Chick neural crest is not the only embryonic cell population in which an ethanol-induced calcium transient initiates apoptosis. Key elements of this pathway (calcium release, CaMKII activation **Figure 2E**) also mediate ethanol-induced apoptosis within zebrafish neural crest progenitors from equivalent developmental stages (Flentke et al., 2014b).  Thus, this pro-apoptotic mechanism of ethanol's action appears to be evolutionarily conserved and occurs in diverse cell lineages, at least within the embryo and fetus. Ethanol also invokes this same phosphoinositide-stimulated calcium transient in the mouse morula/blastocyst; however, it does

not cause apoptosis and instead stimulates pathways that govern implantation and proliferative expansion (Winston and Maro, 1995). This suggests that how cells interpret ethanol's calcium transient is lineage dependent.

# **GENOMIC FACTORS MODIFY CALCIUM-MEDIATED NEURAL CREST APOPTOSIS**

The serendipitous discovery of ethanol-sensitive and -resistant chicken strains provided novel insights into the mechanisms of this ethanol-mediated apoptosis. Layer flocks in the authors' poultry facility are replaced annually and one such exchange revealed that the birds' genetic background affects neural crest vulnerability to ethanol-induced apoptosis. As with inbred mice, commercial layer strains reproducibly fall along a continuum of ethanol responses.  Vulnerability to ethanol-induced cardiac defects is similarly shaped by genetics (Cavieres and Smith, 2000). The ethanol content of eggs

and embryos is equivalent and cannot account for this differential vulnerability. Because commercial chick strains are derived from hybrid crosses of sib-grandparent stocks, it is impractical to identify potential loci using traditional genetic breeding approaches. We therefore turned to deep DNA/RNA sequencing to characterize the transcriptomes of ethanol-sensitive and -resistant strains. The *Gallus gallus* genome was among the first to be sequenced and it is sufficiently annotated for detailed genetic analysis.

This comparison was accomplished using a unique genetic resource, two related chicken lines of the Hy-Line W98 strain that were selected for multiple traits affecting egg production. These lines were maintained as distinct closed flocks for perhaps as many as 40 generations. Line W98S (so designated because it originated from Hy-Line's Spencer, IA, facility) displays a robust calcium transient and high apoptosis in response to ethanol challenge (**Figure 3**; Garic et al., 2014), and it was utilized in many of the authors' ethanol publications in the decade following 2000. In contrast, the related W98 line W98D (originating from Hy-Line's Dallas Center, IA, facility) has ethanol-invoked calcium transients that are 30–40% lower than those of W98S at exposure

**FIGURE 3 | Chick lines of the W98 background differ in their sensitivity to ethanol-induced apoptosis. (A)** There is appreciably more ethanol-induced cell death (white dots) in neural crest and neuroprogenitors in line W98S as compared with ethanol-treated W98D. Cell death levels in the latter do not differ from that in saline-treated controls. Here, cell death is visualized using acridine orange, which detects apoptosis in this model; dorsal view is shown. **(B)** W98S embryos (•) mobilize greater intracellular

calcium concentrations (quantified using Fura2) in response to ethanol challenge than do W98D embryos (◦) at equivalent ethanol exposures. Their calcium release is dose-dependent. **(C)** W98S (•) has significantly more apoptotic cells than does W98D (◦) in response to equivalent ethanol exposures. Values are mean ± SD for 3–8 embryos/treatment; \*p < 0.001 vs. 0 mM ethanol for within-strain comparison; †p < 0.001 between W98S/W98D at equivalent ethanol dose.

to equivalent ethanol concentrations. Both strains achieve different plateau values, indicating that their differential response is not due to a shifted dose–response curve. Non-linear regression of these dose-response curves finds that their ethanol-induced calcium responses share similar Kds (51 mM vs. 55 mM) and different maxima (**Figure 3B**; Garic et al., 2014). This suggests that their neural crest progenitors possess ethanol-binding sites with similar affinities and that they differ in transduction of the ethanol signal.

High-throughput transcriptome sequencing (RNA-Seq) of neural crest-enriched headfolds from W98S and W98D identified genomic difference that might influence ethanol sensitivity. Cells were not exposed to ethanol, so as to identify baseline differences that might potentially shape calcium and/or β-catenin signaling. The analysis identified 363 genes that are differentially expressed between W98S and W98D (Garic et al., 2014). Of these, 171 genes (47.1%) are increased in W98S and 192 genes (52.9%) are significantly decreased. Additionally, 18 genes within the Wnt/β-catenin signaling pathway have significantly differential expression. Importantly, the ethanol-sensitive W98S cells have significantly reduced expression of β-catenin itself (0.916-fold vs. W98D, *p* = 0.00588), as well as the two calmodulin isoforms that detect the calcium transient in these cells (CALM, 0.898 fold, *p* = 0.0170; CALM2, 0.836-fold, *p* = 0.060). W98S also has increased expression of two distinct Wnt/β-catenin antagonists, SHISA2 (1.106-fold, *p* = 2.34 × 10−7) and the secreted frizzled receptor protein SFRP2 (1.193-fold, *p* = 7.08 × 10−8), which is normally enriched in hindbrain neural crest progenitors fated for apoptosis (Ellies et al., 2002). Although these different expression levels represent transcripts and not protein, it suggests that cells derived from these two lines have foundational differences in how they perform Wnt/β-catenin-dependent signaling (**Figure 4**, left). In ethanol-sensitive W98S, the reduced β-catenin expression coupled with elevations in two canonical Wnt antagonists could dampen β-catenin's transcriptional activity within its neural crest as compared with W98D. Similarly, their lower content of both calmodulin isoforms could alter their respective calcium signaling dynamics. Taken together, these changes could shift the ethanol dose–response curve such that W98D cells are buffered against β-catenin transcriptional losses, whereas W98S is more vulnerable to those losses. One caveat to this analysis is that these headfolds are composed of neural crest and neuroprogenitors and thus not all these genes are present in neural crest. However, Wnt signaling genes such as β-catenin and SFRP2 are restricted to neural crest at these stages and their identification in this model likely informs their cellular responses.

Additional analysis of this gene set using the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed 10 biologically characterized pathways with significantly differential representation between the two lines (**Table 1**; Garic et al., 2014). These pathways, in turn, largely contribute to two major cellular networks. In the first, four of these pathways mediate the flow of cellular information from nucleus to cytosol and included the spliceosome (*p* = 7.02 × 10−8), RNA transport (*p* = 0.00676), ribosome (*p* = 1.85 × 10−47), and protein processing in the endoplasmic reticulum (*p* = 0.00109) gene clusters. The second

**FIGURE 4 | Integration of genetic findings with known ethanol-induced signals in neural crest.** The left panel shows how the expressions of genes that participate in ethanol's apoptosis are altered in within ethanol-sensitive W98S as compared against ethanol-resistant W98D. Fold-changes in expression are presented. The right panel proposes a mechanism by which ribosome dysgenesis could interact with MDM2 to stimulate p53-mediated apoptosis. In response to cellular stress such as ethanol, a decrease in ribosome synthesis leads to ribosome proteins interacting with MDM2. MDM2 no longer silences p53 and permits p53-mediated activities, including apoptosis, to increase within cells.

**Table 1 | KEGG pathway enrichments inW98S vs.W98D.**

Data from Garic et al. (2014).

differentially represented network includes two pathways that govern energy generation and include metabolism (*p* = 0.00983) and oxidative phosphorylation (*p* = 1.10 × 10−11), and near significance for glycolysis/gluconeogenesis (*p* = 0.0530). Also differentially represented are KEGG pathways involving the cell cycle (*p* = 0.0140) and epithelial tight junctions (*p* = 0.00309).

# **NEURAL CREST AND RIBOSOME BIOGENESIS**

The most compelling finding from the RNA-Seq comparison of ethanol-vulnerable/resistant neuroprogenitors is the significant differential enrichment of 38 genes that encode ribosome proteins. This includes 27 large and 11 small ribosomal subunit proteins (**Figure 5**), as well as two additional genes that participate in ribosome biogenesis, pescadillo (PES-1, 1.198-fold, *p* = 0.0145) and NSA2 (1.340-fold, *p* = 3.81 × 10−6). Of these ribosomal proteins, 25 were decreased and 13 increased in ethanol-sensitiveW98S. Remarkably, ribosomal gene clusters were also significantly altered in two independent studies of ethanol exposure to mouse neural folds of comparable developmental stages to our chick embryos (Green et al., 2007; Downing et al., 2012b; see this volume). Ribosomal gene clusters additionally emerged from independent studies of ethanol-treated neuronal cultures (Rahman and Miles, 2001; Gutala et al., 2004). Perhaps, further supporting its importance, haploinsufficiency in the methionyl tRNA synthetase (*mars*), which donates the first amino acid to initiate protein translation, also heightens embryo sensitivity to ethanol-induced craniofacial deficits (Swartz et al., 2014). The repeated emergence of ribosomal protein clusters from multiple comparisons of ethanol-treated neuroprogenitors suggests that ribosomal activity may be an integral component of cellular ethanol responses.

Why are ribosomal proteins potentially relevant for neural crest apoptosis? Ribosome synthesis is complex (Granneman and Baserga, 2004). Ribosomes convert messenger

RNA (mRNA) information into protein. Composed of four ribosomal RNAs (rRNA) and approximately 80 proteins, ribosomes bind mRNA, provide docking sites for codon recognition by amino acyl-transfer RNA, and catalyze the synthesis of nascent protein. The cell nucleolus denotes locations of polycistronic rRNA synthesis, processing, and ribosome assembly. The rRNA emerges as a single transcript that is then modified by small nucleolar RNAs (snoRNAs) and protein cofactors that mediate cleavage, methylation, and pseudouridylation of the rRNA. Ribosome biogenesis imposes a significant energy demand and, for rapidly proliferating cells, occupies as much as 80% of the energy budget because cells must disassemble the nucleolus and replenish their ribosomes with each mitotic division (Schmidt, 1999).

Given its high use of cellular resources, ribosome biogenesis has been recently recognized as an important sensor of cellular stress through its regulation of p53 activity (**Figure 4**, right; Kruse and Gu, 2009; Fumagalli and Thomas, 2011). The tumor suppressor protein p53 accumulates in response to stress to effect cell cycle arrest, apoptosis, DNA repair, and other processes. Interactions with the E3 ubiquitin ligase MDM2 silence p53 through MDM2-catalyzed ubiquitination and proteosomal destruction. When ribosome biogenesis is perturbed, as through reduced ATP availability or imbalanced ribosome protein content, ribosomal proteins such as RPL5, RPL11, RPL23, and RPS7 instead interact with MDM2 to suppress its ubiquitinase activity and thereby stabilize p53 and stimulate p53-mediated activities including cell cycle arrest and apoptosis.

