Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD

doi:10.21203/rs.3.rs-4519315/v1

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Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD

https://doi.org/10.21203/rs.3.rs-4519315/v1

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Research indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene ST3GAL3 in this process. Adopting a pathways approach, this study sought to examine the role that ST3GAL3 and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [m_age= 10.40 (0.49); 66% male] and 50 non-ADHD controls [m_age= 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a = 0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that ST3GAL3 and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings.

Biological sciences/Neuroscience/Epigenetics in the nervous system

Health sciences/Pathogenesis

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder primarily characterized by difficulties with inattention and/or impulsivity and hyperactivity with neuroimaging research highlighting broad structural and functional brain differences between those with ADHD and neurotypical individuals (1, 2). Although genetic studies suggest substantial heritability (3), understanding of ADHD pathophysiology is still lacking. Genome-wide association studies (GWAS) indicate complex underlying polygenic architecture (4–6) potentially contributing to aberrant brain structure and function in ADHD, while neuroimaging research of brain white matter microstructure have reported differences in ADHD, with suggestion that this could be attributed to differences in myelination (1, 6–9). In addition to this, broader work suggests potential contribution of environmental factors (7). Examination of epigenetic modifications to gene expression, such as DNA methylation, could aid in unravelling the complex underpinnings of ADHD as it presents a window through which to examine interactions between gene and environment. This highlights a need for epigenetic exploration of ADHD-implicated genes.

Recently, the largest genome-wide association study (GWAS) to date was conducted, with 20,183 diagnosed ADHD cases and 35,191 controls (5). The highest-ranked of twelve loci associated with ADHD was annotated to ST3GAL3, whose function is implicated in myelination (5), a key structural component of brain tissue. Aligning with this gene’s role, neuroimaging research of brain white matter microstructure have reported differences in ADHD, with suggestion that this could be attributed to differences in myelination (1, 8–11). ST3GAL3, located on chromosome 1p34.1 encodes β-galactoside-α2,3-sialyltransferase-III (ST3Gal-III), which is actively involved in sialylation, an integral part of the glycocalyx of glycoproteins and glycolipids (12). With glycolipids forming a fundamental component of myelin, any disruption to intercellular communication through differential glycocalyx formation could ultimately disrupt the formation and function of neural white matter. Human neural sialic acid concentrations appear two to four times higher than other mammals, and in comparison to non-neural cellular membranes, sialic acid is up to twenty times higher in neural cellular membranes (13). In addition to mediating sialylation, in the mouse brain, St3gal3 has also been shown to produce gangliosides and polysialic acid in neural cells, which play crucial roles in synaptogenesis and subsequently neurotransmission and cognition (12, 14). St3gal3-deficient mice exhibit significant reduction in myelin thickness and major myelin proteins (15). In humans, ST3GAL3 is therefore believed to play a vital role in brain development. Mutations of the ST3GAL3 gene have been liked with a range of neurological manifestations including intellectual developmental disorder (16), developmental and epileptic encephalopathy (17), West syndrome (18), and autosomal recessive intellectual disability (19).

Recent epigenetic research into ADHD also implicates ST3GAL3. A methylome-wide analysis on children at birth and at age 7, using cord blood at birth and whole blood at age 7 (6), found DNA methylation of a probe annotated to ST3GAL3 (Illumina Infinium array probe cg09989037) at birth to be significantly associated with ADHD diagnosis at age 7. This probe was found to be hypomethylated in comparison to neurotypical controls. However, this association was not maintained at age 7. The variability of findings in the literature highlights a need for further examination of ST3GAL3 in paediatric ADHD cohorts.

This study therefore aimed to investigate the relationship between DNA methylation of ST3GAL3 and ADHD, both categorically (diagnosis) and dimensionally (symptom severity). In addition to ST3GAL3, probes annotated to all other genes involved in sialic acid metabolism (SAM) were examined. It was hypothesised that variability in DNA methylation of ST3GAL3, and broader SAM genes, will be associated with ADHD diagnosis and symptom severity.

