Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and...

Area of Science:

• Neuroscience
• Genomics
• Biomarkers

Background:

• Alzheimer's disease (AD) poses a significant challenge, necessitating early detection and novel therapeutic strategies.
• Identifying reliable biomarkers at the mild cognitive impairment (MCI) stage is critical for effective intervention.
• Current diagnostic and therapeutic approaches require enhancement for prodromal AD detection.

Purpose of the Study:

• To characterize gene-expression patterns in MCI to identify mRNA measures and gene clusters linked to AD pathogenesis.
• To discover potential therapeutic targets for early-stage Alzheimer's disease.
• To establish a transcriptomic signature for sensitive detection of prodromal AD.

Main Methods:

• Utilized a transcriptomics approach integrating magnetic resonance imaging (MRI) and peripheral blood gene expression data.
• Employed persistent homology (PH) for data integration, followed by kernel-based clustering.
• Validated driver genes in an external dataset for prediction of amyloidosis and clinical diagnosis.

Main Results:

• Identified three gene clusters significantly associated with amnestic MCI diagnosis.
• Biological processes linked to clusters include mitochondrial function, NF-kB signaling, and apoptosis.
• Cluster associations with cortical thickness showed AD-like patterns; driver genes predicted amyloidosis and clinical diagnosis.

Conclusions:

• A disease-relevant transcriptomic signature sensitive to prodromal AD was discovered.
• Identified a subset of potential therapeutic targets implicated in AD pathogenesis.
• This signature can improve early AD risk assessment and guide therapeutic development.