogenic</td><td></td></tr><tr><td>c.429C>T</td><td>D143D</td><td>p.Asp143−</td><td>erythrocytosis</td><td>1.314e-05</td><td>N/A</td><td>N/A</td><td>7.62%</td><td>0.01</td><td>moderate impact</td><td>likely pathogenic</td><td>likely pathogenic</td><td></td></tr><tr><td>c.429C>G</td><td>D143E</td><td>p.Asp143Glu</td><td>Pheo, RCC</td><td>0</td><td>1.04: tolerant</td><td>no impact</td><td>54.00%</td><td>0.23</td><td>severe impact</td><td>VUS</td><td>VUS</td><td></td></tr><tr><td>c.430G>A</td><td>G144R</td><td>p.Gly144Arg</td><td>erythrocytosis</td><td>6.711e-05</td><td>1.11: tolerant</td><td>no impact</td><td>7.62%</td><td>0.36</td><td>moderate impact</td><td>likely pathogenic</td><td>pathogenic</td><td></td></tr><tr><td>c.431G>C</td><td>G144A</td><td>p.Gly144Ala</td><td>cerebellum Hb</td><td>0</td><td>1.11: tolerant</td><td>no impact</td><td>7.62%</td><td>0.11</td><td>moderate impact</td><td>VUS</td><td>likely pathogenic</td><td></td></tr><tr><td>c.431G>A</td><td>G144E</td><td>p.Gly144Glu</td><td>Pheo</td><td>0</td><td>1.11: tolerant</td><td>no impact</td><td>7.62%</td><td>0.01</td><td>no impact</td><td>VUS</td><td>VUS</td><td></td></tr><tr><td>c.460C>G</td><td>P154A</td><td>p.Pro154Ala</td><td>chronic thrombosis</td><td>0</td><td>0.92: slightly tolerant</td><td>severe impact</td><td>30.67%</td><td>0.02</td><td>no impact</td><td>VUS</td><td>VUS</td><td></td></tr><tr><td>c.460C>T</td><td>P154S</td><td>p.Pro154Ser</td><td>VHL type 2B</td><td>0</td><td>0.92: slightly tolerant</td><td>severe impact</td><td>50.05%</td><td>0.03</td><td>no impact</td><td>likely pathogenic</td><td>likely pathogenic</td><td></td></tr><tr><td>c.461C>G</td><td>P154R</td><td>p.Pro154Arg</td><td>erythrocytosis</td><td>0</td><td>0.92: slightly tolerant</td><td>severe impact</td><td>43.04%</td><td>0.04</td><td>moderate impact<sup>‡</sup></td><td>VUS</td><td>pathogenic</td><td></td></tr><tr><td>c.461C>T</td><td>P154L</td><td>p.Pro154Leu</td><td>VHL type 2A</td><td>0</td><td>0.92: slightly tolerant</td><td>severe impact</td><td>69.33%</td><td>0.47</td><td>high impact</td><td>likely pathogenic</td><td>pathogenic</td><td></td></tr><tr><td>c.462A>C</td><td>P154P</td><td>p.Pro154−</td><td>VHL type 2A</td><td>0</td><td>N/A</td><td>N/A</td><td>98.41%</td><td>0.78</td><td>severe impact</td><td>VUS</td><td>pathogenic</td><td></td></tr></tbody></table>

This table summarizes the *In silico* and *In vitro* analyses performed on variants located in exon 2 of the VHL gene. "Frequency" indicates the frequency of the variant in the population. "Protein mutation tolerance" displays the predicted tolerance to as substitutions. "Protein stability" shows the impact on protein stability in HEK293 cells, from no impact to severe impact (Figure 1). "SPiP score" shows predicted impact on splicing. N/A, not applicable. "SpliceAI donor loss" indicates the predicted impact on the canonical donor site of exon 2, with higher score reflecting a greater likelihood of significant weakening or loss of donor site function (A score 0-1, thresholds ≥ 0.2) (0.5 for impact). This may result in aberrant splicing, such as a shift in the splice site usage downstream of exon 2. "Minigene assay" summarizes the impact on splicing across all tested cell lines, providing an overall assessment of the functional consequences on splicing. Arbitrary thresholds were set according to the quantification of the exon 2 skipping rate (Figures 2C and 3C): severe impact (<80%), high impact (80%-50%), moderate impact (50%-20%), and no or low impacts ≥20%.

\*The score derived from RT-qPCR quantification of exon skipping in patients' lymphoblastoid cell lines (LCLs) (Figure 2D). "Criteria" outlines the specific ACMG criteria applied to classify the variant. "ACMG classification" provides the final ACMG classification based on established ACMG guidelines (criteria detailed in supplemental data). A version of this table with a color code for visual guidance is provided in Table S2A. Detailed information is given in Table S2B.

These findings suggest that splicing deregulation, rather than protein instability, may account for the pathogenicity of some exon 2 variants. To further explore this hypothesis, we extended our analysis to variants located in the central region of the exon, where conventional splicing-prediction tools are often less accurate.

## Study of the impact on splicing of variants located in the central region of VHL exon 2

In a next step, we studied a second group of 10 mutations located within the exonic region (Figure 3A). As described above, we conducted *in silico* analyses and performed minigene assays across different cell lines. SPiP predicted a negligible impact on splicing (scores ≤8%)
for all variants but one, the c.429C>G variant that shows a significant risk (54%) for splicing disruption. The minigene assays confirmed a severe exon 2 skipping associated with this variant. However, minigene experiments also revealed an impact on splicing for five additional variants, ranging from moderate (c.413C>T, c.431G>C) to strong effects (c.414A>G, c.429C>G). No impact was observed for the c.397A>T, c.398C>T, c.431C>G, and c.413G>A variants. A similar splicing profile was observed across all cell lines for the entire cluster of variants tested, indicating that the splicing dysregulation caused by these variants is not cell specific (Figures 3B and 3C).

