In utero exposure to maternal smoking is associated with DNA methylation alterations and reduced neuronal content in the developing fetal brain
- Zac Chatterton1, 2, 7,
- Brigham J. Hartley1, 2, 3,
- Man-Ho Seok1, 2, 3,
- Natalia Mendelev1, 2, 7,
- Sean Chen1, 2, 7,
- Maria Milekic5,
- Gorazd Rosoklija5, 8, 9,
- Aleksandar Stankov9,
- Iskra Trencevsja-Ivanovska10,
- Kristen Brennand1, 2, 3,
- Yongchao Ge4,
- Andrew J. Dwork5, 6, 8 and
- Fatemeh Haghighi1, 2, 7Email authorView ORCID ID profile
© The Author(s) 2017
Received: 9 August 2016
Accepted: 9 January 2017
Published: 26 January 2017
Intrauterine exposure to maternal smoking is linked to impaired executive function and behavioral problems in the offspring. Maternal smoking is associated with reduced fetal brain growth and smaller volume of cortical gray matter in childhood, indicating that prenatal exposure to tobacco may impact cortical development and manifest as behavioral problems. Cellular development is mediated by changes in epigenetic modifications such as DNA methylation, which can be affected by exposure to tobacco.
In this study, we sought to ascertain how maternal smoking during pregnancy affects global DNA methylation profiles of the developing dorsolateral prefrontal cortex (DLPFC) during the second trimester of gestation. When DLPFC methylation profiles (assayed via Illumina, HM450) of smoking-exposed and unexposed fetuses were compared, no differentially methylated regions (DMRs) passed the false discovery correction (FDR ≤ 0.05). However, the most significant DMRs were hypomethylated CpG Islands within the promoter regions of GNA15 and SDHAP3 of smoking-exposed fetuses. Interestingly, the developmental up-regulation of SDHAP3 mRNA was delayed in smoking-exposed fetuses. Interaction analysis between gestational age and smoking exposure identified significant DMRs annotated to SYCE3, C21orf56/LSS, SPAG1 and RNU12/POLDIP3 that passed FDR. Furthermore, utilizing established methods to estimate cell proportions by DNA methylation, we found that exposed DLPFC samples contained a lower proportion of neurons in samples from fetuses exposed to maternal smoking. We also show through in vitro experiments that nicotine impedes the differentiation of neurons independent of cell death.
We found evidence that intrauterine smoking exposure alters the developmental patterning of DNA methylation and gene expression and is associated with reduced mature neuronal content, effects that are likely driven by nicotine.
KeywordsBrain DNA methylation Epigenetics Fetal Neuron Nicotine Neurodevelopment Prenatal Smoking Tobacco
Numerous studies have established that maternal smoking during pregnancy is associated with impaired executive function and behavioral problems in the offspring [1–3]. Maternal smoking is associated with altered fetal brain development  and reduced volumes of cortical gray matter in childhood , indicating that exposure to tobacco smoke constituents in utero may impact brain development and subsequently result in neurodevelopmental abnormalities. Offspring exposed to smoking after birth does not exhibit the same adverse trajectories [6, 7], suggesting biologically mediated mechanisms during gestation. Cigarette smoke is a highly complex mixture of more than 5000 chemicals of which approximately 100 are known to be hazardous . Linking specific compound(s) with defined phenotypes has proven difficult. Indirect biological mechanisms caused by cigarette constituents other than nicotine have been proposed, including hypoxia/ischemia and DNA damage . Exposure to nicotine prenatally has a direct impact on brain development. In rodents, prenatal exposure to nicotine is reported to induce abnormal dendritic morphology and reduced synapse density in the cerebral cortex and nucleus accumbens . Additionally, prenatal nicotine exposure during primate brain development up-regulates nicotinic acetylcholine receptors (nAChR), causes cell death, and alters cell size and neurite outgrowth in a regionally dependent manner . Furthermore, nicotine replacement therapy has been suggested to increase the risk for behavioral impairments (for review see [12–17]).