Defects in ribosome biogenesis are clinically significant. Human ribosomopathies are not embryolethal and are associated with anemia, short stature, limb and heart defects, and, of relevance to this discussion, can feature significant craniofacial deficits including a flattened nasal bridge, micrognathia, epicanthal folds, cleft lip/palate, and altered palpebral fissues (Narla and Ebert, 2010; Fumagalli and Thomas, 2011). These facial changes echo those of FASD and suggest that cranial neural crest is especially sensitive to ribosome dysbiogenesis. Animal models confirm that loss-of-function mutations in ribosome proteins or effectors of ribosome biogenesis can cause cell cycle arrest and p53-mediated apoptosis within neural crest progenitors, the best described of these being Treacher-Collins syndrome (Trainor, 2010). Five of the differentially expressed ribosome proteins in W98S/D headfolds are known to regulate MDM2/p53 interactions (**Table 2**; RPL5, RPL11, RPL12, RPS15, RPL23). Additionally, nine of these ribosomal proteins are causative in the craniofacial ribosomopathy Diamond–Blackfan anemia (RPL5, RPL11, RPL26, RPL27, RPL35A, RPL36, RPS10, RPS15, RPS17). Additional attention to ribosome biogenesis arises from the analysis of these same cell populations 6 h following ethanol challenge, wherein the greatest gene cluster change again involves ribosome biogenesis (*p* = 2.2 × 10−21; Garic et al., 2014). Similar to the afore-mentioned reductions in *shh* signaling (Ahlgren et al., 2002) or in *Pdgfra* (McCarthy et al., 2013), genomic-level alterations in ribosomal proteins might disturb the balance of MDM2/p53 regulation that, in of itself, is insufficient to initiate apoptosis, but does so upon the additional stress of ethanol challenge (**Figure 4**). Indeed,

#### **Table 2 | Differentially expressed RPs linked with human ribosomopathies.**

Adapted from Narla and Ebert (2010), Fumagalli and Thomas (2011), and Garic et al. (2014).

interactions between mTOR and ribosome biogenesis control both cellular anabolism and decisions regarding p53 activity (reviewed in Shimobayashi and Hall, 2014), and it is tempting to speculate that these mTOR and ribosome/p53 findings have identified different aspects of the same mechanism. Given the established role for calcium/β-catenin signals in this model of ethanol-induced apoptosis, we additionally speculation that the combination of diminished β-catenin activity and ribosome dysbiogenesis may interact to stimulate these cells' proapoptotic fate. Studies are underway to evaluate this hypothesis in detail.

# **ENERGY METABOLISM AND APOPTOSIS**

Excessive ethanol suppresses energy metabolism through the competition for cellular reducing equivalents, through inhibition of lipolysis, and through direct effects upon mitochondrial activity (Bunout, 1999). Embryos have a high energy demand due to their obvious anabolic and pro-proliferative state. The avian embryo's primary energy source is triglyceride β-oxidation and thus oxidative phosphorylation; unlike mammals, yolk-bearing embryos can convert acetyl-CoA subunits into glucose via gluconeogenesis. Ethanol-vulnerable cells from W98S had significant up-regulation of multiple components of oxidative phosphorylation including many proteins in complex I/NADH dehydrogenase, cytochrome c oxidase, and the ATP synthase (**Figure 6**). However, several components of the cytochrome c reductase and cytochrome c oxidase were significantly lower compared with W98D. It is possible that these changes altered the metabolic flux within W98S relative to W98D, but unfortunately those embryos are no longer available to test this hypothesis. These metabolic differences might also be linked to the ribosomal changes between these strains, given the high energy cost of ribosome biogenesis and its role as a cellular stress sensor. It is likely that these differences in energy metabolism resulted from commercial pressures during line selection, because efficiency in nutrient

utilization and feed conversion are desirable agricultural traits to improve growth and production. Similarly, the emphasis on improved growth may have also created efficiencies in ribosome biogenesis. Given the importance of energy generation for the rapidly growing embryo, relative differences in energy metabolism may have influenced cellular vulnerability to ethanol-induced stress.

# **SUMMARY**

In summary, genetic approaches have informed the multiple mechanisms by which ethanol disrupts craniofacial morphogenesis. The use of haploinsufficient mice has demonstrated how PAE suppresses shh signaling and generates holoprosencephalic features when exposure occurs during gastrulation. Zebrafish mutants implicate additional mechanisms through effects upon mTOR signaling and cell cycle regulation. Transcriptome sequencing of both mouse and avian embryos with

differing ethanol sensitivities has further identified novel potential mechanisms including energy metabolism and/or ribosome biogenesis; in turn, these could be related to each other as well to contributions from mTOR. That ribosomal dysregulation also emerged from multiple, independent analyses of ethanol responsive genes in mouse neural folds make ribosome biogenesis a strong candidate to modify and contribute to ethanol's damage. Our findings suggest that genetic-level differences in neural crest vulnerability to PAE may partially explain why only a percentage of ethanol-exposed pregnancies exhibit a postnatal craniofacial dysmorphology. Next-generation sequencing offers a rapid and comparatively affordable approach to identify additional genomic candidates that modify craniofacial responses to ethanol. Together with advances in epigenetics, this research will greatly advance our understanding of how genes and environment interact to shape individual outcomes in FASD.

# **ACKNOWLEDGMENTS**

This work was supported by an NIH award R37-AA11085 to Susan M. Smith. Ana Garic, George R. Flentke, and Mark E. Berres performed experiments discussed herein. Susan M. Smith drafted the manuscript; Ana Garic and George R. Flentke edited it. The authors approved the manuscript and the work described, and do not have a conflict of interest to report.

# **REFERENCES**

genes related to the ubiquitin-proteasome pathway and protein synthesis. *Alcohol. Clin. Exp. Res.* 28, 1779–1788. doi: 10.1097/01.ALC.0000148117.17707.B4

**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.

*Received: 13 May 2014; accepted: 27 June 2014; published online: 07 August 2014. Citation: Smith SM, Garic A, Berres ME and Flentke GR (2014) Genomic factors that shape craniofacial outcome and neural crest vulnerability in FASD. Front. Genet. 5:224. doi: 10.3389/fgene.2014.00224*

*This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics.*

*Copyright © 2014 Smith, Garic, Berres and Flentke. 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) or licensor are credited andthatthe original publication inthis journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Reduced DNA methylation at the PEG3 DMR and KvDMR1 loci in children exposed to alcohol in utero: a South African Fetal Alcohol Syndrome cohort study

**Matshane L. Masemola1 , Lize van der Merwe2,3 , Zané Lombard1,4,5 , Denis Viljoen6 and Michèle Ramsay1,4\***

1 Division of Human Genetics, National Health Laboratory Service, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

2 Department of Statistics, Faculty of Natural Sciences, University of the Western Cape, Cape Town, South Africa

3 Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

4 Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa

5 School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa

6 Foundation for Alcohol Related Research, Cape Town, South Africa

#### **Edited by:**

Feng C. Zhou, Indiana University School of Medicine, USA

**Reviewed by:** Evan Elliott, Bar-Ilan University, Israel

Thomas Yang, University of Florida, USA

#### **\*Correspondence:**

Michèle Ramsay, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2050, South Africa e-mail<EMAIL_ADDRESS>Fetal alcohol syndrome (FAS) is a devastating developmental disorder resulting from alcohol exposure during fetal development. It is a considerable public health problem worldwide and is characterized by central nervous system abnormalities, dysmorphic facial features, and growth retardation. Imprinted genes are known to play an important role in growth and development and therefore four imprinting control regions (ICRs), H19 ICR, IG-DMR, KvDMR1 and PEG3 DMR were examined. It is proposed that DNA methylation changes may contribute to developmental abnormalities seen in FAS and which persist into adulthood. The participants included FAS children and controls from the Western and Northern Cape Provinces. DNA samples extracted from blood and buccal cells were bisulfite modified, the ICRs were amplified by PCR and pyrosequencing was used to derive a quantitative estimate of methylation at selected CpG dinucleotides: H19 ICR (six CpG sites; 50 controls and 73 cases); KvDMR1 (7, 55, and 86); IG-DMR (10, 56, and 84); and PEG3 DMR (7, 50, and 79). The most profound effects of alcohol exposure are on neuronal development. In this study we report on epigenetic effects observed in blood which may not directly reflect tissue-specific alterations in the developing brain. After adjusting for age and sex (known confounders for DNA methylation), there was a significant difference at KvDMR1 and PEG3 DMR, but not the H19 ICR, with only a small effect (0.84% lower in cases; p = 0.035) at IG-DMR. The two maternally imprinted loci, KvDMR1 and PEG3 DMR, showed lower average locus-wide methylation in the FAS cases (1.49%; p < 0.001 and 7.09%; p < 0.001, respectively). The largest effect was at the PEG3 DMR though the functional impact is uncertain. This study supports the role of epigenetic modulation as a mechanism for the teratogenic effects of alcohol by altering the methylation profiles of imprinted loci in a locus-specific manner.

**Keywords: fetal alcohol syndrome, imprinted genes, epigenetics, PEG3, KvDMR1, H19 ICR, IG-DMR**

### **INTRODUCTION**

Alcohol is a potent teratogen with devastating effects on the developing fetus. The most profound effects of prenatal alcohol exposure are on neuronal development, resulting in adverse cognitive and behavioral outcomes with lifelong implications, distinct dysmorphic features (shortened palpebral fissures, smooth philtrum, and thin vermilion border to the upper lip), and preand postnatal growth retardation (Stratton et al., 1996; Riley and McGee, 2005; Floyd et al., 2006). The outcomes are collectively referred to as fetal alcohol spectrum disorders (FASD) and range in severity with fetal alcohol syndrome (FAS) at the most severe end of the spectrum (Sokol et al., 2003). FAS is the leading cause of preventable mental retardation and developmental disability in the world. It is an international problem that shows no racial boundaries (Clarren and Smith, 1978; Masotti et al., 2006) and the consequences of prenatal alcohol exposure represent a major public health problem worldwide (May and Gossage, 2001; Sokol et al., 2003; Riley et al., 2011).

The worldwide average prevalence of FAS is estimated at 0.97 per 1000 live births, yet in some communities it is much higher (Abel and Hannigan, 1995; May and Gossage, 2001; McKinstry, 2005). Notably, the prevalence of FAS in South Africa is one of the highest reported in the world, at 40.5–46.4 per 1000 children of school going age in the Western Cape Province (May et al., 2000), confirmed in two follow up studies from the same area reporting an increasing prevalence of 65.2–74.2 (Viljoen et al., 2005) and 68–89.2 per 1000 (May et al., 2007). In addition a study in the Northern Cape Province reported a similar prevalence of 67.2 per 1000 (Urban et al., 2008).

Fetal alcohol syndrome is a complex multifactorial condition and although prenatal alcohol exposure is the primary trigger, twin concordance studies and animal models suggest a significant genetic susceptibility for the development of FAS (Streissguth and Dehaene, 1993; Becker et al., 1996). Recent studies have proposed an epigenetic etiology and supporting evidence for such a mechanism is accumulating (Garro et al., 1991; Haycock, 2009; Ungerer et al., 2013). Gene expression disturbances can be caused by changes in DNA methylation, molecular modification of histones and through RNA interference. These mechanisms work together to produce a unique, and reversible epigenetic signature that regulates gene expression through chromatin remodeling. DNA methylation has been investigated extensively as a mechanism of alcohol teratogenesis.

Genomic imprinting is an epigenetic phenomenon resulting in mono-allelic gene expression according to the parent of origin in a locus-specific manner. It is mediated by differential DNA methylation and imprinted loci play an important role during normal development (Jirtle et al., 2000; Rodenhiser and Mann, 2006). The DNA methylation status can be influenced by the environment leading to a functional impact mediated by changes in the epigenome (Jirtle and Skinner, 2007). Imprinted genes are therefore suitable candidates for investigating the effects of teratogens on disease etiology. Almost all imprinted genes contain differentially methylated regions (DMRs) which serve as a mark that differentiates the paternal allele from the maternal allele. Some DMRs which regulate the methylation patterns of a cluster of imprinted genes are referred to as primary DMRs or imprinting control regions (ICRs). The CpG methylation at ICRs is established in the gametes and maintained in somatic tissues of offspring throughout development (Smallwood and Kelsey, 2012). Despite this trend, they may still be subject to tissue-specific effects and extrapolation from the tissue under investigation should be done with care. On the other hand, the imprinting of secondary DMRs is established after fertilization (Geuns et al., 2007). Individual loci may be hyper- or hypomethylated following alcohol exposure. A study by Kaminen-Ahola et al. (2010) reported that maternal alcohol exposure tended to induce hypermethylation at the Avy locus, while Haycock and Ramsay (2009) reported hypomethylation at the *H19 ICR* in mouse placenta following *in utero* alcohol exposure and Stouder et al. (2011) also showed hypomethylation at the *H19 ICR* in the brain and sperm of *in utero* exposed offspring (Stouder et al., 2011). A study by Liu et al. (2009) has demonstrated that alcohol exposure during early neurulation can induce aberrant changes in DNA methylation with associated changes in gene expression in mice. These studies support an epigenetic mechanism as a contributing factor for the development of features observed in FASD. It is widely suggested that the effect is mediated through the interruption of the one carbon pathway that is critical in production of the methyl groups in the maintenance of DNA methylation (Halsted et al., 2002; Liu et al., 2009). Alcohol exposure is correlated with reduced DNA methylation through several plausible mechanisms. Firstly acetaldehyde, a metabolite of alcohol metabolism, inhibits methyltransferase activity, and secondly, folate deficiency as a result of alcohol consumption and poor nutrition, reduces the pool of methyl donors.

