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. 2016 May 3;10:168. doi: 10.3389/fnins.2016.00168

DNA Methylation Profiling at Single-Base Resolution Reveals Gestational Folic Acid Supplementation Influences the Epigenome of Mouse Offspring Cerebellum

Subit Barua 1,†, Salomon Kuizon 1, W Ted Brown 2, Mohammed A Junaid 1,*
PMCID: PMC4854024  PMID: 27199632

Abstract

It is becoming increasingly more evident that lifestyle, environmental factors, and maternal nutrition during gestation can influence the epigenome of the developing fetus and thus modulate the physiological outcome. Variations in the intake of maternal nutrients affecting one-carbon metabolism may influence brain development and exert long-term effects on the health of the progeny. In this study, we investigated whether supplementation with high maternal folic acid during gestation alters DNA methylation and gene expression in the cerebellum of mouse offspring. We used reduced representation bisulfite sequencing to analyze the DNA methylation profile at the single-base resolution level. The genome-wide DNA methylation analysis revealed that supplementation with higher maternal folic acid resulted in distinct methylation patterns (P < 0.05) of CpG and non-CpG sites in the cerebellum of offspring. Such variations of methylation and gene expression in the cerebellum of offspring were highly sex-specific, including several genes of the neuronal pathways. These findings demonstrate that alterations in the level of maternal folic acid during gestation can influence methylation and gene expression in the cerebellum of offspring. Such changes in the offspring epigenome may alter neurodevelopment and influence the functional outcome of neurologic and psychiatric diseases.

Keywords: folic acid, DNA methylation, gestational development, brain development, cerebellum, psychiatric disease

Introduction

DNA reprogramming is essential for early embryonic development; around the time of implantation, de novo methylation is initiated in embryonic cells and is required for complete embryonic development (Li et al., 1992; Lei et al., 1996; Okano et al., 1999; Dean et al., 2001). In mammals, cytosine methylation is highly prevalent at CpG islands that modulate the chromatin structure and binding of transcriptional factors at promoter regions (Jaenisch and Bird, 2003). Thus, alterations in DNA methylation and epigenetic modification that occur during a specific window of gestational development can dysregulate gene expression and are associated with many diseases (Oberlander et al., 2008; Stein et al., 2009; Suter et al., 2010; Gore et al., 2011; Li et al., 2013; Perkins et al., 2013; West et al., 2013; Vanhees et al., 2014). Studies in humans and animal models have shown that variations in intake of the maternal nutrients involved in one-carbon metabolism, including folic acid (FA), during pregnancy can induce persistent changes in the offspring's epigenome and modulate various physiological outcomes (Cooney et al., 2002; Bean et al., 2011; Boeke et al., 2012; Greenop et al., 2014; O'Neill et al., 2014; Barua and Junaid, 2015). Our earlier studies in a mouse model had shown that exposure to high FA supplementation during gestation causes widespread changes in the methylation and gene expression in the cerebral hemisphere of the offspring (Barua et al., 2014b). Moreover, such exposure during gestation and the post-weaning period resulted in moderate changes in behavior (Barua et al., 2014a).

Over the past decades, several studies have shown that the cerebellum (CB) plays a significant role in coordination to motor functions and is involved in various cognitive processes, including perception, attention, and emotional behavior (Leiner et al., 1991; Martin et al., 2003; Schmahmann and Caplan, 2006). Recent studies with post-mortem brain samples have shown widespread aberrant methylation and gene expression in the CB of psychotic patients (Chen et al., 2014). Studies with a mouse model and in human post-mortem CB of individuals with autism have shown altered patterns of DNA methylation (Shpyleva et al., 2014), and indeed, cerebellar abnormalities have been reported in more than 95% of post-mortem examinations of individuals with autism (Marzban et al., 2014). To investigate whether higher supplementation with a methyl diet during gestation impacts the cerebellar development of offspring, in this study, we tested the hypothesis that higher folic acid supplementation during gestation can alter the methylation and gene expression in the CB of offspring.

Materials and methods

Animals and experimental design

All animal experiments were performed in accordance with protocols reviewed and approved by the Institute for Basic Research Institutional Animal Care and Use Committee in conformity with the NIH Guide for Care and Use of Laboratory Animals (NIH publication No. 86-23, revised 1985). One week prior to mating and throughout gestation, adult 8–10 week-old C57BL/6 J female mice were fed a custom AIN-93G amino acid–based diet (Research Diet, Inc., North Brunswick, NJ), having either low maternal folic acid (LMFA), at 0.4 mg/kg (n = 12), or high maternal folic acid (HMFA), at 4 mg/kg (n = 12). These levels of FA supplementation were chosen in this study, as women with a prior history of complicated and neural tube defect (NTD)–affected pregnancy are recommended to take 10-fold higher FA (4 mg/day) in comparison to other pregnant women (400–800 μg/day). FA at the 0.4 mg/kg diet level is necessary for a normal healthy litter, whereas FA at the 4 mg/kg diet level is 10 times higher.

Tissue collection and processing

At post-natal day one (P1), pups from different dams were sacrificed, and CB tissues were dissected. The numbers of tissues collected from the LMFA group were: male pups, n = 15, and female pups n = 15. The numbers of tissues collected from the HMFA were: male pups n = 15, and female pups, n = 15. Tissues were immediately stored at −70°C until further use. From these, tissues were distributed for subsequent DNA/RNA and protein analysis.

DNA extraction

CB tissues were extracted from P1 pups and pooled (n = 3/gender/group, each from an independent dam). DNA was extracted with the Epicenter MasterPure DNA purification kit (Epicenter Biotechnologies, Madison, WI, USA) by following the manufacturer's instructions, and concentration was measured by using a NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA).