<figure><img src="image_4.png" /><figcaption>**Figure 2. Assessing the splicing impact of variants located in the terminal codon of VHL exon 2: predictions versus experimental data**</figcaption></figure>

<figure><img src="image_5.png" /></figure>

(A) Schematic overview of mutations. VHL gene structure with exons E1, E2, and E3, and zoom-in on the terminal codon of exon 2 to detail the specific nucleotide alterations and their respective aa changes set against a light-gray background representing the intron. The mutations are identified by their nucleotide substitutions and the resulting aa impacts. Variants in red are associated with erythrocytosis and those in black with tumorigenesis. The boxes display graphical representations of the universal donor splicing consensus sequences based on nucleotide frequencies around canonical splice sites, where the size of each letter reflects the frequency of each base at each position across all introns. Bases are arranged by frequency, from highest to lowest, from top to bottom.

<figure><img src="image_6.png" /></figure>

(B) Splicing Prediction Pipeline (SPIP) with minigene splicing assays. The upper section of the figure displays SPIP predictions for the impact of mutations on splicing. The colors associated with SPIP predictions are as follows: green indicates no impact, yellow suggests a moderate impact, orange represents a strong impact, and red signifies a severe impact. Minigene assay conducted in cell lines: HEK293, HKC-B, UT7, Hep3B, and lymphoblastoid cell line (LCL). Splicing deregulation was demonstrated by RT-PCR using primers in synthetic exons A and B of the minigene construct. *The slight shift observed in the c.462A>C mutant band was investigated by gel purification and sequencing of the upper band, which confirmed the inclusion of a 40-nt intronic sequence resulting from the use of a cryptic donor splice site located in intron 2.*

<figure><img src="image_7.png" /></figure>

(C) Quantification of exon 2 skipping observed in minigene experiments performed on the different cell line. Arbitrary

thresholds were set according to the quantification of the exon 2 skipping rate in order of severity: red, exon 2 skipping >80%; orange, 80%-50%; yellow, 50%-20%; green, <20%.

(D) The study of endogenous VHL splicing in the LCL patient line carrying the homozygous mutation c.461C>G was conducted using RT-PCR, revealing a 10% increase in exon 2 skipping. The RT-PCR gel is shown on the left, with band quantification displayed on the right (n = 3).

Statistical significance of the differences observed is indicated with *p* values: """*p* ≤ 0.0001, "**p* ≤ 0.001, "*p* ≤ 0.01, and "*p* ≤ 0.05, based on two-way ANOVA with Sidak post hoc test. Data are represented as the mean ± standard error of the mean.
To better understand why certain centrally located variants impacted splicing despite low prediction scores, we compared results of minigene experiments with SPiP outcomes and investigated whether the observed splicing defects could result from altered interaction with predicted splicing regulatory elements or RBPs.

## Analysis of SPiP predictions and splicing regulatory regions

Comparison of splicing defects observed in minigene experiments with SPiP analysis revealed that SPiP accurately predicted splicing outcomes of variants located near splice donor and acceptor sites typically governed by canonical splicing consensus sequences. Among the five variants tested, a discrepancy was observed for c.461C>G. The analysis of patients' cells revealed a splicing defect for this c.461C>G mutant, confirming the *in silico* data and reducing discrepancies to zero (Figure S5A). In contrast, predictions were less accurate for variants located in the central region of the exon, with 5 out of 10 variants being misinterpreted (Figure S5B).

We hypothesized that exon skipping induced by mutations at neighboring nucleotides 413-414 (aa Pro138) and 429-431 (aa Asp143, Gly144) might result from changes in exonic-splicing regulatory sequences, which remain particularly challenging to predict with current *in silico* tools. To explore this further, we investigated whether mutations at nucleotides 413-414 (aa Pro138) and 429-431 (aa Asp143-Gly144), which are associated with exon skipping, might disrupt binding sites for splicing regulatory RBPs. Specifically, we examined the impact on splicing of the mutations c.413C>T, c.413C>G, and c.414A>G. We first used the RBPmap platform to predict potential RBPs that could bind to the WT and mutated *VHL* exon 2 sequences. This analysis identified RBP binding sites within the sequence located at or near the variants (Figure S6A).

These discrepancies highlighted the limitations of current *in silico* tools in predicting effects mediated through ESEs or ESSs. These findings prompted us to experimentally identify RBPs that differentially interact with WT and mutant sequences to better understand the mechanisms underlying the observed splicing changes.

## Identification of hnRNPF and hnRNPAB as RBPs involved in *VHL* gene splicing

In order to identify RBPs that bind *VHL* exonic sequences, we performed RNA-protein pull-down. *VHL* RNA probes, spanning nucleotides 403-441, were synthesized with both the WT and mutated sequences. These probes were then incubated with nuclear extracts from HeLa cells, and the bound proteins were subsequently identified and quantitatively compared using MS.

