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AI Reveals Hidden Genetic Drivers of Alzheimer’s Disease

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AI Reveals Hidden Genetic Drivers of Alzheimer’s Disease

Scientists have created the most detailed maps yet of genetic activity in Alzheimer’s brains. Using a powerful AI system called SIGNET, researchers uncovered how genes control each other across different brain cells. The findings could transform our understanding of this devastating disease. They also reveal new targets for potential treatments.

What the AI Discovery Revealed

Researchers at the University of California, Irvine led this groundbreaking study. They analyzed brain samples from 272 participants in long-term aging studies. The team built genetic maps for six major brain cell types. These maps show cause-and-effect relationships between genes. Therefore, they reveal which genes actively drive harmful changes. Traditional tools only show which genes move together. However, SIGNET goes much further. It identifies true command centers in the genetic network.

Major Changes in Key Brain Cells

The most dramatic disruptions appeared in excitatory neurons. These nerve cells send activating signals throughout the brain. Nearly 6,000 cause-and-effect interactions showed extensive rewiring as Alzheimer’s progressed. The team also identified hundreds of “hub genes.” These function as central regulators. They influence many other genes and likely play key roles in disease progression. For example, the well-known APP gene showed strong control over other genes in inhibitory neurons. This finding adds new understanding to its role in Alzheimer’s.

Why This Matters for Treatment

Alzheimer’s disease affects nearly 14 million Americans by 2060. It remains the leading cause of dementia worldwide. Scientists already know several genes linked to the disease. However, they did not fully understand how these genes disrupt normal brain function. Min Zhang, co-corresponding author, explains the significance. “Different types of brain cells play distinct roles in Alzheimer’s,” she states. Their work shifts the field from observing correlations to uncovering causal mechanisms.

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