Speakers
Description
Protein-protein interaction (PPI) networks are an important resource in systems biology. PPI interactions are identified in tedious experiments. Due to the high number of possible interactions, efforts are limited to testing only major protein isoforms, hence neglecting the considerable influence of Alternative splicing (AS) on the interactome.
To close this gap, we developed DIGGER (Domain Interaction Graph Guided ExploreR), a user-friendly database and web tool to explore the functional impact of AS in human protein interactions (Louadi et al. 2020). DIGGER integrates the interactome from BioGRID with DDIs of Pfam domains reported by DOMINE and 3did. Notably, none of the existing resources annotate individual exons, which is a prerequisite to studying the consequence of AS on DDIs. To mitigate this, DIGGER provides a unique mapping of interface residues of interacting proteins to exons based on experimentally resolved structures in the Protein Data Bank (PDB). We generated a PPI network resolving interactions on a residue-specific level, i.e., for each protein residue on an interaction interface, we derived information on all residues from the interacting protein that is in contact with it. In this way, genomic information on a splicing event can be directly mapped onto three-dimensional protein structures and the impact of the AS event on the PPI interface can be assessed.
Through DIGGER’s user-friendly web interface (https://exbio.wzw.tum.de/digger), researchers can interactively visualize the domain composition for any protein isoform, with detailed information of the interacting domains between the selected protein and of its partners in the PPI network.
To leverage the joint PPI and DDI network in DIGGER for studying the consequences of AS across two or more conditions, we further developed the python tool NEASE (Louadi et al. 2021) (Network Enrichment method for Alternative Splicing Events, https://github.com/louadi/NEASE). The classical approach for studying differential alternative splicing focuses on alternatively spliced genes, thus neglecting the functional consequences on the network level. In contrast, NEASE considers interactions affected by AS and identifies enriched pathways based on affected edges rather than affected genes. Our analysis shows that NEASE outperforms classic gene set enrichment in the context of AS.
The DIGGER database and NEASE tool together provide an unprecedented opportunity to understand the functional impact of tissue-, developmental- and disease-specific AS in a system biology manner.
References:
Louadi, Zakaria, Maria L. Elkjaer, Melissa Klug, Chit Tong Lio, Amit Fenn, Zsolt Illes, Dario Bongiovanni, et al. 2021. “Functional Enrichment of Alternative Splicing Events with NEASE Reveals Insights into Tissue Identity and Diseases.” Genome Biology 22 (1): 327.
Louadi, Zakaria, Kevin Yuan, Alexander Gress, Olga Tsoy, Olga V. Kalinina, Jan Baumbach, Tim Kacprowski, and Markus List. 2020. “DIGGER: Exploring the Functional Role of Alternative Splicing in Protein Interactions.” Nucleic Acids Research, September. https://doi.org/10.1093/nar/gkaa768.
