Researchers have introduced a cutting-edge computational pipeline aimed at pinpointing protein biomarkers linked to complex diseases, such as Alzheimer’s disease (AD). This groundbreaking tool examines biomarkers that can trigger three-dimensional structural changes in proteins, thus offering valuable insights into the underlying mechanisms of the disease and pointing out possible targets for therapy. The results hold promise for improved early detection and treatment approaches for Alzheimer’s disease, which has historically been difficult to treat effectively.
A team of researchers from the Columbia University Mailman School of Public Health has created an innovative computational pipeline aimed at identifying protein biomarkers associated with complex diseases, including Alzheimer’s disease (AD). This tool is designed to analyze biomarkers that can cause 3D structural modifications in proteins, thus providing significant insights into disease mechanisms and identifying potential therapeutic targets. The results, published in Cell Genomics, may facilitate advancements in the early detection and treatment strategies for Alzheimer’s disease, which has long been challenging to effectively manage.
“Alzheimer’s disease is characterized by the accumulation of amyloid-beta plaques and tau neurofibrillary tangles in the brain, which can develop years before symptoms appear. Current methods for early diagnosis tend to be either resource-intensive or invasive,” explained Zhonghua Liu, ScD, assistant professor of Biostatistics at Columbia Mailman School and lead researcher. “Present AD treatments targeting amyloid-beta might provide some symptom relief and could slow disease progression, but they do not completely stop it.” He emphasized the importance of identifying blood-based protein biomarkers that are easier and less invasive for the early detection of Alzheimer’s disease. These advancements could uncover the fundamental mechanisms of the disease and lead to more effective treatment options.
A New Approach to Alzheimer’s Disease
Leveraging data from the UK Biobank, which includes 54,306 participants, combined with a genome-wide association study (GWAS) involving 455,258 subjects (71,880 AD cases and 383,378 controls), the research team identified seven critical proteins—TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55—that show structural variations linked to Alzheimer’s risk.
“We found that certain FDA-approved drugs targeting these proteins could potentially be repurposed for Alzheimer’s treatment,” Liu noted. “Our results highlight the promise of this pipeline in discovering protein biomarkers that can act as new therapeutic targets and offer opportunities for repurposing existing medications in the fight against Alzheimer’s.”
The MR-SPI Pipeline: Precision in Disease Prediction
The MR-SPI (Mendelian Randomization by Selecting genetic instruments and Post-selection Inference) is a new computational pipeline with several key benefits. Unlike conventional methods, MR-SPI does not rely on a large number of candidate genetic instruments (like protein quantitative trait loci) to identify proteins related to diseases. This makes MR-SPI an effective tool for research with just a few available genetic markers.
“MR-SPI is especially useful for identifying causal relationships in complex diseases like Alzheimer’s where traditional methods may not be effective,” Liu said. “Combining MR-SPI with AlphaFold3, a sophisticated tool for predicting protein 3D structures, improves its capability to predict 3D structural changes due to genetic mutations, offering a richer understanding of the molecular mechanisms driving the disease.”
Implications for Drug Discovery and Treatment
The outcomes of this study imply that MR-SPI could have extensive applications beyond just Alzheimer’s disease, providing a robust framework for discovering protein biomarkers for various complex diseases. Additionally, the aptitude to forecast 3D structural changes in proteins opens new avenues for drug discovery and repurposing existing treatments.
“By merging MR-SPI with AlphaFold3, we create a comprehensive computational pipeline that not only identifies potential drug targets but also predicts structural changes at the molecular level,” Liu concluded. “This pipeline holds exciting possibilities for the development of therapeutic strategies and could lead to more effective treatments for Alzheimer’s and other complex diseases.”
“Using large biobank cohorts, cutting-edge statistical and computational strategies, along with AI-based tools like AlphaFold, this research exemplifies innovation that will enhance our understanding of Alzheimer’s disease and other complex health conditions,” remarked Gary W. Miller, PhD, Vice Dean for Research Strategy and Innovation at Columbia Mailman and professor in the Department of Environmental Health Sciences.
The study co-authors include Minhao Yao from The University of Hong Kong; Badri N. Vardarajan from the Taub Institute on Alzheimer’s Disease and the Aging Brain at Columbia University; Andrea A. Baccarelli from the Harvard T.H. Chan School of Public Health; and Zijian Guo from Rutgers University.