MS analysis revealed significant binding of many RBPs to both the *VHL* WT and mutated RNA probes (data deposited; see methods, Figures 4A and S7). After removing background noise generated by the beads alone (control), we listed proteins bound to the RNA probes without binding to the control. RBPs displayed three distinct binding patterns: (1) no difference in binding between the WT and mutated forms, and binding that was either (2) upregulated or (3) downregulated in
the presence of mutations compared to the WT. When we compared the *in silico* predictions of the RBPmap database with the *in vitro* RBP identification results obtained through MS, we found that 12 out of the 17 predicted RBPs were detected: VHL WT (MATR3 and PTBP3), VHL c.413C>G (p.Pro138Arg) (CELF1, FUBP1, PTBP3, and ZC3H14), VHL c.413C>T (p. Pro138Leu) (CELF1, FUBP3, HNRNPDL, KHSRP, ZNF326, PUF60, FUBP1, RC3H1, RBM45, and PTBP3), and VHL c.414A>G (p.Pro138=) (PTBP3 and PUF60) (Figure S6B). Focusing on RBPs involved in splicing (CELF1, KHSRP, ZNF326, and PTBP3), no statistically significant differences in binding (either up or down) were observed across the different variants compared to WT.

<figure><img src="image_8.png" /><figcaption>Figure 3. Assessing the splicing impact of central region of VHL exon 2: predictions versus minigene assays</figcaption></figure>

(A) Structural overview of *VHL* gene mutations in central region of *VHL* exon 2. This panel provides a schematic representation of the *VHL* gene, highlighting exons E1, E2, and E3. A detailed view of exon 2 is presented, showcasing the nucleotide sequence and corresponding aa. Mutations are marked by nucleotide changes leading to specific aa substitutions. Variants implicated in erythrocytosis are marked in red, while those linked to tumors are in black.

<figure><img src="image_9.png" /></figure>

(B) Splicing Prediction Pipeline (SPiP) and minigene splicing assays were performed across five cell lines. Splicing deregulation was demonstrated using primers in exons A and B of the minigene construct by RT-PCR.

(C) Quantification of exon 2 skipping observed in minigene experiments performed on the different cell line. Arbitrary thresholds were set according to the quantification of the exon 2 skipping rate in order of severity: red, exon 2 skipping >80%; orange, 80%–50%; yellow, 50%–20%; green, <20%. Data are represented as the mean ± standard error of the mean.

<figure><img src="image_10.png" /></figure>

We then focused on RBPs that were differentially bound to the probe carrying the *VHL* c.414A>G (p.Pro138=) mutation, which induces the most severe exon 2 skipping.⁹ Significantly deregulated RBPs were cross-referenced with functional annotations from the QuickGO (RNA splicing GO: 0008380), Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG) specifically involved in splicing processes databases, enabling the selection of two candidate RBPs for further characterization based on their potential involvement in splicing regulation. This mutation led to a significant decrease in the binding of hnRNPF (UniProtKB: P52597) (*p* ≤ 0.05) and a significant increase in binding of the
splicing-specific RBP hnRNPAB (UniProtKB: Q99729) (****p ≤ 0.0001) (Figure 4B). Recognition sequence motif for hnRNPF was available, 32 but to our knowledge no such sequence was identified for hnRNPAB. Regarding the binding of hnRNPF to RNA probes, both the WT and the c.414A>G probe contain a segment that matches the hnRNPF motif (Figure S7). To further investigate the role of these targets in VHL splicing, we performed RNA interference experiments by transfecting siRNA in HEK293 cells. The inhibition resulted in a significant reduction in both transcript and protein levels of hnRNPF and hnRNPAB (Figures 4C and 4D). Subsequent analysis of endogenous VHL transcripts was performed using TaqMan probes targeting specific exon-exon junctions, as conventional RT-PCR lacked the sensitivity to detect subtle changes (data not shown). TaqMan-based quantification revealed that siRNA-mediated knockdown of the RBPs had a moderate yet significant impact on VHL expression and/or splicing. A reduction in the E1-E3 isoform level was observed upon hnRNPAB silencing, while knockdown of hnRNPF led to decreased expression of both VHL transcripts (E1E2E3 and E1E3) (Figure 4E).

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>Figure 4. Identification and functional characterization of RBPs differentially binding to VHL WT and c.414A>G RNA probes</figcaption></figure>

(A) Mass spectrometry analysis of proteins fixed to VHL-RNA probes. Volcano plots showed differential protein enrichment across conditions. Each plot displays the negative base-10 logarithm of the *p* value (-log₁₀ *P*) versus the log₂ fold change from pairwise comparisons. The panel compares the WT versus control samples (beads without probes) using one-sided test, and the c.414A>G variant to WT using a two-sided test. Proteins with *p* < 0.01 were considered significantly enriched or depleted. Red points indicate significantly enriched proteins, blue points indicate significantly depleted proteins, and black ones represent predicted protein interaction partners containing RBPmap binding sites (see Figure S6).

(B) Differential binding of hnRNPAB and hnRNPF to VHL WT and c.414A>G RNA probes identified by mass spectrometry. White histograms represent the control (no RNA probe), and black and gray histograms correspond to the VHL WT and c.414A>G probes, respectively. Statistical significance of the differences observed is indicated with *p* values: "***"*p* ≤ 0.0001, "**"*p* ≤ 0.001, "*"*p* ≤ 0.01, and "*"*p* ≤ 0.05, based on one-way ANOVA with Dunnett's post hoc test. Data are represented as the mean ± standard error of the mean (n = 5). (C and D) (C) Impact of siRNA-mediated knockdown of hnRNPF and hnRNPAB in HEK293 cells, with transcript levels quantified by RT-qPCR using SYBR Green (n = 4), and (D) protein levels assessed by western blot analysis (with antibodies specific to respective proteins, upper panels). Protein quantification was performed using ImageLab and normalized on Ponceau S staining of total protein levels (lower panels) (n = 3) (Figure S8).