Prenatal exposure to environmental factors such as alcohol  and industrial chemicals (lead, methylmercury, PCBs, reviewed in [19, 20]) often manifests as neurodevelopmental disorders. Epigenetic modifications such as DNA methylation regulate gene activity necessary for cell differentiation . Exposure to tobacco smoke can induce alterations in epigenetic patterning that are associated with a wide spectrum of human diseases including cardiovascular, pulmonary, neurobehavioral disorders and cancer [22–28]. Maternal smoking during pregnancy alters DNA methylation in the blood of newborns  and can cause DNA methylation changes that persist into childhood . In relation to neurological function, differences in DNA methylation have been reported between offspring of smokers and non-smokers in the promoters of catechol-O-methyltransferase (COMT) and monoamine oxidase A (MAOA), genes thought to be involved in nicotine dependence and other neurobehavioral disorders [31, 32]. Further, an increase in the DNA methylation of the brain-derived neurotrophic factor-6 (BDNF-6) promoter/5′UTR has been found in adolescents exposed to maternal smoking during pregnancy . To our knowledge, no studies have directly examined the epigenetic changes of the developing human brain exposed in utero to maternal cigarette smoking. Here we interrogate DNA methylation patterns in the developing cortex of human fetuses exposed to maternal smoking on a genome scale.
Fetal cortical samples dissected from the second trimester (ST) of gestation
Age in wpc (mean ± SD)
Age in wpc (mean ± SD)
16.63 ± 0.52
22.4 ± 1.14
16.67 ± 0.52
23.25 ± 0.96
Sample dissection and processing
Upon delivery, the products of conception were refrigerated, and within hours, they were moved to a −80 °C freezer. For examination, they were placed at −20 °C overnight. Working quickly over dry ice, the brain was removed without thawing. The cortical plate was sampled in the region that becomes the DLPFC in order to obtain post-migratory NeuN-immunoreactive (NeuN+) neurons, which normally become numerous in cortical layers 4–6 between 14 and 20 weeks gestational age, and in layers 2 and 3 between 20 and 24 weeks (Additional file 1: Figure S1) . This region was chosen because it is involved in decision-making and working memory, and its function is compromised in neurodevelopmental and psychiatric conditions, including autism spectrum disorder (ASD). It is readily identified and accessible in second-trimester fetal brain. During the second trimester, the cerebral hemispheric wall in the frontal region grows from a thickness of ~2 mm at 12 weeks to ~6–8 mm at 18 weeks and ~18 mm at 26 weeks, with cortical plate thickness of ~0.5–1, ~1.5, and ~2 mm, respectively [36–38]. We obtained tissue from the cortical plate from frozen fetal brains by scraping the dorsal prefrontal region of the left hemisphere to a depth of approximately 0.5 mm for the youngest fetuses, where there was no gross demarcation between plate and subplate. In the older fetuses, we were guided by a change in color at the junction of the cortical plate and subplate at approximately the predicted depth. These sample specimens for DNA methylation and gene expression assays were stored at −80 °C for further processing.
Human induced Neuronal Precursor Cells (hiNPC)
All hiNPC lines were derived as previously described . To match the in vivo data generated from postmortem studies, hiNPC lines (NSB553-3-C, NSB2607-4-1 and NSB690-2-1) used in this study were derived from three Caucasian males, and for full details of the donors of the fibroblasts and validation of the hiPSC and NPC lines, please see . Cell culture; NPCs were maintained at high density, grown on growth factor-reduced Matrigel (BD Biosciences)-coated plates in NPC media (Dulbecco’s Modified Eagle Medium/Ham’s F12 Nutrient Mixture (ThermoFisher Scientific), 1× N2, 1× B27-RA (ThermoFisher Scientific) and 20 ng/ml−1 FGF2 and split 1:3 every week with Accutase (Millipore, Billerica, MA, USA). Neural differentiation; NPCs were dissociated with Accutase and plated at 2.0 × 105 cells per cm2 in NPC media onto growth factor-reduced Matrigel-coated plates. For neuronal differentiation, medium was changed to neural differentiation medium (DMEM/F12, 1× N2, 1× B27-RA, 20 ng/ml−1 BDNF (Peprotech), 20 ng/ml−1 GDNF (Peprotech), 1 mM dibutyryl-cyclic AMP (Sigma), 200 nM ascorbic acid (Sigma) 1–2 days later. NPC-derived neurons were differentiated for 3 and 6 weeks before being assayed.
Nicotine (N0267-100MG, Sigma) was diluted at three different concentrations [100 nM (low), 10 μM (med) and 1 mM (high)] in neuronal media and added every 2nd day with a complete media change. Control wells were treated with equal volume of vehicle (ethanol) added to neuronal media.
The cell impermeant nuclei dye TO-PRO3® (ThermoFisher Scientific, T3605) was added at week 3 of differentiation. Three plates, each containing triplicates of a hiNPC line, were imaged with an Odyssey® infrared imaging system (LI-COR). TO-PRO3® fluorescence intensity was normalized to control (vehicle treated) wells.
