RESUMO
Lysine acetylation is a reversible post-translational modification (PTM) involved in multiple physiological functions. Recent studies have demonstrated the involvement of protein acetylation in modulating the biology of Schwann cells (SCs) and regeneration of the peripheral nervous system (PNS). However, the mechanisms underlying these processes remain partially understood. Here, we characterized the acetylome of the mouse sciatic nerve (SN) and investigated the cellular distribution of acetylated proteins. We identified 483 acetylated proteins containing 1442 acetylation modification sites in the SN of adult C57BL/6 mice. Bioinformatics suggested that these acetylated SN proteins were mainly located in the myelin sheath, mitochondrial inner membrane, and cytoskeleton, and highlighted the significant differences between the mouse SN and brain acetylome. Manual annotation further indicated that most acetylated proteins (> 45%) were associated with mitochondria, energy metabolism, and cytoskeleton and cell adhesion. We verified three newly discovered acetylation-modified proteins, including neurofilament light polypeptide (NEFL), neurofilament medium/high polypeptide (NFM/H), and periaxin (PRX). Immunofluorescence illustrated that the acetylated proteins, including acetylated alpha-tubulin, were mainly co-localized with S100-positive SCs. Herein, we provided a comprehensive acetylome for the mouse SN and demonstrated that acetylated proteins in the SN were predominantly located in SCs. These results will extend our understanding and promote further study of the role and mechanism of protein acetylation in SC development and PNS regeneration.
Assuntos
Lisina , Processamento de Proteína Pós-Traducional , Acetilação , Animais , Lisina/metabolismo , Camundongos , Camundongos Endogâmicos C57BL , Proteoma/metabolismo , Nervo Isquiático/metabolismoRESUMO
High-throughput sequencing is gaining popularity in clinical diagnoses, but more and more novel gene variants with unknown clinical significance are being found, giving difficulties to interpretations of people's genetic data, precise disease diagnoses, and the making of therapeutic strategies and decisions. In order to solve these issues, it is of critical importance to figure out ways to analyze and interpret such variants. In this work, BRCA1 gene variants with unknown clinical significance were identified from clinical sequencing data, and then, we developed machine learning models so as to predict the pathogenicity for variants with unknown clinical significance. Through performance benchmarking, we found that the optimized random forest model scored 0.85 in area under receiver operating characteristic curve, which outperformed other models. Finally, we applied the best random forest model to predict the pathogenicity of 6321 BRCA1 variants from both sequencing data and ClinVar database. As a result, we obtained the predictive pathogenic risks of BRCA1 variants of unknown significance.