Abstract
Purpose :
To assess the pathogenicity of five new variants of the BEST-1 gene associated with a Best vitelliform macular dystrophy (BVMD) phenotype. BEST1 variants were analysed for potential pathogenicity by in silico evaluation and correlated to the underlying molecular mechanisms involved in the dysfunction of the encoded bestrophin-1 protein, a calcium-activated chlorine channel (CaCC).
Methods :
57 patients from different Italian hospitals with clinical features of BVMD were evaluated. All patients underwent a complete ophthalmic examination, including best corrected visual acuity (BCVA), anterior and posterior segment examination and imaging. Imaging included fundus photography, infrared reflectance (at 811 nm excitation wavelength), fundus autofluorescence ((at 488 nm excitation wavelength)) and spectral domain optical coherence tomography (SD-OCT, Heidelberg Engineering, Heidelberg, Germany). All patients and 18 relatives underwent genetic testing. Five new variants of the BEST-1 gene were identified and characterized by molecular modelling. CaCCs functionality was estimated computing calcium binding energies (ΔΔEdim and ΔΔECabin) of two bestrophin-1 subunits bearing a mutation. Computational analysis was used to assess the potential pathogenicity of each mutations as a deviation from the native protein functionality.
Results :
Novel BEST-1 gene variants [p.(Val9Gly), p.(Ser108Arg), p.(Asn179Asp), p.(Trp182Arg) and p.(Glu292Gln)] lead to amino acid replacements and consequent impairment of the channel function. Out of the new BEST1 mutations reported in this work, three variants [p.(Val9Gly), p.(Asn179Asp) and p.(Glu292Gln)] seem to affect the Ca2+ ion binding and the calcium-dependent CaCC channel activation. The remaining two variants [p.(Ser108Arg) and p.(Trp182Arg)] seem to affect the channel formation and the pore shape.
Variable penetrance and disease expressivity in patients with the same BEST1 variant was also assessed and confirmed.
Conclusions :
Pathogenicity of the new variants detected was confirmed by computational analysis. This approach may give us an interesting insight into the molecular basis of genetic diseases in order to better understand the correlated clinical phenotypes.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.