Viktoria Haghani1,2†
Sara Mohsen Ali3,4,5†
Noemi Cannizzaro3,4
Ralph Green3,4
Janine M. LaSalle1*
Konstantinos S. Zarbalis3,4,6*- 1Department of Medical Microbiology and Immunology, Genome Center, University of California, Davis, Davis, CA, United States
- 2Department of Molecular and Cellular Biology, Genome Center, University of California, Davis, Davis, CA, United States
- 3Department of Pathology and Laboratory Medicine, University of California, Davis, Davis, CA, United States
- 4Institute for Pediatric Regenerative Medicine, Shriners Hospitals for Children, Northern California, Sacramento, CA, United States
- 5Department of Clinical Pathology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
- 6UC Davis MIND Institute, Sacramento, Davis, CA, United States
Introduction: Folate is crucial for various biological processes, and deficiencies during pregnancy are linked to increased risk for neural tube defects and neurodevelopmental disorders. As a proactive measure, folic acid fortification of staple foods has been mandated in many countries, in addition to dietary supplementation recommendations during pregnancy. However, the risks of excess prenatal folic acid supply have yet to be fully understood.
Methods: To better appreciate molecular changes in mouse brain exposed to in utero 5-fold folic acid excess over normal intake, we investigated the transcriptome and methylome for alterations in gene networks.
Results: RNA-seq analysis of cerebral cortex collected at birth, revealed significant expression differences in 646 genes with major roles in protein translation. Whole genome bisulfite sequencing revealed significant differences in 910 differentially methylated regions including functions enriched in glutamatergic synapse, neurodevelopmental, and glutathione pathways.
Discussion: These molecular alterations conceivably present risks to brain development and provide functional congruencies with disruptions in neuronal connectivity maps that we have described in previous work, underscoring the potential for excess prenatal folic acid exposure to disrupt developing metabolic and neurological pathways.
Introduction
Folate (vitamin B9) is an essential vitamin found in many foods, including dark leafy greens, fruits, legumes, seafood, liver, and more (1, 2). Folate is required for numerous biological processes, such as red blood cell production (3), closure of the neural tube during fetal development (4–8), DNA synthesis and repair (3, 9, 10), and gene expression through methylation of CpG-rich islands in DNA promoter regions (10, 11). Folate has also been shown to have a protective effect against neurodevelopmental disorders, such as autism spectrum disorders (ASD) when taken in the first month of pregnancy, associated with maternal exposure to biological and chemical contaminants (12–19). Due to its vital role in these processes, folate deficiency has been linked to several adverse health conditions, including an increased risk of miscarriages (4, 20), neural tube defects (3), and increased risk of ASD (21–23).
The neural tube, the precursor to the central nervous system, forms in humans during the first 28 days of development (24), often before many women become aware of their pregnancy (25). To address this concern and ensure that pregnant females receive sufficient folate to support neural tube development including other developmental processes, folic acid (FA) fortification became a standard public health measure in many countries, including the United States (26) and Canada (27) in 1998, Australia and New Zealand in 2009 (28), South Africa in 2003 (29), Chile in 2000 (30, 31), Argentina in 2003 (32), Brazil in 2004 (33), and many more. Consequently, a notable reduction in neural tube defects and birth defects was documented (34), with one study demonstrating a 72% protective effect against neural tube defects (5). In addition to FA fortification typically added to grains, it is common practice for pregnant females to take prenatal vitamins that further increase FA intake (35).
While the benefits of FA supplementation during neurodevelopment are well documented, emerging evidence suggests that FA excess (FAE) may have unintended negative consequences (36), such as disruptions to the immune system (37, 38), alterations in DNA methylation patterns (11, 39), metabolic perturbations (40–44), and increased risk of certain cancers (45–47). Additionally, there are indications that FAE could interfere with the regulation of homocysteine levels (48), which is associated with cardiovascular disease (49).
Furthermore, some epidemiological studies suggest that there are risks associated with high prenatal FA taken at any time in pregnancy to neurodevelopment and neurodevelopmental disorders (50–54). Of particular interest, data from the Boston Birth Cohort showed a “U shaped” relationship between maternal multivitamin supplementation frequency and ASD likelihood. A positive association between maternal plasma folate levels at birth and autism likelihood was identified (55) mostly implicating high plasma concentrations of unmetabolized FA (56).
A conceivable mechanism by which FAE may adversely impact neuronal function is its influence on neuronal circuit development. The formation of such circuits involves timely structured synaptogenesis and pruning to shape both local and long-range connectivity patterns (57). During normal brain development, these mechanisms facilitate the maturation of neural networks through selective strengthening and weakening of synaptic connections (58). Disruptions in these fundamental developmental processes have been implicated in various neurodevelopmental disorders, particularly ASD, where altered patterns of neural connectivity have been observed (59). These potential risks highlight the need to investigate the consequences of FAE.
In this study, we evaluated the effects of 5-fold FAE on gene pathways and networks in the neonatal mouse brain, with particular emphasis on changes in gene expression and DNA methylation that could contribute to neurodevelopmental impairments. By comparing RNA-seq and whole genome bisulfite sequencing (WGBS) data between the control and FAE groups, we uncovered dysregulated genes and epigenetic alterations to translational regulation, glutathione metabolism, and glutamatergic neuronal synapses, all functions relevant to neurodevelopment.
Materials and methods
Animal husbandry and diets
Mice were kept in facilities accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International. All procedures were conducted following protocols approved by the Institutional Animal Care and Use Committee at the University of California, Davis, ensuring compliance with ethical guidelines for animal research. Dietary groups were established by feeding C57BL/6NJ dams Clifford/Koury-based L-amino acid-defined rodent diets (60) (Dyets Inc., Bethlehem, PA) with specified folic acid levels, starting 2 weeks before mating and continuing through pregnancy. The breeding pairs remained on their assigned folate-controlled diets until the pups were collected. The experimental groups included: (1) a control group receiving 2 mg/kg of folic acid and (2) a high-folate group receiving 10 mg/kg (equivalent to 5 times the control amount). The control diet with 2 mg folic acid/kg chow satisfies the experimentally defined daily folate requirement for rodents (61), as recommended by the American Institute of Nutrition (62), corresponding to the human recommended intake of 400 μg/day. The 10 mg/kg dietary manipulation produced mild changes in brain folate cycle metabolites, notably, a significant decrease in THF/5-MTHF ratio and in SAM levels as previously reported (63).
Tissue collection and extraction
Newborn pups (P0–P2) were collected within hours after birth. Each pup was decapitated, and the cerebral cortex was rapidly dissected, placed on ice in tubes with brain preservation media which consisted of Hibernate-A and 2% 1X B-27 Supplement, and promptly stored at 4 °C until further processing. Genomic DNA and total RNA were simultaneously extracted from the same cortical tissue sample using the Qiagen AllPrep DNA/RNA/miRNA Universal Kit, following the manufacturer’s protocol to maximize yield. The DNA and RNA were separately eluted and assessed for quality and concentration using a Nanodrop spectrophotometer, with additional verification on agarose gels. DNA and total RNA were submitted to Novogene Corporation Inc. (Sacramento, CA) for RNA-seq and WGBS sequencing. PCR library selection was performed and paired-end sequencing was conducted on the Illumina NovaSeq X Plus platform. Sequencing generated paired-end reads of 150 bp in length, targeting approximately 30–40 million reads per sample for RNA-Seq and 40–50 million reads per sample for WGBS. Libraries were sequenced to a depth of >20× coverage for WGBS. Raw sequence data output was generated in FASTQ format, with quality scores provided in Phred format (Qphred = −10log10(e)), where “e” represents the sequencing error rate. Sequencing results of 7 cerebral cortical samples from the control group and 9 samples from the FAE group were analyzed for this study.
RNA-seq bioinformatics analysis
Raw sequence data was trimmed using Trim Galore (64) (v0.6.10) and aligned to the mmEnsemble107 genome using STAR (65) (v2.5.2b) with the Mus_musculus_Ensemble_107_new GTF file for annotation. The trimmed sequence data was aligned to the hg19, hg38, Lambda, mm10, PhiX, rheMac10, and rn6 genomes using FastQ Screen (66) (v0.15.3) to assess sample origins and screen for possible contamination. Samtools (67) (v1.11) was used to index the aligned BAM files. The proportion of reads mapping to the X vs. Y chromosome was assessed to determine the sex of the samples. MultiQC (68) (v1.14) was run for all raw and processed data to evaluate data integrity and overall quality.
