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Nanoparticle-Enabled Enrichment of Longitudinal Blood Proteomic Fingerprints in Alzheimer's Disease.

ABSTRACT: Blood-circulating biomarkers have the potential to detect Alzheimer's disease (AD) pathology before clinical symptoms emerge and to improve the outcomes of clinical trials for disease-modifying therapies. Despite recent advances in understanding concomitant systemic abnormalities, there are currently no validated or clinically used blood-based biomarkers for AD. The extremely low concentration of neurodegeneration-associated proteins in blood necessitates the development of analytical platforms to address the "signal-to-noise" issue and to allow an in-depth analysis of the plasma proteome. Here, we aimed to discover and longitudinally track alterations of the blood proteome in a transgenic mouse model of AD, using a nanoparticle-based proteomics enrichment approach. We employed blood-circulating, lipid-based nanoparticles to extract, analyze and monitor AD-specific protein signatures and to systemically uncover molecular pathways associated with AD progression. Our data revealed the existence of multiple proteomic signals in blood, indicative of the asymptomatic stages of AD. Comprehensive analysis of the nanoparticle-recovered blood proteome by label-free liquid chromatography-tandem mass spectrometry resulted in the discovery of AD-monitoring signatures that could discriminate the asymptomatic phase from amyloidopathy and cognitive deterioration. While the majority of differentially abundant plasma proteins were found to be upregulated at the initial asymptomatic stages, the abundance of these molecules was significantly reduced as a result of amyloidosis, suggesting a disease-stage-dependent fluctuation of the AD-specific blood proteome. The potential use of the proposed nano-omics approach to uncover information in the blood that is directly associated with brain neurodegeneration was further exemplified by the recovery of focal adhesion cascade proteins. We herein propose the integration of nanotechnology with already existing proteomic analytical tools in order to enrich the identification of blood-circulating signals of neurodegeneration, reinvigorating the potential clinical utility of the blood proteome at predicting the onset and kinetics of the AD progression trajectory.

SUBMITTER: Hadjidemetriou M

PROVIDER: S-EPMC8155389 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and objectiveBlood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.MethodsWe measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis.ResultsAge and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway.ConclusionCombined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

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Nanoparticle-Enabled Enrichment of Longitudinal Blood Proteomic Fingerprints in Alzheimer's Disease.

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