Library construction, sequence alignments, and data analysis

Library construction, sequence alignments, and data analysis were performed by following the detailed protocol previously described (Barua et al., 2014b). Libraries were prepared from 200 to 500 ng of genomic DNA after sequential digestion with 60 units of TaqI and 30 units of MspI (New England Biolabs, Ipswich, MA, USA), and sequencing was performed on an Illumina HiSeq genome analyzer. Sequence reads from bisulfite-treated EpiQuest libraries were identified using standard Illumina base-calling software and then analyzed using a Zymo Research proprietary analysis pipeline, which is written in Python. Bismark (http://www.bioinformatics.babraham.ac.uk/projects/bismark/) was the alignment software in the analysis pipeline. Index files were constructed by bismark_genome_preparation command using the entire reference genome. –non_directional and all the other default parameters were applied for running Bismark. Filled-in nucleotides were trimmed off when doing methylation calling. The number of reads reporting a C was divided by the total number of reads reporting a C or T was used to estimate the methylation level of sample cytosine. For each CpG site, Fisher's exact test was performed, which covered at least five reads. Moreover, for each CpG promoter, gene body and CpG island annotations were added. The total numbers of reads that were taken into account for each CpG site are given in Tables 2, 3, and Tables S1–S4 (column total CpG). All the procedures above were carried out in the Zymo Epigentic Core Services (Zymo Research, Irvine, CA). Sequence data has been deposited at the Sequence Read Archive (accession number SRX1608467) in the National Center for Biotechnology Information (NCBI).

Table 2.

List of hypermethylated CpG/CHH/CHG sites in the gene body/promoter/other chromosomal region of genes from high maternal folic acid diet that were significantly altered after multiple testing corrections.

Chromosome Start End Gene Total CpG LMFA Total CpG HMFA Methylation difference P-value Adj P-value
MALE
CpG
Chr3 101396425 101396426 Atp1a1 48 55 0.69 4.73043E-11 8.61543E-06
Chr4a 120370725 120370726 Kcnq4 38 18 0.73 5.23985E-08 0.000602429
Chr5 32310326 32310327 Bre 8 16 1.00 1.35967E-06 0.00766814
Chr5a 122872838 122872839 Anapc7 20 11 0.90 9.21198E-07 0.005834628
Chr6a 99642691 99642692 Gpr27 46 10 0.80 2.59437E-06 0.012370994
Chr6 125280040 125280041 Scnn1a 21 33 0.68 8.58529E-08 0.000857291
Chr7 29537833 29537834 Sars2 17 17 0.76 5.1294E-06 0.020249774
Chr8 79656982 79656983 Nr3c2 30 24 0.68 2.79663E-07 0.002218414
Chr9 108751023 108751024 Celsr3 32 20 0.70 4.85931E-07 0.003592574
Chr11a 4962328 4962329 Gas2l1 14 18 0.89 5.09083E-07 0.003724971
Chr12 3739974 3739975 Dtnb 15 20 0.77 1.10085E-05 0.034438481
Chr12a 52930473 52930474 Hectd1 31 11 0.91 7.47565E-09 0.000114327
Chr12 83903228 83903229 Rgs6 28 41 0.71 2.68793E-11 8.35061E-06
Chr12 99800383 99800384 Kcnk10 14 32 0.69 9.50913E-06 0.03190158
ChrX 110293664 110293665 Chm 6 24 1.00 1.68414E-06 0.008878596
CHH
Chr3 37380544 37380545 Spata5 10 16 1.00 1.88262E-07 0.018712081
Chr3 37379114 37379115 Spata5 39 54 0.52 4.92021E-09 0.000825025
Chr5a 122872790 122872791 Anapc7 57 38 0.71 2.29993E-11 5.49912E-05
Chr5 129289390 129289391 Rimbp2 22 42 0.62 3.47908E-07 0.031282409
Chr5 142522151 142522152 Sdk1 41 52 0.49 2.95614E-07 0.027788529
Chr8 34944721 34944722 Rbpms 33 40 0.59 1.33163E-07 0.013543758
Chr11 89851744 89851745 Pctp 46 39 0.52 6.98215E-08 0.007876096
Chr17 81118969 81118970 Map4k3 20 36 0.72 6.36563E-08 0.007313615
0.00