To identify differentially expressed genes (DEGs), raw count data output by STAR was read into a Jupyter Notebook (69) (v7.0.7) running R (70) (v4.3.2) via IRkernel (71) (v1.3.2). Two DEG analyses were conducted. The first analysis contrasted control (n = 7) vs. FAE (n = 9) with sex as a covariate. The second analysis was sex-segregated, containing control females (n = 4), FAE females (n = 6), control males (n = 3), and FAE males (n = 3). The following analyses were conducted: control female vs. FAE female, control male vs. FAE male, FAE female vs. FAE male, and control female vs. control male. The raw count data were normalized in R. The data were filtered based on criteria described by Chen et al. (72). Briefly, genes were only kept if they had a minimum count-per-million in at least 70% of samples from the smallest group size and a minimum total count across all samples. The normalized and filtered data were log-transformed and subjected to voom transformation, then fitted to a linear model using limma (73) (3.58.1) in conjunction with edgeR (74) (v4.0.16). Contrasts were defined for each pairwise comparison following the convention of FAE minus control where relevant for consistent interpretation of log fold changes, and the estimated contrasts for each gene were calculated. Finally, empirical Bayes moderation was applied, followed by multiple testing adjustment using the Benjamini–Hochberg method to identify significant DEGs.
In addition to DEG analysis, weighted gene correlation network analysis (WGCNA) was also conducted using the WGCNA package (75) (v1.72.5) to identify co-expression modules of genes, relate these modules to external traits, and identify key hub genes that may play significant roles in the biological processes associated with FAE. Normalized count data were used to generate WGCNA in R. Soft power thresholding was performed, and a power of 17 was selected because the SFT.R.sq value was 0.80600, indicating a strong scale-free topology for the network. This choice of power ensures that the resulting network accurately reflects the underlying biological relationships among the genes. Blockwise modules were generated using a power of 17 to construct the network, with parameters set to optimize module detection for genes exhibiting coordinated expression patterns. Module eigengenes were calculated for modules, representing the combined expression patterns of the genes within each module. Then, module membership was assessed by calculating the biweight midvariance correlation and associated p-values between the genes and the module eigengenes, enabling the evaluation of each gene’s association with the identified modules. Pearson correlation coefficients and associated p-values were calculated between the module eigengenes and the sample metadata, facilitating the assessment of the relationships between gene modules and external traits, namely: condition only (control vs. FAE), sex only, control female vs. FAE female, control male vs. FAE male, and FAE female vs. FAE male.
Gene Ontology (GO) was carried out for both DEG and WGCNA results. Biomart (76) (v2.58.2) was used to assign Entrez gene IDs to Ensembl genes. For the DEG GO analysis, genes were filtered to adjusted p-values <0.05 and submitted to the following Enrichr (77) (v3.2) databases: GO Biological Process 2023, GO Cellular Component 2023, GO Molecular Function 2023, KEGG 2019 Mouse, Panther 2016, Reactome 2016, and RNA-seq Disease Gene and Drug Signatures from GEO. Significant DEGs were categorized as upregulated or downregulated with FAE exposure based on the directionality of log-fold changes. Plots were created using a combination of ggplot2 (78) (v3.4.4) and Enrichr to depict the top enriched terms from the Enrichr databases for all significant DEGs, as well as for upregulated and downregulated DEGs. For WGCNA, genes within each module were submitted as lists to the same Enrichr databases used for the DEG GO analysis. Enriched terms were plotted using ggplot2 and Enrichr.
WGBS bioinformatics analysis
Raw sequence data was processed using Epigenerator (64, 68, 79–81). Briefly, sequence data from multiple lanes for the same sample were merged. The data were trimmed using Trim Galore (64) (v0.6.10). The trimmed sequence data were aligned to the hg19, hg38, Lambda, mm10, PhiX, rheMac10, and rn6 genomes using FastQ Screen (66) (v0.15.3) to assess sample origins and screen for possible contamination. Trimmed data were aligned to the mm10 genome and deduplicated using Bismark (80) (v0.24.0). Data were sorted by chromosome coordinate and insert size metrics were collected using Picard (82) (v2.27.5). Bismark was used to calculate nucleotide coverage and compare it to average genome composition, determine methylation status for genomic cytosines, and generate cytosine reports, detailing cytosine coverage and methylation status. MultiQC (68) (v1.14) was run to ensure sequence quality for processed data. Five samples were excluded from analysis due to low alignment rates (0.1%), which was caused by improper insert sizes (i.e., paired-end fragments are either too short or too long, resulting in insufficient sequence content). The remaining samples were comprised of the following: control females (n = 3), control males (n = 4), FAE females (n = 2), and FAE males (n = 3).
Cytosine reports were used as inputs for DMRichR (83–85) (v1.7.1), which identifies differentially methylated regions (DMRs) and performs enrichment analysis. The analysis was conducted as control vs. FAE with sex as a covariate in the analysis. DMRs were identified when at least 50% of samples in a group exhibited coverage in a specified region displaying at least 10% difference in methylation levels between groups. Additionally, cytosine reports were used as inputs for comethyl (86) (v1.3.0), which performs WGCNA to identify modules of genomic regions with correlated methylation patterns. A minimum coverage of 2 was required for a CpG site to be retained in the analysis with at least 70% of samples meeting the coverage requirement.
Regions, which require a minimum of 3 CpG sites within 150 bp, were filtered to retain those with a minimum coverage of 8 across samples. Modules were generated using a mergeCutHeight of 0.4 with a minimum module size of 60, meaning that clusters of genes were combined if their dissimilarity was less than 0.4, and each module contains at least 60 genes. This allowed for the exploration of biologically relevant relationships between methylated regions, essentially identifying DMRs that may act together as a network due to the interconnectedness of genomic methylation changes.
Integrative RNA-seq and WGBS bioinformatics analysis
Because the RNA-seq and WGBS data were derived from paired samples obtained from the same mice (n = 12; 3 control females, 4 control males, 2 FAE females, and 3 FAE males), the RNA-seq and WGBS data can be correlated. Correlations were conducted between RNA-seq data (transcripts per million, TPM) and WGBS data (percent methylation). To calculate RNA-seq TPM, we utilized raw count data and the mm10 reference genome annotation (mm10.refGene.gtf). We summed the lengths of exons for each gene to obtain the total gene length, which was then converted to kilobases by dividing by 1,000. Reads per kilobase (RPK) were computed by dividing the raw read counts by the gene lengths in kilobases. Finally, TPM was derived from the RPK values by normalizing each gene’s RPK to the total RPK across samples and multiplying by 1,000,000 (i.e., TPM = (RPK/Total RPK) * 1,000,000). To determine the WGBS percent methylation, a total count of unmethylated and methylated cytosines per gene were generated from the cytosine reports. Percent methylation was calculated as the number of methylated cytosines divided by the total cytosines (methylated and unmethylated) times multiplied by 100 (i.e., percent methylation = (methylated cystosines/(methylated cytosines + unmethylated cytosines)) * 100).
To assess the correlation between gene expression and DNA methylation, samples were grouped into control and FAE groups, with each sample in the corresponding group being treated as an observation containing paired data (TPM and percent methylation). Spearman correlation coefficients and their p-values were calculated for every gene in the data sets. Then, overlapping significant DEGs and significant DMRs were identified and subject to a Fisher’s Exact Test to assess the significance of overlap.
RNA-seq and WGBS results were visualized using R (v4.3.2) with an R kernel (r-irkernel v1.3.2) in a Jupyter Notebook (v7.0.7) with the following packages: UpSetR (v1.4.0), openxlsx (v4.2.5.2), readxl (v1.4.3), dplyr (v1.1.14), glue (v1.7.0), ggplot2 (v3.4.4), ggrepel (v0.9.6), enrichr (v3.2), biomaRt (v2.58.2), tidyverse (v2.0.0), WGCNA (v1.72.5), magrittr (v2.0.3), viridis (v0.6.5), and tidyr (v1.3.1).
Results
FAE leads to sex-specific transcriptional dysregulations associated with mitochondrial function and protein synthesis
For the sex combined analysis (control vs. FAE), 646 significant DEGs (adjusted p-value <0.05) were identified (Supplementary Table 1). For the sex-stratified analyses, only the control female vs. FAE female analysis yielded significant DEGs (161, Supplementary Table 2), with the other comparisons (control male vs. FAE male, control male vs. control female) having no significant DEGs. Significant DEGs for the sex-adjusted combined and sex-stratified analyses were compared to identify overlapping genes (i.e., DEGs present in both analyses) that may be consistently affected by FAE (Figure 1 and Supplementary Table 3). There were 267 upregulated DEGs unique to control vs. FAE, 228 downregulated genes unique to control vs. FAE, 10 downregulated DEGs overlapping between the two comparisons, 51 upregulated DEGs overlapping between the comparisons, 7 downregulated DEGs unique to control female vs. FAE female, and 3 upregulated DEGs unique to control female vs. FAE female. Because DEG contrasts were conducted as FAE minus control, “upregulated” refers to genes expressed more highly in the FAE group and “downregulated” refers to genes expressed lower in the FAE group compared to the control group.
Figure 1. UpSet plot of DEGs. Upregulated and downregulated DEGs were overlapped between the sex combined and sex-segregated RNA-seq analyses. The number of upregulated and downregulated genes in each comparison was plotted, with specific emphasis on the intersections between the groups to discern sex-specific DEG results.
The 10 most significantly upregulated and downregulated DEGs were labeled for the control vs. FAE and control female vs. FAE female DEG analyses (Figure 2). A few overlaps were identified among these genes, including Gm55094, Lnp1, Hhex, and Dcst2, which are downregulated with prenatal FAE exposure. Ackr2 overlaps for the top 10 upregulated genes. Notably, Lnp1, which is downregulated, is involved in lipid metabolism, and Ackr2, which is upregulated, is involved in the immune response.
