A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay

Biomolecules. 2022 Nov 6;12(11):1643. doi: 10.3390/biom12111643.

Abstract

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

Keywords: PPI interface; PsymuKB; de novo mutation; developmental delay; protein interactome; protein-protein interaction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Mutation*
  • Protein Interaction Domains and Motifs* / genetics

Grants and funding

The study was supported by grants from the 2030 Science and Technology Innovation Key Program of the Ministry of Science and Technology of China (No. 2022ZD020910001); The National Natural Science Foundation of China (No: 81971292, 82150610506, 12104088); Natural Science Foundation of Shanghai (No: 21ZR1428600); The Medical-Engineering Cross Foundation of Shanghai Jiao Tong University (No. YG2022ZD026); Major Special Program Grant of Shanghai Municipality (Grant #2017SHZDZX01) (to Weihai Ying).