Pathogenic signal peptide variants in the human genome

Author:

Gutierrez Guarnizo Sneider Alexander1,Kellogg Morgana K1,Miller Sarah C1,Tikhonova Elena B1,Karamysheva Zemfira N2,Karamyshev Andrey L1ORCID

Affiliation:

1. Department of Cell Biology and Biochemistry, Texas Tech University Health Sciences Center , Lubbock , TX 79430 , USA

2. Department of Biological Sciences, Texas Tech University , Lubbock , TX 79409 , USA

Abstract

Abstract Secreted and membrane proteins represent a third of all cellular proteins and contain N-terminal signal peptides that are required for protein targeting to endoplasmic reticulum (ER). Mutations in signal peptides affect protein targeting, translocation, processing, and stability, and are associated with human diseases. However, only a few of them have been identified or characterized. In this report, we identified pathogenic signal peptide variants across the human genome using bioinformatic analyses and predicted the molecular mechanisms of their pathology. We recovered more than 65 thousand signal peptide mutations, over 11 thousand we classified as pathogenic, and proposed framework for distinction of their molecular mechanisms. The pathogenic mutations affect over 3.3 thousand genes coding for secreted and membrane proteins. Most pathogenic mutations alter the signal peptide hydrophobic core, a critical recognition region for the signal recognition particle, potentially activating the Regulation of Aberrant Protein Production (RAPP) quality control and specific mRNA degradation. The remaining pathogenic variants (about 25%) alter either the N-terminal region or signal peptidase processing site that can result in translocation deficiencies at the ER membrane or inhibit protein processing. This work provides a conceptual framework for the identification of mutations across the genome and their connection with human disease.

Funder

National Institute of General Medical Sciences

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Applied Mathematics,Computer Science Applications,Genetics,Molecular Biology,Structural Biology

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