Integrated Multi‐Omics Analysis and Cross‐Model Validation Reveal Mitochondrial Signatures in Alzheimer's Disease

TL;DR

This study identifies mitochondrial biomarkers in Alzheimer's disease using multi-omics data and validates them in mouse and cell models, revealing key genes and pathways involved in the disease.

Contribution

The study introduces a novel framework combining multi-omics data and cross-model validation to identify and functionally validate mitochondrial signatures in Alzheimer's disease.

Findings

Key biomarkers like hsa-miR-129-5p and SLC6A12 were identified as pivotal regulators in AD.

A core signature of seven genes, including APOE, CDKN1A, and CLOCK, was consistently dysregulated in both mouse and cell models.

The TCA cycle was highlighted as a critical pathway in mitochondrial dysfunction in AD.

Abstract

Alzheimer's disease (AD) is a devastating neurodegenerative disorder where mitochondrial dysfunction is increasingly recognized as pivotal, yet its comprehensive molecular underpinnings remain incompletely understood. This study aimed to systematically identify and validate mitochondria‐related biomarkers associated with AD risk and brain resilience, thus elucidating the molecular mechanisms underpinning mitochondrial dysfunction in AD.

We innovatively integrated a multi‐omics approach, encompassing genomics, DNA methylation, RNA‐sequencing, and miRNA profiles from the ROSMAP and ADNI cohorts (sample sizes ranging from 638 to 2090 per omic layer). Additionally, we applied 10 distinct machine learning methods to robustly identify and validate critical mitochondrial biomarkers relevant to AD progression. Subsequent validation was performed using a two‐tiered approach: an in vivo AD mouse model to establish phenotypic relevance and an in vitro H2O2‐induced oxidative stress model in HT22 cells to provide direct mechanistic validation.

Our computational analyses identified key biomarkers such as hsa‐miR‐129‐5p and SLC6A12 as pivotal regulators and highlighted the importance of the tricarboxylic acid (TCA) cycle. Experimentally, our AD mouse model exhibited significant cognitive deficits and brain remodeling, linked to a specific transcriptomic signature. Our in vitro model functionally recapitulated mitochondrial dysfunction and oxidative stress. Crucially, a cross‐model analysis revealed a core signature of seven genes (including APOE, CDKN1A, and CLOCK) consistently dysregulated in both the cognitively impaired mouse brain and in neuronal cells subjected to direct oxidative insult. This provides powerful functional evidence linking our computationally derived targets, such as mitochondrial‐epistatic genes (CLOCK), to AD‐relevant pathology.

These functionally validated findings provide deeper insights into the complex mitochondrial regulatory mechanisms involved in AD pathogenesis, offering robust biomarkers and novel potential avenues for developing targeted therapeutic strategies to address this challenging neurodegenerative disease.

This study's framework begins with large‐scale data integration, leveraging the ROSMAP cohort for multi‐omics discovery (genotyping, DNA methylation, transcriptomics, miRNA) and the ADNI cohort for validation. This data feeds into a comprehensive analytical pipeline where differential expression analysis and an ensemble of 10 machine learning algorithms identify robust mitochondrial biomarkers of AD risk and brain resilience. To explore their biological context, a ceRNA network is constructed, and the key findings are subjected to a rigorous two‐tiered validation: in vivo confirmation of gene expression in an AD mouse model, followed by in vitro functional assessment of mitochondrial health (ROS, membrane potential) in an H2O2‐induced oxidative stress model in HT22 cells. This holistic discovery‐to‐validation workflow establishes a robust platform for identifying novel therapeutic targets and advancing our molecular understanding of AD.

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