CHG 0.00
Chr4 108146626 108146627 Zcchc11 112 115 0.34 5.53559E-08 0.006775161
Chr5 134721807 134721808 Gtf2i 57 80 0.49 9.5847E-11 7.42648E-05
Chr5 73446765 73446766 Fryl 64 49 0.44 1.56966E-08 0.002561524
Chr8 34914184 34914185 Rbpms 34 62 0.52 3.10146E-08 0.004794897
FEMALE
CpG
Chr2 18865431 18865432 Pip4k2a 14 8 1.00 3.12725E-06 0.017610194
Chr4 150525065 150525066 Camta1 17 14 0.81 6.84434E-06 0.032886803
Chr7 133845071 133845072 Coro1a 89 14 0.70 5.12126E-07 0.003733256
Chr8 86608595 86608596 Rfx1 45 12 0.70 9.53995E-06 0.042482671
Chr8* 86608595 86608596 Mir709* 45 12 0.70 9.53995E-06 0.042482671
Chr10 79397201 79397202 Arid3a 29 14 0.93 1.53102E-09 1.95146E-05
Chr10 60250203 60250204 Unc5b 25 18 0.70 5.43229E-06 0.027663076
Chr11 102139305 102139306 Asb16 34 19 0.68 1.40228E-06 0.009072618
Chr14 52609319 52609320 Arhgef40 42 8 0.90 9.21996E-07 0.006230346
Chr14* 52609319 52609320 Gm16617* 42 8 0.90 9.21996E-07 0.006230346
Chr16 10979333 10979334 Litaf 38 10 0.95 1.00906E-08 0.00011098
Chr17a 39985399 39985400 Rn45s 20 11 0.90 9.21198E-07 0.006230346
Chr18 75697791 75697792 Ctif 58 11 0.74 5.0559E-06 0.025932481
Chr19a 6428759 6428760 Nrxn2 52 10 0.72 5.74263E-06 0.028897735
ChrXa 137133982 137133983 Tsc22d3 39 8 0.95 1.43104E-07 0.001162634
ChrXa* 53984977 53984978 Fhl1* 46 14 0.83 1.84349E-08 0.000189568
ChrXa* 160346929 160346930 Ap1s2* 52 10 0.72 5.74263E-06 0.028897735
CHH
Chr19a 46072916 46072917 9130011E15Rik 61 6 1.00 1.00205E-08 0.00773454
CHG
Chr9 57880963 57880964 Ccdc33 53 6 1.00 2.21939E-08 0.012821301
Chr9a 98856591 98856592 Foxl2 102 11 0.64 8.54473E-09 0.006910752
Chr9a* 98856591 98856592 Foxl2os* 102 11 0.64 8.54473E-09 0.006910752
Chr19a 37625255 37625256 Exoc6 66 8 0.97 2.98577E-09 0.003018518
OTHER CHROMOSOMAL REGIONS
MALE
CpG
Chr2 117020483 117020484 19 23 0.72 5.75229E-06 0.022192691
Chr4 55291226 55291227 24 17 0.69 1.55743E-05 0.044508695
Chr6 122591925 122591926 17 23 0.71 1.1076E-06 0.006597209
Chr10 92819632 92819633 16 31 0.81 3.98075E-09 7.03909E-05
Chr12 110057962 110057963 16 31 0.75 8.42303E-07 0.00545744
Chr14 87019484 87019485 14 26 0.78 1.29013E-06 0.007324618
Chr17 80418706 80418707 31 23 0.75 5.72815E-09 9.24604E-05
Chr17 80418649 80418650 31 23 0.74 2.14082E-09 4.08331E-05
Chr17 80418656 80418657 31 23 0.74 2.14082E-09 4.08331E-05
Chr17 80418710 80418711 31 22 0.73 5.02628E-09 8.44787E-05
Chr17 80418721 80418722 28 23 0.70 1.02512E-07 0.000977631
Chr17 49319822 49319823 18 47 0.68 8.1497E-07 0.005341511
Chr18 8736817 8736818 32 9 0.81 1.4286E-05 0.04188451
Chr18 8736774 8736775 31 9 0.81 1.83039E-05 0.048931908
Chr18 60275673 60275674 14 22 0.71 3.93805E-06 0.017229446
CHH
Chr3 37105839 37105840 28 60 0.50 4.59752E-07 0.039616449
Chr8 32211528 32211529 45 70 0.57 5.4005E-11 5.49912E-05
Chr8 29104849 29104850 30 44 0.55 1.11592E-07 0.011734506
Chr8 27826273 27826274 36 48 0.51 5.73672E-07 0.0474555
Chr17 85245770 85245771 36 62 0.68 9.22107E-11 5.49912E-05
CHG
Chr3 18872260 18872261 69 40 0.45 6.79721E-10 0.000153587
Chr9 4249915 4249916 41 86 0.53 5.27759E-10 0.000129188
FEMALE
CpG
Chr3 15582599 15582600 64 28 0.73 3.54618E-11 1.16245E-05
Chr7 142676163 142676164 50 6 0.92 6.46782E-06 0.03164917
Chr7 140397403 140397404 46 20 0.67 2.57852E-07 0.001995863
Chr11 116362692 116362693 50 6 0.96 8.62376E-07 0.005883499
ChrX 139577966 139577967 8 18 0.76 0.000397497 0.506460372
ChrX 118333137 118333138 44 13 0.68 8.19276E-06 0.037584248
CHH
Chr8 114228185 114228186 43 6 1.00 7.15112E-08 0.047312275
CHG
Chr18 83201037 83201038 70 12 0.67 1.38883E-08 0.009360419
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a