In this study we examined quantitative changes in DNA methylation in blood and buccal cells from individuals with FAS, compared to unaffected controls, at four ICRs that regulate gene expression at loci that are important during fetal growth and development: *H19 ICR*, *KvDMR1, IG-DMR,* and *PEG3 DMR*.

# **MATERIALS AND METHODS**

# **STUDY PARTICIPANTS AND SAMPLE COLLECTION**

The study participants included 87 individuals with a diagnosis of FAS and 58 controls. All participants were of mixed ancestry, referred to as "Coloreds" in the South African context, and were resident in the Western Cape and Northern Cape provinces of South Africa. The FAS cases were recruited from Wellington in the Western Cape and De Aar and Upington in the Northern Cape. They were diagnosed by a team of trained clinicians from the Division of Human Genetics, NHLS, Braamfontein, Johannesburg, and also the Foundation for Alcohol Related Research (FARR; http://www.farr-sa.co.za), led by Denis Viljoen. The control participants were recruited from the Northern Cape and no phenotype data were collected. The cases and controls were not age matched. The FAS cases has a median age of 9 years (range 1–16 years) and the control participants were 17–26 years of age (median age 20 years). Adult participants provided informed consent and the parents or guardians of minor participants provided informed consent on their behalf. Ethics approval for the study was obtained from the University of the Witwatersrand Committee for Research on Human Subjects (Medical) (Protocol numbers M02/10/41, M03/10/20 and M080548). Venous blood samples were collected into EDTA by qualified phlebotomists and buccal swabs were collected by the research staff.

# **DNA EXTRACTION FROM BLOOD AND BUCCAL TISSUES**

DNA was extracted from whole blood using a manual salting out method according to a modified protocol from Miller et al. (1988). The buccal tissue DNA was extracted using the Gentra Puregene buccal cell kit (Qiagen, Valencia, CA, USA).

# **DNA BISULFITE MODIFICATION AND PCR AMPLIFICATION**

Genomic DNA was bisulfite modified using the EZ-DNA Methylation Gold Kit ™ (Zymo Research, Orange, CA, USA). Published primer sets and custom designed primer sets were used to amplify specific regions within the ICRs of four imprinted loci: *H19 ICR; IG-DMR*; *KvDMR1*; and *PEG3 DMR*. Each locus is described briefly and the details of the PCR and sequencing primers are shown in **Table 1**.

The pre-pyrosequencing PCR step requires that one of the primers is 50 biotin labeled. In this study we used a universal biotin labeled primer (50 -biotin-GACGGGACACCGCTGATCGTTTA-3 0 ) which was included in the PCR cocktail to generate labeled DNA fragments (Colella et al., 2003). The sequences of primers

**Table 1 | Locus specific information for PCR amplification and pyrosequencing.**

Tag-50 -biotin-GACGGGACACCGCTGATCGTTTA-30—universal biotin labeled tag.

that were designed to be biotin labeled therefore had a 23 bp complementary tag sequence added to their 50 ends for the priming of the universal biotin labeled primer. These primers are shown in **Table 1** as "tag" primers. Unless specified to the contrary, primer sets were designed using the PSQ assay design software (Biotage, Uppsala country, Sweden).

The *H19 ICR* contains seven CTCF binding sites, of which the sixth is differentially methylated. The sixth CTCF binding site was the target region in this study and contains five CpGs, but the amplified region included one extra CpG which was also included in the analysis. For the *H19 ICR* amplification, nested PCR was used with an outer and an inner PCR primer sets. The PCR reactions for this region were performed in triplicate.

The amplified *IG-DMR* region contains 15 CpGs, but only 10 CpGs were analyzed using two different sequencing primers (1 and 2), where one analyzed three and the other analyzed seven CpG sites. PCR primers used for amplification of the *KvDMR1* are published primers and the amplicon contains seven CpGs, including a differentially methylated *NotI* site (Bourque et al., 2010). The PCR forward primer and pyrosequencing sequencing primer had a wobble introduced to accommodate an unavoidable CpG site in the sequence template that could either be methylated or unmethylated. The *PEG3 DMR* amplified region contains 14 CpGs but only seven CpGs were analyzed. The PCR assays for *IG-DMR*, *KvDMR1*, and *PEG3 DMR* were run in duplicate.

# **PYROSEQUENCING FOR QUANTIFICATION OF DNA METHYLATION ANALYSIS**

DNA methylation of the different amplified ICRs was quantified by pyrosequencing using the PSQ 96MA system with the PyroGold SQA reagent kit (Biotage, Uppsala, Uppsala country, Sweden). Pyrosequencing assays and sequencing primers (**Table 1**) were designed using PSQ Assay Design Software and the sequencing was done in triplicate (*H19 ICR*) or duplicate (*IG-DMR*, *KvDMR1*, and *PEG3 DMR*). The percentage methylation for each of the CpG sites within the target region was calculated using Pyro Q-CpG software (Biotage, Uppsala, Uppsala country, Sweden). Two non-CpG cytosine bases were included in all the pyrosequencing assays as internal controls to assess successful bisulfite conversion. Samples containing >5% unsuccessfully converted non-CpG cytosines were discarded.

# **STATISTICAL ANALYSIS**

We analyzed methylation data for 145 individuals, 87 FAS cases, 58 controls. Not every individual provided complete data. There was no age overlap due to the cases being of primary school age (younger than 17 years old, mean age 9 years) and the controls being 17 years or older. This means that the age effect (difference between young and older) cannot be distinguished from the fetal alcohol (case-control) effect in this study. However, the effect per additional year of age could be estimated within each group. Both groups had similar gender distributions, as summarized in **Table 2**.

Linear mixed-effects models were used to generate all the results reported here. These analyses are based on joint models, where all the original methylation observations (individual replicates) are put into a single model to simultaneously do the tests. One advantage is that it avoids some false positive results, because all the results are adjusted for each other. These models also enabled us to adjust for different kinds of random variation as random effects: that between sites, and that between individuals, and that within individuals (between replicates). Adjusting for

**Table 2 | Summary table for number of samples, sex and age distribution for the different loci tested in the control and case groups.**

the variation between individuals is a different way of saying we adjusted for the correlation between replicates on the same individual. After confirming, using linear mixed-effects models, that age and sex were confounders, all further models were adjusted for them, as fixed effects. All *p*-values, effects sizes and standard errors (SE) come from interaction terms in the models. All results corresponding to *p*-values below 0.05 are described as significant, below 0.01 as highly significant and below 0.001 as very highly significant.

The observed methylation data are summarized with box plots. Each box extends from the first quartile to the third quartile (interquartile range), the line inside the box is at the median, and the whiskers extend to the non-outlying minimum and maximum, respectively. Outliers are shown as open circles. The freely available environment for statistical computing and graphics, R (R Core Team, 2014), and R package (Pinheiro et al., 2015), were used for these analyses.

#### **RESULTS**

The 87 FAS cases were recruited from several areas of the Western Cape and the Northern Cape, whereas the 58 control participants were mainly recruited from the Northern Cape. There are differences in the numbers of individuals tested per locus, due to failure to amplify in specific samples for specific loci. Similar percentages per sex were tested, 30 (52%) males and 28 (48%) females in the controls and 47 (54%) males and 40 (46%) females in the cases. The control participants (*N* = 58) all donated blood samples and of the 87 FAS cases, eight donated buccal samples and the remainder donated blood. A summary for the number of samples, sex and age distribution at the different loci in the case and control groups is shown in **Table 2**.

To address tissue specificity of DNA methylation at an imprinted locus, we showed that there was no significant difference in percentage methylation at the *H19 ICR* locus CpG sites between buccal and blood samples from 50 random participants from another study (data not shown). Methylation status between the two tissues was not assessed at *KvDMR1*, *IG-DMR*, and *PEG3 DMR*. Based on two previous studies, we concluded that methylation profiles at these ICRs are unlikely to differ between the two tissues. Bourque et al. (2010) compared average methylation profiles at *KvDMR1* between blood and saliva tissues in healthy adults and reported that their methylation patterns were similar. In addition Woodfine et al. (2011) examined the methylation patterns of 17 germline DMRs (including *H19 ICR*, *KvDMR1*, *IG-DMR*, and *PEG3 DMR*) amongst several somatic tissues (including brain, breast, colon, heart, kidney, and liver) and reported that the average methylation did not vary amongst the tissues. It is therefore unlikely that the origin of the tissue for the DNA methylation studies is a significant confounder in this study.

**Figure 1** contains box plots summarizing the observed percentage methylation at individual CpG sites at all loci (*H19 ICR*, *KvDMR1*, *IG-DMR*, and *PEG3 DMR*), in controls (CON) and cases (FAS). **Figure 2** contains box plots summarizing the observed percentage average methylation at each locus: *H19 ICR*, *KvDMR1*, *IG DMR*, and *PEG3 DMR*, in controls (CON) and cases (FAS). It is not possible to visualize the data after correction for age and sex.

#### **AGE AND SEX AS POTENTIAL CONFOUNDERS IN DNA METHYLATION STUDIES ON IMPRINTED LOCI**

Age and sex are reported confounders in DNA methylation studies and their effects were investigated in the present study. The results for sex are summarized in **Table 3** and for age in **Table 4**. The sex effect was highly significant at *PEG3 DMR* in FAS cases, where males had an estimated 1.11% more methylation than females. In contrast, in controls at *PEG3 DMR*, males had a significant estimated 0.84% lower methylation compared to that in females. However estimated methylation did not differ by sex in control nor in FAS cases at any of *H19 ICR*, *IG-DMR.A*, *IG-DMR.B*, and *KvDMR1*. Since there was a significant difference at one locus, sex was adjusted for in downstream analyses. It was observed that *IG-DMR* has a wide variability in methylation at the different CpG sites analyzed. Most of the individuals had methylation of above 70% at CpG 1–5 while CpG site 6–10 have methylation of about 50%. Therefore *IG-DMR* was split into two regions for this analysis: sites 1–5 called *IG-DMR.A* and sites 6–10 called *IG-DMR.B*.

Due to ethical considerations in the selection of control participants, the study design was sub-optimal in terms of age. All cases were below 17 years of age and all controls were 17 years and above, where the latter were able to give individual informed consent, but the parents or guardians consented to the participation of the cases. This means that age is strongly confounded and that it is not possible to tell whether any differences between cases and controls are caused by the age difference or not. However, the effect of age inside each of the groups could and was investigated.

**Table 4** shows estimates of the difference in methylation percentage over 1 year of age, together with its SE and *p*-values in cases and controls.