Participants

Data from the Children’s Attention Project (CAP) study (20) was used in this study. The recruitment protocol and procedures are documented elsewhere (21). Briefly, children were initially recruited at age 6–8 years across Melbourne, Australia, from 43 socio-economically diverse primary schools. Screening was performed using the Conners 3 ADHD Index (22) and a parent face-to-face structured diagnostic interview (NIMH Diagnostic Interview Schedule for Children IV – DISC-IV; (23)) was administered for case-confirmation. At a 36-month follow-up (aged 9–11 years), diagnostic status was re-assessed, and a subset of participants were invited to provide a saliva sample. Participants provided 3 ml of saliva by passive drool into a 50 ml centrifuge tube (Eppendorf South Pacific, NSW, Australia), and stored in fridge at -80°C. Clinical, behavioural, and epigenetic assessments were conducted by researchers blinded to the diagnostic status of the participants. This study was approved by The Royal Children’s Hospital Human Research Ethics Committee (HREC #34071), and parents or guardians gave informed consent for participating children.

Exclusion criteria included intellectual disability, serious medical conditions, genetic disorders, moderate-severe sensory impairment, neurological problems, and parents with insufficient English to complete interviews/questionnaires. Following quality control, one individual was removed due to poor sample performance, the final sample consisted of 90 children with ADHD [m_age= 10.40 (0.49); 66% male] and 50 non-ADHD controls [m_age= 10.40 (0.45); 48% male]. Of the ADHD group, 24% of children were on medication at the time of sample collection. See supplementary material for more information on medication status.

Measures

Demographics and clinical assessment

ADHD diagnostic status was ascertained using the Diagnostic Interview Schedule for Children (DISC-IV) (23). Assessment was conducted upon recruitment (6–8 years) and repeated at the current sampling (9–11 years). Children were categorized in the ADHD group if they had a childhood history of ADHD (meeting criteria at recruitment and/or sampling wave). ADHD symptom severity was assessed using the Conners-3 ADHD Index (parent report) (22). At recruitment, parents/guardians completed a self-report questionnaire that included retrospective questions relating to fetal gestation period (weeks), birth weight (grams), post-natal intensive care stay (y/n) and trimester 1, 2 & 3 maternal alcohol and smoking consumption. Between group differences in these were assessed using either between-groups t-tests or chi-squared tests.

DNA methylation analysis

Probe selection

Rather than examining a single candidate gene site, this study adopted a pathways approach (Fig. 1) whereby the broader biochemical pathway was accounted for during probe selection. In this case, ST3GAL3 is implicated in sialic acid metabolism (SAM). Definition of the SAM pathway can be found on the Reactome website (https://reactome.org/content/detail/R-HSA-4085001) (24). The following protocol outlines how a set of genomic regions of interest was defined for SAM. This region set of interest included probes annotated to all involved genes, as well as probes within associated promoter and enhancer regions.

[INSERT FIGURE 1 HERE]

Figure 1.

Steps taken to identify probes within a genomic region of interest.

Step 1. Microarray pre-processing and quality control

For comprehensive overview of microarray pre-processing and quality control see supplementary material. Briefly, genomic DNA was extracted from saliva samples and pre-processed. Extracted genomic DNA samples were bisulphite treated and hybridised to Infinium MethylationEPIC arrays (EPIC; Illumina, San Diego, CA, USA) at NTX-Dx (Diagenode, Ghent, Belgium). These arrays generate data from over 850,000 CpGs throughout the human genome. Raw intensity data was imported into R (3.6.3, http://cran.r-project.org/). Data underwent subset-quantile normalisation (SQN) (25), followed by data quality assessment using the minfi (v1.34.0) Bioconductor package (26). EPIC probes (n = 865,859) were filtered by removing those with a poor signal to noise ratio (mean detection p-value of > 0.01, n = 113,011), containing a single nucleotide polymorphism at the CpG site (n = 26,455), mapping to sex chromosomes (n = 16,776), or cross-reactivity to multiple genomic locations (n = 34,471) (27).

