https://orcid.org/0000-0002-6422-9600
Figures
Abstract
Attention Deficit-Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with a prevalence around 5% in children and adolescents and 2.5% in adults. Recent reports using GWAS approaches have identified several genetic risk loci for this disorder. However, the epigenetic influence of extrinsic factors during pregnancy or the exposure to environmental factors during childhood, on the onset of the disorder remains unclear. This question has been addressed by analyzing blood or saliva samples from ADHD patients or by postmortem analysis. The aim of this study was to determine differential patterns in DNA methylation in fresh hippocampal samples using a murine model of ADHD. We analyzed the genome-wide pattern of differentially methylated CpG sites using the Illumina Infinium Mouse Methylation BeadChip in fresh hippocampal samples from the prenatal nicotine exposure (PNE) mouse model of ADHD and control animals. Our analysis revealed 218 DMPs including genes associated with growth factors signaling, such as adhesion G protein-coupled receptor B2 (ADGRB2), leukemia inhibitory factor receptor (LIFR) and erb-b2 receptor tyrosine kinase 3 (ERBB3) involved in synaptogenesis, proliferation, and differentiation of neural stem cells. The functional gene enrichment analysis of differentially methylated positions (DMPs) revealed the nervous system development as the biological process with highest enrichment factor. In addition, the GO and KEEG enrichment analysis of 113 differentially methylated regions (DMR) revealed several loci associated with the positive regulation of Hippo signaling (involved in neuronal development) in PNE samples. In addition, our results revealed a DMP previously associated to ADHD patients supporting the PNE murine model of ADHD. These results are relevant in terms of the validation of PNE model of ADHD and for the identification of epigenetic markers of the disorder in humans. In addition, our results are relevant for the characterization of the cellular and molecular mechanisms underlying the ADHD, currently unknown.
Citation: Vidal R, Lopez I, Ugarte G, Contreras D, Piña R, Godoy F, et al. (2025) Genome-wide DNA methylation analysis of hippocampal tissue in a murine model of attention deficit-hyperactivity disorder. PLoS One 20(6): e0323756. https://doi.org/10.1371/journal.pone.0323756
Editor: Kenji Tanigaki, Shiga Medical Center, JAPAN
Received: December 18, 2024; Accepted: April 14, 2025; Published: June 4, 2025
Copyright: © 2025 Vidal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw microarray IDAT data are available in the Gene Expression Omnibus (GEO) GSE290914, and all other relevant data are provided within the paper and the Supplementary files.
Funding: This work was funded by FONDECYT grants from the Agencia Nacional de Investigación y Desarrollo – ANID (grant # 3170249 to RP, grant # 3190897 to DC, grant # 11140403 to CR; by 22 DICYT grants from the Dirección de Investigación Científica y Tecnológica of the University of Santiago (grant # 022343MM (to BM), grant # 022443MM (to BM), grant # 0211843RS (to CR); and ANID Fellowship # 212208073 (to FG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Attention Deficit-Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with a high prevalence (approximately 5%) among children worldwide and is characterized by hyperactivity, inattention and/or impulsivity, which affect learning and sociability at school [1]. The prevalence of ADHD in children and adolescents has a male-female ratio of approximately 4:1 in clinical studies and in adults between 2:1 and 1:1 [2,3].
Several studies support a polygenic cause for ADHD and nongenetic factors involved in the etiology of this disorder. Extrinsic factors such as smoking and alcohol consumption during pregnancy, exposure to contaminants such as lead, diet deficiencies and low educational attainment have been correlated with ADHD [4]. Recently, a genome-wide association study (GWAS) meta-analysis, involving thousands of ADHD and control individuals, identified 27 risk loci for ADHD involving genes that are expressed mainly in excitatory and inhibitory neurons of the frontal cortex [5]. However, the mechanism of interaction among the genetic background and environmental factors involved in the ADHD onset remains unclear. In this context, DNA methylation, an epigenetic biological process that regulates gene expression, may arbitrate environmental or genetic effects. Several studies focused on epigenetic modifications associated with ADHD have been reported [6]. These reports correspond to epigenome-wide analysis studies (EWASs) using different types of samples (peripheral blood or saliva) taken from children, adolescents, or adults. Notably, few common loci with different methylation statuses between control and ADHD patients such as VIPR2 encoding the receptor for vasoactive intestinal peptide (VIP) expressed in the CNS and regional methylation patterns of neurotransmitters-associated pathways have been identified [7–9]. Although these studies have provided important knowledge about gene methylation patterns in ADHD, the most of them have been performed on peripheral blood or saliva where medium-large scale noninvasive sampling is feasible [10–12]. The DNA methylation pattern is tissue- and cell type-specific [13,14], so it is not clear whether ADHD blood DNA methylation results may act as a proxy for the DNA methylation patterns of target ADHD tissues. In this context, investigating the primary ADHD-related tissues, such as the hippocampus, is extremely relevant for advancing our understanding of the cellular and molecular mechanisms involved in ADHD. The meta-analysis of neuroimaging data obtained from children, adolescents and adults with ADHD revealed statistically significant reductions (compared to control groups) in cortical thickness, cortical surface area and the volume of subcortical areas such as the accumbens, amygdala, caudate, putamen and hippocampus. Interestingly, this brain area which is critical for learning and memory processes, remains significantly reduced during later stages of development [15].
