Human post-mortem brain samples from six brain regions were studied. These brain samples were from 55 subjects with age ranging from 16 weeks of gestation to 91 years, who had no history of psychiatric or neurological illness, and their post-mortem brain samples showed no neuropathological evidence of any neuropsychiatric disorder. The brain samples were collected from six brain regions, namely: the cerebellum (n = 47), brainstem (n = 49), amygdala (n = 47), striatum (n = 52), thalamus (n = 52), and frontal cortex (n = 54). These brain samples were procured from the Department of Neuropathology, Institute of Pathology, University of Würzburg, Germany (member of the BrainNet Europe-BNEII) and the London Neurodegenerative Diseases Brain Bank, UK. Informed written consent for tissue donation was obtained from the individuals or the next of kin. The causes of death of the study population are presented in Supplementary Table S6. This study was approved by the Cantonal Ethic Commission of Zurich (Ref. Nr. EK: KEK-ZH-Nr. 2013-0177).

DNA, RNA, and protein were simultaneously isolated from the same frozen human post-mortem brain samples using AllPrep DNA/RNA/Protein Mini Kit (Qiagen, Switzerland). Firstly, frozen brain tissue samples weighing around 27–34 mg were disrupted and homogenized in the buffer provided in the kit using the TissueLyser II (Qiagen, Switzerland). Secondly, tissue lysates were added to the AllPrep DNA spin column, and DNA was eluted. Thirdly, RNA was eluted after adding an ethanol treated sample to a RNeasy spin column. Finally, proteins in the sample were precipitated and pelleted by centrifugation. Since the buffer provided in the kit interferes with protein determination and does not dissolve proteins completely, protein eluates were acetone precipitated, and precipitated proteins were redissolved in urea buffer (7 M urea, 2 M thiourea, 2% CHAPS, and trace bromophenol, pH 8).

DNA isolated from post-mortem human cerebellum (n = 46), striatum (n = 2), thalamus (n = 5), and amygdala (n = 2) were used for genotyping. DNA concentrations, A260/A280, and A260/A230 ratios were measured using a spectrophotometer (NanoVue Plus, GE Healthcare Life Sciences). Real-time polymerase chain reaction (PCR) was used to genotype DAO (rs3918347, rs4623951), and DAOA (rs778293, rs3916971, rs746187) polymorphisms. These single-nucleotide polymorphisms (SNPs) were chosen based on previously reported association with schizophrenia (Allen et al., 2008). DNA, TaqMan^® Genotyping Master Mix (Applied Biosystems, USA), and SNP Genotyping Assays (rs778293 assay number: C_8704507_10; rs3916971 assay number: C_27495752_10; rs746187 assay number: C_1925241_10; rs3918347 assay number: C_27937201_10; and rs4623951 assay number: C_32177440_10, all from Applied Biosystems, USA) were combined in a 384-well plate. Real-time PCR was performed in a C1000^TM CFX384^TM Thermal cycler (Bio-Rad) using TaqMan^® SNP Genotyping Assay PCR standard protocol. Genotypes were determined by the allelic discrimination program of Bio-Rad CFX Manager^TM Software version 2.1. The Hardy–Weinberg equilibrium (HWE) p-value and allele frequencies were computed using PLINK software (Purcell et al., 2007), and p < 0.05 was considered as statistically significant.