Methylation exclusively in the CpG island;

*

Methylation in the promoter regions; HMFA, high maternal folic acid; LMFA, low maternal folic acid. CHH, CHG where H can be A, T or G.

Table 3.

List of hypomethylated CpG/CHH/CHG sites in the gene body/promoter/other chromosomal region of genes from high maternal folic acid diet that were significantly altered after multiple testing corrections.

Chromosome Start End Gene Total CpG Total CpG Methylation P-value Adj P-value
LMFA HMFA difference
MALE
CpG
Chr2 70926781 70926782 Dcaf17 12 8 −1.00 7.9384E-06 0.028133219
Chr3 51477712 51477713 Mgst2 16 15 −0.73 1.6121E-05 0.045534315
Chr5 111715613 111715614 Ttc28 46 19 −0.79 1.95557E-09 3.9055E-05
Chr10 79393579 79393580 Arid3a 18 12 −0.83 2.1967E-06 0.010935514
Chr11 69574236 69574237 Amac1 42 15 −0.80 2.10416E-08 0.000279056
Chr11 89384648 89384649 Ankfn1 16 19 −0.74 9.65017E-06 0.032243587
Chr12 109176310 109176311 Bcl11b 10 31 −0.77 1.73473E-05 0.04752513
Chr13 24228093 24228094 Lrrc16a 13 16 −0.86 3.07969E-06 0.014240168
Chr17 11729485 11729486 Park2 10 10 −1.00 1.08251E-05 0.034194413
CHH
Chr5 150366342 150366343 Wdr95 27 32 −0.85 1.21698E-12 7.55036E-06
Chr6 119463258 119463259 Wnt5b 16 43 −0.69 1.56071E-08 0.002104991
Chr8 119603496 119603497 Pkd1l2 25 56 −0.60 7.02256E-09 0.001146559
Chr9 107166117 107166118 Mapkapk3 24 39 −0.65 2.17476E-07 0.020757812
Chr16 6736334 6736335 Rbfox1 26 36 −0.71 4.2963E-09 0.000740418
0.00
CHG 0.00
Chr4 43744421 43744422 5430416O09Rik 38 79 −0.66 1.89728E-11 4.03799E-05
Chr9 103033308 103033309 Rab6b 23 37 −0.65 9.21745E-09 0.00159268
Chr9 104021165 104021166 Acad11 38 72 −0.58 2.74934E-11 4.03799E-05
Chr11 118130307 118130308 Usp36 38 51 −0.55 2.20911E-10 7.42648E-05
Chr16 15797072 15797073 Prkdc 8 26 −1.00 5.50776E-08 0.006775161
FEMALE
CpG
Chr1a 39592922 39592923 D1Bwg0212e 46 12 −0.72 7.29815E-06 0.034293206
Chr2 54854875 54854876 Galnt13 40 10 −0.85 7.79574E-07 0.005370225
Chr3 152572664 152572665 St6galnac5 46 10 −0.76 1.32097E-06 0.008651764
Chr5 150883968 150883969 Rxfp2 52 6 −0.92 5.18834E-06 0.026484159
Chr5 4009104 4009105 Akap9 47 8 −0.87 2.4664E-06 0.014583234
Chr7 13564506 13564507 Zfp446 17 6 −1.00 9.90619E-06 0.043500018
Chr7 142277612 142277613 Dock1 49 15 −0.71 2.05041E-08 0.000206849
Chr8a 73985459 73985460 Abhd8 66 11 −0.76 1.60125E-06 0.01005433
Chr9 45614148 45614149 Cep164 41 15 −0.80 8.14852E-10 1.23014E-05
Chr9 65575164 65575165 Zfp609 35 8 −0.75 4.59283E-06 0.024030758
Chr11 100924353 100924354 Atp6v0a1 40 6 −0.95 2.98928E-06 0.016967907
Chr11 63783989 63783990 Cox10 24 12 −0.92 7.27024E-08 0.00063948
Chr11 118386794 118386795 Rbfox3 33 10 −0.82 4.17663E-06 0.02232177
Chr11 70466348 70466349 Pfn1 71 18 −0.75 3.48721E-09 4.17015E-05
Chr11 101045277 101045278 Cntnap1 84 16 −0.71 4.67003E-08 0.00043033
Chr13 115629726 115629727 Itga2 52 14 −0.70 1.73075E-06 0.010740731
Chr15 73386877 73386878 Dennd3 42 6 −0.95 2.28171E-06 0.013758747
Chr15 76684078 76684079 Zfp251 43 7 −0.71 9.91146E-06 0.043500018
Chr17 26145026 26145027 Rab11fip3 35 6 −1.00 2.22401E-07 0.00174687
Chr17 34201190 34201191 Col11a2 69 10 −0.71 4.42664E-06 0.023265511
Chr18 64349640 64349641 1700091E21Rik 60 8 −0.72 6.76961E-06 0.032675219
ChrX 130978215 130978216 Drp2 42 6 −0.95 2.28171E-06 0.013758747
CHG
Chr17a 39981459 39981460 Rn45s 215 1801 −0.32 3.15966E-11 0.000127773
OTHER CHROMOSOMAL REGIONS
MALE
CpG
Chr4 114927822 114927823 19 16 −1.00 2.4631E-10 1.6251E-05
Chr4 70356713 70356714 11 8 −1.00 1.32307E-05 0.039611439
Chr4 33145068 33145069 22 23 −0.74 1.15388E-07 0.001082193
Chr6 22702718 22702719 20 38 −0.79 1.68255E-09 3.40026E-05
Chr8 23561782 23561783 10 9 −1.00 1.08251E-05 0.034194413
Chr8 125951782 125951783 6 15 −1.00 1.84284E-05 0.049033126
Chr8 15641954 15641955 33 10 −0.87 1.72635E-07 0.001510603
Chr9 49862607 49862608 10 10 −1.00 1.08251E-05 0.034194413
Chr10 12602192 12602193 17 8 −1.00 9.24578E-07 0.005834628
Chr10 123989391 123989392 17 35 −0.83 4.59988E-09 7.96787E-05
Chr12 44576005 44576006 19 14 −0.80 3.041E-06 0.014104497
Chr12 68850704 68850705 31 14 −0.76 1.20506E-06 0.007029716
Chr14 13972178 13972179 44 9 −0.84 4.38892E-07 0.003296639
Chr15 31799762 31799763 14 16 −0.81 4.79293E-06 0.019439463
Chr16 55473911 55473912 24 11 −0.78 1.52675E-05 0.043927676
Chr17 84101994 84101995 10 10 −1.00 1.08251E-05 0.034194413
Chr17 40509054 40509055 26 26 −0.73 1.66363E-07 0.001486366
CHH
Chr4 62532417 62532418 30 18 −0.80 3.6826E-08 0.004662758
Chr16 39097237 39097238 47 34 −0.70 4.56498E-11 5.49912E-05
Chr18 53985910 53985911 27 30 −0.63 6.04117E-08 0.007071799
CHG
Chr4 44486603 44486604 24 72 −0.53 1.53357E-07 0.017325988
Chr8 97707113 97707114 31 43 −0.52 4.46831E-08 0.006250153
Chr8 91367262 91367263 39 56 −0.46 5.61123E-09 0.001030159
Chr10 93725790 93725791 84 53 −0.46 1.1065E-10 7.42648E-05
Chr11 101785985 101785986 26 44 −0.54 4.99742E-08 0.006672518
FEMALE
CpG
Chr1 72001078 72001079 32 8 −0.75 7.29474E-06 0.034293206
Chr2 178977613 178977614 52 10 −0.88 7.44799E-08 0.000649745
Chr3 69055711 69055712 66 8 −0.85 2.90337E-06 0.016568628
Chr5 48149488 48149489 54 10 −0.80 1.01668E-08 0.00011098
Chr7 76060069 76060070 43 10 −0.77 9.4751E-06 0.042282364
Chr8 4203056 4203057 66 6 −0.91 5.91402E-06 0.029620167
Chr8 66000281 66000282 61 8 −0.72 6.1866E-06 0.030412915
Chr16 22049549 22049550 26 14 −0.71 1.1809E-06 0.007855174
Chr18 83282753 83282754 95 9 −0.83 9.90991E-08 0.000823993
Chr18 67611225 67611226 62 10 −0.74 9.90604E-06 0.043500018
Chr19 45266101 45266102 30 18 −0.80 3.6826E-08 0.00034839
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a

Methylation exclusively in the CpG island; HMFA, high maternal folic acid; LMFA, low maternal folic acid. CHH, CHG where H can be A, T or G.

Quantitative real-time polymerase chain reaction (qPCR) analysis

Total RNA was extracted (n = 12, segregated by gender) by lysing the cells with Trizol reagent (Life Technologies, Inc., Carlsbad, CA) and was further purified by Qiagen RNeasy kit (Qiagen, Valencia, CA), according to the manufacturer's protocol as described earlier (Barua et al., 2014c). For each sample, on-column DNase digestion was performed to remove any DNA contamination. Quantitative RT-PCR was performed with the One-Step iScript kit (BioRad, Hercules, CA) or the Two-step kit (Affymetrix, Santa Clara, CA) by following the manufacturer's instructions. Hprt1 was used as the endogenous control, and the relative expression was calculated using the Pfaffl method. For each gene, the mRNA expression was measured from n = 3/gender/group, each from an independent dam. Each reaction was run in triplicates, and only the expression of those genes that exhibited the same directional changes in independent pools from at least two independent animals was considered significant. The statistical difference between samples was determined by Student's t-test by using Prism Software (GraphPad, San Diego, CA); values are presented as means ± standard deviation. Primers used for qRT-PCR are listed in Table S5.