The access to brain samples from DHD patients has been limited to postmortem analysis [16], lead to the development and characterization of animal models of the disorder. Several rodent models of ADHD have been described including genetic models such as SNAP-25 Knockout (KO) mice, DAT KO mice and LPHN3 KO mice and rats, those induced by exposure to environmental chemicals such as lead and pesticides, and those induced by prenatal exposure to nicotine or alcohol. The prenatal nicotine exposure (PNE) murine model presents several symptoms described in ADHD patients such as increased locomotor activity, low performance in cognitive tests, inattention, and transmissibility until the third generation [17]. In addition, PNE mice exhibit a reduction in dopamine turnover in the frontal cortex and striatum and a significant volume reduction in the cingulate cortex [18]. Our previous works supported the correlation of hyperactive and inattention behaviors in PNE mice with impairment hippocampal long-term potentiation (LTP). These abnormalities were reversed after a single administration of methylphenidate [19]. In the present study we utilized the PNE mouse model of ADHD and the recently developed Infinium Mouse Methylation BeadChip platform (Illumina) to evaluate the genome-wide DNA gene methylation patterns in hippocampus and the pathways associated with this disorder. Our PNE animal ADHD model corroborates some methylation patterns observed in humans and reveals new differentially methylated GpC sites in the hippocampal tissue.
Materials and methods
Murine model of ADHD
Protocol for the care and use of animals were approved by the Bioethical Committee of the University of Santiago of Chile. Prenatal nicotine exposure (PNE) mice were obtained following the protocol described in (18). Briefly, C57BL/6 female mice were treated with 0.1 mg/ml nicotine for three weeks before mating and during the entire pregnancy period. Nicotine was administered orally and dissolved in the drinking water with 2% saccharine ad libitum. The behavioral characterization of PNE and control mice is shown in the S1 Fig.
Tissue collection
PNE and control mice at postnatal day 21 (P21) were sacrificed by decapitation under halothane anesthesia. The brains were removed and transferred into ice-cold dissection solution containing the following (in mM): 125 NaCl, 4 KCl, 10 glucose, 1.25 NaH2PO4, 25 NaHCO3, 0.5 CaCl2, and 2.5 MgCl2 (pH 7.4), which was subsequently equilibrated with a 5% CO2–95% O2 mixture. Transversal 300 µm brain slices containing the hippocampal area were obtained using a vibratome (Leica, Nussloch, Germany) and the hippocampi were further dissected under a low-magnification microscope.
DNA isolation and bisulfite conversion
Bilateral hippocampal tissues from six control (3 males and 3 females) and six treated groups (3 males and 3 females) were homogenized shortly after sacrifice and subsequently handled for DNA isolation with the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. Purity, DNA concentration, and integrity were obtained with a Qubit 4 fluorometer (Thermo Fisher Scientific, MA, USA) and a 2100 Bioanalyzer System (Agilent, CA, USA), respectively. For each sample, one microgram of genomic DNA was utilized for bisulfite conversion, employing the EZ DNA Methylation Kit (Zymo Research, Irvine, CA, USA) and DNA methylation was determined with the Infinium Mouse Methylation BeadChip (Illumina, CA, USA) (GPL30650), following the manufacturer’s recommendations.
DNA methylation analysis
Identification of differentially methylated positions (DMPs) and differentially methylated regions (DMRs).
IDAT files (GSE290914) were exported into R and the Champ package [20] and the GenomeStudio Methylation Module (v1.8) and utilized to perform subsequent analysis, including removal of probes with detection p > 0.01, mapping to Y and X chromosomes, and <3 beads in at least 5% of the samples. The methylation ratios of the CpG sites were described by beta-values (ranging from 0, no methylation to 1, full methylation). Then, the beta-values data were normalized with the MIxture Quantile dilation option [21], amending type I and type II probe bias. To identify general methylation patterns, a principal component analysis (PCA) on methylation levels in all samples was realized using the prcomp library in R (https://github.com/SurajGupta/r-source/blob/master/src/library/stats/R/prcomp.R). Differential methylated CpG sites were determined using a linear model with the package limma and included sex as a covariate. CpGs with a corrected (Benjamini–Hochberg) P-value <0.05 and an absolute Δbeta > 0.20 were considered as DMP. In addition, DMRs were identified with the LASSO methodology [22] and the option ChAMP.DMR, including a minimum of 3 successive CpG sites and a distance no greater than 1,000 bp among 2 contiguous DMPs. DMRs with a corrected (Benjamini–Hochberg) P-value <0.05 were considered as significant DMR. The ChAMP.import option was utilized to annotate all DMRs. The R package ClusterProfiler (version 3.14.3) was utilized to perform analysis for enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DMRs and DMPs.
