New tool looks for evidence of Alzheimer's in the intestinal microbiome

New tool looks for evidence of Alzheimer's in the intestinal microbiome

Cleveland Clinic researchers are using artificial intelligence to uncover the connection between the gut microbiome and Alzheimer's disease.

Previous studies have shown that Alzheimer's patients experience changes in their gut bacteria as the disease progresses. The newly published oneCell ReportsStudy describes a computational method to determine how bacterial byproducts called metabolites interact with receptors on cells and contribute to Alzheimer's disease.

Feixiong Cheng, PhD, founding director of the Cleveland Clinic Genome Center, worked closely with the Luo Ruvo Center for Brain Health and the Center for Microbiome and Human Health (CMHH). The study ranks metabolites and receptors according to the likelihood that they interact with each other and the likelihood that the pair influences Alzheimer's disease. The data represent one of the most comprehensive roadmaps to date for studying metabolism-related diseases.

Bacteria release metabolites into our bodies as they break down the food we eat for energy. The metabolites then interact with and influence cells, stimulating cellular processes that can be helpful or harmful to health. In addition to Alzheimer's disease, researchers have linked metabolites to heart disease, infertility, cancer, and autoimmune diseases and allergies.

Preventing harmful interactions between metabolites and our cells could help fight disease. Researchers are working to develop drugs to activate or block the association of metabolites with receptors on the cell surface. Progress on this approach is slow due to the sheer amount of information required to identify a target receptor.

Intestinal metabolites are the key to many physiological processes in our body, and for every key there is a lock on human health and disease. The problem is that we have tens of thousands of receptors and thousands of metabolites in our system. Therefore, manually figuring out which key fits into which lock was tedious and costly. That’s why we decided to use AI.”

Feixiong Cheng, PhD, founding director, Genome Center, Cleveland Clinic

The team of Dr.

The study's first author and postdoctoral fellow at the Cheng Lab, Yunguang Qiu, PhD, led a team that included J. Mark Brown, PhD, research director, CMMH; James Leverenz, MD, director of the Cleveland Clinic Luo Ruvo Center for Brain Health and director of the Cleveland Alzheimer's Disease Research Center; and neuropsychologist Jessica Caldwell, PhD, ABPP/CN. Director of the Women’s Alzheimer’s Movement Prevention Center at Cleveland Clinic Nevada.

The team used a form of AI called machine learning to analyze over 1.09 million potential metabolite-receptor pairs and predict the likelihood that each interaction contributed to Alzheimer's disease.

The analyzes included:

• genetische und proteomische Daten aus menschlichen und präklinischen Studien zur Alzheimer-Krankheit
• unterschiedliche Rezeptor- (Proteinstrukturen) und Metabolitenformen
• wie sich verschiedene Metaboliten auf von Patienten stammende Gehirnzellen auswirken

The team examined the metabolite-receptor pairs most likely to influence Alzheimer's disease in brain cells from patients with Alzheimer's disease.

One molecule they focused on is a protective metabolite called agmatine, which is thought to protect brain cells from inflammation and related damage. The study found that agmatine most likely interacts with a receptor called CA3R in Alzheimer's disease.

Treating Alzheimer-affected neurons with agmatine directly reduced CA3R levels, suggesting that the metabolite and receptor influence each other. Neurons treated with agmatine also had lower levels of phosphorylated tau proteins, a marker for Alzheimer's disease.

Dr. Cheng says these experiments show how his team's AI algorithms can pave the way for new research avenues into many diseases beyond Alzheimer's.

“We focused specifically on Alzheimer's disease, but metabolite-receptor interactions play a role in almost every disease involving gut microbes,” he said. “We hope that our methods can provide a framework to advance the entire field of metabolite-associated diseases and human health.” Now Dr. Cheng and his team are taking these AI technologies further and applying them to study interactions between genetic and environmental factors (including food and gut metabolites) on human health and disease, including Alzheimer's disease and other complex diseases.

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