The largest effects are seen at *IG-DMR.A* and *IG-DMR.B* in FAS cases, where the estimated methylation percentage decreased

by 0.43 and 0.38% respectively, for a 1 year increase in age. At *KvDMR1*, for every year increase in age, there is a significant estimated methylation increase of 0.19% in controls but in FAS cases there is a significant decrease by 0.11%. Again the highly significant effect is seen at *PEG3 DMR* in the control group, where estimated methylation percentage decreases by 0.22% for every year increase in age. No age effect was observed at *H19 ICR* (either in cases or controls), nor at *IG-DMR.A* and *IG-DMR.B* (in controls) nor at *PEG3 DMR* (in cases). **Table 5** summarizes, for each CpG site, the effect of 1 year of age on methylation, separately for controls and FAS cases, as well as the estimated difference between cases and controls in that effect. There are five CpG sites in *IG-DMR*, one in *KvDMR1*, where the effect of age on methylation is significantly lower in FAS cases and controls. At *IG-DMR* sites 2, 5, 6, 8, and 9, as well as at *KvDMR1* site 6, methylation decreased highly significantly with age in FAS cases but no significant effect was detected in controls. In *PEG3 DMR* site 2, the effect was significantly higher in FAS cases than controls.

**Table 3 | Comparison of methylation within a locus between sexes, separately in FAS and controls.**

CON, controls; FAS, FAS case; Effect, the estimated percentage difference in methylation between males and females in the specific group at the specific locus, using linear mixed-effects models, as described in methods section; SE, standard error of the effect estimate. Significant: p < 0.05. Analyses are adjusted for variation between sites and also for variation between individuals and within individuals as random effects.

In light of these differences, sex and age were adjusted for in the subsequent analyses to assess differences between FAS cases and controls.

# **THE EFFECT OF ALCOHOL ON DNA METHYLATION AT DIFFERENT LOCI (FAS CASES COMPARED TO UNAFFECTED CONTROLS)**

Unadjusted and adjusted results are presented to assess potential differences in methylation percentages at different CpG sites and **Table 4 | The estimated effect of 1 year of age on % methylation per locus per group.**

CON, controls; FAS, FAS case; Effect, estimated percentage difference in methylation between patients of a specific age and those 1 year younger, in the specific group at the specific locus, using linear mixed-effects models, as described in methods section; SE, standard error of the effect. Significant: p < 0.05. Analysis is adjusted for sex (fixed), CpG sites, individuals and replicates (random effects).

also across loci, between controls and FAS cases. The random variation between sites, individuals and replicates per individual was adjusted for in all analyses.

**Table 6** gives a summary of the estimated differences in CpG methylation between FAS cases and controls (FAS-CON), per CpG site, unadjusted and adjusted for age and sex. Both models were adjusted for random variation between and within individuals.

### **Table 5 | The estimated effect of 1 year of age on % methylation per CpG site per locus.**

Effect is the estimated percentage difference in methylation between individuals of a specific age and those 1 year younger, in the specific group at the specific locus, using linear mixed-effects models, as described in methods section.

At *H19 ICR*, all six sites, and at *IG-DMR* sites 2, 5, 6, and 9, the case group had significantly higher methylation than the control group. However after adjusting for age and sex there was no longer a significant difference between controls and cases. The only significant effects detected at *KvDMR1*, were at sites 4 and 7, where methylation was significantly lower in FAS cases than controls, after adjustment for age and sex. At *PEG3 DMR*, across all CpG sites, estimated methylation was very highly significantly lower (all *p*-values < 0.001) in FAS than in controls, with and without adjustment for age and sex.

The estimated methylation percentage difference between controls and cases across each locus is summarized in **Table 7** and the observed percentage methylation is shown in **Figure 2**. At the *H19 ICR* locus, cases showed a highly significant increased average methylation compared to the controls, but this was no longer significant after adjusting for age and sex. At *KvDMR1* locus showed a significant lower average methylation after age and sex were adjusted. In the unadjusted analysis, the average methylation was significantly higher (1.15 and 0.75% respectively) in cases than controls, however after adjusting for age and sex the direction of the effect had changed but the reduced methylation was only significant at region B. The *PEG3 DMR* also showed a highly significant difference between cases and controls and the unadjusted (*p* < 0.001) and adjusted (*p* < 0.001) effect sizes were similar (5.47% lower in cases before adjustment and 7.09% lower in cases after adjustment).

# **DISCUSSION**

Epigenetic modulation is increasingly studied as an important mechanism to explain fetal outcome based on environmental exposures during *in utero* development, with some effects lasting into adulthood. This includes maternal diet and exposure to teratogens, like alcohol, but may also include factors like stress. Since imprinted loci play an important role in fetal development, cellular differentiation and growth, we decided to investigate the levels of CpG methylation at four primary DMRs in children with FAS compared to methylation in unaffected controls. Our understanding of the relationship between DNA methylation with regard to sex, age and cell type remains incomplete, but in addition to inter-individual variation, it is clear that there are locus-specific effects. It is therefore expected that teratogens would also display locus-specific effects explaining their impact on fetal outcome. In addition, tissuespecific DNA methylation and tissue-specific epigenetic responses to prenatal alcohol exposure could potentially confound the interpretation of our study as we examined blood and buccal

The analysis was unadjusted and adjusted for age and sex. Both models were adjusted for random variation between and within individuals.

**Table 7 | Estimated differences in percentage methylation between cases and controls at each locus.**

SE, standard error. Significant: p < 0.05. Analyses were adjusted for variation between sites and variation between individuals, with and without adjustment for age and sex.

DNA from the participants, rather than neuronal tissue derived DNA.

# **SEX AND AGE DEMONSTRATE LOCUS SPECIFIC METHYLATION EFFECTS ON SELECTED ICRs**

The effect of sex on global DNA methylation and locus-specific methylation has been reported. Global DNA methylation has a tendency toward higher methylation levels in males (Fuke et al., 2004; Shimabukuro et al., 2007). Studies on the effect of sex on locus-specific methylation have shown both increases and decreases in DNA methylation (Sandovici et al., 2005; Sarter et al., 2005; Eckhardt et al., 2006; El-Maarri et al., 2007).

In this study, the effect of sex on methylation was shown to be significant at only one locus, *PEG3 DMR*. Interestingly the effects are modest, but opposite in FAS cases and controls, with the former showing increased methylation (1.11%) in males and the latter a decrease of 0.84% in males. It is not clear why the sex effect on methylation is different in the two groups, but it may be due to the fact that the data were not adjusted for age when the analysis was done because it was done as a baseline comparison to decide if sex needed to be adjusted for in the main analysis. *PEG3 DMR* average methylation was shown to decrease in controls for every 1 year increase in age, suggesting that there may be an age sex interaction at this locus. There was no effect of sex on average methylation at *H19 ICR*, *KvDMR1,* and *IG-DMR.*

Age is reported to cause a reduction in global DNA methylation and causes dramatic changes in the distribution of 5 methylcytosine across the genome (Liu et al., 2011). With respect to specific genes, methylation can either be increased or decreased depending on the gene investigated (as reviewed by Liu et al., 2003). Issa et al. (1996) reported that the *IGF2 P2-P4* promoterassociated CpG island is methylated on the silenced maternal allele in young individuals, however with age this methylation also appears on the paternal allele resulting in biallelic methylation (indicating an overall increase in methylation with age). The promoter regions of many genes tend to switch from an unmethylated to a methylated state resulting in gene silencing in an age dependent manner. This includes the promoters of several tumor and aging related genes (Wilson and Jones, 1983; Fuke et al., 2004; Liu et al., 2011). The mechanism contributing to the age dependent changes in global methylation includes a decrease in the expression of *DMNT1* (Lopatina et al., 2002; Liu et al., 2003). Longitudinal research on age effects that study the same individuals at several time points is rare (Florath et al., 2014; Flanagan et al., 2015). In two studies DNA methylation of participants was examined at two ages only, one where they were sampled 6 years apart and the other 8 years apart. It is therefore not yet clear whether age-related changes in methylation at CpG loci associated with age effects occur linearly with age.

We examined the effect of age on the different CpG sites and average methylation across each locus, separately in FAS cases and controls. In the control group, with the exception of *PEG3 DMR* CpG7, there was no CpG site specific age effect. In the FAS cases however, eight out of the 10 *IG-DMR* CpG sites, one *KvDMR1* site and one *PEG3 DMR* site showed a significant age effect. With a single exception, methylation in the FAS group decreased by a modest amount for every additional year of age. When examining the locus-averaged methylation and the effect of age, there was a small but significant effect for *KvDMR1*, but a larger effect in the FAS cases for *IG-DMR* (for both region A and B). This effect was not observed in controls. In contrast, the controls showed an age effect at the *PEG3 DMR*. The measure for an age effect is "difference in methylation per additional year of age"; however there was no overlap in absolute age between cases and controls. From our results, it would appear that age effects are more significant at younger ages (1–16 years) than in older age groups (17–26), in a locus-specific manner.

In this study age was shown to influence methylation at three of the four loci investigated. In alignment with our findings, a study on periconceptional famine exposure (Heijmans et al., 2008) found that within the age group of 43–70 years, the DNA methylation at the *IGF2 DMR* of a 10 year older group was associated with a 3.6% lower methylation (*p* = 0.015) in controls. The magnitude (0.36% per annum) of the effect in their study was greater than that observed in our study.

Since both sex and age showed some effect on DNA methylation at one or more of the imprinted loci in this study, we present sex and age adjusted analyses when comparing DNA CpG methylation between FAS cases and unaffected controls.

# **THE EFFECT OF IN UTERO ALCOHOL EXPOSURE ON DNA METHYLATION AT FOUR IMPRINTED LOCI**

We assessed the possible effect of maternal alcohol consumption on DNA methylation at *H19 ICR*, *KvDMR1*, *IG-DMR*, and *PEG3 DMR*, by comparing methylation levels between FAS cases and unaffected controls. After adjustment for sex and age there was no observed correlation with *in utero* alcohol exposure at the CpG site level at two of the imprinted loci, *H19 ICR* and *IG-DMR*. Interestingly, a modest effect (*p* = 0.035) of decreased methylation (0.84%) for *IG-DMR* Region B was observed in FAS cases. The *IG-DMR* Region B shows roughly 50% methylation, in line with a parent of origin allelic effect whereas Region A had an overall higher methylation percentage.

The *IG-DMR* is a good candidate in terms of its potential biological impact, in line with the features of FAS. The paternally methylated *IG-DMR* is the primary ICR at the *DLK1/GTL2* (*MEG3*) imprinting domain on human chromosome 14q32, where it plays an essential role in regulating the monoallelic expression of several imprinted genes including the paternally expressed *DLK1* and maternally expressed *GTL2* genes (Lin et al., 2003). The methylation on the paternal allele is essential in maintaining the expression of imprinted genes, because failure to maintain the paternal methylation has been shown to result in considerable *Dlk* repression while *Gtl2* expression is increased (Schmidt et al., 2000).

The *DLK1/GTL2* (*MEG3*) imprinting cluster is a critical region for the phenotypes associated with both maternal and paternal uniparental disomy (UPD) of chromosome 14 (Coveler et al., 2002; Kagami et al., 2005; Temple et al., 2007; Buiting et al., 2008). Maternal uniparental disomy 14 [Upd(14)mat] and hypomethylation at the paternally imprinted *IG-DMR* (Ogata et al., 2008) are characterized by pre- and postnatal growth retardation, developmental delays, mild to moderate mental retardation, muscular hypotonia, small hands and feet, premature puberty and truncal obesity. The locus-averaged methylation of the *IG-DMR* was modestly reduced in FAS cases, tending toward hypomethylation and which may potentially contribute to the growth and neuronal deficits in affected individuals. The magnitude of alcohol effects may be tissue specific and may play a more important role in neurogenesis. These findings merit further study and validation.