Western blot analyses

Total cell lysates (n = 16) from the CB of male (M) and female (F) pups were prepared from both the HMFA and the LMFA groups. For each of these two groups, four male CBs (each male pup from an independent dam) and four female CBs (each female pup from an independent dam), for a total eight pups were analyzed. Western blot analyses were done as previously described (Barua et al., 2014c). Targeted proteins were detected by incubating with primary antibodies overnight at 4°C (dilutions for anti-GAD1 1:500 and anti-PARK2 1:200) followed by the secondary antibody coupled with horseradish peroxidase. Densitometric evaluation of the bands was calculated by using ImageJ software (NIH) and was normalized to the densities of β-actin staining as housekeeping control. The average calculated ratios of target protein to β-actin are presented.

Results

Global DNA methylation patterns of the offspring's CB from HMFA

The statistics of the mapping of the methylation profiles of pups CB's of pups from mothers supplemented with LMFA and HMFA are given in Table 1. The ratio of mapped reads to total reads ranged from 40 to 46.34% in male pups and from 56.87 to 61.33% in female pups from the LMFA and HMFA groups, respectively, with an average depth of CpG coverage (12x–13x) in males and (10x–16x) in females. The bisulfate conversion rates were approximately 98% for all the samples. Analysis of the global methylation profile revealed that 19% of the CpG sites were differentially methylated in pups from the HMFA group in comparison to that of the LMFA group in both male and female pups (n = 40.376 for male and n = 44.974 for female). Moreover, the majority of differentially methylated regions (DMRs) were in the intergenic or introns, whereas 19–20% were in exons, and 8–9% were in the promoter regions in the CpG-island sequence (Figures S1A,B). Similar to the CpG sites, the distribution of DMRs in the non-CpG sites revealed that the majority of DMRs were in the intergenic or introns, whereas 9–10% in exons, and 15–23% in the promoter regions (Figure S2). The distribution of methylation ratios and the Pearson's correlation coefficient for the corresponding CpG and non-CpG sites of male and female pups are shown in Figures S3, S4. The hexbin plot (Figures S5–S7) shows the distribution of overlapped CpG and non-CpG sites (P < 0.05) in male and female pups as a result of HMFA. The result of global DNA methylation profiling indicated that HMFA altered the methylation pattern of the epigenome of the offspring's CB.

Table 1.

Descriptive statistics of the mapping of methylation profile of pups' cerebellums from mothers supplemented with FA at 0.4 (LMFA) and 4 mg/kg (HMFA).

Cerebellum Total read Mapped read Mapping ratio (%) Unique CpG CpG Cov. (X) BS conv. rate (%)
CB-04 Male 29,885,144 13,848,250 46.34 4,430,390 12 98.09
CB-4 Male 29,016,284 11,607,234 40.00 3,348,964 13 98.70
CB-04 Female 37,174,068 21,140,555 56.87 4,643,308 16 98.59
CB-4 Female 17,432,488 10,690,857 61.33 3,784,838 10 98.78
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Maternal FA alters DNA methylation status of several genes in the CpG and non-CpG sites in offspring's CB

DNA methylation analysis at the single base level revealed that HMFA resulted in significant alterations in the methylation level of several genes. The alterations of methylation level were found in both CpG and non-CpG (CHG, CHH) sites throughout the entire genome in both male and female pups from HMFA group (Figures S1, S2). Such alterations were evident both in promoter and gene body regions and resulted in either hyper-methylation or hypo-methylation (P ≤ 0.05) in pups supplemented with HMFA. Multiple testing corrections revealed that HMFA resulted in hyper-methylation (Table 2) of several genes that modulate neuronal pathways in male pups (Atp1a1, Kcnq4, Bre, Scnn1a, Celsr3, Kcnk10), whereas the methylation of a gene of the neurexin gene family (Nrxn2) was hyper-methylated in female pups. Further analysis of methylation data revealed significant hypo-methylation (Table 3) of several genes in both male and female pups from the HMFA group. Several genes related to intellectual disability (Dcaf17, Myst4, Park2, Rbfox1) were found to be hypo-methylated in male pups, and genes (Pfn1, Cntnap1, Drp2) related to normal function of the nervous system were hypo-methylated in female pups from the HMFA group. Transcriptional factors in male pups (Gtf2i, Nr3c2) and in female pups (Foxl2, Rfx1) were hyper-methylated, whereas transcriptional factor Rn45s was hypo-methylated in female pups. Of note, the methylation level of sidekick cell adhesion molecule 1 (Sdk1) was hyper-methylated in both male and female pups from the HMFA group.

Further analysis of all P ≤ 0.05 differential methylation sites without correction for multiple testing identified several genes that were hyper-methylated or hypo-methylated in male and female pups, in both the CpG and non-CpG sites (Tables S1–S4). The methylation level of genes associated with autism spectrum disorder (Plxna4, Arid1b, Kdm4c, Runx1, Accn1, Aff2, Chd9, Cntnap2, Grip1, Grin2b, and Mid1); imprinted genes (Peg12, Tsix); transcriptional factors (Ebf2, Lmx1b, Runx3, Sox13, and Mef2a) that modulate neurogenesis; and genes related to neurodevelopment (Grik4, Ntrk2, Sgk1, Cacna1a, Gabrg3, Erbb3, and Gfra1) were found to be altered in CB of both male and female pups from the HMFA group. Our findings suggest that maternal diet during gestation, specifically HMFA, can modulate the methylation profile of several genes, including those involved in neural development in the gene bodies and the promoter, CpG, and non-CpG sites in the CB of pups' brains.

Maternal FA alters expression of several differentially methylated genes in offspring's CB

To extend our findings, we then analyzed whether HMFA induced changes in the overall methylation profile in offspring's CB correlates with the alterations in gene expression. Quantitative RT-PCR analysis of several genes that showed differential methylation (P ≤ 0.05) exhibited variations in expression in pups from the HMFA group. Genes in male pups from the HMFA group that exhibited significant hyper-methylation after multiple testing corrections in CpG sites (Atp1a1, Bre, Celsr3, Kcnq4) and Gtf2i in CHG sites did not exhibit any changes in expression level compared to pups from the LMFA group (Figure 1A). In contrast, expression of Kcnk10, a gene that encodes protein from the potassium channel family and is hyper-methylated in CpG sites, was significantly down-regulated in male pups from the HMFA group (Figure 1A). In female pups from the HMFA group, genes that exhibited hyper-methylation in CpG sites after multiple corrections (Arid3a, Nrxn2, Unc5b) exhibited no significant change in expression, whereas the expression of Coro1a was up-regulated significantly (Figure 1B). Similarly, analysis of expression of genes that were hypo-methylated at CpG sites by HMFA revealed significant up-regulation in expression of Arid3a in male pups whereas the expression of several other genes in male (Dcaf17, Park2, Rbfox1) and in female (Col11a2, Cox10, Drp2, Itga2, Pfn1, and Rxfp2) pups did not exhibit any significant changes (Figures 1C,D).