At 3 and 6 weeks of differentiation, cells were washed once with 1× PBS and then fixed in 4% paraformaldehyde (Electron Microscopy Services) for 15 min. Following 3 washes with 1× PBS, cells were then blocked and permeabilized with 1% v/v BSA Fraction V (BSA, ThermoFisher Scientific) with 0.3% v/v Triton-X 100 (T-100X, Sigma). Primary antibodies (Rb-Ki67, 1:500, Abcam, ab15580 and Ms-TUJ1, 1:1000, Covance, MMS-435P) were added overnight in 1%BSA/0.5%T-100X. Appropriate secondary antibodies (AlexaFluor Dk secondaries, Ms-680 and Rb-800) were incubated for 2.5 h in 1%BSA/0.5%T-100X. Following 3 washes with 1× PBS, plates were imaged with an Odyssey® infrared imaging system (LI-COR). Fluorescence intensity was normalized to control wells. Statistical differences between nicotine-treated and vehicle-treated controls were determined by Student’s ttest using R Language 3.03 .
Illumina Infinium Human Methylation BeadChip sample processing
DNA from fetal brains and hiNPCs were isolated and bisulfite converted (Zymo Research), and CpG methylation was determined using Illumina Infinium Human Methylation BeadChip microarrays (HM450), as described previously .
DNA methylation data preprocessing
The analyses were performed using R Language 3.03  an environment for statistical computing and Bioconductor 2.13 . Raw data files (.idat) were processed by minfi package . All samples displayed a mean probe-wise detection call for the 485,512 array probes <0.0005. The data were normalized, background subtracted and further normalized by SWAN . M values were used in feature selection models. Beta values (logistic transformed M values) were used for sample sex determination and DNA methylation reporting. Probes mapping to multiple locations (N = 19,834), Infinium type I probes with a SNP at the interrogated CpG (N = 13,708) and probes mapping to the X- and Y-chromosomes were removed from analysis (N = 11,648), as described , leaving 452,930 analyzable probes.
DNA methylation analysis
Differentially methylated probes (DMPs) display a mean difference in DNA methylation of at least 20%, corresponding to a methylation difference detectable by the HM450 with 99% confidence . DMPs were mapped to refSeq gene annotations and analyzed using Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems, www.ingenuity.com). Differentially methylated regions (DMR) were found using the bumphunter algorithm applied to DNA methylation M values . Specifically, for each CpG site, we estimate the difference between the M values for the exposed and unexposed adjusting for gestational age, sex and sample chip assignment. An interaction term was included between smoking exposure and gestational age for interaction DMR analysis. The methylation difference estimates are smoothed based on the predefined CpG clusters where the maximal gap between neighboring CpG sites is 500 bp, while the largest cluster size is set to 1500 bp. The smoothed regional methylation difference estimates were obtained using a predefined threshold to identify the putative DMRs, with associated significance levels obtained empirically based on 1000 permutations. Cell-proportion estimates were performed using the methods described in Jaffe et al. . Briefly, publicly available HM450 data from ESC-derived NPC (H9) , adult cortical NeuN+ and NeuN− cells  were quantile normalized together  and 227 unique probes that separated the 3 cell types were used in a nonlinear mixed modeling  to estimate the proportion of each of the 3 cell types within our HM450 fetal dataset. Cell-proportion estimates were also generated for publicly available HM450 data from dissected postnatal DLPFC aged 4, 6 and 10 months, produced by the BrainSpan Consortium .
Gene expression analysis
Total RNA was isolated from the same 24 fetal samples used for DNA methylation analysis (ToTALLY RNA™ Total RNA Isolation Kit, Ambion). Fetal mRNA was analyzed using Nanostring nCounter Elements technology. Gene expression analysis of fetal DLPFC samples was performed for the 2 most significant smoking-DMRs (SDHAP3 and GNA15) and 3 of the 5 genes annotated to the most significant interaction DMRs (C21orf56, POLDIP3 and SYCE3). Housekeeping gene selection: We used the Nanostring nCounter Elements technology and selected 4 housekeeping genes for expression normalization. Previously, Penna et al.  investigated the stability of a panel of housekeeping genes for mRNA normalization in human postmortem brain samples. Additionally, Madden et al.  described a subset of ubiquitously expressed transcripts ideal for using as housekeeping genes within brain tissue. We selected 4 housekeeping genes, 3 of which were identified by both Penna et al. and Madden et al. (GAPDH, YWHAZ and CYC) and SDHA, identified by the former group and that we have previously used successfully in Nanostring interrogation of rat mRNA . Negative control subtraction and normalization to housekeeping genes was performed using the nSolver Analysis Software. Sample fold-changes (FC) were calculated relative to sample FS5777 (one of the 24 fetal samples analyzed chosen at random) gene expression levels for each gene independently. Any expression values of 3 standard deviations from the group mean were deemed outliers and removed from the analysis. No more than 1 result for any assay was removed.