Figure 2. Volcano plots of top 10 significant upregulated and downregulated DEGs. This figure displays volcano plots of the top 10 most significantly differentially expressed genes filtered for adjusted p-values less than 0.05 and a log fold change greater than 1 for (A) control vs. FAE and (B) control female vs. FAE female. The x-axis represents the log2 fold change in gene expression, while the y-axis indicates the −log10 of the adjusted p-values, allowing for the visualization of both the magnitude and significance of expression changes. Upregulated genes are marked in red, downregulated genes are shown in blue, and genes that are not significantly differentially expressed are colored gray. The top 10 upregulated and downregulated genes are also labeled by gene name.
Significant DEGs were used to conduct the GO analysis, which revealed several significant biological processes, cellular components, and molecular functions (Figure 3). Key biological processes corresponding to genes downregulated in FAE included cytoplasmic translation, proton motive force-driven mitochondrial ATP synthesis, translation, macromolecule and peptide biosynthetic processes, and oxidative phosphorylation, indicating a strong impact of FAE on protein metabolism. Notable cellular components identified were various mitochondrial structures, including the mitochondrial inner membrane and ribosomal complexes, suggesting alterations in mitochondrial function and protein synthesis. Additionally, molecular functions associated with NADH dehydrogenase activity, as well as binding to RNA, ribosomes, and various types of rRNA, were identified, indicating alterations in electron transport and ribosomal activity. GO terms corresponding to genes upregulated in FAE included sterol and cholesterol biosynthetic processes, indicating enhanced capacity for lipid metabolism and membrane synthesis. Additionally, positive regulation of phosphatidylinositol 3-Kinase signaling and DNA damage response suggest a potential role in cellular signaling and stress response pathways. Cellular components associated with genes upregulated in FAE include trans-Golgi network, lytic vacuole membrane, mitotic spindle, spindle, and cytoskeleton, which suggests a potential increase in cell growth and proliferation. Molecular functions for upregulated genes are involved in pathways such as DNA exonuclease activity, insulin-like growth factor binding, vascular endothelial growth factor receptor 2 binding, and 3′–5′ DNA exonuclease activity, which is also consistent with cell growth and proliferation.
Figure 3. Enrichr Gene Ontology analysis for DEGs. This figure displays the results of the gene ontology analysis for the top 10 enriched terms identified in the GO Biological Process 2023 (blue), GO Cellular Component 2023 (green), and GO Molecular Function 2023 (red) Enrichr databases. (A) Represents the results associated with upregulated genes for the control vs. FAE comparison. (B) Shows the results associated with downregulated genes for the control vs. FAE comparison. (C) Represents the results associated with upregulated genes for the control female vs. FAE female comparison. (D) Shows the results associated with downregulated genes for the control female vs. FAE female comparison. The x-axis represents the −log10 of the p-values, indicating the significance of each term, with higher values reflecting greater significance. The y-axis lists the enriched terms.
The upregulated genes identified in the control female vs. FAE female analysis were associated with biological processes such as DNA metabolic process, DNA-templated DNA replication maintenance of fidelity, and replication fork processing, indicating major roles in DNA synthesis. Additionally, positive regulation of mRNA catabolic process, post-transcriptional regulation of gene expression, and DNA destabilization provides insight into the regulatory mechanisms that govern gene expression in response to FAE exposure in females. Cellular components included the actin cytoskeleton, preribosome, large subunit precursor, focal adhesion, and cell-substrate junction, which reflects alterations in cytoskeletal dynamics, cell adhesion and signaling pathways, and ribosome biogenesis, each of which may result in morphological changes. Molecular functions include RNA binding, DNA binding, telomerase activity, and mismatched DNA binding, which relates to DNA damage responses. The downregulated genes identified in the control female vs. FAE female analysis were associated with biological processes such as ncRNA processing, rRNA metabolic process, and ribosome biogenesis, indicating significant disruptions in RNA metabolism and protein synthesis. Additionally, processes like translation, cytoplasmic translation, and macromolecule biosynthetic processes highlight the impact of FAE on the cellular machinery responsible for protein production, suggesting a potential reduction in overall cellular function and viability in response to FAE exposure in females. Cellular components included the nucleolus, ribosome, and polysomal ribosome, reflecting alterations in ribosomal structure and function, which may lead to impaired protein synthesis. Molecular functions associated with the downregulated DEGs included rRNA binding, RNA binding, and NADH dehydrogenase activity, which relate to the cellular response to oxidative stress and energy metabolism. The downregulation of these functions suggests a compromised ability to manage oxidative damage and maintain homeostasis. Compared to the sex combined analysis, the female-specific analysis showed a more pronounced impact on ribosomal biogenesis and RNA-related processes. This suggests that while both analyses indicate disruptions in metabolic pathways due to FAE, the effects may manifest differently between sexes. Overall, these results suggest that FAE significantly affects ribosomal pathways, with strongest impacts observed in females.
RNA-seq WGCNA confirms sex-specific dysregulations of gene modules associated with neurological activity
In addition to the DEG analysis, we further explored the relationships between gene expression patterns and sample traits using WGCNA. The subsequent analysis revealed distinct modules of co-expressed genes (Supplementary Table 4), which were correlated with the sample traits, including the pairwise comparisons derived from the differential expression analysis (Figure 4). We focused on modules that showed significant associations with FAE but not litter or sex-only effects. The brown, darkorange, midnightblue, darkgreen, and skyblue modules are significantly positively correlated with the control vs. FAE comparison, including both sex-stratified control vs. FAE comparisons (i.e., control female vs. FAE female and control male vs. FAE male).
Figure 4. Heatmap of module trait pearson correlations from WGCNA. The heatmap displays the Pearson correlation coefficients between gene modules and different experimental conditions. The color scale ranges from dark red, indicating a perfect positive correlation (1), to dark blue, indicating a perfect negative correlation (−1). Each cell in the heatmap represents the correlation coefficient for a specific module-trait pair, with darker shades of red signifying stronger positive correlations and darker shades of blue signifying stronger negative correlations. The traits analyzed include control vs. FAE, control female vs. FAE female, control male vs. FAE male, and FAE female vs. FAE male. For all pairwise comparisons, positive correlations (red) indicate that higher expression levels of specific gene modules are associated with the first group in the comparison. Conversely, negative correlations (blue) indicate that higher expression levels are associated with the second group. The stars represent significant correlations, highlighting relationships that are statistically meaningful.
The brown module is enriched for synapse-related processes such as synapse assembly and organization together with axon-related cellular components, focal-adhesion structures and cell-substrate junctions, and it also contains ion-channel and receptor-kinase activities (Figure 5). KEGG enrichment highlights pathways such as focal adhesion and cardiomyopathy, suggesting that FAE may influence neuronal connectivity, extracellular-matrix interactions and cardiac-related signaling through altered methylation-dependent gene regulation. The darkorange module is dominated by mitotic and cell-cycle terms, including microtubule-cytoskeleton organization in mitosis, sister-chromatid segregation, spindle organization, tubulin and microtubule binding, and DNA-helicase activity. KEGG hits for cell-cycle, DNA replication, and oocyte meiosis point to heightened proliferative or reparative activity that could stem from increased nucleotide synthesis driven by abundant folate. The midnightblue module shares many of these mitotic themes, with particular emphasis on sister-chromatid segregation, spindle elongation, spindle microtubules and nuclear-chromosome localization, reinforcing the notion that FAE perturbs precise control of chromosome segregation and mitotic progression, possibly through altered one-carbon metabolism. The darkgreen module contributes a more focused signal of DNA-polymerase activity, indicating that DNA synthesis or repair processes are also elevated under FAE, consistent with enhanced availability of thymidine and purine precursors. Finally, the skyblue module is characterized by peroxisome-related biology, including establishment of protein localization to peroxisomes, protein targeting to peroxisomes, peroxisomal membrane components and microtubule-based transport, implying that FAE may modulate lipid metabolism, reactive-oxygen-species detoxification, and organelle trafficking through changes in one-carbon flux. Together, these gene network-level signatures illustrate that FAE simultaneously affects neuronal and cardiovascular signaling, cell-division dynamics, DNA maintenance, and metabolic organelle function.
Figure 5. Dot plot of top GO terms across WGCNA modules. This dot plot illustrates the top GO enrichment terms identified across different gene modules derived from WGCNA. Each dot represents a specific GO term from the GO databases being identified in the module (KEGG_Mouse = KEGG 2019 Mouse, GO MF = GO Molecular Function 2023, GO CC = GO Cellular Component 2023, GO BP = GO Biological Process 2023). The size of the dot represents the odds ratio, with a larger dot (higher odds ratios) signifying a stronger association between the gene module and the GO term. Conversely, smaller dots (lower odds ratios) indicate weaker associations. The color of each dot reflects the adjusted p-value, with darker blue shades indicating slight significance and lighter yellow shades indicating greater significance. The y-axis lists the various GO terms, while the x-axis corresponds to the different WGCNA modules. Modules that were not significant for any condition or only for litter and sex effects were excluded.
