Computational extraction of a neural molecular network through alternative splicing

Area of Science:

• Molecular Biology
• Bioinformatics
• Neuroscience

Background:

• Alternative splicing significantly impacts mammalian protein function, yet its effects on molecular networks are understudied.
• Current bioinformatics analyses often overlook functional changes caused by alternative exons altering protein domains.

Purpose of the Study:

• To develop and validate a computational approach for analyzing functional changes induced by alternative exons.
• To explore molecular networks affected by alternative splicing during neuronal cell differentiation.

Main Methods:

• Analyzed 262 differentially alternatively spliced exons from neuronal differentiation data.
• Performed conserved domain searches to identify changes in protein domains.
• Utilized pathway analysis on genes with altered functional domains.

Main Results:

• Identified 49 genes with alternatively spliced exons affecting their primary functions.
• Pathway analysis revealed frequent involvement of the Epidermal Growth Factor Receptor (EGFR) and Mammalian Target of Rapamycin (mTOR) signaling pathways.
• The findings align with existing research linking the mTOR pathway to neuronal differentiation.

Conclusions:

• A more complex bioinformatics approach is needed for exons compared to genes due to functional domain alterations.
• The developed method effectively extracts alternative splicing networks and identifies key affected pathways like EGFR and mTOR.
• This in silico analysis of alternative splicing provides valuable biological insights into neuronal differentiation.