Fetal samples exposed to maternal smoking were matched to unexposed fetal samples by age and sex when available (Table 1). Fetal brain weight and total weight were highly correlated (R 2 = 0.94, Additional file 1: Figure S1a), and within either early or late second-trimester samples, no significant difference in brain weight was observed between exposure groups (p value = 0.3 and 0.8, respectively, Student’s t test) (Additional file 1: Figure S1b, c). The cortical plate was sampled from the presumptive DLPFC in an effort to obtain post-migratory NeuN+ neurons, which normally become numerous in cortical layers 4–6 between 14 and 20 weeks gestational age, and in layers 2 and 3 between 20 and 24 weeks .
Maternal smoking-associated differential DNA methylation in the fetal cortex
Developmental interaction with maternal smoking exposure
No discernable difference in group-wise DNA methylation patterns by exposure/development was evident in the promoter regions of RUN12/POLDIP3 or SYCE3, regions that had been identified using the bumphunter algorithm as significant (Additional file 3: Figure S3a, b). Gene expression analysis of SYCE3 revealed low gene expression in early second-trimester smoking exposed (p value = 0.02) (Additional file 3: Figure S3c), analogous to the SDHAP3 mRNA results indicative of developmental delay in smoking exposed. No difference in POLDIP3 mRNA was found between exposure/development (Additional file 3: Figure S3d).
Global DNA methylation of fetal cortex exposed to maternal smoking
Our cortical plate sectioning of the presumptive DLPFC aimed to enrich for post-migratory NeuN+ neurons (Fig. 3b). Other investigators have established cell deconvolution algorithms with remarkable accuracy in estimating NeuN+ proportions of whole brain tissues using DNA methylation profiles [51, 60]. Using the DNA methylation profiles generated from the fetal cortices, we were able to estimate the cell proportions (CP estimates) of NeuN+, NeuN− and neural precursor cells (NPCs) within the fetal DLPFC sections (“Methods” section). CP estimates revealed our fetal cortical sections contained a high proportion of NPCs (mean = 16%) compared to CP estimates of NPCs of cortical sections (postnatal 4–10 months) produced by the BrainSpan consortium (mean = 7%) [50, 53, 57] (Fig. 3c). These NPCs are presumably undergoing maturation, and thus, our sections provide a rare window into the effects of maternal smoking exposure on neuro-cellular development.
In vitro modeling of neurodevelopment in response to nicotine exposure
In this study, we profiled genome-scale DNA methylation of fetal brain development in response to exposure to maternal smoking in utero. Although limited in number, the fetal brain samples have well-characterized maternal health history and exposure data, thus providing a rare opportunity to investigate the impact of nicotine exposure on early human brain development. Notably, our DNA methylation profiling was performed on whole tissue sections from the developing DLPFC that consist of a mixture of neuronal and non-neuronal cell types. Established methods enable the isolation of neuronal nuclei ; however, due to the fragility of fetal neuronal nuclei, this technique cannot be applied. Estimating cell proportions using DNA methylation profiles revealed our fetal DLPFC sections contained approximately 16% NPCs that are presumably undergoing maturation, providing a rare window into human neuro-cellular development in response to maternal smoking exposure.
Embryogenesis is a stage of rapid neurological transformation and growth in which epigenomic landscapes undergo dramatic change [63, 64]. In the developing DLPFC, genome-wide DNA methylation changes that distinguish early and late second-trimester samples were clearly reduced in fetal cortex of smoking exposed indicating alterations in cell-type differentiation. Indeed, our most significant DMRs were identified by interaction analysis between gestational age and maternal smoking exposure. We found a delay in the up-regulation of expression of SYCE3, a gene that is conserved among mammals and whose loss leads to a block in synapsis initiation resulting in meiotic arrest . We also identified an interaction DMR in the promoter of C21orf56 (also known as SPATC1L). C21orf56 is a spermatogenesis and centriole-associated 1-like gene found on chromosome 21 of little-known function. Although we did not observe developmental/exposure dependent changes in gene expression, we observed a gain in promoter methylation in late second-trimester exposed samples that could reduce the transcriptional potential of C21orf56 later in development.