DNA methylation levels were reported as beta (β) values (proportion of methylated intensity over total intensity values). Beta values were converted to M-values (log₂ ratio of the methylated intensity divided unmethylated intensity) for statistical regression analyses (28).

Step 2. Define the Reactome pathway of interest

The Reactome library links genes to different biochemical pathways throughout the body. Searching of the Reactome library (https://reactome.org/) confirmed ST3GAL3 to play a role in SAM. The Reactome library used in this study was downloaded as a GMT file (https://reactome.org/download/current/ReactomePathways.gmt.zip) in Aug 2023 and was stored and accessed locally.

Step 3. Define SAM gene set

The search resulted in 33 genes associated with the SAM pathway. A summary of these genes can be seen in Table 1.

Table 1

Summary of Sialic Acid Metabolism (SAM) genes and probes
Gene	Probes	Gene	Probes	Gene	Probes
CMAS	18	SCL17A5	25	ST6GALNAC2	27
CTSA	30	SLC35A1	24	ST6GALNAC3	68
GLB1	52	ST3GAL1	94	ST6GALNAC4	33
GNE	31	ST3GAL2	60	ST6GALNAC5	46
NANP	23	ST3GAL4	93	ST6GALNAC6	33
NANS	23	ST3GAL5	72	ST8SIA1	39
NEU1	102	ST3GAL5	44	ST8SIA2	41
NEU2	12	ST3GAL6	44	ST8SIA3	18
NEU3	21	ST6GAL1	74	ST8SIA4	47
NEU4	43	ST6GAL2	50	ST8SIA5	28
NPL	35	ST6GALNAC1	21	ST8SIA6	48

Table 1. Summary of Sialic Acid Metabolism (SAM) genes and probes

[INSERT Table 1 HERE]

Step 4. Extract probes from annotation file (gene names)

Following the definition of SAM genes, a list of probes (n = 1331) was created. Using R, the Illumina annotation file for the EPIC array (https://sapac.support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit/downloads.html) was subset to only include probes annotate to SAM genes.

Step 5. Extract probes from annotation file (gene names)

In addition to the list created in Step 4, a secondary probe list was created based on gene location (n = 696). Again, the Illumina annotation file for the EPIC array was subset to only include probes that were located within 3 kb of the first and last exon of each SAM gene, as well as within the defined gene coding region.

Step 6. Extract associated enhancer and promotor probes

Finally, probes identified as SAM gene associated promoters/enhancers, with a gene association score greater than 50 were also included in the probe list (n = 653). These probes were identified through downloading gene information via the GeneCards database (https://genealacart.genecards.org/) (29) on 02/06/2023. Probes that existed within these defined promotor/enhancer regions were then extracted from the Illumina annotation file for the EPIC array.

Step 7. Generate list of all pathway probes export file for use in differential methylation analysis

Following extraction of gene-associated probes (name and location), as well as associated enhancers/promoters, lists were combined, and duplicates removed to include only unique probes. This resulted in 1419 probes identified across the 33 genes involved the SAM pathway, including ST3GAL3. A summary of the number of probes per gene can be seen in Table 1.

Step 8. Calculation of sample cell heterogeneity

As previously recommended (30), cell-type heterogeneity of samples were controlled for during analysis. Epithelial cell count was estimated using the Epigenetic Dissection of Intra-Sample Heterogeneity (EpiDISH) (31–34) and included as a covariate in all analysis.

Step 9. Conduct differential methylation analysis

The identification of differentially methylated probes was performed using the Bioconductor limma package (35). Following analysis, p-values were adjusted for multiple testing using a false discovery rate (FDR) method (Benjamini and Hochberg, 1995). Differentially methylated CpG probes (DMPs) were considered significant if they were p_FDR < 0.05, however, top ranked probes significant at an uncorrected level of significance were also explored.