(E) Quantification of endogenous VHL transcripts, following transfection of siRNA targeting respective RBP, by RT-qPCR using TaqMan probes specific to the VHL E1-E2 or E1-E3 junctions. Statistical significance of the differences observed is indicated with *p* values: "***"*p* ≤ 0.0001, "**"*p* ≤ 0.001, "*"*p* ≤ 0.01, and "*"*p* ≤ 0.05, based on two-way ANOVA with Sidak post hoc test. Data are represented as the mean ± standard error of the mean (n = 4). NT, not transfected.
Discussion

In this study, we conducted a comprehensive and comparative functional analysis of 15 VHL
exon 2 variants, including three variants identified in the present work: c.398C>T
(p.Thr133Ile), c.460C>G (p.Pro154Ala), and c.461C>G (p.Pro154Arg). Our analysis focused
on their impact on both splicing and protein stability. First, using cycloheximide chase assays
and western blot analysis, we observed that the p. Thr133Ile, p.Pro138Arg, and all variants
targeting the Proline154 exhibited a significant loss of protein stability compared to the WT
pVHL. These results demonstrate that different aa change can adversely affect pVHL stability
(p.Thr133Ser/= p.Thr133Ile, p.Pro138Leu/= p.Pro138Arg), and that proline 154 plays a
particularly crucial role in maintaining protein stability. Mechanisms regulating pVHL protein
stability are still debated. Previous studies on the pVHL p.Pro138Arg variant showed that the
degradation of pVHL is mediated by the proteasome.33 It has also been shown that
the β-domain of the pVHL, particularly aa from 136 to 154, is crucial for binding to Tat-
binding protein-1 (TBP-1), a key component of the proteasome.34 The study, performed on
the p.Pro154Leu variant, showed that, although pVHL-Pro154Leu can still form the VBC
(VHL-Elongin B and C) complex and bind to HIF-1α, it fails to degrade HIF-1α effectively
due to its compromised interaction with TBP-1.34 Given the impact of numerous variants
found here, further investigations should be conducted to better understand the mechanisms
governing VHL protein stability.

The *VHL* gene encodes two main transcript isoforms, with the major isoform including exon2, which is essential for pVHL function as it encodes the protein domain responsible for binding to the oxygen degradation domain of HIF-α. Given this critical role, it is imperative to investigate the impact of *VHL* mutations on splicing. Some studies have assessed the functional consequences of a few variants, notably on the c.413C>T,9,35 c.414A>G,9,36,37 c.413C>G,33 and c.462A>C38 mutations. Here, we performed a comparative analysis of a large set of variants in exon 2 to precisely evaluate their impact on splicing and delineate splicing regulatory regions. We initially focused on the genetic variants located at the terminal codon of exon 2 c.460-462, which encodes Proline154. Notably, we found that the terminal sequence of *VHL* exon 2 is totally identical to the consensus splice donor sequence (CAG[GT intron). Our results demonstrated a relationship between the proximity of a variant to the donor site “GT” of exon 2 and its impact on splicing. The closer the mutation is to the donor site and the more it affects highly conserved positions, the more severe the impact on splicing: the impact of mutations at nucleotide c.460 (p. Pro154Ala, p.Pro154Ser) was less pronounced than those at c.461 (p.Pro154Arg, p.Pro154Leu), which in turn were less impactful than the mutation at c.462 (p.Pro154=). Moreover, the impact on splicing becomes more pronounced as the nucleotide substitution diverges from the consensus sequence.39 Specifically, splicing efficiency is higher when the nucleotide at position □ 3 of the donor splice site is C (CAG[GT, WT) followed by G (c.461C>G [p. Pro154Arg]), and then T (c.461C>T [p.Pro154Leu]) (Figure 2A). Note that the results from the minigene assay of variants in the last codon of *VHL* exon 2 (c.462A>C [p.Pro154=]) demonstrate that proximity to the splicing consensus sequence can also cause a shift in the donor splice site. The *in silico* prediction tools, particularly SpliceAI, effectively anticipated this shift, which leads to the insertion of an intronic sequence into the mature transcript and results in the introduction of a PTC in the coding frame. We have previously shown that non-sense mediated mRNA decay (NMD) is activated in *VHL* transcripts harboring premature stop codons.9 However, since the c.462A>C mutation is located downstream of the final exon-exon junction, it is not expected to trigger NMD according to classical rules, although no material was available to experimentally
confirm this. The absence of detectable mutant transcripts in patients' cells carrying the c.462A>C (p.Pro154=) mutation previously described38 may reflect either limitations in detection sensitivity or degradation via alternative NMD mechanisms. In any case, the profound impact of this variant on splicing supports its classification as a pathogenic loss-of-function mutation.

Regarding the variants located in the central region of exon 2, the minigene assay detected splicing impacts in more mutations (six) than predicted by the SPiP tool (one). These results underscore the current lack of comprehensive data on exonic-splicing regulatory sequences and highlight the critical importance of using minigene assays to accurately assess the impact of mutations on splicing. Altogether, our functional studies enabled the addition of a criterion (PS3; Table S2) for ACMG classification and allowed the classification of variants identified in the current study (c.398C>T, c.460C>G, c.461C>G) as well as the reclassification of previously described variants, resulting in a total of five (re)classified VHL mutants (Table 2). Notably, c.398C>T and c.461C>G mutations, which exhibit splicing defects and/or loss of stability, can be classified as likely pathogenic regarding the development of erythrocytosis when present at homozygous state. In addition, our study reclassifies previously reported VUSs as likely pathogenic (c.431G>C [p.Gly144Ala]) and as pathogenic (c.462A>C [p.Pro154=]). Regarding the c.460C>G (p.Pro154Ala) mutation, incidentally, identified at the heterozygous state in a 27-year-old patient who did not exhibit any VHL-associated disease, we found a significant impact on protein stability. This mutation cannot be definitively classified; however, it is very close (in terms of impact on stability and splicing) to the c.460C>T (p.Pro154Ser) identified in a patient with tumors. Therefore, we recommend close clinical monitoring for potential tumor development in this patient.