Genes such as GNA15 and SDHAP3 that contained maternal smoking-associated DMRs displayed a developmental delay in mRNA up-regulation in smoking exposed. Notably, SDHAP3 is a subunit of the succinate dehydrogenase complex located within the mitochondrial membrane and functions in electron transport chain transfer of electrons to coenzyme Q . It has been reported that mutations within succinate dehydrogenase subunits actually increase levels of oxidative stress . Intriguingly, this same DMR was recently found hypermethylated in the cerebellum of patients diagnosed with ASD  and differentially methylated in the DLPFC of patients diagnosed with schizophrenia (SCZ) within 3 independent studies . Furthermore, in a separate report, GNA15 was found to be differentially methylated in the PFC of ASD patients . Both ASD and SCZ probably have prenatal origins [71–73]. Taken together, these results reveal a potential link between maternal smoking-associated DNA methylation perturbation and potential increase risk for neurodevelopmental abnormalities. Notably, GNA15 is transcriptionally modifiable by acute doses of nicotine in neuroblastoma cell lines , indicating nicotine as a potential causative agent.
Cell deconvolution algorithms have shown remarkable accuracy in estimating NeuN+ proportions from DNA methylation profiles from whole brain tissue [51, 60]. CP estimates within our fetal DLPFC revealed a smoking exposure-associated reduction in NeuN+ cells supporting previous observations of reduced gray matter in the cortex of smoking-exposed children . The adverse effects of maternal smoking on fetal development are well described; however, it was estimated that 30% of smokers attempting to quit smoking use cessation aids that contain nicotine . Nicotine is a well-studied substance in tobacco and has been shown to induce oxidative stress in rodent [76, 77] and human neurons . Exposure of hiNPCs to 100 nM nicotine resulted in the lowest amount of toxicity but the greatest suppression of neuronal differentiation (B3-Tubulin). These results recapitulate the reduction in the estimated proportion of NeuN+ cells we observed in human samples and implicate nicotine as a causative agent in impeding neuronal development. These data provide direct evidence from primary tissue of in utero exposure to teratogenic agents as found in cigarettes—warranting further investigations of the in utero environment on fetal development and how it impacts offspring health and disease risk through the lifespan.
In summary, we have found evidence that intrauterine smoking exposure alters the developmental patterning of DNA methylation and gene expression and is associated with reduced mature neuronal content, effects that are likely driven by nicotine through mechanisms independent of cell death.
autism spectrum disorder
brain-derived neurotrophic factor-6
chromosome 21 open reading frame 56
dorsolateral prefrontal cortex
differentially methylated positions
differentially methylated region
false discovery rate
- GNA15 :
G protein subunit alpha 15
human induced pluripotent stem cell
Illumina Infinium Human Methylation Microarray 450K platform
monoamine oxidase A
nicotinic acetylcholine receptors
- NeuN+ :
neural progenitor cell
RNA, U12 Small Nuclear
sperm-associated antigen 1
ZC, NM, SC, MM, YG and FH designed and performed DNA methylation and gene expression experiments and analysis. ZC, FH, BH, KB and SMH designed and performed cell culture studies. AD, GR, AS and ITI designed and collected mothers’ history and fetal brain samples. AD performed fetal brain dissections. ZC, FH, AD, YG, KB, MM and BH wrote and edited the manuscript. All authors read and approved the final manuscript.
Please see title page for author affiliations and contact information.
This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai.
The authors declare that they have no competing interests.
Availability of data and materials
All raw Illumina HM450 DNA methylation data used in this study have been deposited in GEO (GSE90871).
Consent for publication
All authors have read and consent to the publication of this research article.
Ethical approval and consent to participate
This study was approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. Informed consent was obtained by a psychologist with no involvement in the mothers’ clinical care, who also interviewed them to obtain a medical history, family history and history of environmental exposures.
The Haghighi Laboratory is supported by the National Institute of Health (NIH) Grant R01MH094774. ZC is supported by a NIDA T32 training grant in Drug Abuse Research from the NIH, USA. Kristen J. Brennand is a New York Stem Cell Foundation—Robertson Investigator. The Brennand Laboratory is supported by the Brain and Behavior Research Foundation, NIH Grants R01 MH101454 and R01 MH106056, and the New York Stem Cell Foundation. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD018522. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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