Several modules also displayed significant sex-specific correlations. In males, the lightgreen module negatively correlated with the control male versus FAE male contrast; this module is enriched for extracellular-matrix organization, collagen-fibril assembly, calcium-ion binding and gap-junction channel activity, suggesting that FAE is associated with dysregulation of structural and intercellular-communication pathways. The violet module, also negatively correlated in control vs. FAE males, is dominated by transmembrane transport activities for oxalate, sulfate and SUMO-ligase functions, suggesting that folate-driven metabolic reprogramming may suppress specific ion-homeostasis and post-translational modification processes in the male brain or peripheral tissues. Conversely, the steelblue module exhibited a positive correlation in the male contrast, with significant GO terms being involved in ATP-gated ion-channel activity and broader nucleotide-receptor signaling, implying that FAE may upregulate purinergic signaling cascades uniquely in males. The ivory module, which is negatively correlated in males, is linked to pathogen-related KEGG pathways such as African trypanosomiasis and malaria, a pattern that may reflect a downregulation of immune-oriented gene sets as a result of FAE. The blue module, positively correlated in males, has a dominant neurodevelopmental signature, with significant dysregulation of axon guidance, actin-cytoskeleton remodeling, GTPase regulation and postsynaptic-density components. For the control female vs. FAE female comparison, only the darkmagenta module is significantly unique; however, there are no significant GO terms positively associated with the genes in the module. Despite a lack of significance unique to females, the purple, enriched for terms associated with DNA-metabolic, DNA-repair, and cell-cycle, is significantly negatively correlated with the FAE female vs. FAE male comparison.
Overall, these results reveal significant insights into gene expression networks and the effects of FAE on these networks, highlighting both common and sex-specific pathways that may contribute to neurological, metabolic, and developmental differences.
FAE alters DNA methylation levels in regions associated with neurodevelopmental processes and synapse activity
The WGBS analyses yielded 910 DMRs in the control vs. FAE comparison using sex as a covariate. The significant DMRs that covered 0.02% of the genome (Supplementary Table 5). 70% of the DMRs were hypermethylated, and 30% were hypomethylated. On average, DMRs were 576 base pairs long and contained 11 CpGs. DMRs were significantly enriched for exonic regions, with hypomethylated regions being both exonic and intronic (Figure 6A). Hypermethylated DMRs were significantly enriched for CpG shores and the open sea and hypomethylated DMRs were significantly enriched for CpG islands (Figure 6B).
Figure 6. Genic site and CpG site enrichment based on DMRs. This figure presents an output from the DMRichR (v1.7.1) analysis, highlighting the enrichment of different genic and CpG sites based on the identified DMRs. (A) The x-axis represents the fold enrichment of hypermethylated and hypomethylated DMRs across various genic sites, including promoters (red), 5′ UTRs (orange), exons (orange-yellow), introns (yellow), 3′ UTRs (green), downstream regions (pale blue), and intergenic regions (blue). The y-axis categorizes the genic sites, with the bars indicating the degree of enrichment for hypermethylated and hypomethylated regions as well as a combination of all regions. Positive values on the x-axis (greater than 0) indicate a higher enrichment of DMRs in that specific genic site, while negative values (less than 0) suggest a depletion of DMRs. (B) While panel A focuses on genic sites, panel B illustrates the enrichment of DMRs across various CpG site categories, including CpG islands (green), CpG shores (yellow), CpG shelves (light blue), and open sea regions (dark blue). The x-axis represents the fold enrichment of hypermethylated and hypomethylated DMRs, while the y-axis categorizes the different CpG site types. Positive values indicate a higher enrichment of DMRs in that specific CpG category, while negative values indicate a depletion.
The top 10 DMRs with the highest methylation differences between the control and FAE groups were Txndc2, Fstl4, Cmc1, Ubxn8, Gm35496, Cep250, Xpr1, Pitpnb, Fndc5, Adgrl2, and Shtn1. Txndc2 and Cep250 are involved in oxidative stress responses and spermatogenesis, suggesting potential impacts on male fertility and reproductive health. Fstl4 plays a role in cell differentiation and regulates BDNF, which is a critical player in neurodevelopment. Cmc1 is associated with mitochondrial respiration, indicating that FAE may disrupt energy metabolism. Ubxn8 is a ubiquitin regulatory protein that affects protein degradation, while Xpr1 maintains phosphate homeostasis, both of which are important for metabolic regulation. Pitpnb is significant for phospholipid signaling in neurogenesis, and Fndc5 is linked to insulin sensitivity. Lastly, Adgrl2 and Shtn1 are crucial for neuronal connectivity and function, particularly in neuronal polarization and axonogenesis. Together, these findings suggest that the epigenetic changes observed in these genes may contribute to adverse effects associated with FAE with regards to metabolic, cellular, and neurodevelopmental pathways.
Enrichment analysis of the DMRs identified significant associations with biological processes such as glutamate receptor signaling, axonogenesis, regulation of potassium ion transmembrane, positive regulation of neuron differentiation, and negative regulation of response to stimulus, indicating that FAE may disrupt critical pathways involved in neuronal development and function (Figure 7). Additionally, cellular components like the axon highlights the potential impact on key neuronal structure. Molecular functions, including ionotropic glutamate receptor activity, RNA polymerase II transcription regulatory region activity, and neurotrophin binding suggest that epigenetic modifications resulting from FAE could alter gene expression and synaptic transmission. Collectively, these findings provide additional context on potential mechanisms by which FAE influences health outcomes, particularly in relation to neurological development and function.
Figure 7. Enrichr results for Gene Ontology analysis of DMRs. Results of DMRichR (v1.7.1) analysis, specifically the gene ontology analysis conducted on significant DMRs. The y-axis lists the enriched GO terms categorized into three main categories: Biological Process (blue), Cellular Component (orange), and Molecular Function (green). The x-axis represents the significance of enrichment, indicated by the −log10 (p-value), with higher values reflecting greater statistical significance.
Comethylation region network analysis confirms dysregulated gene networks in FAE are predominantly associated with chemical synapse activity
Comparably to the WGCNA analysis for the RNA-seq data, we further explored the relationships between comethylated genes (i.e., modules) and sample traits using comethyl. This analysis yielded 58 modules, with the hondeydew1, white, brown, brown4, and purple modules being particularly interesting due to their significance in the FAE group (Figure 8 and Supplementary Table 6). The brown, brown4, purple, and white modules were significantly enriched for KEGG terms related to neurological processes, including glutamatergic synapse, GABAergic synapse, dopaminergic synapse, cholinergic synapse, calcium signaling pathway, axon guidance, and more (Figure 9).
Figure 8. Heatmap of module trait biweight midvariance correlations from comethyl. This figure presents an output from the comethyl (v1.3.0) analysis. This heatmap displays the biweight midvariance correlation coefficients between comethylated modules and sample condition. The color scale ranges from red, indicating a perfect positive correlation (1), to blue, indicating a perfect negative correlation (−1). Each cell in the heatmap represents the correlation coefficient for a specific module-trait pair, with darker shades of red signifying stronger positive correlations and darker shades of blue signifying stronger negative correlations. The stars represent significant correlations, highlighting relationships that are statistically meaningful.
Figure 9. Dot plot of top KEGG terms across comethyl modules. This dot plot illustrates the top KEGG pathways identified across different gene modules derived from comethyl. Each dot represents a specific KEGG term from the KEGG_2019_Mouse database being identified in the module. The size of the dot represents the odds ratio, with a larger dot (higher odds ratios) signifying a stronger association between the gene module and the KEGG pathway. Conversely, smaller dots (lower odds ratios) indicate weaker associations. The color of each dot reflects the adjusted p-value, with yellow indicating slight significance and darker blue shades indicating greater significance. The y-axis lists the various KEGG pathways, while the x-axis corresponds to the different comethyl modules. Depicted modules were significant for the FAE condition containing significantly enriched KEGG terms.
Integration of RNA-seq DEGs and WGBS DMRs reveals significant correlations between data sets
Significant DEGs from the RNA-seq analysis were compared to the significant DMRs from the WGBS analysis for the control vs. FAE groups to identify overlaps. It is important to note that the RNA and DNA samples of each sequencing experiment were obtained from the same set of tissue samples. The integration of RNA-seq DEGs and WGBS DMRs revealed an overlap of 20 genes: Naa20, Med10, Epb41l4a, Katnal2, D3Ertd751e, Ccdc93, Itga6, Tacr3, Cald1, Ccl17, Atp2c1, Asprv1, Kcnk10, Fau, Egr1, Mest, Fbh1, Syt10, Urm1, and Arrdc3. Based on the Fisher’s Exact Test, this overlap is significant (p-value = 2.2 × 10−16). In addition to this overlap being significant, 809 genes also showed significant correlations between RNA-seq TPMs and WGBS percent methylation values (Supplementary Table 7). Among these significant transcriptome-methylome correlated genes, 583 were identified in control mice (553 uniquely, 238 positively correlated, 345 negatively correlated), compared to only 288 genes with transcriptome-methylome correlations in the FAE mice (256 unique to FAE group, 110 positively correlated, 178 negatively correlated). These results suggest that part of the dysregulation associated with prenatal FAE may be the disrupted balance of DNA methylation regulatory elements important for gene expression.
