Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Abstract

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at approximately 450,000 CpG sites in 9,732 middle-aged to older adults from 14 community-based studies. Single-CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single-CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10−8), was associated with F2 expression in blood (P = 6.4 × 10−5), and colocalized with FOLH1 expression in brain (posterior probability =0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single-CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis, and multi-omics colocalization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug repositioning analysis indicated antihyperlipidemic agents, more specifically peroxisome proliferator-activated receptor alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood brain barrier disruption.

Choroid Plexus Enlargement Exacerbates White Matter Hyperintensity Growth through Glymphatic Impairment

“…47 Recently, several integrative cross-omics analyses independently implicate a few converged peripheral pathways related to elevated immune pressure in patients with WMH. 48,49 However, whether CP mediates the peripheralcentral immune pressure transfer or represents the neuroinflammatory state in the context of WMH still needs further investigation. Overall, CP, together with its products, participates in the regulation of cholesterol homeostasis, circadian rhythm, and inflammation, which may all contribute to the pathogenesis of WMH.…”

Current and Future Treatments of Vascular Cognitive Impairment

“…Beyond genetic associations, the field of multiomics is gaining traction in the study of cSVD. Recent interesting works have uncovered blood epigenetic and metabolomic signatures that correlate with cSVD severity, 222,223 disease burden quantified by imaging markers, and cognitive function. [224][225][226] These implicate a wide range of cSVD-associated biological processes, ranging from BBB and extracellular matrix organization, to lipid and lipoprotein metabolism and immune responses.…”

Applications of multi‐omics analysis in human diseases

“…The acquisition of phenotypic data of the disease is crucial for omics to reveal the molecular changes in the progression of the disease, and may directly find the key target molecules of the disease. For example, in the multi‐omics discovery of peripheral blood biomarkers of Alzheimer's disease, various cognitive impairment scale scores (MMSE and MoCA), brain imaging (PET and MRI), and other phenotype data play a decisive role in the discovery of diagnostic biomarkers 83–86 . Similarly, the purpose is to know the differentially expressed molecules between the disease and the control, so the experimental design should focus on the sample size 87 ; if the experiment wants to find new molecules, the omics design should focus on a higher technical coverage depth, such as long‐read transcriptome 88 .…”

“…For example, in the multi-omics discovery of peripheral blood biomarkers of Alzheimer's disease, various cognitive impairment scale scores (MMSE and MoCA), brain imaging (PET and MRI), and other phenotype data play a decisive role in the discovery of diagnostic biomarkers. [83][84][85][86] Similarly, the purpose is to know the differentially expressed molecules between the disease and the control, so the experimental design should focus on the sample size 87 ; if the experiment wants to find new molecules, the omics design should focus on a higher…”