Linear regression analysis was conducted for both ADHD diagnostic status (Model A) and ADHD symptom severity (Model B) as separate dependent variables. M-values for 1188 probes within the region of interest were independent variables. Covariates (cov) were epithelial cell count, array batch, age and sex. Sensitivity testing run in GPower (36, 37) with a = 0.05, revealed the current study (n = 140) has 90% power to detect a minimum effect of 6%, with a critical t-score of 1.66.

$$\varvec{M}\varvec{o}\varvec{d}\varvec{e}\varvec{l} \varvec{A}: ADHD Diagnostic Model: Y= \beta 0+ cov+\beta Group$$

$$\varvec{M}\varvec{o}\varvec{d}\varvec{e}\varvec{l} \varvec{B}: ADHD Symptom Model: Y= \beta 0+ cov+\beta Symptom Severity$$

For both models A and B, differentially methylated regions (DMRs) were identified using the DMRcate package (38, 39). Significance testing for DMRs was conducted on M-values using the Bioconductor limma pipeline (35). Similarly to the DMP analysis, p_FDR < 0.05 was established as the cut-off. Where appropriate, DMRs were ranked using Fisher’s multiple comparison statistic.

Cohort characteristics

Independent sample t-tests revealed that the groups did not differ significantly on age (t = 2.013; p = 0.786) or gestation period (weeks) (m_ADHD = 38.578, m_Control = 39.120, t = -1.497, p = 0.136). The groups did differ significantly on birth weight (grams) (m_ADHD = 3278.780, m_Control = 3529.211, t = -2.481, p = 0.014); however, chi-squared tests showed no significant differences in intensive care (y/n) (X² = 0.009, p = 0.925), trimester 1–3 maternal alcohol consumption (X² = 1.175, p = 0.759; X² = 2.368, p = 0.450; X² = 1.681, p = 0.641) or trimester 1–3 maternal smoking (X² = 2.266, p = 0.519; X² = 3.269, p = 0.352; X² = 4.094, p = 0.252).

DNA methylation of SAM genes by ADHD diagnosis

Initially we investigated associations between SAM genes and diagnostic classification of ADHD. At an uncorrected level, 38 probes reached statistical significance at a = 0.05 and three of these were annotated to ST3GAL3 (cg25630069, cg05180596, cg19326856). A summary of these probes can be found in Table 2. Of the 33 total SAM genes, 20 were represented in the 38 significant probes with 8 genes each contributing one probe (GLB, NANP, NLP, SLC17A5, ST3GAL2, ST6GALNAC5, ST8SIA5), 5 genes each contributing 2 probes (NANS, ST3GAL4, ST6GALNAC1, ST6GALNAC4, ST8SIA1) and 7 genes contributing 3 probes each (NEU4, ST3GAL1, ST3GAL3, ST6GAL1, ST6GALNAC1, ST8SIA6) (Fig. 2). A summary of delta beta values (Δβ; average difference of beta values between groups) for these probes can be seen in Fig. 2. The ST3GAL3 probe previously reported by Walton et al (2017) (cg09989037) was ranked 275 of 1188 probes (Δβ = -4.58 x 10^− 5; t = 0.75; p = 0.45; p_FDR = 0.98). No individual probes survived correction for multiple comparisons.