Discussion
The findings of this study provide insight into the biological consequences of 5-fold excess FA exposure during pregnancy on cerebral cortical DNA methylation and gene expression. Our analysis revealed significant alterations in gene networks associated with metabolic and neurological functions. The identification of 646 DEGs in the RNA-seq analysis highlights the impact of FAE on gene expression. Notably, the significant downregulation of genes critical for neurodevelopment, such as Hhex, Lnp1 and Dcst2, and significant upregulation of Ackr2 (87, 88), raises concerns about the long-term implications of FAE on brain development and function. These genes are known to play pivotal roles in neuronal differentiation and the formation of neural circuits and synpases, suggesting that prenatal exposure to high levels of FA may disrupt normal neurodevelopmental trajectories (89–92). Furthermore, the RNA regulatory mechanisms identified in the female FAE GO analysis suggest that while significant changes at the RNA expression level may not be evident, there could be substantial alterations at the proteomic level. These processes can lead to the degradation of specific mRNAs, preventing their translation into proteins, which may mask the true extent of gene expression changes seen when only analyzing RNA levels. Additionally, indications of compromised genomic integrity may lead to aberrant protein synthesis or the production of dysfunctional proteins. This disconnect between RNA and protein expression highlights the need to investigate gene network level interactions and potentially expand into proteomic analysis methods to fully understand the biological consequences of FAE exposure, as the effects on protein function and interaction networks may be more pronounced than would otherwise be evident.
Folate is required for the generation of S-adenosyl-L-methionine (SAM), the universal methyl donor used for methylation of DNA, RNA, and proteins (93, 94). Thus, excess maternal FA supplementation may also influence RNA methylation, which may further influence its translation through a variety of mechanisms, including by influencing mRNA structure and accessibility. In this study, several downregulated genes in FAE females were enriched for RNA-related processes, including ncRNA processing, rRNA metabolism, and ribosome biogenesis, pathways closely linked to RNA methylation, particularly N6-methyladenosine (m6A). As m6A regulates RNA stability, translation, and degradation while interacting with DNA methylation (95, 96), the observed female-specific dysregulation of RNA-binding and processing genes may reflect broader alterations in RNA methylation dynamics. Future studies examining enzymes such as METTL3 or FTO (96, 97) could clarify whether these pathways contribute to the transcriptional effects of prenatal FAE.
The WGBS analysis identified 910 DMRs, with a predominance of hypermethylation, which is generally associated with gene silencing. The enrichment of DMRs in exonic regions and their association with critical biological processes, such as glutamate receptor signaling and axonogenesis, suggests that FAE may disrupt essential pathways involved in neuronal development and synaptic function. Furthermore, comethylated regions also suggested significant effects on several pathways involved in neurological function.
The findings of this study also reveal important connections between FAE and other related health issues. The significant downregulation of Lnp1, a fusion partner of NUP98 associated with hematopoietic malignancies, underscores the potential cancer-related implications of FAE, aligning with results from other studies that have linked folate metabolism to increased cancer risk (98). The increased cancer risk may be a result of enhanced cell growth and proliferation, as seen in the GO analysis for genes upregulated with FAE exposure. This is further supported by the comethyl analysis, which identified significant enrichment for the following KEGG terms: adrenergic signaling in cardiomyocytes, arrhythmogenic right ventricular cardiomyopathy (ARVC), dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and choline metabolism in cancer.
In addition to other organ-specific health outcomes, the presence of addiction-related GO terms in the WGBS comethylation modules suggests that FAE may influence pathways relevant to substance use disorders. Current studies regarding the role of FA in addiction pathways suggest that FA deficiencies are an outcome of substance abuse (99–101); however, it may be worth to further explore these associations to determine the impact of FAE and deficiencies on substance use disorders.
While the role of FA metabolism in widespread health outcomes and addiction pathways warrants further investigation, previous studies have linked disruptions to insulin metabolism to FAE (40–42). This aligns with our findings revealing significant hypomethylation of Fndc5, a gene involved in the modulation of energy expenditure and insulin sensitivity (102). Hypomethylation is typically associated with increased gene expression, suggesting that the elevated Fndc5 expression may lead to dysregulation of insulin sensitivity pathways and contribute to insulin resistance. Comethylation modules significant for the FAE group were also significantly enriched for genes involved in insulin secretion.
Interestingly, our results also revealed sex-specific differences in the response to FAE, with distinct patterns of gene expression and methylation observed. The identification of WGCNA modules that were positively correlated with FAE in sex-specific contexts suggests that the effects of FAE may manifest differently based on sex. The hypomethylation of Txndc2 and Cep250, both linked to oxidative stress and spermatogenesis (103, 104), supports a potential impact of prenatal FAE exposure on neurodevelopmental outcomes of male offspring. Conceivably, male offspring may be predisposed to adverse neurodevelopmental outcomes by interference with oxidative stress pathways that are critical for normal brain development (105). Additionally, the alterations in gene expression and methylation patterns observed in this study suggest that FAE could negatively affect male reproductive health, potentially leading to fertility issues later in life. Understanding these sex-specific responses to FAE is vital for developing targeted nutritional guidelines and interventions that consider the unique metabolic and developmental needs of males and females.
Overall, our study provides novel insights into the molecular consequences of prenatal FAE exposure, revealing significant disruptions in gene expression and epigenetic regulation in the neonatal mouse brain. Further, our findings suggest that neurons derived from offspring exposed to FAE during pregnancy may exhibit diminished capacity for establishing functional synaptic connections and creating coherent neuronal networks compared with controls. These findings highlight the importance of understanding the potential risks associated with high levels of FA intake and call for further research to determine the mechanisms underlying these effects. Future studies should investigate the long-term implications of these molecular changes and their relevance to the development of neurodevelopmental disorders, ultimately informing guidelines for prudent FA supplementation recommendations in pregnant populations. The implications of these findings are significant, as they suggest that while FA fortification has been a successful public health measure in reducing neural tube defects, the potential risks associated with excessive intake after the periconceptional period of demonstrated protection warrant further investigation. The observed alterations in gene expression and methylation patterns may have long-term consequences for metabolic health, immune function, and neurodevelopment, underscoring the need for a balanced approach to FA supplementation during pregnancy.
Conclusion
This study investigated the impact of prenatal FAE exposure on gene expression and DNA methylation in the neonatal mouse brain. Our results demonstrate that FAE can lead to widespread alterations in the expression of gene networks critical for neurodevelopment, with potential long-term implications for metabolic health and neurological function. The integration of RNA-seq and WGBS analyses revealed a complex interplay between epigenetic modifications and gene regulation, highlighting the interconnectedness of these processes in shaping developmental outcomes. Additionally, the identification of sex-specific differences in response to excessive prenatal FA exposure further underscores the necessity of considering offspring sex and timing of supplementation in research design and public health recommendations. While FA fortification and periconceptional use of prenatal vitamins has been instrumental in reducing neural tube defects, our results raise important questions regarding the potential risks associated with FAE in other aspects of neurodevelopment and other critical biological pathways. In summary, this study contributes to the growing body of evidence suggesting that while FA is essential for healthy prenatal development, careful consideration of dosage and timing of administration is crucial to avoid unintended adverse effects. Our findings warrant further research to explore the long-term consequences of these molecular changes and to refine guidelines for FA supplementation in pregnant individuals, ensuring optimal health outcomes for both mothers and their offspring.
Data availability statement
Sequence data generated for this study can be found using the BioProject Accession Number PRJNA1223393, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1223393. All scripts used in the RNA-seq and WGBS analysis can be found here: https://github.com/vhaghani26/Mouse_FAE_RNAseq_WGBS.
Ethics statement
The animal study was approved by the Institutional Animal Care and Use Committee at the University of California, Davis. The study was conducted in accordance with the local legislation and institutional requirements.
Author contributions
VH: Data curation, Writing – review & editing, Methodology, Writing – original draft, Visualization, Formal analysis, Investigation. SMA: Methodology, Visualization, Validation, Investigation, Writing – review & editing, Formal analysis. NC: Writing – review & editing, Methodology, Investigation. RG: Funding acquisition, Project administration, Supervision, Methodology, Writing – review & editing, Conceptualization. JML: Resources, Funding acquisition, Conceptualization, Writing – original draft, Project administration, Supervision, Validation, Writing – review & editing, Investigation, Methodology. KSZ: Writing – review & editing, Funding acquisition, Project administration, Writing – original draft, Conceptualization, Supervision.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by NICHD R01HD107489, by a UC Davis Interdisciplinary Research Grant, and the Powell Family Charitable Trust. Additional support to KSZ was provided by Shriners Hospitals for Children. SMA was supported by Ph.D. scholarship GM 1153 from the Egyptian Ministry of Higher Education and Scientific Research.