RT-qPCR.
Three DMR genes were randomly selected for RT-qPCR analysis. Total RNA was isolated using Trizol (Invitrogen, USA) and RNeasy Mini KIT (Qiagen, USA) and the genomic DNA residue was removed with TURBO DNAse treatment (Invitrogen, USA), according to the manufacturer’s instructions. The qualities, concentrations, and integrities of RNA extracts were evaluated with a Qubit 3.0 fluorometer (Life Technologies, USA; Qubit RNA HS kit) and a Bioanalyzer 2100 (Agilent Technologies, USA; RNA Nano 6000 Assay Kit). Reverse transcription was performed with a blend of random primers and oligo(dT) and iScript reverse transcriptase (iScript cDNA synthesis kit, Bio-Rad, USA) according to the manufacturer’s instructions. DMR specific primers for each of the selected genes were designed with Primer3 Plus (https://www.primer3plus.com/index.html; CORO7, F:5’TACTGGGCATTGTGCCTCTG-3´, R:5´AAGGGCGAGAAGTCCAAGTC-3´; ERBB3, F:5’-CTTGCCTCGATGTCCTAGCC-3´, R:5’- CTGAAAAGCAAGCCCAGCAC-3´; MYL4, F:5´-GCTCATCTCTCTGGCTGCTC-3’, R:5´- ACAGGCTGATCCCTTCTTGC-3’). Quantitative real-time PCR was conducted using RT² SYBR Green ROX qPCR Mastermix (Qiagen, USA) and Rotor-Gene Q (Qiagen). qPCR cycling consisted of 95°C for 4 min, followed by 40 cycles of: 95°C for 6 s and 60°C for 45 s, with a final melting curve analysis at 95°C for 12 s, 60°C for 60 s, and 95°C for 15 s. All RT-qPCRs were performed in triplicate and the Rpl13a gene was utilized as normalizer [23]. The relative expression of the selected genes was estimated using the 2–ΔΔCt method [24]. The PCR efficiency of each pair of primers was calculated with the LinRegPCR V. 2021.2 package, based on raw fluorescence data [25].
Results
Overall methylation profile of hippocampal samples from PNE and control mice
After quality control of the chips and samples, box plots were used to characterize the data. The results revealed that the β-normalized experimental values were uniformly distributed, implying that the data obtained were proportional. A density plot revealed a similar bimodal methylation pattern among control and PNE mice, with 2 peaks at higher (~0.85) and lower (~0.07) β-average values (Fig 1).
(A) Box plots showing the methylation profiles of male (M) and female (H) hippocampal cells from control (wt) and PNE mice. (B) Density plot of the averaged β-values of male (M) and female (H) CpG sites of controls (wt) and PNE mice.
To identify general methylation patterns, we performed PCA on the overall methylation levels in the hippocampal samples of the four experimental groups revealed a tight clustering according to sex and prenatal nicotine exposure (Fig 2).
Principal component analysis (PCA) showing the distribution of the averaged β-values of the CpG sites between the four groups of samples (males and females of control and PNE groups). PC1, principal component 1; PC2, principal component 2.
Identification of differentially methylated positions (DMPs) in PNE and control mice in hippocampus
The comparison between hippocampal samples from PNE and control mice revealed a total of 281 DMPs (adjusted P value <0.05 and delta absolute value >0.2) between 276,649 CpG probes (see S1 Table). One hundred twenty-two DMPs were hypomethylated (43.42%), and one hundred fifty-nine displayed a hypermethylated pattern (56.58%) in the hippocampus of PNE mice compared with the controls. The top 20 DMP-associated genes are shown in Table 1.
An analysis of the genomic distribution of DMPs revealed that the highest proportion were in the body region (Table 2).
The comparison between PNE and control mice revealed that DMPs are distributed across all chromosomes (Fig 3).
The blue and brown lines represent the hypermethylated and hypomethylated DMPs, respectively. Y-axis corresponds to the delta beta-value.
