Genotype-phenotype relations of the von Hippel-Lindau tumor suppressor inferred from a large-scale analysis of disease mutations and interactors

Abstract

Familiar cancers represent a privileged point of view for studying the complex cellular events inducing tumor transformation. Von Hippel-Lindau syndrome, a familiar predisposition to develop cancer is a clear example. Here, we present our efforts to decipher the role of von Hippel-Lindau tumor suppressor protein (pVHL) in cancer insurgence. We collected high quality information about both pVHL mutations and interactors to investigate the association between patient phenotypes, mutated protein surface and impaired interactions. Our data suggest that different phenotypes correlate with localized perturbations of the pVHL structure, with specific cell functions associated to different protein surfaces. We propose five different pVHL interfaces to be selectively involved in modulating proteins regulating gene expression, protein homeostasis as well as to address extracellular matrix (ECM) and ciliogenesis associated functions. These data were used to drive molecular docking of pVHL with its interactors and guide Petri net simulations of the most promising alterations. We predict that disruption of pVHL association with certain interactors can trigger tumor transformation, inducing metabolism imbalance and ECM remodeling. Collectively taken, our findings provide novel insights into VHL-associated tumorigenesis. This highly integrated in silico approach may help elucidate novel treatment paradigms for VHL disease.

The Genotype-Phenotype Association of Von Hipple Lindau Disease Based on Mutation Locations: A Retrospective Study of 577 Cases in a Chinese Population

“…So, the classification of mutations based on the VHL-HIF pathway is not suitable for all VHL-associated phenotypes. Mutations at different locations are likely to change the intrinsic structure and interfaces of the VHL protein (Minervini et al, 2019), resulting in deregulation of the corresponding pathways and tumorigenesis. Further studies should classify mutations based on their effects on the structure and functions of the VHL protein rather than merely on the basis of mutation locations.…”

Algorithmic assessment of missense mutation severity in the Von-Hippel Lindau protein

“…An additional set of 1380 mutations was generated to represent all possible theoretical missense mutations (APMM) of VHL from a SNP ( S1 File ). Finally, hot spot mutations and mutation lists associated with different pathogenic outcomes were selected from the literature [ 26 , 29 , 33 – 35 ] ( S3 File ). A total of 1665 mutations were therefore used in our multiparametric analysis.…”

“…PTMs confer additional specificity to the overall structure and function of a given protein, and contribute to the ability of a protein to interact with different binding partners [56][57][58]. To assess the specific roles of PTMs in our algorithmic assessment of VHL disease, missense mutations that occurred at a position known to be post-translationally modified were positively scored [33].…”

“…domain, the β-domain, and the N-terminal coil region [33]. We determined that mutations scored higher if they occurred in the α and β domains compared to the N-terminus ( Fig 2B).…”

“…In this study, we created an advanced weighted-score multiparametric approach to validate the use of a computational algorithm to assess the potential disease severity of genetic missense mutations in pVHL. We focused not only on mutations that can affect the overall proteostasis of pVHL, but also noted the specific mutations that would impact VHL-specific functional properties [ 28 , 29 , 33 ]. Our multiparametric algorithm for VHL included a set of eight biophysical parameters with individually weighted scores that gave an overall assessment of the ability of a given missense mutation in VHL to result in protein impairment: 1. aggregation propensity; 2. protein-protein interactions; 3. secondary structure; 4. conformational flexibility; 5. solvent accessibility; 6. protein stability; 7. post-translational modifications, and 8. translational rate [ 9 , 14 , 18 , 32 ].…”