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A cross-omics approach identified four distinct molecular profiles of Alzheimer’s Disease, one of these profiles was associated with worse cognitive function and neuropathological features, according to researchers at Beth Israel Deaconess Medical Center (BIDMC) and their collaborators. The symptoms in this poorer prognosis group included significantly higher Clinical Dementia Rating at death, shorter lifespan after symptom onset, more severe neurodegeneration, and neuroinflammation and decreased levels of metabolomic profiles.
The team’s work appeared in PLOS BIOLOGY. The lead author is Abdallah M. Eteleeb. The senior authors are Bruno A. Benitez, MD, a human geneticist and director of the Neurobiorepository in the department of neurology BIDMC, and Oscar Harari of the department of neurology, division of nurogenetics, The Ohio State University.
The team used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles to provide novel critical molecular insights into Alzheimer’s Disease (AD).
AD is known to be a heterogeneous multifactorial neurodegenerative disorder pathologically characterized by amyloid (Aβ) plaques, neurofibrillary tangles (NFTs), neuroinflammation, and synaptic and neuronal loss. Recently, distinct spatiotemporal trajectories of tau pathology, brain atrophy, postmortem brain transcriptomics profiles, or cerebrospinal fluid proteomics have all been associated with multiple clinical and pathological AD features.
One big question is whether the disease can be reliably separated into different subtypes.
This team has previously leveraged high-throughput brain molecular data from AD patients and healthy individuals using transcriptomic, proteomic, metabolomic, and single-cell omics, separately, to study the signatures for AD. However, they write, “We recognize that omics layers are interdependent and interconnected. Thus, we hypothesized that by combining multiple signals from various molecules, novel significant biological hubs, or pathway changes in AD that are otherwise missed with single-omic analyses will emerge.”
In this study, the worst prognosis molecular profile was present in multiple affected cortical regions associated with more advanced disease—that is, those with more of the tau protein pathology seen in AD as well as significant dysregulation of synapse-related genes and signaling pathways altered in AD early and late stages.
Cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, they leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. They identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition.
The team’s results suggest that cross-omics analyses can capture differences and identify molecular variations in complex and heterogeneous diseases like Alzheimer’s Disease that are otherwise missed in single-modality omics approaches.Using this approach, they say, scientists can “better understand this disease—and others—and pave the way for new breakthroughs and potential therapies.’
Cross-omics approaches can serve as a powerful tool in investigators’ efforts to understand Alzheimer’s Disease, Benitez said. “These approaches are not only superior to single-omic analyses, but also provide invaluable molecular insights into the pathogenesis of this disease.”
He added, “We have identified molecular markers for an early stage of the disease reflecting synaptic dysfunction, cognition, and AD staging, paving the way for precision medicine. Our research serves as a beacon of hope for those affected by AD, inspiring us to continue our relentless pursuit of knowledge and progress; we expect to replicate these findings in BIDMC patients with Alzheimer’s disease.”
News & FeaturesAlzheimer's diseaseBiomarkersClinical study and study designsMachine learningMultiomicsPrecision medicine
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