Functional enrichment analysis of DMPs show association with the nervous system development and ErbB-3-dependent intracellular cascade
The GO functional gene enrichment analyses of the DMP-associated genes revealed one biological process (GO:0007399: nervous system development) and 24 molecular functions terms that were significantly enriched (see S2 Table). Abnormal time course of volume increase of neocortex and subcortical areas during childhood of ADHD patients suggesting a delay in neurodevelopment is well documented [15]. In addition, the GO molecular functions terms showed a relevant fold enrichment for GO terms associated to P-type calcium transporter activity involved in the regulation of presynaptic cytosolic calcium ion concentration (GO:1905056), neuregulin binding (GO:0038132) and ErbB-3 class receptor binding (GO:0043125). Interestingly, Neuregulin-ERBB signaling is involved in several neural processes such as myelination, neurotransmission and synaptic plasticity and neuropsychiatric disorders such as schizophrenia, bipolar disorder, and depression. In addition, KEGG enrichment analysis revealed one pathway enriched corresponding to mineral absorption (mmu04978) (Fig 4). Calcium, sodium, and potassium ions are required for the excitability of neuronal cells and chemical neurotransmission.
Percentage of enrichment is on the X-axis; counts number is proportional to symbol size and color code to -log10 of p-value. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Gene and Genomes; BP, biological process; MF, molecular function.
Identification of Differentially methylated regions (DMRs) between PNE and control mice in hippocampus
To gain insight into the connections among DMPs and expressed genes in PNE and control animals, we evaluated differentially methylated regions (DMRs). A total of 113 (including 58 hypomethylated and 55 hypermethylated) DMRs were filtered in PNE mice (see S3 Table). Among these DMRs, the top five genes with the highest level of hypomethylation in DHD-mice corresponded to PPCDC, TMOD3, MYL3, TTC9C and CACNA1I. Similarly, the top five genes with the highest level of hypermethylation in PNE mice corresponded to PHC3, H2-D1, RPGRIP1I, HAND1 and MAPT. We further performed GO and KEGG functional enrichment analyses of these DMRs. The GO functional gene enrichment analyses of the DMR-associated genes revealed one biological process (GO:0035332: positive regulation of hippo signaling; Fold Enrichment: 96) and three molecular functions terms significantly enriched (GO:000378: actin monomer binding; Fold Enrichment: 28) (GO:0019838 growth factor binding; Fold Enrichment: 139) (GO:0008092 cytoskeletal protein binding; Fold Enrichment: 3.1). KEGG analysis did not revealed any enriched pathway.
To validate the relationships among DMR genes and their transcript levels, one hypermethylated (CORO7) and two hypomethylated (ERBB3 and MYL4) DMR genes were selected.
All the primer pairs designed for the three DMR genes chosen for RT-qPCR validation had efficiencies between 94.57 and 98.02%, which is within the ideal range for qPCR analysis. The relative mRNA levels of the selected genes agreed with the microarray results supporting the relationship between DNA methylation status and gene expression level of target genes (Fig 5).
The delta of beta-values for CORO7, ERBB3 and MYL4 obtained from the microarray data (PNE versus control mice) are plotted as blue solid bars. Relative mRNA levels were obtained from levels of transcripts (evaluated by RT-qPCR) in PNE samples normalized to levels detected in control samples are plotted as orange solid circles.
Discussion
ADHD corresponds to a neurodevelopmental disorder with a high index of heritability (approximately 80%) and several risk genes have recently been identified using GWAS approaches. Among those genes (mainly expressed in excitatory and inhibitory neurons) there are transcription factors expressed in brain tissue and structural components of postsynaptic density previously associated with neurodevelopmental disorders [5]. However, the effects of extrinsic factors during pregnancy and postnatal development on the incidence of this disorder through epigenetic modifications are poorly understood. In this work we analyzed DNA methylation in brain samples from the PNE murine model of ADHD to identify putative epigenetic biomarkers of the disorder to develop further clinical studies in humans. The use of animal models for human neurological disorders or diseases is limited considering the divergence in brain neuroanatomy, timeline of neurodevelopment and cognitive properties. However, the current use of murine models for neurodevelopmental and neurodegenerative diseases such as Parkinson’s disease, Alzheimer’s disease and Amyotrophic Lateral Sclerosis has been useful for describing the molecular pathways underlying these disorders and for the development and testing of new drugs in preclinical studies [26].
By another part, several reports focused on the study of the epigenome in ADHD have used saliva or blood samples from patients or postmortem brain samples [9,12,16]. The present study employed fresh hippocampal tissue dissected from control and PNE mice. Considering that DNA methylation patterns are cell type specific our results are relevant in terms of describing the DNA methylation-associated epigenetic changes that occurs in the hippocampus, one of the subcortical areas significantly reduced in volume in children and adults with ADHD [15].