After adjustment of sex and age, two *KvDMR1* CpG sites (4 and 7) showed significantly decreased DNA methylation in FAS cases which contributed to a locus-averaged decrease of 1.49% methylation in the *KvDMR1*. The functional impact of this difference is not clear. The biggest effect (a decrease of 7.09% methylation in FAS cases) was observed at the *PEG3 DMR*. Interestingly, it is the two maternally imprinted loci, *KvDMR1* and *PEG3 DMR*, which are significantly affected by *in utero* alcohol exposure and both show a decrease in methylation following alcohol exposure.

One of the key features of FAS is pre- and post-natal growth retardation and dysregulation of imprinting at *H19 ICR* has been associated with growth disorders (Reik et al., 1995; Gicquel et al., 2005; Ideraabdullah et al., 2008). The findings of our study are, however, in agreement with a study done in a mouse model by Haycock and Ramsay (2009) where they reported no difference in methylation at the *H19 ICR* of mouse embryos exposed to alcohol during the preimplantation period, when compared to unexposed control embryos. Interestingly *H19 ICR* hypomethylation was observed in the mouse placentas suggesting a localized effect on the extra-embryonic tissue, which could explain the effect on fetal growth. In two other related studies subtle differential DNA methylation was observed. Knezovich and Ramsay (2012) reported a significant decrease in methylation at the *H19 ICR* in mouse offspring following preconception paternal alcohol exposure and Downing et al. (2011) reported a subtle decrease in methylation at the mouse *Igf2 DMR1* locus in embryos following *in utero* alcohol exposure.

The hypomethylation at *KvDMR1* and *PEG3 DMR* is aligned to our original hypothesis suggesting that alcohol reduces DNA methylation through the one carbon metabolism pathway and its effect on reducing folate levels. In the next sections the potential implications of hypomethylation at these loci are explored.

# **THE FUNCTIONAL IMPACT OF REDUCED KvDMR1 METHYLATION IN FAS CASES IS UNCLEAR**

*KvDMR1* CpG site-specific and average locus-wide hypomethylation in response to *in utero* alcohol exposure would suggest a loss of methylation on the maternally methylated ICR which regulates the monoallelic expression of several imprinted genes located in the *CDKN1C*/*KCNQ1OT1* imprinting domain cluster. This imprinting domain harbors the paternally expressed non-coding antisense transcript to *KCNQ1* called *KCNQ1OT1*, and other maternally expressed protein coding genes including *KCNQ1* and *CDKNIC1*. Loss of imprinting, or hypomethylation, at the *KvDMR1* has been widely implicated in the Beckwith– Wiedemann syndrome (BWS; Gaston et al., 2001; Diaz-Meyer et al., 2003; Azzi et al., 2009), a congenital disorder characterized by pre- and postnatal overgrowth, organomegaly, and a high risk of childhood tumors (Weksberg et al., 2010). Paradoxically, the FAS cases showed significant hypomethylation at CpG sites 4 and 7 (1.67 and 2.1%, respectively), yet FAS affected individuals are growth restricted. It is unclear whether hypomethylation of only two of the seven CpG sites in this ICR will affect the levels of expression of the imprinted genes in the cluster and what the functional effect may be.

To gain further insight into gene regulation at this locus will require both gene expression and DNA methylation studies to more fully understand the impact of altered methylation at the *KvDMR1*. This is the first study to show the effect of alcohol on methylation status at *KvDMR1* and the findings are counter intuitive given that hypomethylation is associated with an overgrowth phenotype (BWS).

# **UNDERSTANDING THE ROLE OF ALCOHOL INDUCED HYPOMETHYLATION AT THE PEG3 IMPRINTED GENE CLUSTER IN THE PATHOGENESIS OF FAS REQUIRES FURTHER KNOWLEDGE OF THE ICR CONTROLLED GENE EXPRESSION IN THIS REGION**

The *PEG3* imprinting cluster is located on human chromosome 19q13.4 and is regulated by a maternally methylated ICR, the *PEG3 DMR*. The cluster includes several imprinted genes including the paternally expressed 3 gene (*PEG3*), the imprinted zincfinger gene 2 (*ZIM2*) gene and the *USP29* gene, all of which are paternally expressed. Although these loci are syntenic in mouse and human, there are some interesting differences regarding their regulation, their tissue specific expression, and their exon structure and genomic arrangement. The *PEG3* gene is expressed in embryonic tissues, including the hypothalamus and brain, and in adult mouse and human brain, but most highly in human ovary, but not mouse ovary. *PEG3* encodes a DNA binding protein based on its multiple zing finger motifs (Relaix et al., 1996; Iuchi, 2001) and is an imprinted transcription factor that has multiple target genes (Thiaville et al., 2013). It has a proposed tumor suppressive function (Nye et al., 2013) and has been shown to induce *p53* mediated apoptosis in multiple cell types (Yamaguchi et al., 2002).

We observed that maternal alcohol consumption is correlated with a significant reduction of ∼7% methylation at the *PEG3 DMR* in FAS cases. The highly significant decrease in methylation was observed for all the CpG sites analyzed for this locus and also for the average methylation across this locus. It is possible that this change in the *PEG3 ICR* may affect multiple imprinted genes in the region. *PEG3* is expressed from the paternal allele and is reciprocally repressed on the maternal allele, suggesting that alcohol induced demethylation likely affects the maternal allele thus leading to derepression of the *PEG3* gene on the maternal allele, and therefore biallelic expression of *PEG3*. This would lead to an overall increase in *PEG3* expression. Several studies have focussed on the effects of reduced *Peg3*, but none has explored the phenotypic outcome of over expression of *PEG3*.

Gene expression studies, without correlation to their imprinting status, have demonstrated upregulation of *PEG3* (as well as several other genes) in intrauterine growth restriction (IUGR) placentas (reviewed in Ishida and Moore, 2013). Since IUGR is a cause of reduced fetal growth, this study supports our finding that the proposed increase in *PEG3* expression could be associated with a growth restriction phenotype. The role of the *PEG3 DMR* in regulating the imprinted gene cluster in humans requires further investigation.

# **CONCLUSION**

Despite limitations in the study design, including the lack of age matching between cases and controls, the relatively small sample size, and the inaccessibility of neuronal tissue, significant differences in DNA methylation were observed at two primary DMRs when comparing FAS cases with unaffected controls. The observed hypomethylation at the *KvDMR1* has uncertain functional impact on gene expression and the FAS phenotype. The largest epigenetic effect among the loci investigated, was a locus-averaged 7% reduction in DNA methylation at the *PEG3 DMR* which was observed across all seven CpG sites. This ICR orchestrates a complex pattern of gene expression across the region with reported differences in mouse models compared to humans. It is proposed that hypomethylation of the *PEG3 DMR* would result in an increase in the paternally expressed *PEG3* gene. *PEG3* has a DNA binding motif and is considered an imprinted transcription factor, and therefore its function is most likely mediated by altered expression of its targets. Although there is some spatiotemporal congruence of gene expression in line with the developmental origins of the FAS related phenotype, the effect and mechanism of altered expression of *PEG3* and the other imprinted genes controlled by the *PEG3 DMR* remains unclear. Despite the uncertainty of the functional biological mechanism of the locus-specific hypomethylation of important ICRs in the blood of FAS cases, these findings support the role of an epigenetic mechanism in the development of FAS.

# **ACKNOWLEDGMENTS**

We would like to thank the participants and their families and to acknowledge the National Health Laboratory Service (NHLS) Research Trust, the National Research Foundation (NRF) and the Medical Faculty Research Endowment Fund (University of the Witwatersrand) for funding; MCM received an NRF Ph.D. bursary and MR is supported by the South African Research Chairs Initiative of the Department of Science and Technology and NRF of South Africa.

# **REFERENCES**

**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.

*Received: 30 May 2014; accepted: 17 February 2015; published online: 10 March 2015.*

*Citation: Masemola ML, van der Merwe L, Lombard Z, Viljoen D and Ramsay M (2015) Reduced DNA methylation at the PEG3 DMR and KvDMR1 loci in children exposed to alcohol in utero: a South African Fetal Alcohol Syndrome cohort study. Front. Genet. 6:85. doi: 10.3389/fgene.2015.00085*

*This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics.*

*Copyright* © *2015 Masemola, van der Merwe, Lombard, Viljoen and Ramsay. 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) or licensor 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.*

# Molecular pathways underpinning ethanol-induced neurodegeneration

# *Dan Goldowitz 1 †, Alexandre A. Lussier 1 †, Julia K. Boyle1 †, Kaelan Wong1, Scott L. Lattimer 2 , Candis Dubose2 , Lu Lu2 , Michael S. Kobor 1,3 and Kristin M. Hamre2 \**

1 Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute – Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada

2 Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA

3 Human Early Learning Partnership, School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada

#### *Edited by:*

Stephen Mason, Indiana University School of Medicine, USA

#### *Reviewed by:*

Alice Hudder, Lake Erie College of Osteopathic Medicine, USA Shiva M. Singh, University of Western Ontario, Canada

#### *\*Correspondence:*

Kristin M. Hamre, Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, 855 Monroe Avenue, Memphis, TN 38163, USA e-mail<EMAIL_ADDRESS>
†Dan Goldowitz, Alexandre A. Lussier and Julia K. Boyle have contributed equally to this work.

While genetics impacts the type and severity of damage following developmental ethanol exposure, little is currently known about the molecular pathways that mediate these effects. Traditionally, research in this area has used a candidate gene approach and evaluated effects on a gene-by-gene basis. Recent studies, however, have begun to use unbiased approaches and genetic reference populations to evaluate the roles of genotype and epigenetic modifications in phenotypic changes following developmental ethanol exposure, similar to studies that evaluated numerous alcohol-related phenotypes in adults. Here, we present work assessing the role of genetics and chromatin-based alterations in mediating ethanol-induced apoptosis in the developing nervous system. Utilizing the expanded family of BXD recombinant inbred mice, animals were exposed to ethanol at postnatal day 7 via subcutaneous injection (5.0 g/kg in 2 doses). Tissue was collected 7 h after the initial ethanol treatment and analyzed by activated caspase-3 immunostaining to visualize dying cells in the cerebral cortex and hippocampus. In parallel, the levels of two histone modifications relevant to apoptosis, γH2AX and H3K14 acetylation, were examined in the cerebral cortex using protein blot analysis. Activated caspase-3 staining identified marked differences in cell death across brain regions between different mouse strains. Genetic analysis of ethanol susceptibility in the hippocampus led to the identification of a quantitative trait locus on chromosome 12, which mediates, at least in part, strain-specific differential vulnerability to ethanol-induced apoptosis. Furthermore, analysis of chromatin modifications in the cerebral cortex revealed a global increase in γH2AX levels following ethanol exposure, but did not show any change in H3K14 acetylation levels.Together, these findings provide new insights into the molecular mechanisms and genetic contributions underlying ethanol-induced neurodegeneration.

**Keywords: QTL, apoptosis, hippocampus, cerebral cortex, chromosome modifications, histone marks**

# **INTRODUCTION**

Alcohol exposure during development induces a number of lasting physiological changes that result in a host of abnormalities in brain function. It has been consistently shown that the type and severity of ethanol-induced changes can be modulated by the genetics of the individual as shown in studies in both animals (Goodlett et al., 1989; Gilliam and Irtenkauf, 1990; Boehm et al., 1997; Gilliam et al., 1997; Ogawa et al., 2005; Downing et al., 2009) and humans (Christoffel and Salafsky, 1975; Chasnoff, 1985; Streissguth and Dehaene, 1993; Riikonen, 1994). As the mechanisms underlying this phenomenon remain unknown, rigorous examination of the relationship between ethanol exposure, its key physiological targets, and genetic variation will enable the identification of the molecular underpinnings of the resulting damage.