RNA isolated from post-mortem human cerebellum (n = 44), brainstem (n = 40), amygdala (n = 39), striatum (n = 35), thalamus (n = 37), and frontal cortex (n = 36) were used for quantitative real-time reverse transcription-PCR (qRT-PCR). A spectrophotometer (NanoVue Plus, GE Healthcare Life Sciences) was used to measure RNA concentrations, A260/A280, and A260/A230 ratios. RNA integrity was analyzed using Experion automated electrophoresis system (Bio-Rad, Switzerland) in a subset of samples. RNA (500 ng) was reverse transcribed using iScript^TM cDNA Synthesis Kit (Bio-Rad, Switzerland) as per manufacturer’s protocol. In a subset of samples, negative controls were prepared with RNA without reverse transcriptase enzyme. qRT-PCR was performed using cDNA, QuantiFast SYBR Green PCR kit (Qiagen, Switzerland), 1 μM forward and reverse DAO primers (Microsynth, Switzerland), and reference genes [β-actin (ACTB) (QT01680476), aminolevulinate synthetase (ALAS1) (QT00073122), ribosomal protein L13a (RPL13A) (QT00089915), alanyl-tRNA synthetase (AARS) (QT00054747), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (QT01192646), peptidylprolyl isomerase A (PPIA) (QT00866137), ribosomal RNA (R18S) (QT00019936), and X-prolyl aminopeptidase 1 (XPNPEP1) (QT00051471); all from Qiagen, Switzerland]. These eight reference genes were selected based on previous reports of their stability in human post-mortem brain (Durrenberger et al., 2012). The DAO primers used in this study have been described by Verrall et al. (2007) (forward primer: CGCAGACGTGATTGTCAACT; reverse primer: GGATGATGTACGGGGAATTG). DAO mRNA levels were normalized to the reference genes. PCR efficiencies were calculated using LinRegPCR program (Ruijter et al., 2009) and mean PCR efficiencies for all studied amplicons were found to be between 88 and 95%. Normalized DAO mRNA levels were quantified by importing qRT-PCR quantification cycle (Cq) values of the gene of interest and reference genes across brain regions and lifespan into qBASE plus software (Biogazelle, Belgium) which utilizes gene-specific amplification efficiencies, and allows normalization with multiple reference genes. The qBASE plus software automatically selects multiple stably expressed reference genes in the samples across brain regions and lifespan by taking into account their stability. The software carries out a normalization of the gene of interest with multiple reference genes, ultimately producing calibrated normalized relative quantities of the gene of interest which was used to perform statistical analysis (Hellemans et al., 2007). To detect DAOA mRNA in human post-mortem brain, qRT-PCR was performed using several different primers for DAOA gene described by Benzel et al. (2008), Cheng et al. (2014), and pre-designed primers [QT00058863 (Qiagen), Hs.PT.58.555086 (IDT), 4331182 (Thermo Fisher Scientific), qHsaCEP0024792 (Bio-Rad)]. As a positive control, these primers were also tested on the cDNA prepared from human neuroblastoma cells (SK-N-SH and SH-SY5Y) overexpressing DAOA, and these primers detected DAOA mRNA in these cells, but not in the human post-mortem brain samples (data not shown). DAOA mRNA was still undetectable in human post-mortem brain after increasing the cDNA amounts in qRT-PCR. Furthermore, amplification using QuantiTect Whole Transcriptome Kit (207043, Qiagen) followed by qRT-PCR to detect DAOA mRNA levels was not successful. In summary, we were unable to quantify DAOA mRNA levels with any of the aforementioned methods in human post-mortem brain.

Protein isolated and acetone-precipitated from post-mortem human cerebellum (n = 40), brainstem (n = 47), amygdala (n = 46), striatum (n = 48), thalamus (n = 48), and frontal cortex (n = 54) were used for enzyme-linked immunosorbent assay (ELISA). Total protein concentration was quantified using the Bradford assay (Sigma-Aldrich; Bradford, 1976). DAO and DAOA protein concentrations were quantified using a commercially available DAO ELISA kit (SEJ298Hu; Cloud-Clone Corp.) with a high specificity and a sensitivity of 0.56 ng/mL and a commercially available DAOA ELISA kit (SEJ297Hu; Cloud-Clone Corp.) with a high specificity and a sensitivity of 0.061 ng/mL according to the manufacturer’s instructions. A standard curve was obtained by plotting absorbance versus different concentration of protein standards provided with the kit, and DAO and DAOA protein concentrations were determined from the standard curve. Normalized DAO and DAOA protein concentrations were calculated by dividing DAO and DAOA protein concentrations by the total protein concentration measured using Bradford assay. The specificity of DAO and DAOA ELISA kits was proved using sodium dodecyl sulfate polyacrylamide gel electrophoresis and Western blotting which is described in detail in Supplementary Methods S1.