Despite complex genotype-phenotype correlations in the VHL-associated diseases, our results add arguments in favor of the continuum model for VHL tumor suppression: the stronger the impact of the mutation on loss of VHL function, the more severe the phenotype.10,40 Notably, none of the mutations associated with erythrocytosis exhibited both splicing deregulation and protein instability. For the same impact on protein stability, the p.Pro154Arg variant associated with erythrocytosis showed a lower impact on splicing than other Pro154 mutations, associated with cancer. However, definitive conclusions are best supported by synonymous variants, which affect splicing without altering the aa sequence, thereby avoiding confounding effects on protein structure, function, or interactions. Here, we showed that the mutations associated with the development of tumors (c.414A>G and c.462A>C) have a stronger impact on splicing than the mutant associated with erythrocytosis (c.429C>T).

Our study also sheds new light on the presence of splicing regulatory sequences in VHL exon 2. We aimed to identify RBPs involved in splicing regulation and focused our investigation on the region spanning nucleotides 403–441, which includes the positions 413–414 corresponding to p.Pro138=/Leu/Arg mutants that showed variable impacts on splicing. When comparing computational predictions with experimental results from MS-based identification following the RNA-protein pulldown, we detected 12 out of 17 RNA-binding proteins predicted by RBPmap. However, none of these proteins showed significant deregulation compared to the WT condition.

This highlights the limitations of current prediction models in capturing the full complexity of RNA-protein interactions and underscores the need for more advanced approaches that account for factors beyond simple sequence recognition.
We then focused our analysis on the c.414A>G mutation, which exhibited increased exon skipping compared to its counterparts, c.413C>G and c.413C>T, and identified two RBP candidates for the splicing regulation of exon 2: hnRNPF and hnRNPAB. Pan-cancer analysis in ccRCC showed that hnRNPAB is significantly overexpressed in renal cancer, and hnRNPF has been shown to play a role in many aspects of tumor development,41 which makes them good candidates in the context of VHL disease. In RNA pull-down experiments, we observed that both RBPs bind to the *VHL* WT probe; however, the c.414A>G variant disrupts hnRNPF binding while enhancing hnRNPAB binding. The result of inhibition of hnRNPF expression using siRNA in HEK cells argues for a moderate inhibitory role of hnRNPF in promoting the skipping of exon 2 in *VHL* transcript, in agreement with the literature describing hnRNPs to mostly generate exon skipping.32 With these results, the skipping of exon 2 observed in the mutated context might not be attributable to the reduced hnRNPF binding. However, the siRNA induced a decreased expression of both *VHL* isoforms. This suggests that hnRNPF may contribute to RNA stability, influencing other genes involved in *VHL* splicing or interacting with additional regulatory sequences within the *VHL* gene. In addition, based on the hnRNPF recognition motif,32 potential hnRNPF binding sites are predicted in region c.429-431 whose recruitment may be modified directly or via interactions with other splicing factors.

The inhibition of hnRNPAB specifically decreases the level of the E1E3 isoform, indicating its inhibitory role in exon 2 inclusion. This result suggests that hnRNPAB may bind to an ESS sequence (either newly created or reinforced by the mutation). The identification of such a sequence in *VHL* might indeed offer a promising therapeutic strategy to reduce the levels of E1E3 transcripts, potentially through the use of antisense oligonucleotides (AONs).42 We may then be able to target the sequences responsible for exon 2 skipping and thus achieve optimum splicing of all *VHL* transcripts. Knowing that the E1-E3 isoform is a natural endogenous isoform whose proportion is not negligible in cells, this therapeutic strategy may increase the expression of functional *VHL* from both alleles (if not mutated on the specific ESS). VHL disease could therefore be a new candidate pathology for treatment with AONs designed to increase functional protein levels, as successfully demonstrated for Duchenne muscular dystrophy (DMD [MIM: 310200]) (Eteplirsen), and spinal muscular atrophy (SMA1 [MIM: 253300]) (Nusinersen).43

Taken together, our results highlight the need for integrative approaches combining *in silico* predictions and functional assays to improve the accuracy of variant classification. Notably, these results demonstrate the complexity and challenges of accurately predicting and analyzing splicing anomalies. Our findings reveal the limitations of current predictive tools and underscore the need for more advanced algorithms to accurately model the effects of mutations, particularly in splicing regulatory sequences located at distance from canonical sites. Moreover, while minigene assays are valuable for rapidly assessing splicing variants when patient-derived materials are unavailable, they have also limitations. We identify specific RBPs involved in *VHL* gene splicing revealing regulatory interactions and demonstrate how their dysregulation can significantly affect splicing. This research will help refine splicing-prediction algorithms, enhancing our understanding of the molecular mechanisms governing splicing regulation and improving the accuracy of disease-related splicing variant predictions, ultimately leading to better diagnostic and therapeutic strategies.
**Data and code availability**

All MS raw data and annotated tandem MS (MS/MS) spectra of single-peptide identifications have been deposited to the ProteomeX change Consortium via the PRIDE (PRoteomics IDEntifications Database) partner repository<sup>44</sup> with the dataset identifiers PXD056515.

**Acknowledgments**

This study was supported by grants from the Région des Pays de la Loire (project "EryCan"), the Agence Nationale de la Recherche (ANR-15-CE17-0021-04 "GenRED" and ANR-20-CE14-0043-01 "SplicHypoxia"), the VHL Alliance USA, the VHL France, the Génavie associations, and the Fondation Maladies Rares (FMR). This work was supported by the DIM Thérapie Génique Paris Ile-de-France Région, IBiSA, and the Labex GR-Ex (ANR-18-IDEX-0001).