Table 2

Summary Rank of Differentially Methylated Probes from Group Regression Modelling (uncorrected p < 0.05)
	Probe	Gene	Chr	Position	Rel 1	Rel 2	t	p	p_FDR	Dβ
1	cg02637438	ST3GAL1	8	134584246	Tss200	Island	3.394	0.001	0.884	-0.010
2	cg06575763	NANP	20	25604643	1stExon;5'utr	Island	3.014	0.003	0.998	-0.009
3	cg04684105	ST6GALNAC4	9	130677075	Body;5'utr	N Shelf	-2.844	0.005	0.998	0.010
4	cg15832710	NANS	9	100818990	5'utr;1stExon	Island	2.686	0.008	0.998	-0.009
5	cg16970851	ST8SIA1	12	22487658	Tss200	Island	2.672	0.008	0.998	-0.008
6	cg27169166*	SLC35A1	6	88182413	Tss1500	Island	2.643	0.009	0.998	0.006
7	cg02472348	ST8SIA1	12	22487781	Tss200	Island	-2.575	0.011	0.998	-0.019
8	cg12841113	NANS	9	100818782	Tss200	Island	2.564	0.011	0.998	-0.007
9	cg01477546	ST3GAL5	2	86096098	Tss1500;Body	Open Sea	2.530	0.012	0.998	0.006
10	cg25630069*	ST3GAL3	1	44385621	3'utr	Open Sea	2.522	0.013	0.998	0.004
11	cg12715330	ST6GAL1	3	186698055	5'utr	Open Sea	-2.471	0.014	0.998	0.003
12	cg05180596	ST3GAL3	1	44226543	Body	Open Sea	2.452	0.015	0.998	0.013
13	cg17702024	ST3GAL5	2	86116326	Tss200	Island	2.361	0.019	0.998	0.006
14	cg09810707	ST3GAL4	11	126274745	5'utr;Tss1500	Open Sea	2.283	0.024	0.998	0.013
15	cg23290912	NEU4	2	242750944	5'utr; Tss1500	N Shelf	-2.235	0.027	0.998	0.006
16	cg27558802	NPL	1	182797087	Body;3'utr	Open Sea	2.219	0.028	0.998	-0.016
17	cg13215049	GLB1	3	33089948	Body	Open Sea	2.163	0.032	0.998	0.011
18	cg13191925	ST8SIA5	18	44292608	Body	Open Sea	2.145	0.033	0.998	-0.010
19	cg02923228	ST3GAL1	8	134480356	Body	Open Sea	-2.142	0.034	0.998	0.011
20	cg14497130	ST6GALNAC3	1	76717350	Body	Open Sea	-2.121	0.035	0.998	-0.020
21	cg13153065	ST6GALNAC4	9	130674860	Body	Island	2.119	0.036	0.998	0.005
22	cg25638604*	ST6GALNAC1	17	74623627	Body	Open Sea	2.116	0.036	0.998	0.006
23	cg13460167	SLC17A5	6	74308079	Body	Open Sea	-2.094	0.038	0.998	0.021
24	cg21604970	ST6GALNAC5	1	77505574	Body	Open Sea	-2.084	0.039	0.998	-0.008
25	cg23680447	ST3GAL4	11	126238371	5'UTR	Open Sea	-2.081	0.039	0.998	0.006
26	cg15026574	ST6GAL1	3	186683429	5'UTR	Open Sea	2.070	0.040	0.998	0.008
27	cg05816879*	ST8SIA6	10	17440929	Body	Open Sea	2.067	0.040	0.998	0.008
28	cg14207785	ST8SIA6	10	17440426	Body	Open Sea	2.067	0.040	0.998	0.003
29	cg00579505	ST3GAL1	8	134477188	ExonBnd;Body	Open Sea	2.055	0.042	0.998	0.005
30	cg18485872*	NEU4	2	242750355	5'utr;1stExon	N Shelf	2.052	0.042	0.998	0.010
31	cg00163462	ST6GALNAC3	1	76723043	Body	Open Sea	2.047	0.042	0.998	0.007
32	cg04490113	ST6GALNAC3	1	76704467	Body	Open Sea	2.041	0.043	0.998	0.012
33	cg05065226	ST6GALNAC1	17	74636203	Body;5'utr	Open Sea	2.011	0.046	0.998	0.010
34	cg15211453*	NEU4	2	242749533	Tss1500	Open Sea	2.004	0.047	0.998	0.005
35	cg22189991	ST3GAL2	16	70468543	5'utr	Open Sea	-2.001	0.047	0.998	0.007
36	cg05650740	ST3GAL5	2	86113142	Body	N Shelf	-1.991	0.048	0.998	0.007
37	cg09023643	ST6GAL1	3	186646931	Tss1500	N Shore	-1.988	0.048	0.998	-0.006
38	cg19326856*	ST3GAL3	1	44170220	3'utr;Body	N Shelf	1.986	0.049	0.998	-0.007
Note: Chr = Chromosome; Position = Chromosomal coordinate of the CpG (Build 37); Rel 1 = Gene region feature category ( ; Rel 2 = Location of CpG relative to CpG Island; t = t-statistic, p = uncorrected p-value; p_FDR = FDR corrected p-value, Coeff = Standardised Regression Coefficient; Dβ = Delta Beta; *Probes also associated with symptom severity at an uncorrected level.

