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1.
Glycobiology ; 34(11)2024 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-39360848

RESUMEN

SRD5A3-CDG is a congenital disorder of glycosylation (CDG) resulting from pathogenic variants in SRD5A3 and follows an autosomal recessive inheritance pattern. The enzyme encoded by SRD5A3, polyprenal reductase, plays a crucial role in synthesizing lipid precursors essential for N-linked glycosylation. Despite insights from functional studies into its enzymatic function, there remains a gap in understanding global changes in patient cells. We sought to identify N-glycoproteomic and proteomic signatures specific to SRD5A3-CDG, potentially aiding in biomarker discovery and advancing our understanding of disease mechanisms. Using tandem mass tag (TMT)-based relative quantitation, we analyzed fibroblasts derived from five patients along with control fibroblasts. N-glycoproteomics analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) identified 3,047 glycopeptides with 544 unique N-glycosylation sites from 276 glycoproteins. Of these, 418 glycopeptides showed statistically significant changes with 379 glycopeptides decreased (P < 0.05) in SRD5A3-CDG patient-derived samples. These included high mannose, complex and hybrid glycan-bearing glycopeptides. High mannose glycopeptides from protocadherin Fat 4 and integrin alpha-11 and complex glycopeptides from CD55 were among the most significantly decreased glycopeptides. Proteomics analysis led to the identification of 5,933 proteins, of which 873 proteins showed statistically significant changes. Decreased proteins included cell surface glycoproteins, various mitochondrial protein populations and proteins involved in the N-glycosylation pathway. Lysosomal proteins such as N-acetylglucosamine-6-sulfatase and procathepsin-L also showed reduced levels of phosphorylated mannose-containing glycopeptides. Our findings point to disruptions in glycosylation pathways as well as energy metabolism and lysosomal functions in SRD5A3-CDG, providing clues to improved understanding and management of patients with this disorder.


Asunto(s)
3-Oxo-5-alfa-Esteroide 4-Deshidrogenasa , Trastornos Congénitos de Glicosilación , Fibroblastos , Proteínas de la Membrana , Proteómica , Humanos , Fibroblastos/metabolismo , Proteínas de la Membrana/metabolismo , Proteínas de la Membrana/genética , Proteínas de la Membrana/deficiencia , 3-Oxo-5-alfa-Esteroide 4-Deshidrogenasa/metabolismo , 3-Oxo-5-alfa-Esteroide 4-Deshidrogenasa/genética , 3-Oxo-5-alfa-Esteroide 4-Deshidrogenasa/deficiencia , Trastornos Congénitos de Glicosilación/metabolismo , Trastornos Congénitos de Glicosilación/genética , Trastornos Congénitos de Glicosilación/patología , Glicosilación , Glicoproteínas/metabolismo , Glicoproteínas/genética , Espectrometría de Masas en Tándem
2.
Int J Mol Sci ; 23(15)2022 Aug 05.
Artículo en Inglés | MEDLINE | ID: mdl-35955863

RESUMEN

Advances in research have boosted therapy development for congenital disorders of glycosylation (CDG), a group of rare genetic disorders affecting protein and lipid glycosylation and glycosylphosphatidylinositol anchor biosynthesis. The (re)use of known drugs for novel medical purposes, known as drug repositioning, is growing for both common and rare disorders. The latest innovation concerns the rational search for repositioned molecules which also benefits from artificial intelligence (AI). Compared to traditional methods, drug repositioning accelerates the overall drug discovery process while saving costs. This is particularly valuable for rare diseases. AI tools have proven their worth in diagnosis, in disease classification and characterization, and ultimately in therapy discovery in rare diseases. The availability of biomarkers and reliable disease models is critical for research and development of new drugs, especially for rare and heterogeneous diseases such as CDG. This work reviews the literature related to repositioned drugs for CDG, discovered by serendipity or through a systemic approach. Recent advances in biomarkers and disease models are also outlined as well as stakeholders' views on AI for therapy discovery in CDG.


Asunto(s)
Trastornos Congénitos de Glicosilación , Inteligencia Artificial , Biomarcadores , Trastornos Congénitos de Glicosilación/genética , Reposicionamiento de Medicamentos , Humanos , Enfermedades Raras
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