**Author contributions**

V.K. conducted the *in silico* analyses, minigene assays, protein stability tests, RNA-protein pull-down assays, and siRNA experiments and reviewed the clinical data. L.S. performed minigene variant assays focused on the central region of exon 2 in HEK293, Hep3b, and UT7 cell lines. Y.A. and F.C. carried out stability tests on endogenous pVHL in LCLs. E.-F.G. led the MS team for RBP identification. M.L. performed nLC-MS/MS analyses and statistical analysis of MS data. M.L.G. was responsible for annotating RBPs identified by MS. S.C. provided the HKC-8 cell line used in the study. N.M, B.C., L.L., V.T., S.R., and F.G. contributed to variant identification and analysis, either as clinicians or through the NGS platform. J.M. and S.T.-G. provided critical assistance with the RBP project. B.G. supervised the study, coordinated the research activities, and contributed to manuscript writing and editing. S.I. provided essential support to the research team and contributed to the proofreading and editing of the article.

**Declaration of interests**

The authors declare that there are no conflicts of interest regarding the publication of this study. All research was conducted independently, and no financial or personal relationships influenced the outcomes of this work. Any affiliations, funding sources, or connections that could be perceived to affect the impartiality of the research have been disclosed and appropriately managed in accordance with institutional and ethical guidelines.

**References**
1. Frew, I.J., and Krek, W. (2008). pVHL: a multipurpose adaptor protein. Sci. Signal. 1, pe30. https://doi.org/10.1126/scisignal.124pe30.

2. Jaakkola, P., Mole, D.R., Tian, Y.M., Wilson, M.I., Gielbert, J., Gaskell, S.J., von Kriegsheim, A., Hebestreit, H.F., Mukherji, M., Schofield, C.J., et al. (2001). Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science 292, 468-472. https://doi.org/10.1126/science.1059796.

3. Stebbins, C.E., Kaelin, W.G., and Pavletich, N.P. (1999). Structure of the VHL-ElonginC-ElonginB complex: implications for VHL tumor suppressor function. Science 284, 455-461. https://doi.org/10.1126/science.284.5413.455.

4. Jiang, B.H., Semenza, G.L., Bauer, C., and Marti, H.H. (1996). Hypoxia-inducible factor 1 levels vary exponentially over a physiologically relevant range of O2 tension. Am. J. Physiol. 271, C1172-C1180. https://doi.org/10.1152/ajpcell.1996.271.4.C1172.

5. Maher, E.R., Iselius, L., Yates, J.R., Littler, M., Benjamin, C., Harris, R., Sampson, J., Williams, A., Ferguson-Smith, M.A., and Morton, N. (1991). Von Hippel-Lindau disease: a genetic study. J. Med. Genet. 28, 443-447. https://doi.org/10.1136/jmg.28.7.443.

6. Lonser, R.R., Glenn, G.M., Walther, M., Chew, E.Y., Libutti, S. K., Linehan, W.M., and Oldfield, E.H. (2003). von Hippel-Lindau disease. Lancet 361, 2059-2067. https://doi.org/10.1016/S0140-6736(03)13643-4.

7. Ang, S.O., Chen, H., Hirota, K., Gordeuk, V.R., Jelinek, J., Guan, Y., Liu, E., Sergueeva, A.I., Miasnikova, G.Y., Mole, D., et al. (2002). Disruption of oxygen homeostasis underlies congenital Chuvash polycythemia. Nat. Genet. 32, 614-621. https://doi.org/10.1038/ng1019.

8. Bento, C., Almeida, H., Maia, T.M., Relvas, L., Oliveira, A.C., Rossi, C., Girodon, F., Fernandez-Lago, C., Aguado-Diaz, A., Fraga, C., et al. (2013). Molecular study of congenital erythrocytosis in 70 unrelated patients revealed a potential causal mutation in less than half of the cases (Where is/are the missing gene(s)?). Eur. J. Haematol. 91, 361-368. https://doi.org/10.1111/ejh.12170.

9. Lenglet, M., Robriquet, F., Schwarz, K., Camps, C., Couturier, A., Hoogewijs, D., Buffet, A., Knight, S.J.L., Gad, S., Couvé, S., et al. (2018). Identification of a new VHL exon and complex splicing alterations in familial erythrocytosis or von Hippel-Lindau disease. Blood 132, 469-483. https://doi.org/10.1182/blood-2018-03-838235.

10. Couvé, S., Ladroue, C., Laine, E., Mahtouk, K., Guégan, J., Gad, S., Le Jeune, H., Le Gentil, M., Nuel, G., Kim, W.Y., et al. (2014). Genetic evidence of a precisely tuned dysregulation in the hypoxia signaling pathway during oncogenesis. Cancer Res. 74, 6554-6564. https://doi.org/10.1158/0008-5472.CAN-14-1161.

11. Richard, S., Gardie, B., Couvé, S., and Gad, S. (2013). Von Hippel-Lindau: how a rare disease illuminates cancer biology. Semin. Cancer Biol. 23, 26-37. https://doi.org/10.1016/j.semcancer.2012.05.005.
12. Latif, F., Tory, K., Gnarra, J., Yao, M., Duh, F.M., Orcutt, M.L., Stackhouse, T., Kuzmin, I., Modi, W., Geil, L., et al. (1993). Identification of the von Hippel-Lindau disease tumor suppressor gene. *Science* **260**, 1317–1320.