GO analysis of hippocampal DMPs revealed the nervous system development as a biological process, was significantly enriched in PNE samples which is consistent with the categorization of ADHD as a neurodevelopmental disorder. A total of 281 DMPs, almost 57% of which were hypermethylated, located principally in the body region, were detected. Methylation localized in gene body regions will alter the gene transcription process through the optimization of alternative splicing events or transcription and elongation [27]. Among the 20 top DMP-mapped genes, several loci associated with neural development and physiology such as ERBB3 (erb-b2 receptor tyrosine kinase 3), ADGRB2 (adhesion G protein-coupled receptor B2) and LIFR (leukemia inhibitory factor receptor) were identified. ADGRB2 codes for the adhesion-GPCR member B2 receptor (BAI2) and belongs to a subfamily of GPCRs (BAI1-3) highly expressed in the brain and involved in synaptogenesis. BAI-2 suppresses the expression of VEGF and BAI2 KO mice exhibit an enhanced adult neurogenesis in the hippocampus associated with the recovery of VEGF-dependent neurogenesis [28]. In addition, LIF signaling is involved in the proliferation and differentiation of human cortical stem cells (or basal radial glia) in excitatory neurons, astrocytes and oligodendrocytes [29]. Recently, it has been reported that LIF promotes the differentiation of these cells into inhibitory interneurons in cortical primary cultures and forebrain organoids [30]. These results are consistent with the etiology of ADHD, which is considered a neurodevelopmental disorder characterized by a decreased volume of the neocortex and subcortical areas such as the hippocampus [15]. In addition, adolescents with ADHD exhibit smaller gray matter volume in several brain regions correlated with lower performance in working memory tests [31]. This impairment in working memory is consistent with our previous reports showing that PNE mice exhibit a significant impairment in hippocampal LTP with a lower density of dendritic spines in CA1 pyramidal neurons [19,32].
Although DMP analyses are frequently reported in epigenomic studies, DMR analyses are biologically more robust, showing a higher correlation with gene expression levels, because of the substantial correlation between contiguous CpGs [33]. In the present study we detected a total of 113 DMR-genes (58 hypomethylated and 55 hypermethylated) between PNE and control mice in hippocampus. Notably only one of these DMR-genes, SPTBN2 (spectrin beta non-erythrocytic 2) have also been found in children with ADHD in a hypomethylated status by analysis of DNA methylation in blood samples [34]. The SPTBN2 gene encodes for the beta-III spectrin (protein forming part of cytoskeleton), which is expressed mainly in the cerebellum (affecting glutamate turnover) and several mutations have been associated with Spinocerebellar Ataxia Type 5 (SCA5) in humans [35]. SPTBN2-KO mice exhibit ataxia and progressive degeneration of Purkinje cells in the cerebellum. In addition, neurons from the prefrontal cortex (involved in attention) of these mice show abnormal dendritic development and memory impairment as evaluated in object recognition-based tests [36]. In addition, among the genes significantly hypomethylated in our DMR analysis, the CACNA1i gene was detected. CACNA1i codes the alpha1 subunit of the Cav3.3 (T-type) voltage-dependent calcium channel and is involved in the modulation of the neuronal firing pattern [37]. These calcium currents in CA1 pyramidal neurons can be reduced by the activity of D5 dopamine receptors decreasing the firing rate of these cells [38].
The GO biological process terms and KEGG enrichment analysis of DMRs included genes associated with positive regulation of the Hippo pathway as the main biological process distinctive between control and PNE mice. The Hippo signaling pathway is involved in several cellular processes such as cell proliferation, differentiation and migration during development and organ regeneration [39]. Interestingly, the downstream YAP1/TAZ transcriptional coactivators of the Hippo pathway are expressed in adult hippocampal neural stem cells (NSCs) and stimulate their activation [40]. In addition, the WW and C2 domain-containing protein 1 (WWC1) have been described as upstream regulatory components of Hippo intracellular cascade [39]. Cell surface proteomics analysis of hippocampal tissue suggested that WWC1 facilitates the anchoring of AMPA receptors in the postsynaptic density enhancing learning and memory in mice [41]. In addition, it has been reported that WWC1/2 KO mice exhibit lower spine density in the hippocampus and impaired learning and memory [42]. Consistently, the CORO7 gene (encoding coronin 7, a scaffold protein involved in the Hippo intracellular cascade) which in our analysis exhibited a hypermethylated status (PNE compared with control samples) was correlated with lower levels of transcripts as expected (see Fig 4). Our previous reports suggest a delay in spine maturation in CA1 hippocampal neurons of PNE mice compared with controls [19]. In this context, to evaluate the modulation of spinogenesis and spine maturation in PNE animals by the Hippo pathway as an epigenetic target of nicotine exposure during pregnancy became relevant. Moreover, among the molecular functions emerged from enrichment GO analysis with highest rich factor, the P-type calcium channels, the neuregulin binding and the ErbB3 membrane receptor binding were found. These proteins are relevant in the context of synaptic plasticity processes such as hippocampal LTP. The P/Q-type calcium channels (Cav2.1) are expressed mainly in presynaptic terminals involved in the Ca2+-triggered fusion of vesicles containing neurotransmitters during the chemical synaptic transmission in the central and peripheral nervous systems [43]. The conditional CAV2.1 KO mice exhibit impaired synaptic transmission and deficits in learning and memory processes [44]. In addition, several reports support the regulation of hippocampal neurogenesis and synaptic plasticity by growth factors such as BDNF and neuregulins [45,46]. ErbB3 is a membrane receptor expressed in the hippocampus and its activation by neuregulin-1 is involved in neurogenesis in the ventral dentate gyrus [47]. Interestingly, the NGR3 gene (encoding for neuregulin 3 ligand of the ErbB3 receptor) has been reported as a DMR in the EWAS of postmortem brain samples from individuals with ADHD [16].