The central nervous system (CNS) is especially susceptible to developmental defects following alcohol exposure, with ethanol causing aberrant mitosis and cell migration, as well as alterations in neuronal process outgrowth and connectivity (as reviewed in Sulik et al., 1988; Kumada et al., 2007; Sadrian et al., 2013). However, one of the most common ethanol-induced alterations is cell death including apoptotic cell death. Cell death occurs in a time and dose-dependent fashion although there are windows of time when specific cell populations are particularly vulnerable to ethanolinduced cell death. For example, the work of Sulik and colleagues demonstrates that developing neuroblasts are particularly vulnerable to ethanol-induced cell death shortly after neural tube closure (Dunty et al., 2001, 2002) while Olney et al. (2002a,b), Dikranian et al. (2005) demonstrated that the more mature neurons in the developing cerebral cortex and other forebrain structures are particularly vulnerable to alcohol during the early postnatal period in mice. In the present study, ethanol was administered to mice postnatally during the time of the brain growth spurt (Dobbing, 1974). During the brain growth spurt, neurons are completing migration and actively establishing connections (Dobbing, 1974). The equivalent stage of brain growth in humans begins during

the third trimester and continues the first 1–2 years after birth (Dobbing and Sands, 1979).

The genetic contributions to a phenotype are often explored using knockout animals (e.g., de Licona et al., 2009; Noel et al., 2011). While this strategy is effective in establishing the function of a specific gene, it does not reflect the spectrum and complexity of variation observed across a population. To circumvent this drawback, we have harnessed the natural variation present within mice through the use of BXD recombinant inbred strains, generated by crossing the C57BL/6J and DBA/2J strains (Morse et al., 1979; Peirce et al., 2004).

Furthermore, genetic background also contributes to the distribution of epigenetic patterns established during early development (Weng et al., 1995; Padjen et al., 2005; Schilling et al., 2009). As histone modifications and DNA methylation also respond to various environmental and cellular cues, epigenetic marks may provide a link between genetic variation and susceptibility to ethanolinduced cell death (Meaney, 2010; Kobor and Weinberg, 2011). Previous studies have investigated the effect of different exposure paradigms on histone modifications in the brain, finding that chromatin structure responds to various teratogens during development (Cronican et al., 2013; Jordi et al., 2013; Luo et al., 2014). Recent evidence also shows that acute ethanol exposure alters dimethylation levels on lysine 9 and 27 of histone 3, which partially mediate ethanol's teratogenic effects in the brain (Subbanna et al., 2013).

In the present study, a high dose of ethanol was administered to mice during the brain growth spurt at postnatal day 7 (P7) (Dobbing, 1974). Previously, strain differences in levels of ethanolinduced cell death were observed following early prenatal ethanol exposure (Chen et al., 2011) and the current study expanded this to neonatal exposure. In order to identify the contribution of the genetic background to variable susceptibility to ethanol-induced cell death, activated caspase-3 immunostaining was performed on the hippocampus and different layers of the cerebral cortex in BXD strains. Quantitative trait locus (QTL) analysis was performed to identify chromosomal locations involved in the observed differences. Finally, as an initial analysis of chromatinbased modifications in ethanol-induced apoptosis, the effect of alcohol exposure on the levels of two histone modifications, phosphorylated H2A.X and acetylated lysine 14 of histone H3, was investigated using protein blots.

# **MATERIALS AND METHODS**

# **ANIMALS**

**Figure 1** illustrates the experimental flow from the generation of mice through to the analysis. All animals were maintained at the University of Tennessee Health Science Center (UTHSC). Mice were maintained on a 12:12 light:dark cycle and given food and water ad libitum. The BXD strains, generated by crossing the C57BL/6J (B6) and DBA/2J (D2) parental strains and inbreeding the resulting offspring for over 20 generations (Morse et al., 1979; Peirce et al., 2004), as well as the B6 and D2 parental strains, were used in the current analyses. All experiments were conducted with approval of the Institutional Animal Care and Use Committee at UTHSC. All adult mice used to generate the neonates were a minimum of 90 days of age.

Timed matings were used to generate offspring used in this study. Males and females were mated for 4 h starting between 9 and 10 a.m. daily. After the 4 h, the females were removed and checked for the presence of a vaginal plug. The presence of a vaginal plug was termed day 0 of gestation. For the neonatal mice, the day of birth was recorded and animals were exposed to ethanol or control solution on postnatal day 7 (P7).

# **ETHANOL EXPOSURE AND TISSUE COLLECTION**

The protocol of Olney et al. (2002a) was followed. Postnatal day 7 (P7) mice were given ethanol (20% v/v in saline) via subcutaneous injection. The total dose of ethanol was 5.0 g/kg given in two injections of 2.5 g/kg separated by 2 h. Controls were given isovolumetric saline. A maximum of one male and one female in each group from each litter were used and multiple litters were evaluated from each strain. Animals were sacrificed 7 h after the initial injection.

The P7 neonates used for cell count analysis were lightly anesthetized on ice and sacrificed by decapitation. Each brain was dissected from the skull and placed into a tissue chopper where 1 mm slabs were cut. Each slab was fixed overnight in 4% PFA and changed to buffer for tissue processing. For epigenetic analyses, the brain was dissected from the skull and microdissected into various brain regions including the hippocampus and cortex. Each region was placed into a separate microfuge tube, frozen in liquid nitrogen and stored at –80◦C until processing.

# **TISSUE PROCESSING**

One slab from each brain, corresponding approximately to the region Bregma –1.955 to –2.48 mm, was picked for sectioning in a cryostat and cryoprotected using 30% sucrose in PBS and embedded in OCT (Sakura). Coronal sections were cut at 16 μm thickness in a cryostat and directly mounted on glass slides (SuperFrost Plus, Fisher Scientific).

# **DETECTION OF CELL DEATH**

For detection of apoptotic cells in P7 brains, we performed immunohistochemistry using an antibody specific for activated caspase-3 (Abcam). Tissue sections were put in boiling 10 mM citrate buffer for 6 min and then treated with 0.3% hydrogen peroxide to quench endogenous peroxidases. Sections were incubated in blocking solution, containing 30% bovine serum albumin (1:100, Sigma-Aldrich), normal goat serum (1:10, Bethyl Laboratories), and triton X-100 (1:100, Fisher Scientific) in PBS, for 20 min, after which they were incubated in primary antibody at a 1:1000 dilution at room temperature overnight. The sections were rinsed and incubated for 1 h with biotinylated goat anti-rabbit IgG (ABC Elite Kit, Vector Laboratories). Sections were next incubated with Avidin and Biotinylated horseradish peroxidase (Vector Laboratories) for 30 min at room temperature. Immunostaining was visualized using Diaminobenzidine and tissues were counterstained with methyl green.

# **QUANTIFICATION OF CELL DEATH**

The tissues were examined with a Zeiss fluorescence microscope and photomicrographs were taken with Axio Vision software

(version 4.6). Area measurements were performed using ImageJ software (National Institutes of Health, version 1.43s). To quantify cell death, three sections from each sectioned slab were chosen, each separated by five sections. A 200 μm wide band of the CA1 region that was just distal to the CA1/2 border of the hippocampus and overlying cortex were chosen for quantification. The areas of CA1 or cortex that fell within the band were measured for caspase 3-positive cells and total cell number. Cortical layers were determined by visual assessment of cell morphology. To obtain a measure of cell death, the total number of cells within the region of interest in each section was estimated by obtaining a count of the number of cells within a 50 μm × 50 μm bin and extrapolating over the total area. The total number of stained cells within the 200 μm wide band was counted to determine a percentage of activated caspase 3-positive cells.

# **QTL ANALYSIS**

Cell death data were registered in GeneNetwork, an open access online database containing BXD genomic information (http://www.genenetwork.org). Genome-wide interval mapping of QTLs regulating cell death was performed using WebQTL, a module of GeneNetwork. The likelihood ratio statistic (LRS) was computed to assess the strength of genotype–phenotype association of the genomic scans. Permutation test of 2000 permutations was computed to establish the significant and suggestive thresholds where the LRS values corresponded to a genome-wide *p*-value of 0.05 and 0.63, respectively. A significant QTL is referred to as a chromosomal region with LRS score equal to or above the genome-wide significant level (*p* = 0.05). A suggestive QTL is a region of the chromosome with LRS score equal or above the genome-wide suggestive level (*p* = 0.63).

# **PROTEIN BLOT ANALYSIS OF HISTONE MARKS**

Nuclear histones were extracted from the cerebral cortex of male P7 mice (three control and four ethanol-treated) using previously described methods (Rumbaugh and Miller, 2011). Histones were loaded onto 15% SDS-polyacrylamide gels and separated by electrophoresis. Proteins were transferred onto nitrocellulose membranes and blocked with 5% milk for 2 h at room temperature. Membranes were incubated for 2 h with rabbit primary antibody at room temperature, followed by 16 h incubation at 4◦C with mouse primary antibody. They were then incubated with secondary antibodies against mouse and rabbit (1/15,000) for 1 h at room temperature. Membranes were washed for 3 × 5 min between incubations with 0.1% Tween-20 Tris-buffered saline (TBST). Bands were imaged using the Li-Cor Odyssey scanner.

The antibodies used were as follows: 1/1000 rabbit polyclonal to histone H2A.X (ab10475, Abcam), 1/1000 mouse monoclonal to H2A.X (phospho-S139) (ab18311, Abcam), 1/2000 mouse monoclonal to histone H3 (ab10799, Abcam), 1/2000 rabbit polyclonal antibody to acetyl-histone H3 (Lys14) (06- 911, Millipore), IRDye® 800CW conjugated Goat (polyclonal) anti-mouse IgG (926-32210, Li-Cor Biosciences), IRDye® 680 conjugated Goat (polyclonal) anti-rabbit IgG (926-32221, Li-Cor Biosciences).

# **QUANTIFICATION OF PROTEIN BLOTS**

Using ImageStudioLite software (LiCor, Lincoln, NE), boxes were placed around each band of interest, which returned values of raw intensity. Background was removed from raw values using the median correction function to obtain the signal intensity for each protein band. γH2A.X signal intensity was normalized to total H2AX to obtain the relative ratio of γH2A.X/H2A.X for each sample and acetylated H3 (Lys14) signal was normalized to total H3 to obtain the relative ratio of H3K14ace/H3. Statistically significant differences (*p* < 0.05) were identified using Student's *t*-test in Graphpad Prism 6.

# **RESULTS**

The following study was designed to identify genetic differences in susceptibility to ethanol-induced cell death, as well as chromatin-based mechanisms that could modulate alcohol's teratogenic effect. As shown in **Figure 1**, mice from BXD strains were exposed to a high ethanol dose at P7 by subcutaneous injection. Brains were harvested and specific regions of the hippocampus and cerebral cortex analyzed for the level of ethanol-induced cell death. In parallel, whole brain regions were dissected from other samples for histone extractions and subsequent analyses of salient chromatin modifications.

# **GENETIC BACKGROUND ALTERED LEVELS OF ETHANOL-INDUCED CELL DEATH**

In spite of the similarity observed between B6 and D2 progenitor strains, considerable differences in susceptibility to apoptosis were observed between various BXD recombinant inbred strains following ethanol treatment at P7.

