Cross-tissue correlations of genome-wide DNA methylation in Japanese live human brain and blood, saliva, and buccal epithelial tissues

Abstract

Neuroepigenetics considers genetic sequences and the interplay with environmental influences to elucidate vulnerability risk for various neurological and psychiatric disorders. However, evaluating DNA methylation of brain tissue is challenging owing to the issue of tissue specificity. Consequently, peripheral surrogate tissues were used, resulting in limited progress compared with other epigenetic studies, such as cancer research. Therefore, we developed databases to establish correlations between the brain and peripheral tissues in the same individuals. Four tissues, resected brain tissue, blood, saliva, and buccal mucosa (buccal), were collected from 19 patients (aged 13-73 years) who underwent neurosurgery. Moreover, their genome-wide DNA methylation was assessed using the Infinium HumanMethylationEPIC BeadChip arrays to determine the cross-tissue correlation of each combination. These correlation analyses were conducted with all methylation sites and with variable CpGs, and with when these were adjusted for cellular proportions. For the averaged data for each CpG across individuals, the saliva-brain correlation (r = 0.90) was higher than that for blood-brain (r = 0.87) and buccal-brain (r = 0.88) comparisons. Among individual CpGs, blood had the highest proportion of CpGs correlated to the brain at nominally significant levels (19.0%), followed by saliva (14.4%) and buccal (9.8%). These results were similar to the previous IMAGE-CpG results; however, cross-database correlations of the correlation coefficients revealed a relatively low (brain vs. blood: r = 0.27, saliva: r = 0.18, and buccal: r = 0.24). To the best of our knowledge, this is the fifth study in the literature initiating the development of databases for correlations between the brain and peripheral tissues in the same individuals. We present the first database developed from an Asian population, specifically Japanese samples (AMAZE-CpG), which would contribute to interpreting individual epigenetic study results from various Asian populations.

Fig. 1. Location of each resected brain tissue according to the recorded Montreal Neurological Institute (MNI) coordinates.

(A) front, (B) top, and (C) left-side views. Each sample is represented by a colored circle.

Fig. 2. Tissue specificity and ancestral differences in genome-wide DNA methylation between the AMAZE-CpG and IMAGE-CpG datasets.

A Multidimensional scaling (MDS) of genome-wide DNA methylation levels from brain, blood, saliva, and buccal samples using a Euclidian distance plot. B Principal components (PCs) for population stratification based on DNA methylation. Top: First and second PC from PC_0bp. Bottom: Second and third PC from PC_0bp. Solid circles represent the AMAZE-CpG dataset, whereas open circles represent the IMAGE-CpG dataset.

Fig. 3. Density scatter plots of cross-tissue correlation for each tissue combination.

(A) Raw, and (B) cell proportion adjusted datasets. AMZ: AMAZE-CpG dataset, and IMG: IMAGE-CpG dataset. Red line: regression line.

Fig. 4. Histograms depicting the distribution of correlations (rho) of the individual CpGs between the brain and blood (left), saliva (center), and buccal (right).

(red) Datasets in raw and (blue) cell proportion adjusted form. AMZ AMAZE-CpG dataset, and IMG IMAGE-CpG dataset.

Fig. 5. Comparison of correlation coefficients between the datasets.

Correlation density scatter plots of the Spearman’s rho between the datasets for each tissue combination for (A) raw and (B) cell proportion adjusted datasets. Venn diagrams summarizing the number of correlations between the brain and each peripheral tissue that did not differ within Δrho < 0.2 between the datasets for (C) Raw and (D) cell proportion adjusted datasets. AMZ AMAZE-CpG dataset, and IMG IMAGE-CpG dataset. The regression line is in red. The dashed line represents the boundary line within Δrho < 0.2 between the datasets.