The processed and normalized methylation beta values from two datasets: GEO accession number GSE61431 as described in the study of Pidsley et al. (2014) and GSE63347 as described by Horvath et al. (2015) were downloaded from the GEO website¹. From these datasets, the methylation beta values for DAO and DAOA CpG sites from the cerebellum and frontal cortex of controls were extracted. DAO (cg03868278, cg22621535, cg22801690) and DAOA (cg00809502) CpG sites in the repeat masker region sites were excluded from the analysis. DAO CpG sites: cg01694331, cg25362648, cg18037826, cg12592321 lie in the exon 1 and cg26725638 in the exon 2 of DAO gene. DAOA CpG sites: cg22773522, cg25623522, cg01297020 are in the promoter region, cg13846327 and cg20888753 in the intron 2, and cg11374446 in the intron 3 region of the DAOA gene. In the GSE61431 dataset, there were 23 control cerebellum and frontal cortex samples ranging from ages 25 to 96 years. In the GSE63347 dataset, there were nine control cerebellum samples in the ages between 38 and 64 years and 17 control frontal cortex samples between ages 32 and 64 years.

IBM^® SPSS^® Statistics (version 21) software was used for statistical analysis. Shapiro–Wilk test with Lilliefors significance correction was used to assess the normality of the distribution of mRNA, protein expression, and DNA methylation data. DAO mRNA, DAO and DAOA protein expression, and DNA methylation data showed both normal and non-normal distribution across different brain regions. In order to maintain consistency between statistical evaluations, non-parametric tests were used even for normally distributed data. Linear regression with brain regions as dummy variables was used after a logarithmic transformation of the non-normally distributed data to determine the differences in DAO mRNA, DAO and DAOA protein expression in the brain regions studied; p < 0.05 was taken as statistically significant. The differences in DAO mRNA, and DAO and DAOA protein levels across different age groups were assessed by the Kruskal–Wallis test followed by pair-wise comparisons with the Mann–Whitney test by adjusting p-value based on the number of comparisons (Bonferroni correction, p < 0.005). As we do not have post-mortem brain samples from ages 2 to 20 years, we divided the ages for correlation into two groups, namely less than 2 years and more than 20 years. Then, Spearman’s rank correlation test was used to assess the correlation between age groups (<2 years and >20 years) and DAO mRNA, DAO and DAOA protein levels; p < 0.05 was taken as statistically significant. We correlated DAO mRNA with DAO protein levels, and DAO protein levels with DAOA protein levels across six brain regions by controlling for age using partial correlation, p < 0.05 was taken as statistically significant. To analyze the effect of DAO and DAOA SNP genotypes on DAO mRNA, DAO and DAOA protein expression, analysis of covariance (ANCOVA) with age as a covariate was used after a logarithmic transformation of not normally distributed DAO mRNA and DAOA protein expression data and the inverse transformation of the non-normally distributed DAO protein expression data; p < 0.05 was taken as statistically significant. The differences in methylation levels between the cerebellum and frontal cortex at each CpG site was assessed using the Mann–Whitney test by adjusting p-value based on a number of CpG sites analyzed (Bonferroni correction, p < 0.005). The correlation between age and methylation levels at each CpG site in the cerebellum and frontal cortex was assessed using Spearman’s rank correlation test, and p < 0.05 was considered statistically significant. GraphPad Prism software (version 6.01) was used to plot the graphs. The post hoc power analyses for DAO mRNA, DAO and DAOA protein expression across five age groups were conducted using G*Power software (Faul et al., 2009), the effect sizes were determined from means of age groups, and the alpha level was set at 0.05 (Supplementary Table S7).

The current study aimed to assess brain region-specific DAO and DAOA expression across the lifespan in normal human post-mortem brain using qRT-PCR and ELISA techniques. The human post-mortem brain samples were obtained from 55 subjects with no clinical or neuropathological evidence of neuropsychiatric disorders. The demographic characteristics of the study sample across five age groups are presented in Supplementary Table S1. In order to assess any confounding factors inherent in human post-mortem studies, we addressed factors such as post-mortem interval (PMI) and gender. The length of PMI did not differ significantly between different age groups as assessed by the Kruskal–Wallis test. Gender had a significant effect on DAO mRNA (p = 0.048) and protein (p = 0.047) expression in the thalamus and frontal cortex, respectively (Mann–Whitney test, p < 0.05). DAO mRNA in the thalamus and DAO protein in the frontal cortex was significantly more expressed in males than in females. Gender had no significant effect on DAOA protein expression in all brain areas studied. There was no statistically significant correlation between PMI and DAO mRNA, DAO and DAOA protein levels in the studied brain regions (Spearman’s rank correlation, p > 0.05).