Table 2. Summary Rank of Differentially Methylated Probes from Group Regression Modelling (uncorrected p < 0.05)

[INSERT Table 2 HERE]

[INSERT FIGURE 2 HERE]

Figure 2.

Waterfall plot of delta beta values of significant differentially methylated probes (uncorrected p < .05) for ADHD compared to controls from group regression modelling.

Enrichment analysis

To explore the possibility of SAM pathway and/or regional enrichment between those with and without ADHD, a series of t-tests of the resultant regression t-scores were conducted. In summary, significant differences in t-score distribution were found for the entire SAM pathway (m = 0.093, t = 3.414, p = 6.623 x 10^− 4), as well as CpG island shelves (regions 2–4 kb upstream (5’) of CpG islands; m = 0.325, t = 2.474, p = 0.016); probes located in open sea (regions of the genome that are located far from any known CpG islands; m = 0.139, t = 3.426, p = 6.562 x 10^− 4), probes within the 5’ untranslated region between transcriptional start site and ATG start site (m = 0.208, t = 3.894, p = 1.202 x 10^− 4) and probes located in the body of the gene between ATG start site and stop codon (m = 0.077, t = 2.013, p = 0.045). This indicates that for the SAM pathway, and probes that lie in these regions, children with ADHD show a subtle pattern of increased DNA methylation. For histograms of t-distributions see Figure S2.

ADHD symptom severity

Secondly, we investigated associations between SAM genes and ADHD symptom severity as estimated by the Conners-3 ADHD Index. At an uncorrected level, 63 probes reached statistical significance at a = 0.05 and 4 of these were annotated to ST3GAL3 (cg25630069, cg10874168, cg06176087, cg22397365). A summary of these probes can be found in Table S1. These probes were distributed across 27 genes. Of the 33 total SAM genes, 27 were represented in the 63 significant probes with 9 genes contributing one probe (NANP, NANS NEU4, SLC35A1, ST3GAL2, ST6GALNAC1, ST6GALNAC6, ST8SIA4, ST8SIA5), 5 genes contributing 2 probes (NPL, ST3GAL1, ST3GAL5, ST6GALNAC4, ST8SIA6), 3 genes contributing 3 probes each (GLB1, NEU1, ST6GALNAC5), 4 genes contributing 4 probes (NEU3, ST3GAL3, ST6GALNAC3, ST8SIA2) 1 gene contributing 5 probes (ST6GAL2) and 2 genes contributing 6 probes (ST3GAL4, ST6GAL1). The ST3GAL3 probe previously reported by Walton et al (2017) (cg09989037) was ranked 539 of 1188 probes (Δβ = -4.584 x 10^− 5; t = 1.201; p = 0.232; p_FDR = 0.943). No individual probes survived correction for multiple comparisons.

When compared to the diagnostic regression, there was an overlap of 7 probes indicating that DNA methylation of these probes was significantly associated with both ADHD diagnostic status and symptom severity at an uncorrected level. These probes are marked with a star in the relevant tables.

Despite the understanding that ADHD is heritable with a neurophysiological basis (40, 41), understanding of the biological mechanisms and drivers is still lacking. Both genetic and epigenetic research have highlighted the potential role of ST3GAL3 in ADHD (42). Given the role this gene plays in brain formation and function (14), and the broader neuroimaging evidence indicating structural and functional brain differences between children with and without ADHD (1, 2), this work set out to investigate the epigenetic contribution of ST3GAL3 to ADHD, along with other genes apart of the sialic acid metabolism (SAM) pathway.