13. Kibel, A., Iliopoulos, O., DeCaprio, J.A., and Kaelin, W.G. (1995). Binding of the von Hippel-Lindau tumor suppressor protein to Elongin B and C. *Science* **269**, 1444–1446. https://doi.org/10.1126/science.7660130.

14. Iliopoulos, O., Ohh, M., and Kaelin, W.G. (1998). pVHL19 is a biologically active product of the von Hippel-Lindau gene arising from internal translation initiation. *Proc. Natl. Acad. Sci. USA* **95**, 11661–11666. https://doi.org/10.1073/pnas.95.20.11661.

15. Hascoet, P., Chesnel, F., Jouan, F., Le Goff, C., Couturier, A., Darrigrand, E., Mahe, F., Rioux-Leclercq, N., Le Goff, X., and Arlot-Bonnemains, Y. (2017). The pVHL172 isoform is not a tumor suppressor and up-regulates a subset of pro-tumorigenic genes including TGFB1 and MMP13. *Oncotarget* **8**,75989–76002. https://doi.org/10.18632/oncotarget.18376.

16. Black, D.L. (2003). Mechanisms of alternative pre-messenger RNA splicing. *Annu. Rev. Biochem.* **72**, 291–336. https://doi.org/10.1146/annurev.biochem.72.121801.161720.

17. Matlin, A.J., Clark, F., and Smith, C.W.J. (2005). Understanding alternative splicing: towards a cellular code. *Nat. Rev. Mol. Cell Biol.* **6**, 386–398. https://doi.org/10.1038/nrm1645.

18. Fu, X.-D., and Ares, M., Jr. (2014). Context-dependent control of alternative splicing by RNA-binding proteins. *Nat. Rev. Genet.* **15**, 689–701. https://doi.org/10.1038/nrg3778.

19. Zhu, J., Mayeda, A., and Krainer, A.R. (2001). Exon identity established through differential antagonism between exonic splicing silencer-bound hnRNP A1 and enhancer-bound SR proteins. *Mol. Cell* **8**, 1351–1361. https://doi.org/10.1016/s1097-2765(01)00409-9.

20. Beckmann, B.M., Castello, A., and Medenbach, J. (2016). The expanding universe of ribonucleoproteins: of novel RNAbinding proteins and unconventional interactions. *Pflugers Arch.* **468**, 1029–1040. https://doi.org/10.1007/s00424-016-1819-4.

21. Esmaillie, R., Ignarski, M., Bohl, K., Krüger, T., Ahmad, D., Seufert, L., Schermer, B., Benzing, T., Müller, R.-U., and Fabretti, F. (2019). Activation of Hypoxia-Inducible Factor Signaling Modulates the RNA Protein Interactome in Caenorhabditis elegans. *iScience* **22**, 466–476. https://doi.org/10.1016/j.isci.2019.11.039.

22. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W.W., Hegde, M., Lyon, E., Spector, E., et al. (2015). Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. *Genet. Med.* **17**, 405–424. https://doi.org/10.1038/gim.2015.30.

23. Baux, D., Van Goethem, C., Ardouin, O., Guignard, T., Bergougnoux, A., Koenig, M., and Roux, A.-F. (2021). MobiDetails: online DNA variants interpretation. *Eur. J. Hum. Genet.* **29**, 356–360. https://doi.org/10.1038/s41431-020-00755-z.
24. Delamare, M., Le Roy, A., Pacault, M., Schmitt, L., Garrec, C., Maaziz, N., Myllykoski, M., Rimbert, A., Karaghiannis, V., Aral, B., et al. (2023). Characterization of genetic variants in the EGLN1/PHD2 gene identified in a European collection of patients with erythrocytosis. Haematologica 108, 3068–3085. https://doi.org/10.3324/haematol.2023.282913.

25. Gaildrat, P., Killian, A., Martins, A., Tournier, I., Frébourg, T., and Tosi, M. (2010). Use of splicing reporter minigene assay to evaluate the effect on splicing of unclassified genetic variants. Methods Mol. Biol. 653, 249–257. https://doi.org/10.1007/978-1-60761-759-4_15.

26. Mougel, G., Mohamed, A., Burnichon, N., Giraud, S., Pigny, P., Bressac-de Paillerets, B., Mirebeau-Prunier, D., Buffet, A., Savagner, F., Romanet, P., et al. (2024). Update of the UMD-VHL database: classification of 164 challenging variants based on genotype-phenotype correlation among 605 entries. J. Med. Genet. 61, 378–384. https://doi.org/10.1136/jmg-2023-109550.

27. Miro, J., Bourgeois, C.F., Claustres, M., Koenig, M., and Tuffery- Giraud, S. (2018). Identification of Splicing Factors Involved in DMD Exon Skipping Events Using an In Vitro RNA Binding Assay. Methods Mol. Biol. 1687, 157–169. https://doi.org/10.1007/978-1-4939-7374-3_11.

28. Chen, X., Yang, H.T., Zhang, B., Phillips, J.W., Cheng, D., Rigo, F., Witte, O.N., Xing, Y., and Black, D.L. (2023). The RNA-binding proteins hnRNP H and F regulate splicing of a MYC-dependent HRAS exon in prostate cancer cells. Proc. Natl. Acad. Sci. USA 120, e2220190120. https://doi.org/10.1073/pnas.2220190120.

29. Wang, X., Lin, L., Zhong, Y., Feng, M., Yu, T., Yan, Y., Zhou, J., and Liao, M. (2021). Cellular hnRNPAB binding to viral nucleoprotein inhibits flu virus replication by blocking nuclear export of viral mRNA. iScience 24, 102160. https://doi.org/10.1016/j.isci.2021.102160.

