Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Quantifying individual transcript isoform expression is challenging, particularly in diseases like cancer and neurodegeneration with high isoform diversity.
• Existing methods for analyzing differential splicing (exon-, isoform-, and event-based) have limitations in sensitivity.

Purpose of the Study:

• Introduce a novel junction-based method, differential splicing frequency analysis (DSFA), for sensitive detection of differential splicing using RNA-seq data.
• Apply DSFA to Alzheimer's disease (AD)-associated genes to identify and characterize major APP gene isoforms and their expression patterns.

Main Methods:

• Developed and applied differential splicing frequency analysis (DSFA), a method quantifying splice junction usage.
• Utilized large-scale RNA-seq data mining for analyzing APP gene isoforms in various cellular contexts.
• Investigated the role of U1 snRNA in regulating APP splice junction usage in human embryonic stem cell (hESC)-derived neurons.

Main Results:

• Established APP770, APP751, APP695, and APP752 as major APP gene isoforms.
• Identified APP752 isoform specificity in immune cells and its increased proportion during iPSC differentiation into microglia.
• Observed APP751 isoform predominance in cancer and immune cells, and APP695 isoform increase during iPSC differentiation into neurons.
• Detected differentially expressed splice junctions APP/58417N (human) and App/52804N (mouse).
• Demonstrated that U1 snRNA overexpression decreases APP/58417N usage in neurons, mirroring AD sample effects.

Conclusions:

• DSFA offers a more sensitive approach for detecting differential splicing compared to existing methods.
• Major gene isoforms can exhibit distinct tissue and cell-type specific expression patterns.
• Findings provide insights into APP isoform regulation and potential roles in AD and other conditions.