The DNA methylation data included in this report used as input DNA samples from whole hippocampal homogenates. Mouse hippocampus comprises glutamatergic neurons, GABAergic interneurons, astrocytes, oligodendrocytes, vessels-associated cells, and other cell types. Considering that the estimated proportion of neuronal cells corresponds to approximately 50% [48], the contribution of non-neuronal cells to the CpG-associated signals in our analysis must be considered. Further single-cell epigenome and transcriptome analyses will be needed to determine the cell-specific epigenetic modifications.
In addition, the DNA methylation analysis of other brain areas such as the prefrontal cortex (involved in attention) in PNE mice to determine whether the epigenetic modifications found in the hippocampus are specific is also needed. Furthermore our previous studies revealed that PNE mice exhibit motor hyperactivity, inattention and impaired hippocampal LTP evaluated in ex-vivo experiments [19]. These defects were not observed in PNE animals treated with a single oral administration of methylphenidate, which is used as a pharmacotherapy for ADHD in children [49]. Therefore, further analysis of epigenetic modifications in PNE mice treated with methylphenidate will be useful for determining whether the recovery of abnormal behavior and hippocampal synaptic plasticity involves changes in the epigenetic status of the same DMPs and DMRs described in this report.
Supporting information
S1 Fig. Behavioral characterization of prenatal nicotine exposure (PNE) and control mice.
(A) Top, Video tracking of spontaneous locomotor activity. Bottom. Quantification of the average total distance traveled. Significant differences were observed between PNE animals and control mice (PNE: 78.37 ± 2.01 m; n = 6; Control = 51.57 ± 3.33 m; n = 6), t = 6,883 df = 10, p < 0,0001. (B) Top, Diagram of the object-based attention test experimental protocol and video tracking of the retention stage. Bottom, PNE mice showed a decreased recognition index percentage compared to control mice (PNE: 24.00 ± 1.03%, n = 6; Control: 53.17 ± 4.09%, n = 6), t = 6,921 df = 10, p < 0,0001. The detailed descriptions of the behavioral tests are described in Contreras et al. (2022).
https://doi.org/10.1371/journal.pone.0323756.s001
(TIF)
S1 Table. List of differentially methylated positions (DMPs) identified in hippocampal samples from PNE and control mice.
https://doi.org/10.1371/journal.pone.0323756.s002
(XLSX)
S2 Table. Functional gene enrichment analysis of DMP-associated genes identified in hippocampal samples from PNE and control mice.
https://doi.org/10.1371/journal.pone.0323756.s003
(XLSX)
S3 Table. List of differentially methylated region (DMR)-associated genes identified in hippocampal samples from PNE and control mice.
https://doi.org/10.1371/journal.pone.0323756.s004
(XLSX)
References
- 1. Feldman HM, Reiff MI. Attention deficit-hyperactivity disorder in children and adolescents. N Engl J Med. 2014;370(9):838–46. pmid:24571756
- 2. Faraone SV, Asherson P, Banaschewski T, Biederman J, Buitelaar JK, Ramos-Quiroga JA, et al. Attention-deficit/hyperactivity disorder. Nat Rev Dis Primers. 2015;1:15020. pmid:27189265
- 3. Martin J. Why are females less likely to be diagnosed with ADHD in childhood than males? Lancet Psychiatry. 2024;11(4):303–10. pmid:38340761
- 4. Faraone SV, Banaschewski T, Coghill D, Zheng Y, Biederman J, Bellgrove MA, et al. The World Federation of ADHD International Consensus Statement: 208 evidence-based conclusions about the disorder. Neurosci Biobehav Rev. 2021;128:789–818. pmid:33549739
- 5. Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Nielsen TT, et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet. 2023;55(2):198–208. pmid:36702997
- 6. Cecil CAM, Nigg JT. Epigenetics and ADHD: reflections on current knowledge, research priorities and translational potential. Mol Diagn Ther. 2022;26(6):581–606. pmid:35933504
