Design of a stable human acid-β-glucosidase: towards improved Gaucher disease therapy and mutation classification

0570872 - ÚFCH JH 2024 RIV GB eng J - Článek v odborném periodiku
Pokorná, Šárka - Khersonsky, O. - Lipsh-Sokolik, R. - Goldenzweig, A. - Nielsen, R. - Ashani, Y. - Peleg, Y. - Unger, T. - Albeck, S. - Dym, O. - Tirosh, A. - Tarayra, R. - Hocquemiller, M. - Laufer, R. - Ben-Dor, S. - Silman, I. - Sussman, J. L. - Fleishman, S. J. - Futerman, A. H.
Design of a stable human acid-β-glucosidase: towards improved Gaucher disease therapy and mutation classification.
FEBS Journal. Roč. 290, č. 13 (2023), s. 3383-3399. ISSN 1742-464X. E-ISSN 1742-4658
Institucionální podpora: RVO:61388955
Klíčová slova: Gaucher disease * gene therapy * in silico mutation classification * pross * Rosetta * SNPs
Obor OECD: Physical chemistry
Impakt faktor: 5.4, rok: 2022
Způsob publikování: Open access

Acid-β-glucosidase (GCase, EC3.2.1.45), the lysosomal enzyme which hydrolyzes the simple glycosphingolipid, glucosylceramide (GlcCer), is encoded by the GBA1 gene. Biallelic mutations in GBA1 cause the human inherited metabolic disorder, Gaucher disease (GD), in which GlcCer accumulates, while heterozygous GBA1 mutations are the highest genetic risk factor for Parkinson's disease (PD). Recombinant GCase (e.g., Cerezyme®) is produced for use in enzyme replacement therapy for GD and is largely successful in relieving disease symptoms, except for the neurological symptoms observed in a subset of patients. As a first step toward developing an alternative to the recombinant human enzymes used to treat GD, we applied the PROSS stability-design algorithm to generate GCase variants with enhanced stability. One of the designs, containing 55 mutations compared to wild-type human GCase, exhibits improved secretion and thermal stability. Furthermore, the design has higher enzymatic activity than the clinically used human enzyme when incorporated into an AAV vector, resulting in a larger decrease in the accumulation of lipid substrates in cultured cells. Based on stability-design calculations, we also developed a machine learning-based approach to distinguish benign from deleterious (i.e., disease-causing) GBA1 mutations. This approach gave remarkably accurate predictions of the enzymatic activity of single-nucleotide polymorphisms in the GBA1 gene that are not currently associated with GD or PD. This latter approach could be applied to other diseases to determine risk factors in patients carrying rare mutations.
Trvalý link: https://hdl.handle.net/11104/0342198