In a new study published in cell reportResearchers have developed an advanced systems biology approach that combines artificial intelligence (AI), genetics, and multi-omics analysis to investigate how metabolites produced by gut bacteria influence Alzheimer’s disease. investigated.
This 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 that target these interactions, offering hope for the treatment or even prevention of Alzheimer’s disease.
Alzheimer’s disease is a progressive neurodegenerative disease that primarily affects older adults and is characterized by a decline in cognitive functions such as memory and reasoning. It is characterized by the accumulation of amyloid beta plaques and tau protein tangles in the brain, which interfere with nerve function and lead to cell death.
The exact cause of Alzheimer’s disease is not completely understood, but a combination of genetic, lifestyle, and environmental factors are thought to affect the brain over time. As the disease progresses, it has a significant impact on daily life and independence and is one of the most common causes of dementia in the elderly.
Previous studies have demonstrated that Alzheimer’s patients experience changes in their gut bacteria as the disease progresses. These bacteria produce metabolites that can affect brain health and can contribute to the development of disease. However, the specific pathways by which these metabolites act remain largely enigmatic.
This gap in understanding has led to new research aimed at elucidating the interactions between these metabolites and the human receptors they affect. The study was conducted by Feixiong Chen and his team, bringing together experts from the Cleveland Clinic Genome Center, the Luo Lubo Center for Brain Health, and the Center for Microbiome and Human Health.
The researchers used machine learning algorithms to analyze more than 1 million potential metabolite-receptor pairs to predict which interactions are most likely to impact the disease. Genetic data, including Mendelian randomization, complemented these predictions by assessing causality and receptor involvement.
“Intestinal metabolites are the key to many physiological processes in our bodies, and all have keys to human health and disease,” Chen said. “The problem is that there are tens of thousands of receptors and thousands of metabolites in our system, so manually determining which key goes into which lock is time-consuming and expensive. That’s why we decided to use AI.”
The study also included experimental validation using neurons from Alzheimer’s patients to test the effects of specific metabolites on tau protein levels, an important biomarker of disease progression. This multifaceted approach allowed researchers to map out key interactions within the gut-brain axis and shed light on potential therapeutic targets for Alzheimer’s disease.
One of the most impressive results of this 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 for which natural activators are unknown, and discovered that certain metabolites can activate these receptors. This discovery is of particular interest as it opens a new route for drug development that has the potential to target these receptors and modulate their activity in favor of disease prevention or mitigation. .
Among the metabolites studied, phenethylamine and agmatine stood out due to their effects on the tau protein, which is involved in the neurological breakdown that is a hallmark of Alzheimer’s disease. This study demonstrated that these metabolites can significantly alter the levels of phosphorylated tau protein in neurons from Alzheimer’s disease patients. Agmatine in particular showed protective effects by reducing harmful tau phosphorylation, suggesting that this may be a potential candidate for therapeutic development.
The application of machine learning models has been pivotal in predicting interactions between more than a million metabolite-receptor pairs. This high-throughput approach not only streamlined the identification of relevant interactions, but also improved our understanding of the complex mechanisms by which the gut microbiome influences brain health. By integrating genetic analysis and experimental data, the researchers were able to test these predictions and further understand the gut-brain axis in relation to Alzheimer’s disease.
Although promising, the study’s authors acknowledge some limitations. The complexity of the gut-brain axis means that the findings are preliminary and require further validation through experimental and clinical studies. Future studies should confirm these interactions in vivo and explore therapeutic possibilities to modulate these pathways.
Furthermore, this study primarily focused on biochemical interactions at the molecular level, and broader physiological and environmental factors that may influence these processes in living systems were not considered. Is not …
Nevertheless, this study provided a valuable framework for understanding how gut bacterial metabolites influence brain health and disease. The implications of these findings extend beyond Alzheimer’s disease, as these methodologies and insights may be applicable to other neurological and systemic diseases that are influenced by the gut microbiome.
“Although we focused specifically on Alzheimer’s disease, metabolite-receptor interactions are involved in almost all diseases that involve gut bacteria,” Chen said. “We hope that our method can provide a framework for advancing the field of metabolite-related diseases and human health as a whole.”
the study, “Systematic characterization of the multiomic landscape between gut microbial metabolites and GPCRomes in Alzheimer’s disease” authors are 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, Feixiong . Chen.
