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After studying 140,000 versions of the Aβ42 peptide, scientists identified some of the earliest molecular interactions that drive the formation of harmful amyloid protein aggregates found in Alzheimer’s disease cases. Their findings are published in a Science Advances paper titled, “Massively parallel genetic perturbation suggests the energetic structure of an amyloid beta transition state.”
The study was done by scientists from the Wellcome Sanger Institute, the Centre of Genomic Regulation (CRG), and the Institute for Bioengineering of Catalonia. According to the paper, they used large-scale genomics data and machine learning to analyze versions of Aβ42 to understand how changing the genetics of Aβ affects the rates of aggregation reactions.
Insights into how Aβ works could help scientists develop novel therapies that help millions of people living with Alzheimer’s disease. According to one estimate, over 55 million people globally are impacted by dementia, and between 60–70% of these individuals are living with Alzheimer’s disease. As Richard Oakley, PhD, associate director of research and innovation at Alzheimer’s Society, noted, “Dementia is the biggest health and social care issue of our time.” This study “harnesses the power of technology to fill a key piece of the puzzle in how toxic amyloid proteins accumulate in the brain and improves our understanding of how genetics influences the way this protein forms plaques.”
Furthermore, “with more than 130 drugs currently being tested in Alzheimer’s disease clinical trials and an urgent need to develop more effective and safer treatments, research like this is critical to continue growing our understanding of the highly complex processes involved in Alzheimer’s disease,” he added.
In the brain, amyloid beta peptides tend to clump and aggregate, forming elongated structures known as amyloid fibrils. Over time, these fibrils accumulate into plaques, which are the pathological hallmarks of more than 50 neurodegenerative diseases, including Alzheimer’s disease. As free-flowing Aβ peptides convert into stable, structured fibrils, they pass through a short-lived, high-energy transition state.
Understanding these structures and reactions is essential to developing therapies that could treat and prevent neurodegenerative diseases. However, studying the short-lived high-energy transition states using classical methods is difficult. And that has made it challenging to study the origins of Aβ aggregation.
In this study, the researchers combined three techniques to study Aβ42. First, they used massively parallel DNA synthesis to study how changing amino acids in Aβ affects the amount of energy needed to form a fibril. They used genetically engineered yeast cells to measure the rate of reaction. They then used machine learning to analyze the data and generate a complete energy landscape of amyloid beta aggregation reaction, showing the effect of all possible mutations in this protein on how fast fibrils are formed. These techniques enabled the researchers to analyse more than 140,000 versions of Aβ42 simultaneously.
They found that a few key interactions between specific parts of the amyloid protein had a strong influence on the speed of fibril formation. Specifically, they found that the Aβ42 aggregation reaction begins at the C-terminal region. Based on these results, they suggest that treatments that can target and prevent interactions in the C-terminal region may protect against and treat Alzheimer’s disease.
“We have created the first comprehensive map of how individual mutations alter the energy landscape of amyloid beta aggregation, a process central to the development of Alzheimer’s disease,” said Anna Arutyunyan, PhD, co-first author on the paper and postdoctoral fellow at the Wellcome Sanger Institute. “Our data-driven model offers the first high-resolution view of the reaction’s transition state, opening the door to more targeted strategies for therapeutic intervention.”
Although this study focused on plaques in Alzheimer’s, the researchers believe that their methods could be used to study short-lived protein transition states in the context of other neurodegenerative diseases.
“Our study is novel for two reasons: Firstly, our ‘kinetic-selection’ method measures how fast reactions occur—and it does so for thousands of reactions in parallel, capturing the true rate-limiting steps of the aggregation reaction,” explained Benedetta Bolognesi, PhD, co-senior author on the paper and group leader at the Institute for Bioengineering of Catalonia. “Secondly, by combining mutations, we can systematically probe the interactions between different parts of the protein as the aggregation reaction initiates.” This is not only crucial for understanding “the first events in the process of protein aggregation that leads to dementia, but it also offers a powerful framework to dissect the key initiating steps of many biological reactions, not just those we’ve studied so far. I look forward to seeing all the ways in which this strategy will be employed in the future.”