In the CA1 region of the hippocampus, mean levels of caspase-3 positive cells following alcohol exposure varied between 2 and 6% of total cells within the analyzed area (**Figure 2**). Out of fourteen different lines, four exhibited cell death levels greater than 4% (BXD 1, 2, 96, 100), which were flagged as higher susceptibility backgrounds for subsequent analyses. In contrast, three strains (BXD 20, 60, 71) only displayed 2% or less mean apoptosis and were thus labeled as low vulnerability strains. Caspase-3 immunostaining of the hippocampal CA1 region in high (BXD96) and low (BXD20) susceptibility strains is illustrated in **Figure 2**.

In the cerebral cortex, ethanol-induced apoptosis was mainly localized to Layers 2/3, and 5 (**Figure 3**), with the highest levels of neurodegeneration occuring in Layer 2/3. Cell death occurred mainly in the superficial portion of Layers 2/3, while Layer 5 displayed a more homogeneous pattern of apoptosis. This specific localization was maintained across strains showing differential cell death levels. However, mean levels of apoptosis differed between genetic backgrounds (**Figure 3B**), ranging from below 5–20% of all cells within the analyzed area. Out of fourteen different strains, three (BXD 71, 80, 100) exhibited mean levels of cell death greater than 15% in Layer 2/3, and were identified as high vulnerability strains. Alternatively, three lines (BXD 32, 39, 51) showed 5% or less caspase-3 positive cells, and were flagged as low susceptibility backgrounds. In almost all cases, the percentage of cell death in Layer 5 was lower than Layer 2/3. However, the trend was similar between strains, where higher or lower cell death in a given line was observed in both regions. Caspase-3 immunostaining of the cerebral cortex in high (BXD80) and low (BXD20) vulnerability strains is illustrated in **Figure 3A**.

# **IDENTIFICATION OF AN ETHANOL-SUSCEPTIBILITY QUANTITATIVE TRAIT LOCUS**

Previous studies have examined malformations in various strains of mice following ethanol exposure, subsequently identifying the chromosomal locations that modulated these strain differences (Downing et al., 2012a). In order to identify potential genetic drivers of variable ethanol vulnerability, QTL analysis was performed to establish correlations between cell death levels and the genotypes across BXD lines. First, levels of caspase-3 positive cells in the hippocampus and cerebral cortex were compared in order to identify a relationship between region-specific apoptosis and genetic background. However, in the examined BXD lines, no significant correlation was observed between mean levels of ethanol-induced cell death in these regions (**Figure 4**). Thus, the hippocampus and cerebral cortex were treated as separate entities for subsequent QTL analyses.

Quantitative trait locus analysis of caspase-3 positive cells in the hippocampal CA1 region revealed a significant locus on chromosome 12 (91.0–92.3 Mb, *p* = 0.05, LRS > 26.13), which is implicated in susceptibility to ethanol-induced cell death in the hippocampus (**Figure 5A**). Genes located within the locus on chromosome 12 include Dio2 deiodinase and a number of RIKEN cDNAs.

In contrast, QTL analysis of cell death in the cerebral cortex revealed a number of suggestive loci (LRS > 12.16), though none were significant (**Figures 5B,C**). QTL analysis for Layer 5 was performed separately and only identified a suggestive locus on chromosome 3 and two loci on chromosome 15 (**Figure 5C**). No suggestive loci overlapped between the different layers of the cortex, and neither had similarities with the hippocampal CA1 region.

# **ETHANOL EXPOSURE ALTERED H2A.X PHOSPHORYLATION**

Given that both ethanol exposure and apoptosis are linked to the chromatin structure, we next examined their intersection on two physiologically relevant histone marks. An initial examination was conducted to assess the contribution of chromatin-based changes to cell death by measuring histone modification levels in the cerebral cortex of male P7 C57/BL6 mice treated with ethanol or saline. Two different modifications were examined to ascertain whether or not ethanol-exposure ubiquitously affects histone marks and if it alters modifications related to apoptosis. The first, phosphorylation of serine 139 on H2A.X (γH2A.X), was chosen due to its direct correlation with DNA damage and apoptosis. This histone H2A variant becomes locally phosphorylated in response to DNA damage to produce γ-H2AX foci in the vicinity of doublestranded breaks (Rogakou et al., 1998). While it does not play an active role in apoptosis, the generation of γ-H2AX during DNA fragmentation is essential for subsequent apoptotic phosphorylation of H2B (Rogakou et al., 2000; Fernandez-Capetillo et al., 2004). Thus, this modification provides a quantitative measure of ethanol-induced DNA damage, which may subsequently lead to cell death. The second, acetylation of lysine 14 on histone H3 (H3K14ace), was selected due to its association with transcriptional activation and presence in active enhancers, which may be indicative of changes in apoptotic gene expression (Karmodiya et al., 2012). A more recent study also showed that ethanol exposure increases levels of this mark in exon 1 of G9a, a histone demethylase involved in alcohol-induced apoptosis (Subbanna et al., 2014).

Using protein blots, analysis of γH2A.X in the cerebral cortex revealed a stark difference between ethanol- and saline-treated

animals (**Figure 6A**). Alcohol exposure significantly increased the ratio of γH2A.X/H2A.X in the cerebral cortex when compared to saline-treated animals (*p* = 0.04; **Figure 6B**). This change in ethanol-treated animals was equivalent to a 1.54 fold increase compared to controls and was indicative of

increased double-stranded breaks in DNA following ethanol exposure. Moreover, initial results also hinted at an increased γH2A.X/H2A.X ratio in both the cerebellum and hippocampus of P7 mice following acute ethanol exposure (data not shown).

In contrast, total nuclear H3 and H3K14 acetylation were quantified to obtain the ratio of H3K14ace/H3, which is indicative of the relative amount of acetylated H3K14 (**Figure 7A**). However, this analysis did not reveal any differences between saline and ethanoltreated animal (**Figure 7A**), as the ratio of H3K14ace/H3 was almost identical for both treatment groups (*p* = 0.32, **Figure 7B**).

# **DISCUSSION**

In the present study, lines of recombinant inbred mice were examined to determine if genetic variation influences the extent and localization of ethanol-induced cell death in the developing cerebral cortex and hippocampus at P7. Differential strain sensitivity to ethanol was observed, with hippocampal data translating into the identification of a QTL on chromosome 12, which mediates these strain-specific differences. To assess whether acute ethanol exposure also causes chromatin-based alterations in the developing brain, two histone modifications were examined in the cerebral cortex. Interestingly, acute ethanol exposure increased levels of γH2AX, a histone mark associated with DNA fragmentation, which is characteristic of apoptosis.

Several studies have previously examined cell death in the developing cerebral cortex and hippocampus following neonatal ethanol exposure in both rats and mice (Olney et al., 2002a,b; Wozniak et al., 2004; Young and Olney, 2006). Several of these studies utilized the same alcohol exposure paradigm and one of the same strains, B6, as the present study, which allows for direct comparisons between our results and published data. While Olney et al. (2002a,b) examined more global changes due to ethanol, rather than focusing on specific regions within structures, it is

interesting to note that these papers also showed high levels of cell death within the CA1 region of the hippocampus and in much of the cerebral cortex. The type of cell death in the hippocampus has also been examined and all studies to date have shown evidence that these cells are dying via apoptotic mechanisms (e.g., Olney et al., 2002a; Wozniak et al., 2004; Young et al., 2005; Young and Olney, 2006; Ullah et al., 2011) consistent with the present study.

Additionally, examination of neuron number following neonatal ethanol exposure has been examined in the hippocampus. Because hippocampal neurons are generated prenatally, any decrease in neuronal number must result from cell death. These studies demonstrate that, within the hippocampus, the CA1 cells are highly susceptible to ethanol-induced cell death following exposure during the brain growth spurt (Livy et al., 2003; Tran and Kelly, 2003; de Licona et al., 2009). These studies are consistent with the results of the present study.

A caveat of the present experiment is that the type of cell that is undergoing apoptosis is unknown. Ethanol has been shown to result in cell death in both neurons and glia (e.g.,Guerri et al.,2001; Dikranian et al., 2005) suggesting that either population could be the target. However, the location and morphology of the developing cells support the hypothesis that neurons are the vulnerable cell population in this ethanol exposure paradigm. The identification of the vulnerable population can provide insights to the identification of susceptibility genes that underlie the QTL and this will be examined in further studies using this model.

One of the issues in the present study is the small number of strains and how that may impact the ability to detect significant

QTLs as well as the reliability of the significant QTL that was detected. It is well known that the ability to detect QTLs is strongly influenced by the number of genes involved such that when there are a small number of genes with large effects on the phenotypic outcome, significant QTLs can be detected with a low number of strains (Flint et al., 2005). In the present experiment, a significant QTL was detected in the hippocampus suggesting that there is a gene within the QTL that has a large impact on genetic differences in ethanol-induced neurodegeneration in that brain region. In contrast, the small number of strains used in the present study translates into lower statistical power that can mean that significant QTLs are not detected. In the cerebral cortex in the present experiment, this is the case and if more strains had been examined, significant, rather than suggestive, QTLs may have been identified in the cortex as well. In regards to the issue of the reliability of significant QTLs, the presence of outliers is an important consideration. Confidence in the validity of a QTL is lessened if there are outliers that are strongly impacting the strain distribution in the phenotypic readout (Hayat et al., 2008; Yang et al., 2009). In the present experiment, the strains show a relatively even distribution and therefore, outliers are not influencing the current analyses providing support that this is a reliable QTL.

Quantitative trait locus analysis demonstrated that the strainspecific differences in ethanol-induced cell death in the hippocampus are modulated, at least in part, by a gene located on Chromosome 12 at approximately 90 Mb. This is a novel chromosomal location with no previously established relationship to ethanol's teratogenic actions (Downing et al., 2012a). This region

is relatively narrow with only two genes, six RIKEN clones, and one EST located within the QTL interval. Cell death, and in particular apoptosis, can be caused by a number of mechanisms including loss of growth factors and excitotoxicity (e.g., Bhutta and Anand, 2002; Nikoli´c et al., 2013). It is of interest therefore, that proximal to the region of the QTL there are several genes that are related to growth factors including the latent transforming growth factor protein 2 (ltbp2), placental growth factor (pgf), and transforming growth factor beta (Tgf beta). However, while these are certainly interesting candidates that cannot be excluded at this time, the rapid nature of the cell death induction in the present analysis led to the hypothesis that the causal gene underlying the QTL is more likely to have a direct link to cell death and the analysis was focused on these genes.

This region of the genome contained several genes that were more directly linked to apoptosis and cell death. However, all the potential candidate genes have the following caveats: (1) none has been linked to cell death following any form of alcohol exposure, and (2) while present in the vicinity of the Ch 12 QTL, none are within the 1LOD interval of the QTL. The candidate gene with the strongest link to apoptosis is Rbm25, an RNA binding protein that has been shown to modulate the expression of isoforms of Bcl2 (Zhou et al., 2008). However, Rbm25 is located farthest from the QTL and its role in apoptosis within the CNS remains unknown. Additionally, both fos and the fos receptor (fosr) as well as jun dimerization protein (jdp2 or jund2) are located close to the QTL. While Fos and jdp2 have been linked to apoptosis (e.g., Lerdrup et al., 2005; Durchdewald et al., 2009), these intermediate early genes have well-documented functions in a number of processes

and thus, the specificity of the effects to the apoptotic processes is currently unknown.

Based on the current data, the best candidate is neuroglobin (Ngb), a relatively recently described gene that encodes a protein that functions to provide oxygen to the CNS (Fiocchetti et al., 2013). Ngb sits close to the QTL region and, as shown onWebQTL, possesses several sequence polymorphisms between the B6 and D2 genome. Genes that lack sequence polymorphisms, and therefore are identical between the two strains, are less likely to mediate strain differences. Moreover, Ngb has been linked to apoptosis caused by a range of factors including oxidative stress (Li et al., 2008) and arsenic toxicity (Liu et al., 2013) while also playing a neuroprotective role following stroke (Yu et al., 2013). Given that hypoxia has been suggested to play a role in ethanol's teratogenic effects (Mukherjee and Hodgen, 1982; Mitchell et al., 1998; Parnell et al., 2007), neuroglobulin is an intriguing candidate for mediating strain differences in ethanol-induced cell death.