- 7. Wilmot B, Fry R, Smeester L, Musser ED, Mill J, Nigg JT. Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2. J Child Psychol Psychiatry. 2016;57(2):152–60. pmid:26304033
- 8. Chen Y-C, Sudre G, Sharp W, Donovan F, Chandrasekharappa SC, Hansen N, et al. Neuroanatomic, epigenetic and genetic differences in monozygotic twins discordant for attention deficit hyperactivity disorder. Mol Psychiatry. 2018;23(3):683–90. pmid:28322272
- 9. Mooney MA, Ryabinin P, Wilmot B, Bhatt P, Mill J, Nigg JT. Large epigenome-wide association study of childhood ADHD identifies peripheral DNA methylation associated with disease and polygenic risk burden. Transl Psychiatry. 2020;10(1):8. pmid:32066674
- 10. Walton E, Pingault J-B, Cecil CAM, Gaunt TR, Relton CL, Mill J, et al. Epigenetic profiling of ADHD symptoms trajectories: a prospective, methylome-wide study. Mol Psychiatry. 2017;22(2):250–6. pmid:27217153
- 11. van Dongen J, Zilhão NR, Sugden K, BIOS Consortium, Hannon EJ, Mill J, et al. Epigenome-wide association study of attention-deficit/hyperactivity disorder symptoms in adults. Biol Psychiatry. 2019;86(8):599–607. pmid:31003786
- 12. Choi Y-J, Cho J, Hong Y-C, Lee D-W, Moon S, Park SJ, et al. DNA methylation is associated with prenatal exposure to sulfur dioxide and childhood attention-deficit hyperactivity disorder symptoms. Sci Rep. 2023;13(1):3501. pmid:36859453
- 13. Loyfer N, Magenheim J, Peretz A, Cann G, Bredno J, Klochendler A, et al. A DNA methylation atlas of normal human cell types. Nature. 2023;613(7943):355–64. pmid:36599988
- 14. Liu H, Zhou J, Tian W, Luo C, Bartlett A, Aldridge A, et al. DNA methylation atlas of the mouse brain at single-cell resolution. Nature. 2021;598(7879):120–8. pmid:34616061
- 15. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ, et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry. 2017;4(4):310–9. pmid:28219628
- 16. Shastri GG, Sudre G, Ahn K, Jung B, Kolachana B, Auluck PK, et al. Cortico-striatal differences in the epigenome in attention-deficit/ hyperactivity disorder. Transl Psychiatry. 2024;14(1):189. pmid:38605038
- 17. Regan SL, Williams MT, Vorhees CV. Review of rodent models of attention deficit hyperactivity disorder. Neurosci Biobehav Rev. 2022;132:621–37. pmid:34848247
- 18. Zhu J, Zhang X, Xu Y, Spencer TJ, Biederman J, Bhide PG. Prenatal nicotine exposure mouse model showing hyperactivity, reduced cingulate cortex volume, reduced dopamine turnover, and responsiveness to oral methylphenidate treatment. J Neurosci. 2012;32(27):9410–8. pmid:22764249
- 19. Contreras D, Piña R, Carvallo C, Godoy F, Ugarte G, Zeise M, et al. Methylphenidate restores behavioral and neuroplasticity impairments in the prenatal nicotine exposure mouse model of ADHD: evidence for involvement of AMPA receptor subunit composition and synaptic spine morphology in the hippocampus. Int J Mol Sci. 2022;23(13):7099. pmid:35806103
- 20. Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33(24):3982–4. pmid:28961746
- 21. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189–96. pmid:23175756
- 22. Xiong Y, Ling Q-H, Han F, Liu Q-H. An efficient gene selection method for microarray data based on LASSO and BPSO. BMC Bioinformatics. 2019;20(Suppl 22):715. pmid:31888444
- 23. Bruckert G, Vivien D, Docagne F, Roussel BD. Normalization of reverse transcription quantitative PCR data during ageing in distinct cerebral structures. Mol Neurobiol. 2016;53(3):1540–50. pmid:25663136
- 24. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001;25(4):402–8. pmid:11846609
- 25. Ramakers C, Ruijter JM, Deprez RHL, Moorman AFM. Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett. 2003;339(1):62–6. pmid:12618301
- 26. Dawson TM, Golde TE, Lagier-Tourenne C. Animal models of neurodegenerative diseases. Nat Neurosci. 2018;21(10):1370–9. pmid:30250265
- 27. Lev Maor G, Yearim A, Ast G. The alternative role of DNA methylation in splicing regulation. Trends Genet. 2015;31(5):274–80. pmid:25837375
- 28. Duman JG, Tu Y-K, Tolias KF. Emerging roles of BAI Adhesion-GPCRs in synapse development and plasticity. Neural Plast. 2016;2016:1–9. pmid:26881134
