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AI connects gut bacteria metabolites to Alzheimer’s disease progression

In a new study published in Cell ReportsResearchers have developed a sophisticated systems biology approach that combines artificial intelligence (AI), genetics and multi-omics analyzes to explore how metabolites produced by gut bacteria might influence Alzheimer’s disease.

The study identifies specific receptors in the human body with which these metabolites interact, potentially opening new avenues for therapeutic intervention. This important discovery could lead to the development of new drugs targeting these interactions, thus offering hope for the treatment, or even prevention, of Alzheimer’s disease.

Alzheimer’s disease is a progressive neurodegenerative disease primarily affecting older adults, characterized by the decline of cognitive functions such as memory and reasoning. It is marked by the accumulation of beta-amyloid plaques and tau protein tangles in the brain, which interfere with neuronal function and lead to cell death.

The exact cause of Alzheimer’s disease is not fully understood, but it is thought to involve a combination of genetic, lifestyle and environmental factors that affect the brain over time. As the disease progresses, it seriously affects daily life and independence, making it one of the most common causes of dementia in older adults.

Previous research has established that Alzheimer’s patients experience changes in their gut bacteria as the disease progresses. These bacteria produce metabolites that can influence brain health and potentially contribute to the development of disease. However, the specific pathways by which these metabolites act remain largely a mystery.

This gap in understanding motivated the new study, aiming to map the interactions between these metabolites and the human receptors they affect. The study was led by Feixiong Cheng and his team, bringing together experts from the Cleveland Clinic Genome Center, the Luo Ruvo Center for Brain Health, and the Center for Microbiome and Human Health.

Researchers analyzed more than a million potential metabolite-receptor pairs using machine learning algorithms to predict which interactions were most likely to influence disease. Genetic data, including Mendelian randomization, complemented these predictions by assessing causality and receptor involvement.

“Gut metabolites are the key to many physiological processes in our body, and for every key there is a lock for human health and disease,” Cheng said. “The problem is that we have tens of thousands of receptors and thousands of metabolites in our system, so manually determining which key goes in which lock has been slow and expensive. This is why we decided to use AI.

The study also involved experimental validation using neurons derived from Alzheimer’s disease patients, where specific metabolites were tested for their effects on levels of tau protein, a key biomarker of disease progression. This multifaceted approach allowed researchers to map significant interactions within the gut-brain axis, thereby shedding light on potential therapeutic targets for Alzheimer’s disease.

One of the most striking results of the study was the identification of specific G protein-coupled receptors (GPCRs) that interact with metabolites produced by gut bacteria. The researchers focused on orphan GPCRs – receptors whose natural activators are unknown – and found that certain metabolites can activate these receptors. This finding is particularly intriguing because it opens new avenues for drug development, targeting these receptors to potentially modulate their activity in favor of disease prevention or mitigation.

Among the metabolites studied, phenethylamine and agmatine stand out for their effects on tau proteins, involved in the neurological degradation characteristic of Alzheimer’s disease. The study demonstrated that these metabolites could significantly alter the levels of phosphorylated tau proteins in neurons derived from Alzheimer’s disease patients. Agmatine, in particular, showed a protective effect by reducing harmful tau protein phosphorylation, suggesting that it could be a potential candidate for therapeutic development.

The application of machine learning models has played a critical role in predicting interactions between more than a million metabolite-receptor pairs. This high-throughput approach has not only streamlined the identification of relevant interactions, but also improved understanding of the complex mechanisms by which the gut microbiota can influence brain health. By integrating genetic analyzes and experimental data, researchers were able to validate these predictions and refine their understanding of the gut-brain axis in the context of Alzheimer’s disease.

Although promising, the study authors acknowledge several limitations. The complexity of the gut-brain axis means that the results are preliminary and require further validation through experimental and clinical studies. Future research will need to confirm these interactions in living organisms and explore the therapeutic potential of modulating these pathways.

Furthermore, the study has primarily focused on biochemical interactions at the molecular level, without considering the broader physiological and environmental factors that may influence these processes in a living system.

Nonetheless, the research has provided a valuable framework for understanding how gut bacteria metabolites might influence brain health and disease. The implications of these findings extend beyond Alzheimer’s disease, as the methodologies and insights could potentially be applied to other neurological and systemic diseases influenced by the gut microbiota.

“We specifically focused on Alzheimer’s disease, but metabolite-receptor interactions play a role in almost all diseases involving gut microbes,” Cheng said. “We hope that our methods can provide a framework for progress across the entire field of metabolite-associated diseases and human health.” »

The study, “Systematic characterization of the multi-omics landscape between intestinal microbial metabolites and the GPCRome in Alzheimer’s disease,” was authored by Yunguang Qiu, Yuan Hou, Dhruv Gohel, Yadi Zhou, Jielin Xu, Marina Bykova, Yuxin Yang, James B. Leverenz. , Andrew A. Pieper, Ruth Nussinov, Jessica ZK Caldwell, J. Mark Brown, and Feixiong Cheng.

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