With the aim of investigating brain region-specific patterns of DAO and DAOA, we assessed their expression in the normal brain. We found that DAO mRNA was significantly more expressed in the cerebellum (set to 100%), followed by the brainstem (17.6%), thalamus (4.8%), striatum (2.5%), amygdala (0.9%), and the frontal cortex (0.7%) as assessed by linear regression (Figures 1A,B). In contrast to DAO mRNA, DAO protein was significantly less expressed in the cerebellum than in the remaining brain regions. In the amygdala (set to 100%), DAO protein was significantly more expressed than in the brainstem (79.2%; Figure 1C). DAOA protein was significantly more expressed in the frontal cortex (set to 100%) than in the cerebellum (65.2%), amygdala (80.4%), and the thalamus (69.6%). In the striatum (set to 100%), DAOA protein was significantly more expressed than in the amygdala (66.7%) and the cerebellum (54.1%). DAOA protein was significantly less expressed in the thalamus (57.7%) than in the brainstem (85.6%) and striatum (set to 100%; Figure 1D). Thus, DAO and DAOA exhibit region-specific expression patterns in the human brain.

The expression patterns across lifespan were analyzed using two approaches. The first approach was by grouping ages into five groups, while the second approach was by correlating ages with expression levels which provided information on expression patterns during brain development, maturation, and degeneration. We assessed DAO and DAOA expression across age groups in six brain regions to understand critical and sensitive periods during brain development. As the sample size in each age group was small, we performed a power analysis which showed reasonable power (>0.76) in the cerebellum, striatum, and thalamus for DAO mRNA as well as reasonable power (>0.64) in the cerebellum, striatum, and frontal cortex for DAO protein, but the power was too low in the brain regions studied for DAOA protein to conduct statistical analysis (Supplementary Table S7).

We found statistically significant (p < 0.05) differences in DAO mRNA levels across the five different age groups, as assessed by the Kruskal–Wallis test, in the cerebellum, brainstem (Figure 2B), amygdala, striatum, and thalamus. However, differences in DAO mRNA levels were found to be statistically non-significant in the frontal cortex (Figure 2F). Pair-wise comparisons using the Mann–Whitney test showed statistically significant differences (p < 0.005) in DAO mRNA levels between age groups 0–2 years and >61 years in the cerebellum (Figure 2A) and striatum (Figure 2D), between prenatal and >61 years in the amygdala (Figure 2C), and between prenatal and >61 years, 0–2 years and 36–60 years, 0–2 years and >61 years in the thalamus (Figure 2E).

The differences in DAO protein levels across the five different age groups was found to be statistically significant (p < 0.05), as assessed by the Kruskal–Wallis test, in the cerebellum (Figure 3A), striatum (Figure 3D), and frontal cortex (Figure 3F). However, differences in DAO protein levels were found to be statistically non-significant in the brainstem (Figure 3B), amygdala (Figure 3C), and thalamus (Figure 3E). Pair-wise comparisons with the Mann–Whitney test showed statistically significant differences (p < 0.005) in DAO protein levels for age groups >61 years versus prenatal and 0–2 years in the frontal cortex (Figure 3F). The differences in DAOA protein levels across the five different age groups were found to be statistically significant (p < 0.05) in the brainstem as assessed by the Kruskal–Wallis test. There were no statistically significant differences in DAOA protein levels in the cerebellum, amygdala, striatum, thalamus, and frontal cortex (Figure 4). The specificity of DAO and DAOA ELISA kits was demonstrated by Western blot as presented in Supplementary Figure S2.

