Network-based discovery of regulatory drivers of cognitive decline in alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder marked by progressive cognitive decline, yet its transcriptional regulatory architecture remains poorly understood. Here, we model sample-specific gene regulatory networks (GRNs) from dorsolateral prefrontal cortex transcriptomes of 87 individuals with AD and 67 non-cognitively impaired (NCI) controls and use a machine learning classifier to detect consistent disease-specific network features. This sample-specific network approach captures inter-individual variation in transcriptional regulation and revealed 22 key transcription factor-gene regulations that distinguish AD from NCI with 96% weighted accuracy. The key transcription factor-gene interactions were enriched in pathways central to AD pathology, including synaptic signalling, mitochondrial function, proteostasis, and neuroinflammation. Network analysis uncovered significant differences in regulatory connectivity between AD and controls, with ZNF225, ZNF849, and ZNF548 emerging as AD-specific regulatory hubs. Moreover, several key regulatory edges showed significant correlations with longitudinal cognitive decline, supporting their clinical relevance. Our findings highlight pervasive transcriptional dysregulation in AD, emphasizing sample-specific GRN modelling's value in uncovering regulatory mechanisms.