Phenotype-Dependent Coexpression Gene Clusters: Application to Normal and Premature Ageing

Area of Science:

• Genetics
• Molecular Biology
• Computational Biology

Background:

• Hutchinson Gilford progeria syndrome (HGPS) causes premature aging with unclear molecular underpinnings.
• Gene expression changes in HGPS show overlaps with normal aging.
• Limited sample sizes hinder standard regression analysis for identifying aging-associated genes.

Purpose of the Study:

• To develop and apply a novel computational approach for identifying gene expression patterns associated with normal aging and HGPS.
• To investigate the biological processes underlying normal aging and HGPS by comparing gene expression profiles.

Main Methods:

• Developed an iterative multiple regression approach leveraging co-expressed gene clusters.
• Applied the method to RNA-seq data from HGPS patient and normal fibroblast cell cultures at different ages.
• Performed comparative analysis of biological processes in aging and HGPS.

Main Results:

• The novel regression approach effectively identifies gene clusters co-varying with age and/or HGPS.
• Results confirm known aging processes and suggest novel biological pathways specific to aging and HGPS.
• The method demonstrated robustness in analyzing limited sample sizes.

Conclusions:

• The developed iterative regression method is effective for identifying phenotype-associated gene clusters in studies with limited sample sizes.
• Comparative analysis revealed both shared and unique biological processes in normal aging and HGPS.
• This approach can be valuable for gene discovery in rare diseases and other research areas with small datasets.