Decoding the role of transcriptomic clocks in the human prefrontal cortex

Area of Science:

• Neuroscience
• Genomics
• Aging Research

Background:

• Aging exhibits significant interindividual variability, often measured by biological aging clocks.
• Transcriptomic clocks, derived from gene expression data, offer insights into health outcomes.
• The human prefrontal cortex (PFC) is crucial for cognitive functions affected by aging.

Purpose of the Study:

• To comprehensively profile transcriptomic clocks in the human PFC.
• To compare the predictive accuracy and stochastic components of different transcriptomic clocks.
• To investigate associations with mental disorders and elucidate functional implications.

Main Methods:

• Profiling of multiple transcriptomic aging clocks across three human PFC datasets.
• Characterization of stochastic components and predictive accuracy of linear vs. deep learning models.
• Network analysis to identify convergent biological mechanisms and associations with health outcomes.

Main Results:

• Substantial heterogeneity observed in transcriptomic age prediction across different clock signatures.
• Deep learning models captured more stochastic variation than linear methods.
• A consistent relationship was found between transcriptomic age and alcohol use.
• Convergent biological mechanisms, including immune signaling and extracellular matrix remodeling, were identified across clocks.
• Associations between transcriptomic age and psychiatric traits were revealed.

Conclusions:

• Transcriptomic clocks exhibit variability, with deep learning models showing potential for capturing non-deterministic aging components.
• Despite limited gene overlap, different clocks converge on shared biological pathways relevant to aging and health.
• Transcriptomic age in the PFC is associated with alcohol use, psychiatric traits, and specific biological pathways, offering insights into aging and health risks.