Acknowledgments
The authors would like to thank Dr. Osman Sharifi, Dr. Aron Mendiola, and Dr. Blythe Durbin-Johnson for their insights on the RNA-seq and WGCNA analysis and Dr. Ian Korf for his computational insight and advice. This article was submitted as a preprint to BioRxiv as Haghani et al. (106): https://doi.org/10.1101/2025.05.07.652681.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author KZ declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1699376/full#supplementary-material
References
1. National Institutes of Health Office of Dietary Supplements. (2022). Fact sheet for health professionals. Available online at: https://ods.od.nih.gov/factsheets/Folate-HealthProfessional/. (Accessed November 13, 2024)
2. Ross, AC, Caballero, BH, Cousins, RJ, Tucker, KL, and Ziegler, TR. Modern nutrition in health and disease. 11th ed. Philadelphia, PA: Wolters Kluwer Health (2012).
4. Czeizel, AE, and Dudás, I. Prevention of the first occurrence of neural-tube defects by periconceptional vitamin supplementation. N Engl J Med. 327:1832–5. doi: 10.1056/NEJM199212243272602
5. MRC Vitamin Study Research Group. Prevention of neural tube defects: results of the Medical Research Council vitamin study. Lancet. (1991) 338:131–7. doi: 10.1016/0140-6736(91)90133-A
6. De Wals, P, Tairou, F, Van Allen, MI, Uh, SH, Lowry, RB, Sibbald, B, et al. Reduction in neural-tube defects after folic acid fortification in Canada. N Engl J Med. (2007) 357:135–42. doi: 10.1056/NEJMoa067103,
7. Kancherla, V. Neural tube defects: a review of global prevalence, causes, and primary prevention. Childs Nerv Syst. (2023) 39:1703–10. doi: 10.1007/s00381-023-05910-7,
8. Slattery, ML, and Janerich, DT. The epidemiology of neural tube defects: a review of dietary intake and related factors as etiologic agents. Am J Epidemiol. (1991) 133:526–40. doi: 10.1093/oxfordjournals.aje.a115924,
9. Wang, X, Yu, J, and Wang, J. Neural tube defects and folate deficiency: is DNA repair defective? Int J Mol Sci. (2023) 24:2220. doi: 10.3390/ijms24032220,
10. Duthie, SJ. Folate and cancer: how DNA damage, repair and methylation impact on colon carcinogenesis. J Inherit Metab Dis. (2011) 34:101–9. doi: 10.1007/s10545-010-9128-0,
11. Crider, KS, Yang, TP, Berry, RJ, and Bailey, LB. Folate and DNA methylation: a review of molecular mechanisms and the evidence for folate’s role. Adv Nutr. (2012) 3:21–38. doi: 10.3945/an.111.000992,
12. Loftus, CT, Hazlehurst, MF, Szpiro, AA, Ni, Y, Tylavsky, FA, Bush, NR, et al. Prenatal air pollution and childhood IQ: preliminary evidence of effect modification by folate. Environ Res. (2019) 176:108505. doi: 10.1016/j.envres.2019.05.036,
13. Goodrich, AJ, Volk, HE, Tancredi, DJ, McConnell, R, Lurmann, FW, Hansen, RL, et al. Joint effects of prenatal air pollutant exposure and maternal folic acid supplementation on risk of autism spectrum disorder. Autism Res. (2018) 11:69–80. doi: 10.1002/aur.1885,
14. Schmidt, RJ, Kogan, V, Shelton, JF, Delwiche, L, Hansen, RL, Ozonoff, S, et al. Combined prenatal pesticide exposure and folic acid intake in relation to autism spectrum disorder. Environ Health Perspect. (2017) 125:097007. doi: 10.1289/EHP604,
15. Shin, HM, Schmidt, RJ, Tancredi, D, Barkoski, J, Ozonoff, S, Bennett, DH, et al. Prenatal exposure to phthalates and autism spectrum disorder in the MARBLES study. Environ Health. (2018) 17:85–14. doi: 10.1186/s12940-018-0428-4,
16. Oulhote, Y, Lanphear, B, Braun, JM, Webster, GM, Arbuckle, TE, Etzel, T, et al. Gestational exposures to phthalates and folic acid, and autistic traits in Canadian children. Environ Health Perspect. (2020) 128:027004. doi: 10.1289/EHP5621,
17. Kim, B, Shah, S, Park, HS, Hong, YC, Ha, M, Kim, Y, et al. Adverse effects of prenatal mercury exposure on neurodevelopment during the first 3 years of life modified by early growth velocity and prenatal maternal folate level. Environ Res. (2020) 191:109909. doi: 10.1016/j.envres.2020.109909,
18. Schmidt, RJ, Lyall, K, and Hertz-Picciotto, I. Environment and autism: current state of the science. Cut Edge Psychiatry Pract. (2014) 1:21–38. doi: 10.65031/ibek1613,
19. Dickerson, AS, and Schmidt, RJ. Invited perspective: protect and serve—the potential role of folate in lead risk reduction. Environ Health Perspect. (2024) 132:101302. doi: 10.1289/EHP16216,
20. Scholl, TO, and Johnson, WG. Folic acid: influence on the outcome of pregnancy. Am J Clin Nutr. (2000) 71:1295S–303S. doi: 10.1093/ajcn/71.5.1295s,
21. Schmidt, RJ, Tancredi, DJ, Ozonoff, S, Hansen, RL, Hartiala, J, Allayee, H, et al. Maternal periconceptional folic acid intake and risk of autism spectrum disorders and developmental delay in the CHARGE (CHildhood Autism Risks from Genetics and Environment) case-control study. Am J Clin Nutr. (2012) 96:80–9. doi: 10.3945/ajcn.110.004416,
22. Surén, P, Roth, C, Bresnahan, M, Haugen, M, Hornig, M, Hirtz, D, et al. Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children. JAMA. (2013) 309:570–7. doi: 10.1001/jama.2012.155925,
23. Wang, M, Li, K, Zhao, D, and Li, L. The association between maternal use of folic acid supplements during pregnancy and risk of autism spectrum disorders in children: a meta-analysis. Mol Autism. (2017) 8:51. doi: 10.1186/s13229-017-0170-8
24. Cavalli, P. Prevention of neural tube defects and proper folate periconceptional supplementation. J Prenat Med. (2008) 2:40–1.
25. Watson, K, and Angelotta, C. The frequency of pregnancy recognition across the gestational spectrum and its consequences in the United States. Perspect Sex Reprod Health. (2022) 54:32–7. doi: 10.1363/psrh.12192,
26. Federal Register. (1996). Food standards: amendment of standards of identity for enriched grain products to require addition of folic acid. Available online at: https://www.federalregister.gov/documents/1996/03/05/96-5014/food-standards-amendment-of-standards-of-identity-for-enriched-grain-products-to-require-addition-of. (Accessed November 26, 2024)
27. Lindzon, G, and O’Connor, DL. Folate during reproduction: the Canadian experience with folic acid fortification. Nutr Res Pract. (2007) 1:163–74. doi: 10.4162/nrp.2007.1.3.163
28. Food Standards Australia New Zealand. Monitoring of folic acid fortification. Available online at: https://www.foodstandards.gov.au/consumer/food-fortification/folic-acid/monitoring-folic. (Accessed December 18, 2024)
29. FoodFacts. Food fortification. Available online at: https://foodfacts.org.za/food-fortification/. (Accessed December 18, 2024)
30. Camelo, L, and Santiago, J. Fortification of flours with folic acid reduces the frequency of neural tube defects in South America; Argentine Society of Genetics. BAG J Basic Appl Genet. (2010) 21:1–6.
