Newly developed data analysis method uncovers causal mechanisms of disease

• Public health researchers use newly developed data analysis method to build the first cell type-specific gene regulatory maps for Alzheimer’s disease, revealing the genetic mechanisms operating within patients’ brains.
• The study also identified numerous influential “hub genes” that offer promising new targets for early detection and therapeutic intervention.
• The National Institute on Aging and the National Cancer Institute provided support.

Researchers led by Min Zhang and Dabao Zhang of the University of California, Irvine’s Joe C. Wen School of Population & Public Health have created the most detailed maps to date showing how genes causally regulate one another across different types of brain cells affected by Alzheimer’s disease.

Using their newly developed machine learning framework, SIGNET, which reveals cause-and-effect relationships rather than simple genetic correlations, they uncovered key biological pathways that may drive memory loss and brain degeneration.

Pipeline of cell-type-specific causal gene regulatory network (GRN) construction

(A) The workflow to construct cell-type-specific GRNs. It starts with preprocessing both single-nucleus RNA-sequencing and whole-genome sequencing data, followed by the construction of GRNs using SIGNET. (B) Constructed cell-type-specific GRNs for each cell type. Downstream analyses were performed to decipher the complex regulatory relationships and identify hub genes of interest. 

The study, published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, also identifies new genes that could serve as targets for future treatments. The work was funded in part by the National Institute on Aging and the National Cancer Institute.

Alzheimer’s disease, the leading cause of dementia, is projected to affect nearly 14 million Americans by 2060. Scientists have already found many genes linked to the disease, such as APOE and APP, but they still don’t fully understand how these genes disrupt healthy brain function.

“Different types of brain cells play distinct roles in Alzheimer’s disease, but how they interact at the molecular level has remained unclear,” said Min Zhang, co-corresponding author and professor of epidemiology and biostatistics. “Our work provides cell type-specific maps of gene regulation in the Alzheimer’s brain, shifting the field from observing correlations to uncovering the causal mechanisms that actively drive disease progression.”

To create these maps, team members analyzed single-cell molecular data from brain samples of 272 participants in long-term memory and aging studies in the Religious Orders Study and the Rush Memory and Aging Project. They developed SIGNET as a scalable, high-performance computing method that integrates single-cell RNA sequencing and whole-genome sequencing data and reveals cause-and-effect relationships among all genes.

The researchers identified causal gene regulatory networks for six major types of brain cells. This allowed them to determine which genes are likely controlling other genes, something traditional correlation-based tools cannot reliably do.

“Most gene-mapping tools can show which genes move together, but they can’t tell which genes are actually driving the changes,” said Dabao Zhang, co-corresponding author and professor of epidemiology and biostatistics. “Some methods also make unrealistic assumptions, such as ignoring feedback loops between genes. Our approach takes advantage of information encoded in DNA to enable the identification of true cause-and-effect relationships between genes in the brain.”

The scientists found that the most dramatic gene disruptions in Alzheimer’s disease occur in excitatory neurons – the nerve cells that send activating signals – with analyses of nearly 6,000 cause-and-effect interactions indicating that these cells undergo extensive rewiring as the disease progresses.

They also pinpointed hundreds of “hub genes” that act as major control centers, influencing many other genes and likely playing key roles in driving harmful changes. These could serve as new targets for early detection and therapeutic intervention. In addition, the team discovered new regulatory roles for well-known genes such as APP, which strongly controlled other genes in inhibitory neurons.

Importantly, the researchers confirmed these findings using an independent set of human brain samples, strengthening confidence that these gene-to-gene relationships reflect real biological mechanisms involved in Alzheimer’s disease.

SIGNET can also be used to study many other complex diseases, including cancer, autoimmune disorders and mental health conditions.

Source – University of California, Irvine