Adopting an alternative approach to candidate-gene studies, this work employed a pathway-approach, whereby genes implicated in the broader SAM pathway were also examined. Once correcting for multiple comparisons across 1188 probes, no group-level differences in DNA methylation were found for ST3GAL3 or SAM probes, at an uncorrected level, there were 38 significant (unadjusted p < 0.05) probes, 3 of which were annotated to ST3GAL3 (see Table 2). These lay within the body of the gene and the 3’ untranslated region (cg05180596, cg25630069, cg19326856). Post-hoc analysis also suggested an overall subtly increased pattern of DNA methylation across the entire SAM pathway for the ADHD group. In addition to this, overall increases were seen in the ADHD group compared to the controls in SAM probes located in shelves 2–4 kb upstream (5’) of CpG islands, open sea, within 5’ untranslated region between transcriptional start site and ATG start site, and probes located in the body of the gene between ATG start site and stop codon. These results indicate that nuanced aberration in sialic acid metabolism may play a role in ADHD.

Enrichment analysis of the SAM pathway showed an overall increase in DNA methylation for the ADHD group. However, caution must be taken with this interpretation as the relationship between DNA methylation and gene expression can vary at different sites across the genome (43–45). At the pathway level, this may indicate an overall reduced expression of genes associated with sialic acid metabolism. Examination of probes significant at an uncorrected level showed that 84% of these probes (n = 32) were annotated to genes involved in the biosynthesis of sialic acid rather than its catabolism. Of these 32 probes, 28 were annotated to sialyltransferases (ST6GAL1, ST3GAL1-5, ST6GALNAC1,3–5, and ST8SIA1,5,6). Though the specific role of each sialyltransferase varies, their primary role is to catalyse the addition of sialic acid from CMP-Sia (a nucleotide sugar donor) to the terminus of the oligosaccharide chain of a glycoprotein or glycolipid, ultimately resulting in the glycoconjugate structure transported to the bilipid membrane layer (46). In the brain, the glycome is dominated by gangliosides (sialylated glycosphingolipids) which carry roughly 75% of the brain’s sialic acid, functioning as both intra- and extra-cellular recognition and regulation molecules(12). Knock-out mouse models have shown that mutations to sialyltransferase genes involved in ganglioside biosynthesis impact axon-myelin interactions, with mice experiencing extensive motor deficits accompanied by significant (47, 48). Interestingly, human brain imaging studies of ADHD cohorts show disruptions to wide-spread white matter networks (2, 49–51). It is difficult to ascertain from these studies, however, whether disruptions to the axon-myelin relationship lay at the heart of these differences. Diffusion weighted imaging is the dominant form of white matter neuroimaging in ADHD, yet differences in white matter microstructure metrics are not specific to myelin and could also represent other elements of white matter microstructure such as fibre architecture, axon diameter and cell swelling (52). This highlights the need for future work to specifically focus on brain white matter myelin in ADHD and the potential epigenetic contribution of SAM

Due to its dynamic nature, epigenetic state is generally temporal. Similarly, brain development, starting in utero and continuing into early adulthood, presents a consistently varying landscape (53). Although ADHD is believed to stem from a disruption to brain development, the underlying timing of behavioural, brain and epigenetic changes are not well understood, highlighting a need for longitudinal work. One such study adopted a methylome-wide prospective investigation with ADHD symptom trajectories (7–15 years). Of the 13 probes found to be differentially methylated at birth (cord blood) between high and low ADHD symptom trajectories, one was annotated to ST3GAL3 – a SAM sialyltransferase (6). Interestingly, concurrent DNA methylation of this probe at age 7 (whole blood) was not associated with either high or low ADHD symptom trajectory, aligning with the current work. This could indicate potential epigenetic staging effects whereby differences in DNA methylation of ST3GAL3 may precede neurological and behavioural manifestations of ADHD (42). Although our study failed to replicate effects of the ST3GAL3 probe highlighted in Walton et al., at birth, results do align with analysis conducted at age 7 whereby no association was found with the probe and ADHD presentation. Interestingly however, here the SAM pathway as a whole, was seen to be hypermethylated in the ADHD cohort. Together, this information suggests disruption to SAM in ADHD. Sialic acid (specifically polysia – multiple sialic acids) plays a fundamental role in neurogenesis (54–59) and appears to both positively and negatively regulate synaptogenesis postnatally (60). In rat brains, different stages of synaptic outgrowth are marked by distinct polysia profiles whereby initially polysia labels the entire synapse yet is progressively reduced to pre- and post-synaptic membranes and ultimately lost altogether as synapses are formed (60). The differential expression profiles of sialic acid during neurogenesis and synaptogenesis highlights the presence of epigenetic regulation of SAM genes throughout pre- and post- natal development and underscores the potential widespread yet subtle consequences of disruption to this pathway. Future work is encouraged to not only adopt longitudinal study designs, but to also examine the relationship between DNA methylation of SAM genes at birth and ADHD behavioural manifestations where possible.

