Researchers at Columbia University Mailman School of Public Health have crafted an innovative computational pipeline, known as MR-SPI (Mendelian Randomization by Selecting genetic instruments and Post-selection Inference), aimed at identifying protein biomarkers linked to complex diseases, with a particular focus on Alzheimer’s disease (AD). This cutting-edge tool delves into biomarkers capable of inducing 3D structural changes in proteins, offering crucial insights into disease mechanisms and spotlighting potential targets for therapeutic interventions. The findings, published in Cell Genomics, hold the promise of advancing early detection and treatment strategies for a disease that has long proven resistant to effective therapies.
Alzheimer’s disease is characterized by the accumulation of amyloid-beta plaques and tau neurofibrillary tangles in the brain, which start building up decades before symptoms manifest. Current early diagnostic methods are either resource-intensive or invasive. Moreover, existing AD therapies targeting amyloid-beta may offer some relief from symptoms and potentially slow the disease’s progression, yet they fall short of completely halting it. As Zhonghua Liu, ScD, assistant professor of Biostatistics at Columbia Mailman School and senior investigator, notes, “Our study highlights the urgent need to identify blood-based protein biomarkers that are less invasive and more accessible for early detection of Alzheimer’s disease. Such advancements could unravel the underlying mechanisms of the disease and pave the way for more effective treatments.”
Utilizing data from the UK Biobank, which encompasses 54,306 participants, along with a genome-wide association study (GWAS) involving 455,258 subjects (71,880 AD cases and 383,378 controls), the research team pinpointed seven key proteins—TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55—that display structural alterations associated with Alzheimer’s risk. “We discovered that certain FDA-approved drugs already targeting these proteins could potentially be repurposed to treat Alzheimer’s,” Liu added. “Our findings underscore the potential of this pipeline to identify protein biomarkers that can serve as new therapeutic targets, as well as provide opportunities for drug repurposing in the fight against Alzheimer’s.”
The MR-SPI pipeline comes with several notable benefits. Unlike conventional methods, it doesn’t require a vast number of candidate genetic instruments (such as protein quantitative trait loci) to identify disease-related proteins. It’s a potent tool specifically designed for studies where only a limited number of genetic markers are available. “MR-SPI is particularly valuable for elucidating causal relationships in complex diseases like Alzheimer’s, where traditional approaches struggle,” Liu explained. “The integration of MR-SPI with AlphaFold3—an advanced tool for predicting protein 3D structures—further enhances its ability to predict 3D structural changes caused by genetic mutations, providing a deeper understanding of the molecular mechanisms driving disease.”
The study’s outcomes suggest that MR-SPI could have far-reaching applications beyond Alzheimer’s disease, presenting a powerful framework for identifying protein biomarkers across diverse complex diseases. Additionally, the capacity to predict 3D structural changes in proteins unlocks new avenues for drug discovery and the repurposing of existing treatments. “By combining MR-SPI with AlphaFold3, we can achieve a comprehensive computational pipeline that not only identifies potential drug targets but also predicts structural changes at the molecular level,” Liu concluded. “This pipeline offers exciting implications for therapeutic development and could pave the way for more effective treatments for Alzheimer’s and other complex diseases.”
As Gary W. Miller, PhD, Columbia Mailman Vice Dean for Research Strategy and Innovation and professor in the Department of Environmental Health Sciences, put it, “By leveraging large cohorts with biobanks, innovative statistical and computational approaches, and AI-based tools like AlphaFold this work represents a convergence of innovation that will improve our understanding of Alzheimer’s and other complex diseases.”
The study’s 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.
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