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Researchers Use Cerebrospinal Fluid To Unlock Pathways For Alzheimer’S Disease

by Emma Miller

A groundbreaking study from Washington University School of Medicine in St. Louis has for the first time linked disease-related proteins and genes through the analysis of cerebrospinal fluid (CSF) collected from living patients, shedding light on the cellular pathways involved in the onset and progression of Alzheimer’s disease. The findings, published in Nature Genetics, could pave the way for new therapeutic targets in the fight against this devastating neurodegenerative disorder.

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Historically, research into Alzheimer’s has faced challenges due to the difficulty of examining the living brain at a molecular level. Previous studies often relied on postmortem brain tissue, which only provides insights into the later stages of the disease. Blood plasma has also been studied, but it does not specifically reflect the changes occurring in the affected brain tissues. In contrast, CSF serves as a valuable proxy for brain activity, enabling researchers to better understand the disease’s pathology.

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Carlos Cruchaga, PhD, the Barbara Burton and Reuben Morriss III professor of psychiatry and director of the NeuroGenomics and Informatics Center at Washington University, emphasized the significance of using human-derived data for this research. “Our goal is to identify risk-linked and protective genes, and also to understand their causal roles,” he said. “CSF is a good representation of the pathology of the disease, which is why we conducted a large proteomic study.”

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Over the past 15 years, researchers have identified nearly 80 regions of the genome associated with Alzheimer’s. However, knowing which genes are linked to the disease is just the beginning. By analyzing the proteomic profiles—essentially the active proteins and their levels—of individuals with and without Alzheimer’s, the research team aimed to uncover dysfunctional cellular pathways.

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“Within a region of DNA associated with Alzheimer’s, there are often many genes, and we don’t always know which ones are driving the condition,” Cruchaga explained. “By incorporating protein data, we can pinpoint the specific genes involved and the molecular pathways they affect, as well as discover new protein-to-protein interactions.”

The research team utilized extensive databases, including the Knight Alzheimer Disease Research Center (Knight-ADRC) and the Dominantly Inherited Alzheimer Network (DIAN), to access genetic information and CSF samples from 3,506 individuals, both healthy and those diagnosed with Alzheimer’s. By cross-referencing CSF proteomic data with existing genomic studies, the researchers identified 1,883 proteins from a total of 6,361 in the CSF proteomic atlas. They employed three established statistical analyses to confidently identify genes and proteins linked to biological pathways leading to Alzheimer’s.

From their analysis, the team determined that 38 proteins likely have causal effects on the progression of Alzheimer’s, with 15 of these identified as potential targets for therapeutic intervention. “The novelty and strength of this analysis lie in defining proteins that modify risk,” Cruchaga noted. “Now that we have established the causal pathways, we can better understand their implications in the brain.”

Conclusion

The implications of this study for understanding and treating Alzheimer’s are significant. Cruchaga believes that the techniques developed for CSF proteomics could also be applied to other neurological conditions, including Parkinson’s disease and schizophrenia. “Once you have an atlas of genetic variants and protein levels, you can apply this approach to any disease,” he stated.

In addition to proteins, Cruchaga is investigating metabolites—substances released by cells during their normal processes that are also found in CSF. In a separate paper published in Nature Genetics, he and his collaborators reported associations between specific metabolites and conditions such as Parkinson’s disease, diabetes, and dementia, further highlighting the potential of CSF analysis in neurological research.

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