Figure 1.

Figure 1

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Relative expression of the genes that exhibited hyper-methylation (A,B) and hypo-methylation (C,D). The results were normalized to Hprt transcript expression and were expressed as relative values in comparison with corresponding transcripts from low maternal folic acid (LMFA). Results represent mean ± standard deviation (SD); asterisks denote statistically significant change (*P < 0.05, **P < 0.01, ***P < 0.001).

To further reveal the impact of maternal FA, we next assessed whether HMFA-induced changes of methylation (P ≤ 0.05, without corrections) alter the expression of genes related to neuronal pathways (Figures 1A–D). Our results showed significant down-regulation in the expression of genes (Gfra1, Plxna4, and Grin2b), up-regulation in the expression of genes (Grik4, Cacna1g, Dock4, Unc5b, and Gabrr1), and no changes in the expression level of genes (Ntrk2, Cacna1a) in male pups that exhibited hyper-methylation at CpG or non-CpG sites (Figure 1A). In female pups from the HMFA group, several genes that were hyper-methylated at CpG or non-CpG sites also exhibited changes in expression level (Figure 1B).

Although the expression of some genes (Gfra1, Gabrg3) was down-regulated and of other genes (Cntnap2, Grin2b, Sgk1, Gad1, Grip1, Kcnb1, and Slc25a12) was up-regulated, the expression of some other genes (Mid1, Ntrk2, Park2, Cacna1a, Park2, and Kcnq1) was unaltered. Similarly, expression analysis of genes that exhibited hypo-methylation (P ≤ 0.05, without corrections) in CpG and non-CpG sites revealed down-regulation in the expression of genes in male (Sgk1, Myst4, Gabrg3) and in female (Gfra1, Runx1) pups (Figures 1C,D). HMFA resulted in up-regulation in the expression of some genes in male (Gabrb1, Kcnab2, Kcnq1, Scn8a, Mid1, Cntnap2, Cox10) and in female (Grik4, Fmr1, Kcnq2) pups, whereas the expression of other genes in male (Runx1, Grip1) and in female (Plxna4, Dnmt1) pups remained unaltered (Figures 1C,D).

Maternal FA down-regulates expression of gad1p in offspring's CB

Next, we analyzed whether the expression of two genes (Gad1, hyper-methylated at CpG in female pups and Park2, hyper- and hypo-methylated at both CpG and non-CpG sites in male and female pups) that play a role in neuronal pathways including autism are altered at the protein level. Western blot analysis and densitometric quantification of the protein levels revealed that the expression of Gad1p (Figure 2A) in male pups remains unaltered; however, the expression of Gad1p in female pups from the HMFA group was significantly reduced (Figure 2B). In contrast, the expression of Park2p remained unchanged in both male and female pups from the HMFA group (Figures 3A,B). These results show that HMFA during gestation can modulate the expression of genes in offspring's CB and that such impact can be gender-specific.

Figure 2.

Figure 2

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Western blot analysis showing the expression of Gad1p in the cerebellum of (A) male (M) and (B) female (F) pups from LMFA (0.4 mg/kg diet) and HMFA (4 mg/kg diet) groups. The left panel shows one representative blot, and the right panel shows the mean densitometric evaluation. The error bar ± SD represents the inter-variability among independent samples (n = 4). Asterisks denote statistically significant change (**P < 0.05) by unpaired T-test.

Figure 3.

Figure 3

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Western blot analysis showing the expression of Park2p in the CB of (A) male and (B) female pups from LMFA (0.4 mg/kg diet) and HMFA (4 mg/kg diet). The left panel represents one representative blot, and the right panel shows the mean densitometric evaluation. The error bar ± SD represents the inter-variability among independent samples (n = 4).

Maternal FA modulates sex-specific alterations in expression of genes in the offspring's CB

To gain insight into whether HMFA-induced changes in methylation profiles modulate sex-specific alterations in the expression of genes, we analyzed the expression of several genes that exhibited alterations in methylation in the opposite sex. In male pups, the expression of the genes Coro1a, Gad1, Kcnb1, and Fmr1 (which exhibited changes in methylation and gene expression in female pups) was not altered (Figure 4A); in contrast, the expression of Drp2, Itga2, and Pfn1 was altered in male pups from the HMFA group, although it exhibited no alterations in methylation profile in comparison to the LMFA group. However, the expression of the genes Nrxn2, Col11a2, Dnmt1, and Rxfp2 was unaltered; while Slc25a12, and Kcnq2 was altered in pups of both genders from the HMFA group. Similarly, in female pups, the expression of genes (Figure 4B) Kcnk10, Cacna1g, Dock4, Gabrr1, Gabrb1, Kcnab2, Scn8a, and Myst4 (which exhibited changes in methylation and gene expression in male pups) was not altered, in contrast to the expression of Gtf2i being altered in female pups from the HMFA group, although it exhibited no alterations in methylation profile in comparison to the LFMA group. However, expression of the genes Atp1a1, Bre, Celsr3, Kcnq4, Dcaf1, and Rbfox1 was unaltered in pups of both genders from the HMFA group.

Figure 4.

Figure 4

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Quantitative real time reverse transcription-polymerase chain reaction (qRT-PCR) showing relative expression of the transcripts of genes in (A) male pups and (B) female pups from the HMFA group that exhibited no alterations in the methylation profile in promoter and gene body in the cerebellum compared with the LMFA. The results were normalized to Hprt transcript expression and were expressed as relative values in comparison with corresponding transcripts from LMFA. Results represent mean ± standard deviation (SD); asterisks denote statistically significant change (*P < 0.05, **P < 0.01, ***P < 0.001).

Discussion

DNA methylation in the early embryo has long been associated with the maintenance of genomic integrity, with its crucial role in modulating gene expression and genomic imprinting (Farthing et al., 2008; Messerschmidt et al., 2014). Epigenetic alterations of effectors in the developing brains of mammals can impair normal development and result in neurodevelopmental disorders (Schanen, 2006; LaSalle and Yasui, 2009; LaSalle, 2011). In this study, we performed genome-wide methylation analysis to test the hypothesis that exposure to HMFA during gestation induces epigenetic changes in the mouse offspring's CB and contributes to alterations in gene expression. We found distinct alterations in the methylation profile in The CB of offspring from mothers that received HMFA in comparison to LMFA. Such changes were widespread throughout the genome and were evident in both promoter and intragenic regions. These data establish that in addition to its role in preventing NTD's, FA during gestation can induce epigenetic changes in the brain.