The pathway approach adopted here proved a strong and viable alternative to the long-adopted candidate-gene study (CGS) approach. Similarly to CGS, the study research questions, and design were informed by the broader literature; however, the inclusion of genes involved in the broader biochemical pathways offers an opportunity to not only examine a singular gene (or genes) but also those involved in the same biological processes. This means that relationships can be examined at a greater degree, compared to targeted approaches. For example, instead of focusing on a few CpG sites in the promoter, we can investigate CpG sites throughout the whole gene (or pathway) and associated regulatory elements. From a financial perspective, genome-wide assaying, as conducted here with the Illumina EPIC array, may prove more fruitful compared to targeted assays. Although the overarching cost of genome-wide assay is more at face value, they continue to become relatively cheaper compared to targeted assays as the utility gained could be considered worthwhile. Associated time and labour are less, and the resultant data allows for more comprehensive, nuanced research questions to be answered.

The results of this study should be interpreted considering a number of limitations. Firstly, the sample size (n = 140) is considered small for an epigenetic study, though was constrained by the existing cohort size. Secondly, peripheral tissues samples were collected in the form of saliva. Although this reduces the sensitivity of conclusions drawn relating to brain function, studies indicate that the correlation between DNA methylation of brain tissues and saliva samples is strong (r = 0.90) (61), however it is yet to be established whether this relationship holds true within clinical cohorts. Lastly, the categorical approach to ADHD assessment in both epigenetic and genetic studies potentially masks the specificity of results. A more dimensional approach, such as we adopted here with symptom severity, may help with more targeted outcomes, thus paving the way for more individualised aid, in both diagnosis and intervention. In addition, adoption of more functionally relevant behavioural and cognitive measures may be useful in adequately capturing the true multi-dimensionality of ADHD and further explore the role of epigenetics.

In conclusion, our study is the first to adopt a pathways approach to explore the epigenetic role of ST3GAL3 and other sialic acid metabolism genes in ADHD. While effects of less than 1% were seen for individual probes, a broader pattern of hypermethylation was found for the entire SAM pathway. This indicates that ST3GAL3, as well as other sialyltransferase genes and the broader SAM pathway, could contribute to disrupted epigenetic regulation in ADHD. Future longitudinal pre- and post-natal research across broad developmental age ranges is necessary to further explore these findings.

Conflict of Interest

All authors report no conflict of interest, financial or otherwise.

Acknowledgements

The NICAP cohort is supported by the National Health and Medical Research Council of Australia (NHMRC #1065895 and #2029361) and a grant from the Waterloo Foundation. This research was supported by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the NCRIS-funded Australian Research Data Commons (ARDC). Authors gratefully acknowledge the contribution to this work of the Victorian Operational Infrastructure Support Program received by the Burnet Institute.

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Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD

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Abstract

Figures

Introduction

Materials and methods

Demographics and clinical assessment

Probe selection

Step 1. Microarray pre-processing and quality control

Step 2. Define the Reactome pathway of interest

Step 3. Define SAM gene set

Step 4. Extract probes from annotation file (gene names)

Step 5. Extract probes from annotation file (gene names)

Step 6. Extract associated enhancer and promotor probes

Step 8. Calculation of sample cell heterogeneity

Step 9. Conduct differential methylation analysis

Results

Enrichment analysis

Discussion

Declarations

Conflict of Interest

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1