Using the second approach, we correlated age (<2 years and >20 years) with DAO and DAOA expression across different brain regions to understand the critical developmental period and brain region in DAO and DAOA expression. We found a statistically significant positive correlation (p < 0.05) as assessed by Spearman’s rank correlation test, between age less than 2 years and DAO mRNA levels in the cerebellum and amygdala (Table 1). Furthermore, there was a significant positive correlation (Spearman’s rank correlation, p < 0.01) between age more than 20 years and DAO protein concentration in the frontal cortex (Table 1). There was no statistically significant correlation between age and DAOA protein concentration in the brain regions studied (Table 1).

In order to assess whether there is a concomitant expression of DAO mRNA and its protein, we correlated the two in various brain regions. We did not find any statistically significant correlation (partial correlation controlled for age) between DAO mRNA and DAO protein in each of the studied brain regions (Supplementary Figure S3).

As we concomitantly measured DAO and DAOA proteins in the human brain, we examined whether these two proteins correlate in each brain region. A statistically significant positive correlation (partial correlation controlled for age) was found between DAO and DAOA protein expression in the cerebellum [r(36) = 0.74, p < 0.0001; Figure 5A], the brainstem [r(43) = 0.84, p < 0.0001; Figure 5B], the amygdala [r(36) = 0.88, p < 0.0001; Figure 5C], the striatum [r(43) = 0.84, p < 0.0001; Figure 5D], and the thalamus [r(44) = 0.91, p < 0.0001; Figure 5E]. However, there was no statistically significant correlation between DAO and DAOA protein concentration in the frontal cortex [r(49) = 0.27, p = 0.06; Figure 5F].

We assessed the potential effect of DAO and DAOA SNPs on DAO and DAOA expression to understand whether such genetic variations known to be risk factors in schizophrenia play a role in the regulation of their expression. The DAO and DAOA SNPs were in HWE (p > 0.05), and the minor allele frequency (MAF) of DAO and DAOA SNPs were similar to HapMap CEU MAF (Supplementary Table S2). In the thalamus, DAO mRNA was significantly more expressed in rs3918347 DAO genotype GG (minor allele G) than in rs3918347 DAO genotypes (GA, AA), and DAO mRNA was less expressed in rs3918347 DAO genotypes GG + GA than in rs3918347 DAO genotype AA as assessed by ANCOVA with age as covariate (Supplementary Table S3). However, for the DAO protein, no statistically significant difference was detected among rs3918347 and rs4623951 DAO genotypes (Supplementary Table S4). For DAOA protein expression, there was a significantly higher expression in rs3916971 DAOA genotype TT (minor allele T) than in rs3916971 DAOA genotypes (CT, CC, CT + CC) in the amygdala, as assessed by ANCOVA with age as a covariate (Supplementary Table S5). Nonetheless, from our findings, we cannot conclusively comment on the effect of DAO and DAOA SNPs on their expression due to their rather small sample size.

From our DAO and DAOA mRNA and protein findings, we hypothesized a possible regulation of their expression at the transcriptional levels. To elucidate this, we analyzed in silico DNA methylation across DAO and DAOA CpG sites in the cerebellum and frontal cortex to indirectly explain the differential DAO and DAOA expression in the human brain. In GSE61431 and GSE63347 datasets, gender had no statistically significant effect on DNA methylation levels at DAO and DAOA CpG sites in the cerebellum and frontal cortex (Mann–Whitney test, p > 0.005). In GSE61431 and GSE63347 datasets, DAO CpG site cg18037826 is significantly more methylated in the cerebellum than in the frontal cortex as assessed by the Mann–Whitney test, p < 0.001 (Supplementary Figure S1A). In the GSE61431 dataset, all studied DAOA CpG sites except cg13846327 are statistically significantly (p < 0.001) more methylated in the cerebellum than in the frontal cortex (Supplementary Figure S1B). In GSE63347 dataset, DAOA CpG sites cg22773522, cg20888753, and cg11374446 are significantly more methylated in the cerebellum than in the frontal cortex (Supplementary Figure S1B). There was no statistically significant correlation between age and DNA methylation levels at DAO and DAOA CpG sites in the cerebellum and frontal cortex in both studied datasets.