BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration
The growing landscape of Alzheimer's disease (AD) datasets creates opportunities to integrate heterogeneous evidence and systematically discover disease effectors. We present BRIDGE-AD, an interpretable network medicine framework that transforms multimodal data into a unified, disease-specific gene representation for AD effector prioritisation. We integrated more than 30 datasets and curated…
Researchers have unveiled a new framework called BRIDGE-AD that can uncover Alzheimer's disease (AD) effectors through the integration of diverse data sources. This interpretable network medicine approach combines over 30 datasets and resources, including omics, functional, genetic, and prior disease knowledge layers.
When tested against recent pretrained and modality-specific gene embeddings, BRIDGE-AD demonstrated superior performance in recovering AD-associated genes. It also generated a comprehensive genome-wide resource of potential AD effectors. These identified effectors were grouped into 19 functional clusters, shedding light on the global molecular landscape of AD biology.
One notable finding was the SPP1-centred cross-compartment hypothesis, which proposed that SCARB2, a poorly characterized gene, could be a promising candidate for further functional validation. Upon closer examination, SCARB2 was found to rewire lysosomal, lipid-handling, and autophagic programs in microglia. Additionally, disruptions in SCARB2 glycosylation in AD patients suggested altered processing and function of this gene.
To facilitate further exploration and hypothesis generation, the researchers have created an accompanying website at explore-bridgead.com. This platform allows users to access the curated evidence and generate mechanistic insights into the complex biology of Alzheimer's disease.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