31. Cortés, F, Mellado, C, Pardo, RA, Villarroel, LA, and Hertrampf, E. Wheat flour fortification with folic acid: changes in neural tube defects rates in Chile. Am J Med Genet A. (2012) 158A:1885–90. doi: 10.1002/ajmg.a.35430,
32. Calvo, EB, and Biglieri, A. Impact of folic acid fortification on women’s nutritional status and on the prevalence of neural tube defects. Arch Argent Pediatr. (2008) 106:492–8. doi: 10.1590/S0325-00752008000600004,
33. Chakraborty, H, Nyarko, KA, Goco, N, Moore, J, Moretti-Ferreira, D, Murray, JC, et al. Folic acid fortification and women’s folate levels in selected communities in Brazil—a first look. Int J Vitam Nutr Res. (2014) 84:286–94. doi: 10.1024/0300-9831/a000215,
34. Boulet, SL, Yang, Q, Mai, C, Kirby, RS, Collins, JS, Robbins, JM, et al. Trends in the postfortification prevalence of spina bifida and anencephaly in the United States. Birth Defects Res A Clin Mol Teratol. (2008) 82:527–32. doi: 10.1002/bdra.20468,
35. De-Regil, LM, Peña-Rosas, JP, Fernández-Gaxiola, AC, and Rayco-Solon, P. Effects and safety of periconceptional oral folate supplementation for preventing birth defects. Cochrane Database Syst Rev. (2015) 2015:CD007950. doi: 10.1002/14651858.CD007950.pub3,
36. Patel, KR, and Sobczyńska-Malefora, A. The adverse effects of an excessive folic acid intake. Eur J Clin Nutr. (2017) 71:159–63. doi: 10.1038/ejcn.2016.194,
37. Troen, AM, Mitchell, B, Sorensen, B, Wener, MH, Johnston, A, Wood, B, et al. Unmetabolized folic acid in plasma is associated with reduced natural killer cell cytotoxicity among postmenopausal women1. J Nutr. (2006) 136:189–94. doi: 10.1093/jn/136.1.189,
38. Paniz, C, Bertinato, JF, Lucena, MR, de Carli, E, Amorim, PMS, Gomes, GW, et al. A daily dose of 5 mg of folic acid for 90 days is associated with increased serum unmetabolized folic acid and reduced natural killer cell cytotoxicity in healthy Brazilian adults. J Nutr. (2017) 147:1677–85. doi: 10.3945/jn.117.247445,
39. Cravo, ML, Pinto, AG, Chaves, P, Cruz, JA, Lage, P, Nobre Leitão, C, et al. Effect of folate supplementation on DNA methylation of rectal mucosa in patients with colonic adenomas: correlation with nutrient intake. Clin Nutr. (1998) 17:45–9. doi: 10.1016/s0261-5614(98)80304-x,
40. Deshmukh, U, Katre, P, and Yajnik, CS. Influence of maternal vitamin B12 and folate on growth and insulin resistance in the offspring. Nestle Nutr Inst Workshop Ser. (2013) 74:145–54. doi: 10.1159/000348463
41. Krishnaveni, GV, Veena, SR, Karat, SC, Yajnik, CS, and Fall, CHD. Association between maternal folate concentrations during pregnancy and insulin resistance in Indian children. Diabetologia. (2014) 57:110–21. doi: 10.1007/s00125-013-3086-7,
42. Keating, E, Correia-Branco, A, Araújo, JR, Meireles, M, Fernandes, R, Guardão, L, et al. Excess perigestational folic acid exposure induces metabolic dysfunction in post-natal life. J Endocrinol. (2015) 224:245–59. doi: 10.1530/JOE-14-0448,
43. Xita, N, and Tsatsoulis, A. Adiponectin in diabetes mellitus. Curr Med Chem. (2012) 19:5451–8. doi: 10.2174/092986712803833182,
44. Popkin, BM, and Doak, CM. The obesity epidemic is a worldwide phenomenon. Nutr Rev. (1998) 56:106–14. doi: 10.1111/j.1753-4887.1998.tb01722.x,
45. Mason, JB, Dickstein, A, Jacques, PF, Haggarty, P, Selhub, J, Dallal, G, et al. A temporal association between folic acid fortification and an increase in colorectal cancer rates may be illuminating important biological principles: a hypothesis. Cancer Epidemiol Biomarkers Prev. (2007) 16:1325–9. doi: 10.1158/1055-9965.EPI-07-0329,
46. Cole, BF, Baron, JA, Sandler, RS, Haile, RW, Ahnen, DJ, Bresalier, RS, et al. Folic acid for the prevention of colorectal adenomas a randomized clinical trial. JAMA. (2007) 297:2351–9. doi: 10.1001/jama.297.21.2351,
47. Choi, SW, and Mason, JB. Folate status: effects on pathways of colorectal carcinogenesis. J Nutr. (2002) 132:2413S–8S. doi: 10.1093/jn/132.8.2413S,
48. Mokgalaboni, K, Mashaba, GR, Phoswa, WN, and Lebelo, SL. Folic acid supplementation on inflammation and homocysteine in type 2 diabetes mellitus: systematic review and meta-analysis of randomized controlled trials. Nutr Diabetes. (2024) 14:22. doi: 10.1038/s41387-024-00282-6,
49. Wierzbicki, AS. Homocysteine and cardiovascular disease: a review of the evidence. Diab Vasc Dis Res. (2007) 4:143–9. doi: 10.3132/dvdr.2007.033,
50. Beard, CM, Panser, LA, and Katusic, SK. Is excess folic acid supplementation a risk factor for autism? Med Hypotheses. (2011) 77:15–7. doi: 10.1016/j.mehy.2011.03.013,
51. DeSoto, MC, and Hitlan, RT. Synthetic folic acid supplementation during pregnancy may increase the risk of developing autism. J Pediatr Biochem. (2012) 2:251–61. doi: 10.1055/s-0036-1586421
52. Egorova, O, Myte, R, Schneede, J, Hägglöf, B, Bölte, S, Domellöf, E, et al. Maternal blood folate status during early pregnancy and occurrence of autism spectrum disorder in offspring: a study of 62 serum biomarkers. Mol Autism. (2020) 11:7. doi: 10.1186/s13229-020-0315-z,
53. Compañ Gabucio, LM, García de la Hera, M, Torres Collado, L, Fernández-Somoano, A, Tardón, A, Guxens, M, et al. The use of lower or higher than recommended doses of folic acid supplements during pregnancy is associated with child attentional dysfunction at 4–5 years of age in the INMA project. Nutrients. (2021) 13:327. doi: 10.3390/nu13020327,
54. Valera-Gran, D, Navarrete-Muñoz, EM, Garcia de la Hera, M, Fernández-Somoano, A, Tardón, A, Ibarluzea, J, et al. Effect of maternal high dosages of folic acid supplements on neurocognitive development in children at 4–5 y of age: the prospective birth cohort Infancia y Medio Ambiente (INMA) study. Am J Clin Nutr. (2017) 106:878–87. doi: 10.3945/ajcn.117.152769,
55. Raghavan, R, Riley, AW, Volk, H, Caruso, D, Hironaka, L, Sices, L, et al. Maternal multivitamin intake, plasma folate and vitamin B12 levels and autism spectrum disorder risk in offspring. Paediatr Perinat Epidemiol. (2018) 32:100–11. doi: 10.1111/ppe.12414,
56. Raghavan, R, Selhub, J, Paul, L, Ji, Y, Wang, G, Hong, X, et al. A prospective birth cohort study on cord blood folate subtypes and risk of autism spectrum disorder. Am J Clin Nutr. (2020) 112:1304–17. doi: 10.1093/ajcn/nqaa208,
57. Keunen, K, Counsell, SJ, and Benders, MJNL. The emergence of functional architecture during early brain development. NeuroImage. (2017) 160:2–14. doi: 10.1016/j.neuroimage.2017.01.047,
58. Tau, GZ, and Peterson, BS. Normal development of brain circuits. Neuropsychopharmacol. (2010) 35:147–68. doi: 10.1038/npp.2009.115,
59. Minshew, NJ, and Williams, DL. The new neurobiology of autism. Arch Neurol. (2007) 64:945–50. doi: 10.1001/archneur.64.7.945,
60. Bills, ND, Koury, MJ, Clifford, AJ, and Dessypris, EN. Ineffective hematopoiesis in folate-deficient mice. Blood. (1992) 79:2273–80. doi: 10.1182/blood.V79.9.2273.2273,
61. Miller, JW, Nadeau, MR, Smith, J, Smith, D, and Selhub, J. Folate-deficiency-induced homocysteinaemia in rats: disruption of S-adenosylmethionine’s co-ordinate regulation of homocysteine metabolism. Biochem J. (1994) 298:415–9. doi: 10.1042/bj2980415,
62. Reeves, PG, Nielsen, FH, and Fahey, GC. AIN-93 purified diets for laboratory rodents: final report of the American Institute of Nutrition ad hoc writing committee on the reformulation of the AIN-76A rodent diet. J Nutr. (1993) 123:1939–51. doi: 10.1093/jn/123.11.1939,
63. Tat, L, Cannizzaro, N, Schaaf, Z, Racherla, S, Bottiglieri, T, Green, R, et al. Prenatal folic acid and vitamin B12 imbalance alter neuronal morphology and synaptic density in the mouse neocortex. Commun Biol. (2023) 6:1133. doi: 10.1038/s42003-023-05492-9,
64. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. (2011) 17:10–2. doi: 10.14806/ej.17.1.200
65. Dobin, A, Davis, CA, Schlesinger, F, Drenkow, J, Zaleski, C, Jha, S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. (2013) 29:15–21. doi: 10.1093/bioinformatics/bts635,
66. Wingett, SW, and Andrews, S. FastQ screen: a tool for multi-genome mapping and quality control. F1000Res. (2018) 7:1338. doi: 10.12688/f1000research.15931.2,
67. Li, H, Handsaker, B, Wysoker, A, Fennell, T, Ruan, J, Homer, N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. (2009) 25:2078–9. doi: 10.1093/bioinformatics/btp352,
68. Ewels, P, Magnusson, M, Lundin, S, and Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. (2016) 32:3047–8. doi: 10.1093/bioinformatics/btw354,
69. Kluyver, T, Ragan-Kelley, B, Pérez, F, Granger, B, Bussonnier, M, and Frederic, J. (2016). Jupyter Notebooks—a publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas. 87–90.
70. R. Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing (2023).
71. Kluyver, T, Angerer, P, Schulz, J, and Ram, K. (2024). IRkernel: native R kernel for the Jupyter Notebook. Available online at: https://github.com/irkernel/irkernel (Accessed November 22, 2024).
