Evolutionary rate covariation is pervasive between glycosylation pathways and points to potential disease modifiers

• Published in: PLoS genetics Vol. 20; no. 9; p. e1011406
• Main Authors: Thorpe, Holly J, Partha, Raghavendran, Little, Jordan, Clark, Nathan L, Chow, Clement Y
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.09.2024
• Subjects: Analysis; Biology and Life Sciences; Diseases; Engineering and Technology; Enzymes; Evolution; Genetic aspects; Genetic research; Genomics; Glycosylation; Identification and classification; Medicine and Health Sciences; Physiological aspects; Proteins; Research and Analysis Methods; United States
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1011406

Mutations in glycosylation pathways, such as N-linked glycosylation, O-linked glycosylation, and GPI anchor synthesis, lead to Congenital Disorders of Glycosylation (CDG). CDG typically present with seizures, hypotonia, and developmental delay but display large clinical variability with symptoms affecting every system in the body. This variability suggests modifier genes might influence the phenotypes. Because of the similar physiology and clinical symptoms, there are likely common genetic modifiers between CDG. Here, we use evolution as a tool to identify common modifiers between CDG and glycosylation genes. Protein glycosylation is evolutionarily conserved from yeast to mammals. Evolutionary rate covariation (ERC) identifies proteins with similar evolutionary rates that indicate shared biological functions and pathways. Using ERC, we identified strong evolutionary rate signatures between proteins in the same and different glycosylation pathways. Genome-wide analysis of proteins showing significant ERC with GPI anchor synthesis proteins revealed strong signatures with ncRNA modification proteins and DNA repair proteins. We also identified strong patterns of ERC based on cellular sub-localization of the GPI anchor synthesis enzymes. Functional testing of the highest scoring candidates validated genetic interactions and identified novel genetic modifiers of CDG genes. ERC analysis of disease genes and biological pathways allows for rapid prioritization of potential genetic modifiers, which can provide a better understanding of disease pathophysiology and novel therapeutic targets.