Researchers headed by a team at the University of California, Irvine, Joe C. Wen School of Population & Public Health have built what they suggest is the first cell type-specific gene regulatory network (GRN) map for Alzheimer’s disease (AD), which shows how genes causally regulate one another across different types of brain cells affected by AD. The researchers developed a machine learning framework, SIGNET (Statistical Inference on Gene Regulatory Networks), which reveals cause-and-effect relationships rather than simple genetic correlations, and applied this to uncover key biological pathways that may drive memory loss and brain degeneration. Their results pointed to numerous influential “hub genes” that offer promising potential new targets for early detection and therapeutic intervention. The investigators say their methodology is also applicable to other complex diseases, including cancer. Research leads Min Zhang, MD, PhD, and Dabao Zhang, PhD, and colleagues reported on the development and application of SIGNET in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, in a paper titled “From correlation to causation: cell-type-specific gene regulatory networks in Alzheimer’s disease.” In their paper, the researchers concluded, “By identifying novel AD-associated hubs and key pathways as potential biomarkers, this study advances our understanding of the molecular mechanisms driving AD and offers significant potential for developing targeted diagnoses and treatments.” Alzheimer’s disease is the leading cause of dementia and is projected to affect nearly 14 million Americans over the age of 65 by 2060. Scientists have already linked many genes, such as apolipoprotein E (APOE) and amyloid precursor…