- 29. Chandrasekaran A, Avci HX, Leist M, Kobolák J, Dinnyés A. Astrocyte differentiation of human pluripotent stem cells: new tools for neurological disorder research. Front Cell Neurosci. 2016;10:215. pmid:27725795
- 30. Andrews MG, Siebert C, Wang L, White ML, Ross J, Morales R, et al. LIF signaling regulates outer radial glial to interneuron fate during human cortical development. Cell Stem Cell. 2023;30(10):1382–1391.e5. pmid:37673072
- 31. Li CS, Chen Y, Ide JS. Gray matter volumetric correlates of attention deficit and hyperactivity traits in emerging adolescents. Sci Rep. 2022;12(1):11367. pmid:35790754
- 32. Piña R, Rozas C, Contreras D, Hardy P, Ugarte G, Zeise ML, et al. Atomoxetine reestablishes long term potentiation in a mouse model of attention deficit/hyperactivity disorder. Neuroscience. 2020;439:268–74. pmid:31809728
- 33. Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol. 2012;41(1):200–9. pmid:22422453
- 34. Li S-C, Kuo H-C, Huang L-H, Chou W-J, Lee S-Y, Chan W-C, et al. DNA Methylation in LIME1 and SPTBN2 genes is associated with attention deficit in children. Children (Basel). 2021;8(2):92. pmid:33572947
- 35. Durr A. Autosomal dominant cerebellar ataxias: polyglutamine expansions and beyond. Lancet Neurol. 2010;9(9):885–94. pmid:20723845
- 36. Lise S, Clarkson Y, Perkins E, Kwasniewska A, Sadighi Akha E, Schnekenberg RP, et al. Recessive mutations in SPTBN2 implicate β-III spectrin in both cognitive and motor development. PLoS Genet. 2012;8(12):e1003074. pmid:23236289
- 37. Cain SM, Snutch TP. Contributions of T-type calcium channel isoforms to neuronal firing. Channels (Austin). 2010;4(6):475–82. pmid:21139420
- 38. Mustafá ER, McCarthy CI, Portales AE, Cordisco Gonzalez S, Rodríguez SS, Raingo J. Constitutive activity of the dopamine (D5) receptor, highly expressed in CA1 hippocampal neurons, selectively reduces CaV 3.2 and CaV 3.3 currents. Br J Pharmacol. 2023;180(9):1210–31. pmid:36480023
- 39. Zhong Z, Jiao Z, Yu F-X. The Hippo signaling pathway in development and regeneration. Cell Rep. 2024;43(3):113926. pmid:38457338
- 40. Fan W, Jurado-Arjona J, Alanis-Lobato G, Péron S, Berger C, Andrade-Navarro MA, et al. The transcriptional co-activator Yap1 promotes adult hippocampal neural stem cell activation. EMBO J. 2023;42(11):e110384. pmid:37083045
- 41. Stepan J, Heinz DE, Dethloff F, Bajaj T, Zellner A, Hafner K, et al. Hippo-released WWC1 facilitates AMPA receptor regulatory complexes for hippocampal learning. Cell Rep. 2022;41(10):111766. pmid:36476872
- 42. Cao R, Zhu R, Sha Z, Qi S, Zhong Z, Zheng F, et al. WWC1/2 regulate spinogenesis and cognition in mice by stabilizing AMOT. Cell Death Dis. 2023;14(8):491. pmid:37528078
- 43. Andrade A, Brennecke A, Mallat S, Brown J, Gomez-Rivadeneira J, Czepiel N, et al. Genetic associations between voltage-gated calcium channels and psychiatric disorders. Int J Mol Sci. 2019;20(14):3537.
- 44. Mallmann RT, Elgueta C, Sleman F, Castonguay J, Wilmes T, van den Maagdenberg A, et al. Ablation of Ca(V)2.1 voltage-gated Ca²⁺ channels in mouse forebrain generates multiple cognitive impairments. PLoS One. 2013;8(10):e78598. pmid:24205277
- 45. Mei L, Nave K-A. Neuregulin-ERBB signaling in the nervous system and neuropsychiatric diseases. Neuron. 2014;83(1):27–49. pmid:24991953
- 46. Wang CS, Kavalali ET, Monteggia LM. BDNF signaling in context: From synaptic regulation to psychiatric disorders. Cell. 2022;185(1):62–76. pmid:34963057
- 47. Mahar I, Tan S, Davoli MA, Dominguez-Lopez S, Qiang C, Rachalski A, et al. Subchronic peripheral neuregulin-1 increases ventral hippocampal neurogenesis and induces antidepressant-like effects. PLoS One. 2011;6(10):e26610. pmid:22028923
- 48. Zhang M, Pan X, Jung W, Halpern AR, Eichhorn SW, Lei Z, et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature. 2023;624(7991):343–54. pmid:38092912
- 49. Cortese S. Pharmacologic Treatment of Attention Deficit-Hyperactivity Disorder. N Engl J Med. 2020;383(11):1050–6. pmid:32905677