72. Chen, Y, Lun, A, and Smyth, G. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res. (2016) 5:1438. doi: 10.12688/f1000research.8987.2
73. Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. (2015) 43:e47. doi: 10.1093/nar/gkv007,
74. Robinson, MD, McCarthy, DJ, and Smyth, GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. (2010) 26:139–40. doi: 10.1093/bioinformatics/btp616,
75. Langfelder, P, and Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. (2008) 9:559. doi: 10.1186/1471-2105-9-559,
76. Smedley, D, Haider, S, Ballester, B, Holland, R, London, D, Thorisson, G, et al. BioMart—biological queries made easy. BMC Genomics. (2009) 10:22. doi: 10.1186/1471-2164-10-22,
77. Kuleshov, MV, Jones, MR, Rouillard, AD, Fernandez, NF, Duan, Q, Wang, Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. (2016) 44:W90–7. doi: 10.1093/nar/gkw377,
79. Laufer, BI, Neier, K, Valenzuela, AE, Yasui, DH, Schmidt, RJ, Lein, PJ, et al. Placenta and fetal brain share a neurodevelopmental disorder DNA methylation profile in a mouse model of prenatal PCB exposure. Cell Rep. (2022) 38:110442. doi: 10.1016/j.celrep.2022.110442,
80. Krueger, F, and Andrews, SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. (2011) 27:1571–2. doi: 10.1093/bioinformatics/btr167,
81. Haghani, V. (2024). vhaghani26/epigenerator. Available online at: https://github.com/vhaghani26/epigenerator. (Accessed December 18, 2024)
82. Picard Toolkit. (2019). Broad Institute, GitHub repository. Available online at: http://broadinstitute.github.io/picard/ (Accessed December 18, 2024).
83. Hansen, KD, Langmead, B, and Irizarry, RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. (2012) 13:R83. doi: 10.1186/gb-2012-13-10-r83,
84. Korthauer, K, Chakraborty, S, Benjamini, Y, and Irizarry, RA. Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing. Biostatistics. (2019) 20:367–83. doi: 10.1093/biostatistics/kxy007,
85. Laufer, BI, Hwang, H, Jianu, JM, Mordaunt, CE, Korf, IF, Hertz-Picciotto, I, et al. Low-pass whole genome bisulfite sequencing of neonatal dried blood spots identifies a role for RUNX1 in Down syndrome DNA methylation profiles. Hum Mol Genet. (2021) 29:3465–76. doi: 10.1093/hmg/ddaa218,
86. Mordaunt, CE, Mouat, JS, Schmidt, RJ, and LaSalle, JM. Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease. Brief Bioinform. (2022) 23:bbab554. doi: 10.1093/bib/bbab554,
87. Chen, Y, Wu, T, Jiang, Y, Nie, L, Li, G, Zhang, Y, et al. The mechanism of chemokine receptor ACKR2 in cognitive impairment in young rats with traumatic brain injury. Am J Transl Res. (2023) 15:1654–66.
88. Lindsay, HG, Hendrix, CJ, Gonzalez Murcia, JD, Haynie, C, and Weber, KS. The role of atypical chemokine receptors in neuroinflammation and neurodegenerative disorders. Int J Mol Sci. (2023) 24:16493. doi: 10.3390/ijms242216493,
89. Ghila, L, and Gomez, M. The evolutionarily conserved gene LNP-1 is required for synaptic vesicle trafficking and synaptic transmission. Eur J Neurosci. (2008) 27:621–30. doi: 10.1111/j.1460-9568.2008.06049.x,
90. Casey, AK, Chen, S, Novick, P, Ferro-Novick, S, and Wente, SR. Nuclear pore complex integrity requires Lnp1, a regulator of cortical endoplasmic reticulum. Mol Biol Cell. (2015) 26:2833–44. doi: 10.1091/mbc.E15-01-0053,
91. Zhu, ZH, Yin, XY, Cai, Y, Jia, NN, Wang, PJ, Qi, Q, et al. Association between the HHEX polymorphism and delayed memory in first-episode schizophrenic patients. Schizophr Res Cogn. (2024) 36:100304. doi: 10.1016/j.scog.2024.100304,
92. Simpson, MT, Venkatesh, I, Callif, BL, Thiel, LK, Coley, DM, Winsor, KN, et al. The tumor suppressor HHEX inhibits axon growth when prematurely expressed in developing central nervous system neurons. Mol Cell Neurosci. (2015) 68:272–83. doi: 10.1016/j.mcn.2015.08.008,
93. Bekdash, RA. Methyl donors, epigenetic alterations, and brain health: understanding the connection. Int J Mol Sci. (2023) 24:2346. doi: 10.3390/ijms24032346,
94. Irwin, RE, Pentieva, K, Cassidy, T, Lees-Murdock, DJ, McLaughlin, M, Prasad, G, et al. The interplay between DNA methylation, folate and neurocognitive development. Epigenomics. (2016) 8:863–79. doi: 10.2217/epi-2016-0003,
95. Zhang, M, Zhai, Y, An, X, Li, Q, Zhang, D, Zhou, Y, et al. DNA methylation regulates RNA m6A modification through transcription factor SP1 during the development of porcine somatic cell nuclear transfer embryos. Cell Prolif. (2024) 57:e13581. doi: 10.1111/cpr.13581,
96. Zhu, C, Huang, T, Fu, J, Li, M, Tan, L, Zhang, X, et al. Global m6A RNA and whole 5mC DNA methylation specifically contribute to cell replicative and premature senescence induced by extrinsic oxidative stress. mSystems. (2025) 10:e0164724. doi: 10.1128/msystems.01647-24,
97. Selberg, S, Blokhina, D, Aatonen, M, Koivisto, P, Siltanen, A, Mervaala, E, et al. Discovery of small molecules that activate RNA methylation through cooperative binding to the METTL3-14-WTAP complex active site. Cell Rep. (2019) 26:3762–3771.e5. doi: 10.1016/j.celrep.2019.02.100,
98. Gough, SM, Slape, CI, and Aplan, PD. NUP98 gene fusions and hematopoietic malignancies: common themes and new biologic insights. Blood. (2011) 118:6247–57. doi: 10.1182/blood-2011-07-328880,
99. Bemanian, M, Vold, JH, Chowdhury, R, Aas, CF, Gjestad, R, Johansson, KA, et al. Folate status as a nutritional Indicator among people with substance use disorder; a prospective cohort Study in Norway. Int J Environ Res Public Health. (2022) 19:5754. doi: 10.3390/ijerph19095754,
100. Halsted, CH, Villanueva, JA, Devlin, AM, and Chandler, CJ. Metabolic interactions of alcohol and folate. J Nutr. (2002) 132:2367S–72S. doi: 10.1093/jn/132.8.2367S,
101. Allen, LH. Causes of vitamin B12 and folate deficiency. Food Nutr Bull. (2008) 29:S20–34. doi: 10.1177/15648265080292S105
102. Rabiee, F, Lachinani, L, Ghaedi, S, Nasr-Esfahani, MH, Megraw, TL, and Ghaedi, K. New insights into the cellular activities of Fndc5/Irisin and its signaling pathways. Cell Biosci. (2020) 10:51. doi: 10.1186/s13578-020-00413-3,
103. Javadirad, SM, and Mokhtari, M. TXNDC2 joint molecular marker is associated with testis pathology and is an accurate predictor of sperm retrieval. Sci Rep. (2021) 11:13064. doi: 10.1038/s41598-021-92603-3,
104. Floriot, S, Bellutti, L, Castille, J, Moison, P, Messiaen, S, Passet, B, et al. CEP250 is required for maintaining centrosome cohesion in the germline and fertility in male mice. Front Cell Dev Biol. (2021) 9:754054. doi: 10.3389/fcell.2021.754054,
105. Liu, X, Lin, J, Zhang, H, Khan, NU, Zhang, J, Tang, X, et al. Oxidative stress in autism spectrum disorder—current Progress of mechanisms and biomarkers. Front Psych. (2022) 13:813304. doi: 10.3389/fpsyt.2022.813304,
Keywords: folate, cerebral cortex, mouse, transcriptomics, DNA methylation
Citation: Haghani V, Ali SM, Cannizzaro N, Green R, LaSalle JM and Zarbalis KS (2025) Excess prenatal folic acid supplementation alters cortical DNA methylation and gene expression networks. Front. Nutr. 12:1699376. doi: 10.3389/fnut.2025.1699376
Edited by:
Yuma Kitase, International University of Health and Welfare, Narita, JapanReviewed by:
Milena Leseva, Stephan Angeloff Institute of Microbiology, BulgariaKrishna Rao Gurugubelli, Andhra Medical College, India
Copyright © 2025 Haghani, Ali, Cannizzaro, Green, LaSalle and Zarbalis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Konstantinos S. Zarbalis, a3phcmJhbGlzQGhlYWx0aC51Y2RhdmlzLmVkdQ==; Janine M. LaSalle, am1sYXNhbGxlQGhlYWx0aC51Y2RhdmlzLmVkdQ==
†These authors have contributed equally to this work