30. Karaghiannis, V., Maric, D., Garrec, C., Maaziz, N., Buffet, A., Schmitt, L., Antunes, V., Airaud, F., Aral, B., Le Roy, A., et al. (2023). Comprehensive in silico and functional studies for classification of EPAS1/HIF2A genetic variants identified in patients with erythrocytosis. Haematologica 108, 1652–1666. https://doi.org/10.3324/haematol.2022.281698.

31. de Sainte Agathe, J.-M., Filser, M., Isidor, B., Besnard, T., Gueguen, P., Perrin, A., Van Goethem, C., Verebi, C., Masingue, M., Rendu, J., et al. (2023). SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation. Hum. Genomics 17, 7. https://doi.org/10.1186/s40246-023-00451-1.

32. Huelga, S.C., Vu, A.Q., Arnold, J.D., Liang, T.Y., Liu, P.P., Yan, B.Y., Donohue, J.P., Shiue, L., Hoon, S., Brenner, S., et al. (2012). Integrative genome-wide analysis reveals cooperative regulation of alternative splicing by hnRNP proteins. Cell Rep. 1, 167–178. https://doi.org/10.1016/j.celrep.2012.02.001.
33. Mathó, C., Fernández, M.C., Bonanata, J., Liu, X.-D., Martin, A., Vieites, A., Sansó, G., Barontini, M., Jonasch, E., Coitiño, E.L., and Pennisi, P.A. (2022). VHL-P138R and VHL-L163R Novel Variants: Mechanisms of VHL Pathogenicity Involving HIF-Dependent and HIF-Independent Actions. *Front. Endocrinol.* *13*, 854365. https://doi.org/10.3389/fendo.2022.854365.

34. Corn, P.G., McDonald, E.R., Herman, J.G., and El-Deiry, W.S. (2003). Tat-binding protein-1, a component of the 26S proteasome, contributes to the E3 ubiquitin ligase function of the von Hippel–Lindau protein. *Nat. Genet.* *35*, 229–237. https://doi.org/10.1038/ng1254.

35. Lanikova, L., Lorenzo, F., Yang, C., Vankayalapati, H., Drachtman, R., Divoky, V., and Prchal, J.T. (2013). Novel homozygous VHL mutation in exon 2 is associated with congenital polycythemia but not with cancer. *Blood* *121*,3918–3924.

36. Flores, S.K., Cheng, Z., Jasper, A.M., Natori, K., Okamoto, T., Tanabe, A., Gotoh, K., Shibata, H., Sakurai, A., Nakai, T., et al. (2019). Synonymous but Not Silent: A Synonymous VHL Variant in Exon 2 Confers Susceptibility to Familial Pheochromocytoma and von Hippel-Lindau Disease. *J. Clin. Endocrinol. Metab.* *104*, 3826–3834. https://doi.org/10.1210/jc.2019-00235.

37. Liu, F., Calhoun, B., Alam, M.S., Sun, M., Wang, X., Zhang, C., Haldar, K., and Lu, X. (2020). Case report: a synonymous VHL mutation (c.414A > G, p.Pro138Pro) causes pathogenic familial hemangioblastoma through dysregulated splicing. *BMC Med. Genet.* *21*, 42. https://doi.org/10.1186/s12881-020-0976-7.

38. Martella, M., Salviati, L., Casarin, A., Trevisson, E., Opocher, G., Polli, R., Gross, D., and Murgia, A. (2006). Molecular analysis of two uncharacterized sequence variants of the VHL gene. *J. Hum. Genet.* *51*, 964–968. https://doi.org/10.1007/s10038-006-0054-9.

39. Hernando, C.E., Sanchez, S.E., Mancini, E., and Yanovsky, M. J. (2015). Genome wide comparative analysis of the effects of PRMT5 and PRMT4/CARM1 arginine methyltransferases on the Arabidopsis thaliana transcriptome. *BMC Genom.* *16*,192. https://doi.org/10.1186/s12864-015-1399-2.

40. Berger, A.H., Knudson, A.G., and Pandolfi, P.P. (2011). A continuum model for tumour suppression. *Nature* *476*, 163–169. https://doi.org/10.1038/nature10275.

41. Li, J., Pan, T., Chen, L., Wang, Q., Chang, Z., Zhou, W., Li, X., Xu, G., Li, X., Li, Y., and Zhang, Y. (2021). Alternative splicing perturbation landscape identifies RNA binding proteins as potential therapeutic targets in cancer. *Mol. Ther. Nucleic Acids* *24*, 792–806. https://doi.org/10.1016/j.omtn.2021.04.005.

42. Quemener, A.M., Bachelot, L., Forestier, A., Donnou-Fournet, E., Gilot, D., and Galibert, M.-D. (2020). The powerful world of antisense oligonucleotides: From bench to bedside. *Wiley Interdiscip. Rev. RNA* *11*, e1594. https://doi.org/10.1002/wrna.1594.

43. Kuijper, E.C., Bergsma, A.J., Pijnappel, W.W.M.P., and Aartsma-Rus, A. (2021). Opportunities and challenges for antisense oligonucleotide therapies. *J. Inherit. Metab. Dis.* *44*, 72–87. https://doi.org/10.1002/jimd.12251.
44. Perez-Riverol, Y., Bai, J., Bandla, C., García-Seisdedos, D., Hewapathirana, S., Kamatchinathan, S., Kundu, D.J., Prakash, A., Frericks-Zipper, A., Eisenacher, M., et al. (2022). The PRIDE database resources in 2022: a hub for mass spectrometry- based proteomics evidences. Nucleic Acids Res. 50, D543–D552.
https://doi.org/10.1093/nar/gkab1038.