As a pteroylmonoglutamate, FA cannot breach the blood brain barrier, with only methylfolate appearing in the cerebrospinal fluid (CSF). After FA is consumed, metabolically it is chemically reduced to tetrahydrofolate by the enzyme dihydrofolate reductase (DHFR) in the liver, where it collects a formyl group, which is reduced to methyl to form 5-methyltetrahydrofolic acid (Barua et al., 2014c). However, excess supplementation with FA may overwhelm this process and possibly saturate the DHFR activity. Perhaps the benefit of high doses of FA may be limited by saturation of DHFR, as studies have shown that DHFR activity in human liver is extremely slow and variable (Bailey and Ayling, 2009). We speculate that mechanistically one possibility is that excess FA in blood may compete to bind with the folate receptor and inhibit methyl-tetrahydrofoalte transport, resulting in changes in brain homeostasis (Smith et al., 2008). Alternatively, elevated methylfolate via maternal metabolism may concentrate against a gradient in the CSF by the choroid plexus and thus interfere with the regulatory functions of the brain. Because the developing brain is potentially vulnerable to maternal nutrient availability (Antonow-Schlorke et al., 2011), such exposure to HMFA may impact the brain function of offspring with alterations in methylation and gene expression, as is evident from the data of our study.

To date, much progress has been made to characterize the dynamic changes of DNA methylation in CpG sites and its role in functional outcome. However, the research to elucidate the precise mechanism by which methylation in non-CpG sites affects gene expression and its role in functional outcomes is still in its infancy. Studies have shown that Dnmts have sequence specificity and can influence the methylation in non-CpG sites (Ramsahoye et al., 2000; Gowher and Jeltsch, 2001; Mund et al., 2004). Similar to our previous findings (Barua et al., 2014b), the data generated from this study suggest that alterations in FA intake during gestation can modulate the methylation profile of both CpG and non-CpG sites in the brains of offspring. Our results revealed that HMFA increases the expression levels of Dnmt3b in male pups and of Dnmt3a, Dnmt3b in female pups (Figure 4). Such evidence supports the idea that HMFA may increase the activity of Dnmts which can modulate neuronal differentiation (Luo et al., 2013); perhaps neuronal cells have a higher variation in DNA methylation than non-neuronal cells (Zhang et al., 2010; Melka et al., 2014). This study further establishes the theory of variations in methylation of neuronal cells, with distinct methylation patterns of CpG and non-CpG sites throughout the genome, including promoters and intergenic and intragenic regions in the CB of pups from the HMFA group in comparison to the LMFA group. This finding is in agreement with our previous reports and suggests that HMFA during gestation can induce unbiased distribution of methylation in the brains of offspring. Such differences in the methylation pattern in the intragenic region may have different correlations to the expression of transcriptional levels, as is evident from our study. Indeed, studies in mammalian cells and human brains correlated changes in methylation of intragenic regions with both up-regulation and down-regulation of transcripts (Ball et al., 2009; Rauch et al., 2009; Maunakea et al., 2010).

Our data also highlighted that several genes that play a role in neuronal pathways exhibited alterations in expression in the CB of offspring. For example, pups from HMFA exhibited significant changes in the expression of several voltage-dependent ion channels (Cacna1g, Scna8a, Kcnk10, Kcnab2, Kcnq1, Kcnq2), which may result in modulation in neuronal synaptic transmission and excitability (Vacher et al., 2008).

Similarly, several genes (Grik4, Gabrr1, Gabrg3, Gabrb1, and Gad1) involved in GABAergic (gamma-aminobutyric acid) or glutamatergic synaptic transmissions exhibited alterations in expression in pups from HMFA. Studies have reported that GABAergic interneurons play a central role in the modulation of excitatory output in the brain and have been documented with a wide variety of psychiatric diseases, including mood disorders, schizophrenia and autism (Fatemi and Folsom, 2014). Biochemical studies have revealed that the expressions of GAD65/67 are decreased in the CB of subjects with developmental disorders such as autism and schizophrenia (Bullock et al., 2008; Blatt and Fatemi, 2011). In the current study, we found that HMFA decreased the expression of Gad1p in female pups, where the expression in the male pups remains unaltered.

Such alterations in the expression of Gad1p may induce alterations in learning and social behavior, as studies with Gad67-deficient mice have been reported to exhibit behavioral abnormalities and reduced sociability (Sandhu et al., 2014; Zhang et al., 2014). Indeed, our previous studies showed that gestational and post-weaning HMFA resulted in changes in behavior, with an increase in ultrasonic vocalization as neonates (Barua et al., 2014a). Such findings indicate that maternal nutritional status, specifically variations in the methyl diet, can induce changes in DNA methylation and thus modulate the expression of genes involved in neuronal pathways and may impact the behavioral outcomes.

Off note, neuronal cells are found to exhibit more inter-individual variations compare to non-neuronal cells (Iwamoto et al., 2011). Moreover, studies with post-mortem brain samples from patient with major depression have shown significant cell specific epigenetic variation in between brain regions (Guintivano et al., 2013). One of the limitations of our current study is that we used the whole cerebellum from pups for methylation and expression analysis; thus some of the alterations of DNA methylation profile may results due to cellular heterogeneity.

Previous studies have reported sexual dimorphism and asymmetry in animal and human cerebellum (Ramirez and Jimenez, 2002; Fan et al., 2010). It is also reported that perinatal exposure to chemical and physical perturbations impact differential neurodevelopment and behavioral effects in males and females (Nguon et al., 2005); other studies with mouse placenta have shown that maternal diet impacts gene expression differentially in males and females (Mao et al., 2010; Gabory et al., 2012). Our data also shows a significant bias between genders in methylation and expression of genes in the offspring CB as a result of HMFA. This finding is congruent with our previous report that HMFA induces gender-specific methylation changes in the brains of offspring (Barua et al., 2014b). We speculate that such differential methylation could results from alterations in the uterine environment because of excess FA, and biased sensitivity to nutritional perturbations resulted from gender specific distribution of specific receptors and methylation of imprinted genes.

