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. 2023;93(3):1135-1151.
doi: 10.3233/JAD-221113.

Genome-Wide Mapping Implicates 5-Hydroxymethylcytosines in Diabetes Mellitus and Alzheimer's Disease

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Genome-Wide Mapping Implicates 5-Hydroxymethylcytosines in Diabetes Mellitus and Alzheimer's Disease

Alana V Beadell et al. J Alzheimers Dis. 2023.

Abstract

Background: Diabetes mellitus (DM) is a recognized risk factor for dementia. Because DM is a potentially modifiable condition, greater understanding of the mechanisms linking DM to the clinical expression of Alzheimer's disease dementia may provide insights into much needed dementia therapeutics.

Objective: In this feasibility study, we investigated DM as a dementia risk factor by examining genome-wide distributions of the epigenetic DNA modification 5-hydroxymethylcytosine (5hmC).

Methods: We obtained biologic samples from the Rush Memory and Aging Project and used the highly sensitive 5hmC-Seal technique to perform genome-wide profiling of 5hmC in circulating cell-free DNA (cfDNA) from antemortem serum samples and in genomic DNA from postmortem prefrontal cortex brain tissue from 80 individuals across four groups: Alzheimer's disease neuropathologically defined (AD), DM clinically defined, AD with DM, and individuals with neither disease (controls).

Results: Distinct 5hmC signatures and biological pathways were enriched in persons with both AD and DM versus AD alone, DM alone, or controls, including genes inhibited by EGFR signaling in oligodendroglia and those activated by constitutive RHOA. We also demonstrate the potential diagnostic value of 5hmC profiling in circulating cfDNA. Specifically, an 11-gene weighted model distinguished AD from non-AD/non-DM controls (AUC = 91.8%; 95% CI, 82.9-100.0%), while a 4-gene model distinguished DM-associated AD from AD alone (AUC = 87.9%; 95% CI, 77.5-98.3%).

Conclusion: We demonstrate in this small sample, the feasibility of detecting and characterizing 5hmC in DM-associated AD and of using 5hmC information contained in circulating cfDNA to detect AD in high-risk individuals, such as those with diabetes.

Keywords: 5-hydroxymethylcytosine; Alzheimer’s disease; cell-free DNA; dementia; diabetes mellitus; epigenetics.

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Conflict of interest statement

Competing interests

CH was the scientific founder of Epican Genetech, which obtained a license from the University of Chicago to develop the 5hmC-Seal technique for clinical applications. WZ was an advisor of Epican Genetech, and he received research support from the company.

Figures

Figure 1.
Figure 1.. Study design.
A cohort of age- and sex-matched circulating cfDNA and DLPFC brain tissue gDNA samples from the Memory and Aging Project (Rush University Medical Center) was used to identify genomic distributions of 5hmC in four different disease/control groups (AD-only, DM-only, AD+DM, and controls), to perform differential 5hmC-gene analysis to determine 5hmC-associated biological features in the different groups, and to explore the diagnostic potential of 5hmC in circulating cfDNA for AD using a machine learning approach. AD: Alzheimer’s Disease; DM: Diabetes Mellitus; control: non-AD/non-DM individuals; QC: quality control; DLPFC: dorsolateral pre-frontal cortex; cfDNA: cell-free DNA; gDNA: genomic DNA.
Figure 2.
Figure 2.. Genome-wide distributions of 5hmC in circulating cfDNA and brain tissue.
Genome-wide 5hmC profiles are summarized for various genomic feature types, including gene bodies, promoter regions, exons, and enhancers. A. Gene bodies exhibit enrichment of 5hmC-Seal reads compared to flanking regions regardless of AD status. TSS: transcription start site; TES: transcription end site; AD+: AD-only and AD+DM samples; AD-: DM-only and non-AD/non-DM controls. B. and C. Co-localization of 5hmC-Seal reads with histone modifications identified in specific tissues from the Epigenomics Roadmap Project are shown for two enhancer markers: B. H3K27ac; and C. H3K4me1. AD+: AD-only and AD+DM samples; AD-: DM-only and non-AD/non-DM controls. D. 5hmC-Seal reads demonstrate high correlation between person-matched brain tissue and circulating cfDNA samples. Blue, green, red, and black lines indicate shared top genes between cfDNA and brain tissue in AD-only, DM-only, AD+DM, and control samples, respectively. Dotted line indicates the number of simulated shared genes. AD: Alzheimer’s Disease; DM: Diabetes Mellitus; CTL: non-AD/non-DM individuals.
Figure 3.
Figure 3.. Differential analysis and hierarchical clustering using circulating cfDNA suggests strong correlations between 5hmC distribution and disease states.
Differential analysis using multivariable logistic regression detects gene bodies differentially 5hmC-modified in cfDNA for hierarchical clustering: A. AD-only vs. controls (CTL); B. AD-only vs. AD+DM; and C. DM-only vs. AD+DM. D. Venn diagram of the differential genes identified in three comparisons: AD+DM vs. DM-only; AD+DM vs. AD-only; AD-only vs. non-AD/non-DM controls. Abbreviations as above and in main text.
Figure 4.
Figure 4.. Gene annotation analyses of differential genes using circulating cfDNA suggests distinct biological pathways affected in persons with AD in different backgrounds.
A. B. Comparison of gene annotation terms associated with differential genes reveals similarities and differences in associated biological pathways for the different disease and control backgrounds. Dashed box highlights annotation terms in common to the two comparisons of persons with AD+DM versus each single disease (≥ 5% of input genes and p-value <0.01). PFC, pre-frontal cortex; ECM, extracellular matrix; ER, endoplasmic reticulum; NPCs, neural precursor cells; H3me, methylation of histone H3. AD: Other abbreviations as above and in main text.
Figure 5.
Figure 5.. Machine-learning suggests diagnostic potential of 5hmC in cfDNA for AD.
Statistical modeling using an elastic net regularization and multivariable logistic regression approach identifies: A. a weighted model comprised of 11 gene bodies that distinguishes AD from controls (CTL); B. a 4-gene weighted model that distinguishes AD from AD+DM; and C. a 4-gene weighted model that distinguishes DM from AD+DM. The AUC (area under the curve) and 95% CI (confidence interval) indicate performance of selected features evaluated by the LOOCV (leave-one-out cross-validation) procedure. Abbreviations as above and in main text.

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