Acta neuropathologica · 2025
Brain-derived 5-hydroxymethylcytosine epigenetic scores are related to Alzheimer's disease pathology and cognitive decline.
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Zhang Zhou, Capuano Ana W, Beadell Alana, Tasaki Shinya, Cui Xiaolong, Mehta Rupal I, Bennett David A, He Chuan, Zhang Wei, Arvanitakis Zoe
Аннотация
BACKGROUND: Cytosine modifications play critical roles in gene regulation and disease pathogenesis. Elucidating novel epigenetic contributions to Alzheimer's disease (AD) could advance diagnostic, prognostic, and therapeutic strategies. 5-hydroxymethylcytosine (5hmC), a stable and dynamic DNA modification, has emerging links to AD pathophysiology and potential uses as a biomarker. Cognitive decline, a hallmark of AD progression, varies across individuals and is not fully explained by classic pathological markers. Here, we aimed to develop and validate brain-derived 5hmC-based epigenetic scores to distinguish AD from non-AD pathology and examine their relationship to individual cognitive trajectories.
METHODS: Genome-wide 5hmC profiles were generated using 5hmC-Seal and next-generation sequencing on 1016 postmortem human brain prefrontal cortex samples from well-characterized, deceased participants in a longitudinal, clinical-pathologic research study on aging. Samples were processed in independent training and validation sets. Genomic features (e.g., gene bodies, enhancers) were summarized, followed by differential analysis and machine learning-based feature selection to construct classification models distinguishing AD from non-AD.
RESULTS: After quality control and batch correction of 5hmC data, 1005 participants were included, with 655 classified as having AD and 350 as non-AD, according to NIA-AA neuropathologic criteria. In the training set (n = 859), 136 candidate gene bodies and 96 enhancers were selected based on variability and relaxed significance thresholds (p < 0.1). Pathway enrichment analyses implicated cardiovascular function, endocytosis, and MAPK signaling pathways. Using these features, we developed machine learning models that distinguished AD from non-AD with high performance in both the training set (AUC = 87.0%; 95% CI 84.2-89.7%) and validation set (n = 146; AUC = 91.4%; 95% CI 86.6-96.2%). Moreover, the resulting AD-score was significantly associated with rates of global and five domain-specific cognitive decline.
CONCLUSION: This study extends prior work by translating brain 5hmC profiles into epigenetic scores that distinguish AD pathology and reflect individual cognitive trajectories. These findings highlight the potential of brain-derived 5hmC modifications as biomarkers for AD and as tools to advance research on disease progression, offering a new direction for epigenetics-informed clinical applications in AD.
METHODS: Genome-wide 5hmC profiles were generated using 5hmC-Seal and next-generation sequencing on 1016 postmortem human brain prefrontal cortex samples from well-characterized, deceased participants in a longitudinal, clinical-pathologic research study on aging. Samples were processed in independent training and validation sets. Genomic features (e.g., gene bodies, enhancers) were summarized, followed by differential analysis and machine learning-based feature selection to construct classification models distinguishing AD from non-AD.
RESULTS: After quality control and batch correction of 5hmC data, 1005 participants were included, with 655 classified as having AD and 350 as non-AD, according to NIA-AA neuropathologic criteria. In the training set (n = 859), 136 candidate gene bodies and 96 enhancers were selected based on variability and relaxed significance thresholds (p < 0.1). Pathway enrichment analyses implicated cardiovascular function, endocytosis, and MAPK signaling pathways. Using these features, we developed machine learning models that distinguished AD from non-AD with high performance in both the training set (AUC = 87.0%; 95% CI 84.2-89.7%) and validation set (n = 146; AUC = 91.4%; 95% CI 86.6-96.2%). Moreover, the resulting AD-score was significantly associated with rates of global and five domain-specific cognitive decline.
CONCLUSION: This study extends prior work by translating brain 5hmC profiles into epigenetic scores that distinguish AD pathology and reflect individual cognitive trajectories. These findings highlight the potential of brain-derived 5hmC modifications as biomarkers for AD and as tools to advance research on disease progression, offering a new direction for epigenetics-informed clinical applications in AD.