Conclusions

Given the role of maternal FA in the methylation pathway, it remains an open questions how such epigenetic modifications impact the brain and behavior of offspring. Our findings support the idea that epigenetic variations may have distinct sex biased functional consequences to certain neuropsychiatric disorders. It further highlights the relevance of studying both sexes in both experimental model and clinical studies to study the epigenetic impact of maternal diet.

In summary, a key finding of this study is that FA during gestation, aside from its role in preventing NTDs, can induce alterations in the methylation of several genes in both CpG and non-CpG regions in the offspring's CB, and such alterations in methylation are gene- and sex- specific. Such changes in DNA methylation during gestation can induce alterations in gene regulatory structures, and given the role the CB in regulation of higher order functions, including motor function and cognition (Schmahmann, 2004; Tavano et al., 2007), such changes may modulate the functional outcome of neurologic and psychiatric diseases.

Author contributions

WB and MJ conceived the experiments; SB, WB, and MJ designed the experiments; SB, SK, and MJ performed the experiments; SB and MJ analyzed the data; SB, SK, WB, and MJ contributed reagents/materials/analysis tools; SB wrote the paper, and WB and MJ critically revised the manuscript.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SK and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Acknowledgments

Financial support from the March of Dimes Foundation (12-FY12-170) and the New York State Office for People with Developmental Disabilities is gratefully acknowledged. We acknowledge Ms. Maureen Marlow for help with editorial corrections with the manuscript.

Glossary

Abbreviations

FA

Folic acid

NTDs

Neural tube defects

LMFA

Low maternal folic acid

HMFA

High maternal folic acid

5 mC

5-methylcytosine

CB

Cerebellum.

Supplementary material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnins.2016.00168

Figure S1

Distribution of differentially methylated sites in CpG island sequences. (A) Male; low maternal folic acid (LMFA) vs. high maternal folic acid (HMFA). (B) Female LMFA vs. HMFA.

Click here for additional data file. (1.5MB, TIF)
Figure S2

Distribution of differentially methylated sites in non-CpG (CHG/CHH) sites of cerebellum. (A) Male low maternal folic acid (LMFA) vs. high maternal folic acid (HMFA). (B) Female LMFA vs. HMFA.

Click here for additional data file. (2MB, TIF)
Figure S3

Scatter plot representing the distribution of the methylation ratio for corresponding sites (A) CpG, (B) CHG, and (C) CHH of LMFA vs. HMFA of male pups. Pearson's correlation coefficient is denoted in the center of each scatter plot.

Click here for additional data file. (4.5MB, TIF)
Figure S4

Scatter plot representing the distribution of the methylation ratio for corresponding sites (A) CpG, (B) CHG, and (C) CHH of LMFA vs. HMFA of female pups. Pearson's correlation coefficient is denoted in the center of each scatter plot.

Click here for additional data file. (3.3MB, TIF)
Click here for additional data file. (973.5KB, TIF)
Click here for additional data file. (454.7KB, TIF)
Figures S5–S7

Hexbin plot representing the overlapped sites in the CpG (n = 2715), CHG (n = 111), and CHH (n = 192) regions between male and female pups from the HMFA group in comparison with the LMFA group from all the significant (P < 0.05) differential methylation sites. Each dot on hexbin plot is one of the overlapped sites. The colors blue, green, yellow and red represent the dot density from lower to higher order, in accordance with the prevalence of the overlapping sites.

Click here for additional data file. (434.9KB, TIF)
Table S1

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hyper methylation (P < 0.05) compared with genes in male offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (299KB, XLS)
Table S2

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hypo methylation (P < 0.05) compared with genes in male offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (268.5KB, XLS)
Table S3

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hyper methylation (P < 0.05) compared with genes in female offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (283.5KB, XLS)
Table S4

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hypo methylation (P < 0.05) compared with genes in female offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (276KB, XLS)
Table S5

List of primers used in this study.

Click here for additional data file. (20.7KB, DOCX)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1

Distribution of differentially methylated sites in CpG island sequences. (A) Male; low maternal folic acid (LMFA) vs. high maternal folic acid (HMFA). (B) Female LMFA vs. HMFA.

Click here for additional data file. (1.5MB, TIF)
Figure S2

Distribution of differentially methylated sites in non-CpG (CHG/CHH) sites of cerebellum. (A) Male low maternal folic acid (LMFA) vs. high maternal folic acid (HMFA). (B) Female LMFA vs. HMFA.

Click here for additional data file. (2MB, TIF)
Figure S3

Scatter plot representing the distribution of the methylation ratio for corresponding sites (A) CpG, (B) CHG, and (C) CHH of LMFA vs. HMFA of male pups. Pearson's correlation coefficient is denoted in the center of each scatter plot.

Click here for additional data file. (4.5MB, TIF)
Figure S4

Scatter plot representing the distribution of the methylation ratio for corresponding sites (A) CpG, (B) CHG, and (C) CHH of LMFA vs. HMFA of female pups. Pearson's correlation coefficient is denoted in the center of each scatter plot.

Click here for additional data file. (3.3MB, TIF)
Click here for additional data file. (973.5KB, TIF)
Click here for additional data file. (454.7KB, TIF)
Figures S5–S7

Hexbin plot representing the overlapped sites in the CpG (n = 2715), CHG (n = 111), and CHH (n = 192) regions between male and female pups from the HMFA group in comparison with the LMFA group from all the significant (P < 0.05) differential methylation sites. Each dot on hexbin plot is one of the overlapped sites. The colors blue, green, yellow and red represent the dot density from lower to higher order, in accordance with the prevalence of the overlapping sites.

Click here for additional data file. (434.9KB, TIF)
Table S1

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hyper methylation (P < 0.05) compared with genes in male offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (299KB, XLS)
Table S2

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hypo methylation (P < 0.05) compared with genes in male offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (268.5KB, XLS)
Table S3

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hyper methylation (P < 0.05) compared with genes in female offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (283.5KB, XLS)
Table S4

Genes in male offspring from high maternal folic acid (HMFA) diet, which exhibited hypo methylation (P < 0.05) compared with genes in female offspring from low maternal folic acid (LMFA) in CpG/CHG/CHH contexts.

Click here for additional data file. (276KB, XLS)
Table S5

List of primers used in this study.

Click here for additional data file. (20.7KB, DOCX)

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