Cited by 2Bioinformatics and Genomic NetworksAlzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer’s Disease

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Xie, Linhui, Raj, Yash, Varathan, Pradeep, He, Bing, Yu, Meichen, Nho, Kwangsik, Salama, Paul, Saykin, Andrew J. +1 more·2024·Journal of Alzheimer's Disease

Abstract

Background: There are various molecular hypotheses regarding Alzheimer’s disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

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  • MoFNet incorporates prior biological knowledge to dynamically link SNPs, genes, and proteins, facilitating the identification of subnetworks involved in processes like neuroinflammation and synaptic dysfunction.

    “Advanced models such as the multi-omics data fusion network (MoFNet) incorporate prior biological knowledge to dynamically link SNPs, genes, and proteins. This facilitates the identification of subnetworks involved in processes like neuroinflammation and synaptic dysfunction286.”
  • MoFNet integrating DNA, RNA, and protein expression reveals distinct pathways related to neurotransmission and immune regulation.

    “Deep multi-omics models such as MoFNet illustrate how integrating DNA, RNA, and protein expression reveals distinct pathways related to neurotransmission and immune regulation, thereby moving the field toward individualized mechanistic understanding286.”

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  1. [13]Wan L, Zeiler M, Zhang S, Cun YL, Fergus R (2013) Regularization of Neural Networks using DropConnect, PMLR, Proceedings of Machine Learning Research.
  2. [14]Sundararajan M, Taly A, Yan Q (2017) Axiomatic Attribution for Deep Networks, PMLR, Proceedings of Machine Learning Research.
  3. [15]Kokhlikyan N, Miglani V, Martin M, Wang E, Alsal-lakh B, Reynolds J, Melnikov A, Kliushkina N, Araya C, Yan S, Reblitz-Richardson O (2020) Captum: A unified and generic model interpretability library for PyTorch. arXiv:2009.07896. · arXiv

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Authors
Xie, L., Raj, Y., Varathan, P., He, B., Yu, M., Nho, K., Salama, P., Saykin, A. J., & Yan, J.
Affiliations
  • Indiana University – Purdue University Indianapolis (US)
  • Indiana University School of Medicine (US)
Corresponding author
Yan, Jingwen
Venue
Journal of Alzheimer's Disease
Published
2024-05-14
Volume
99
Issue
2
Pages
715-727
ISSN
  • 1387-2877 (ISSN-L)
  • 1875-8908
OpenAlex
W4396792959
Cited by
2
References
63
Citations by year
2025: 2
FWCI
0.27
Type
journal article
Language
en
Open access
Locations
  • Journal of Alzheimer s Disease (publishedVersion, public-domain)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion)
Funders
  • National Science Foundation (1942394, CAREER)
  • Rush University
  • National Institutes of Health
  • National Institute on Aging (R01AG017917, P30-AG 10161, U01 AG32984, R01AG030146, R01 AG30146, R01‐AG17917, R01 AG15819, AG068057, R01-AG36836, R21AG066135, R01AG036836, U01AG046152, R01AG015819, P30AG010161, U01AG032984, R21AG072101, U01‐AG46152, U19AG074879, U01AG068057)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease
MeSH terms
Deep Learning, Alzheimer Disease, Humans, Polymorphism, Single Nucleotide, Gene Regulatory Networks

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