Differences in chromatin regulation may also be contributing to the strain-specific differences in cell death. Epigenetic marks are emerging as major regulators of gene-by-environment interactions and have been implicated in the etiology of ethanol-induced neurodegeneration. In fact, G9a-mediated increases of H3K9 and H3K27 dimethylation regulate proteolytic cleavage of histones by caspase-3 and subsequent neurodegeneration in the hippocampus and neocortex following acute, low-dose ethanol exposure (Subbanna et al., 2013). Furthermore, the same group recently showed H3K14 acetylation levels on exon1 of G9a increase following low-dose ethanol exposure (Subbanna et al., 2014).

Our results also support a role for chromatin-based marks in ethanol-induced apoptosis of cells in the CNS. However, similar to previous studies examining cocaine exposure (Jordi et al., 2013), this remains a global analysis of the genome, rather than a targeted gene approach, and does not identify changes in H3K14 acetylation or γH2A.X in specific regions of the genome. While the cerebral cortex did not show any differences in the levels of H3K14 acetylation following alcohol exposure, the ratio of γH2A.X to H2A.X was increased by an acute ethanol treatment. Although these two marks do not perform the same function, these results suggest that ethanol does not have a broad impact over different histone modifications, but, rather, acts in a specific fashion by altering at least a subset of chromatin-based mechanisms linked to apoptosis. Moreover, this effect seemed to occur across all analyzed brain regions (cortex; preliminary data: cerebellum, hippocampus), implying that this is a global response to ethanol exposure, rather than a region-specific event.

The observed difference of γH2A.X in the cerebral cortex following alcohol exposure is likely caused by increased rates of double-stranded breaks (Rogakou et al., 2000). This may be a direct effect of ethanol, where its exposure increases reactive oxygen species (ROS) levels in the cell, which, in turn, cause more DNA damage (Kotch et al., 1995). Widespread DNA damage might then cause the affected cells to undergo apoptosis. In this situation, γH2A.X might play a mechanistic role in ethanolinduced neuroapoptosis. However, the effect may also be indirect, where, instead, ethanol exposure activates apoptotic programming through other mechanisms, leading to DNA fragmentation and subsequent H2A.X phosphorylation (Rogakou et al., 2000).

Here, γH2A.X would simply be a passenger to neurodegeneration, a simple consequence of cell death. Though there is evidence in the field for both these possibilities, the current data does not allow for a distinction to be made between the two. However, further experiments investigating the link between ROS and γH2A.X following ethanol exposure and the activation patterns of apoptotic programs may provide additional insight into their plausibility.

Similar to the issue discussed above, protein blot analysis of the whole cerebral cortex does not allow for differentiation between the types of cells affected by ethanol. As the brain is composed of many different cell types, unaffected cells may dampen some signal in the event that a single cellular species is affected by ethanol in this fashion. In turn, this would explain the relatively low fold change of ethanol-treated animals compared to control (1.54). Higher resolution techniques, such as immunofluorescence will be better suited for this type of analysis in additional mouse strains.

Several studies have also begun to characterize DNA methylation changes following prenatal and postnatal ethanol exposure (Haycock, 2009; Haycock and Ramsay, 2009). In mouse models, ethanol exposure during embryonic days 9–11 was shown to cause global genomic hypomethylation and decreased DNA methyltransferase activity in the fetal genome (Garro et al., 1991). However, recent studies have shown that some regions become hypermethylated and others hypomethylated in embryonic cultures exposed to ethanol (Liu et al., 2009; Chen et al., 2013). In vivo animal studies have corroborated these findings, showing that fetal alcohol exposure results in long-lasting alteration to the brain's DNA methylome, notably in regulatory sequences and imprinted regions containing non-coding RNA (Laufer et al., 2013). Combined with data on histone modifications, these studies support a role for epigenetic changes in ethanol-induced developmental defects.

In summary, we demonstrate that acute neonatal ethanol exposure causes cell death in the developing cerebral cortex and hippocampus in a strain-specific manner. Moreover, we show that a region on chromosome 12 could, at least in part, mediate differential strain sensitivity to ethanol in the hippocampus and identify a number of putative candidate genes that may underlie this QTL. Further studies are required to pinpoint the gene(s) that mediates this vulnerability to ethanol-induced apoptosis in the hippocampus, as well as other brain regions. Identification will be facilitated by identifying whether any genes within the QTL exhibit strainspecific changes in expression following ethanol exposure similar to other studies examining stain-specific expression differences (Green et al., 2007; Downing et al., 2012b). This study also begins to examine whether chromatin-based modifications contribute to differential strain sensitivity, showing that acute ethanol exposure can indeed alter histone modifications. Additional studies into the level of epigenetic alteration between different genotypes will be required to identify their role in variable susceptibility to ethanol-induced neurodegeneration.

# **ACKNOWLEDGMENTS**

Funding for this project was provided by NeuroDevNet, a Canadian Network of Centres of Excellence. The authors also wish to thank Clara van Ommen for assistance in the histological analyses and Megan Mulligan for assistance in the QTL interpretation. Michael S. Kobor is a Senior Fellow of the Canadian Institute for Advanced Research and a Scholar of the Mowafaghian Foundation.

# **REFERENCES**

**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.

*Received: 31 March 2014; accepted: 17 June 2014; published online: 15 July 2014.*

*Citation: Goldowitz D, Lussier AA, Boyle JK, Wong K, Lattimer SL, Dubose C, Lu L, Kobor MS and Hamre KM (2014) Molecular pathways underpinning ethanol-induced neurodegeneration. Front. Genet. 5:203. doi: 10.3389/fgene.2014.00203*

*This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics.*

*Copyright © 2014 Goldowitz, Lussier, Boyle, Wong, Lattimer, Dubose, Lu, Kobor and Hamre. 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) or licensor 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.*

**REVIEW ARTICLE** published: 26 August 2014 doi: 10.3389/fgene.2014.00285

# Epigenetic regulation of the neural transcriptome and alcohol interference during development

# *Marisol Resendiz 1,2 , Stephen Mason3 , Chiao-Ling Lo2,3 and Feng C. Zhou1,2,3 \**

1 Stark Neuroscience Research Institute, Indianapolis, IN, USA

2 Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA

3 Department of Anatomy and Cell Biology, Indiana University School of Medicine, Indianapolis, IN, USA

#### *Edited by:*

Beverly Sara Muhlhausler, The University of Adelaide, Australia

#### *Reviewed by:*

Ian C. G. Weaver, Dalhousie University, Canada Abhijit Shukla, Harvard Medical School, USA

#### *\*Correspondence:*

Feng C. Zhou, Department of Anatomy and Cell Biology and Indiana Alcohol Research Center, Indiana University School of Medicine, MS508, 635 Barnhill Drive, Indianapolis, IN 46202, USA e-mail<EMAIL_ADDRESS>
Alcohol intoxicated cells broadly alter their metabolites – among them methyl and acetic acid can alter the DNA and histone epigenetic codes. Together with the promiscuous effect of alcohol on enzyme activities (including DNA methyltransferases) and the downstream effect on microRNA and transposable elements, alcohol is well placed to affect intrinsic transcriptional programs of developing cells. Considering that the developmental consequences of early alcohol exposure so profoundly affect neural systems, it is not unfounded to reason that alcohol exploits transcriptional regulators to challenge canonical gene expression and in effect, intrinsic developmental pathways to achieve widespread damage in the developing nervous system.To fully evaluate the role of epigenetic regulation in alcohol-related developmental disease, it is important to first gather the targets of epigenetic players in neurodevelopmental models. Here, we attempt to review the cellular and genomic windows of opportunity for alcohol to act on intrinsic neurodevelopmental programs.We also discuss some established targets of fetal alcohol exposure and propose pathways for future study. Overall, this review hopes to illustrate the known epigenetic program and its alterations in normal neural stem cell development and further, aims to depict how alcohol, through neuroepigenetics, may lead to neurodevelopmental deficits observed in fetal alcohol spectrum disorders.

**Keywords: neuroepigenetics, neural stem cells, DNA methylation, histone modification, miRNA, epigenomics, neural developmental pathway, gene–environment interaction**

# **PART ONE: NORMAL EPIGENETIC PROGRAM IN DIFFERENTIATING NEURAL STEM CELLS (NSCs) INTRODUCTION**

When neural precursors begin their journey into specified, mature neurons they undergo much transcriptional re-programming. This involves the silencing of pluripotency genes that act to keep the cell in a primordial stage as well as the activation of neuron-specific genes that permit the morphological and functional capabilities of the mature cell. It comes as no surprise then that a host of chromatin remodeling proteins, including epigenetic machinery, undergo considerable transformation during this time. After all, to accommodate the transcriptional changes necessary for cellular specification, relevant DNA regions must undergo structural changes to either facilitate or hinder the accessibility of the loci to transcriptional machinery. In just the last decade, an unprecedented growth in our understanding of the molecular underpinnings of these structural changes has occurred. We have uncovered and expanded the investigation of several classes of epigenetic modifications from histone to DNA and more recently, non-coding elements of the genome which can also play a role in shaping which genes are expressed during the critical, developmental phases of neural maturation. Skepticism regarding the gravity of these epigenetic factors in normal mammalian development has been answered by the revelation that deleting critical enzymes, such as DNA methyltransferases, decreases the viability

of offspring or results in embryonic lethality (Li et al., 1992; Okano et al., 1999). Likewise, mutations in the genes of other epigenetic machinery have been linked to developmental diseases such as the MeCP2 mutation in Rett syndrome, a disease which results in detrimental nervous system development (Amir et al., 1999; Guy et al., 2001). Together, this evidence suggests that epigenetic machinery not only plays a role but is required for the progression of normal neural development.

Much effort has been made to understand how epigenetic markers are altered during neuronal differentiation. Directed differentiation of neuronal fates from totipotent embryonic cells (*in vitro*) as well as live developmental study of mammalian animal models have shown that epigenetic transformation, in line with transcriptome reorganization, is robust and dynamic. Importantly, these changes occur in very cell (lineage)-specific ways and follow strict spatial and temporal cues. Altogether, we propose that an epigenetic program is necessary to drive the transcriptional profiles that differentiate a neural precursor. Here we present just a fraction of the hundreds of neural epigenetic targets that contribute to the development of a neuron. Particularly, we discuss these genes in the context of developmental signaling pathways known to be required for the specificity of mature neural cells-everything from cell cycle arrest to inhibition of neuronal apoptosis and the onset of neuron and glia differentiation. Throughout, we note that some genes are targets for multiple

epigenetic modifications and that one modification often begets another. Indeed, the epigenetic drivers of neurodevelopmental pathways are complex and highly integrated with one another thus allowing external influences a host of downstream opportunities from a single starting position. From this vantage point it is easier to understand why developmental time points are so much more sensitive to external stimuli and how these early exposures can drive lasting change in a neural system (more of which will be discussed in part two).

# **NEURAL STEM CELL DIFFERENTIATION: GLOBAL TRANSFORMATIONS**

The earliest cellular commitment of a neural cell occurs when embryonic, totipotent stem cells become neural progenitors. During this time many investigators have noted global changes occurring in the epigenetic profile of these transformative cells. Histone acetylation increases among maturing neural progenitors *in vivo*, this is likely occurring to accommodate the increasing rates of RNA synthesis occurring in the cell (recall that acetylation of chromatin results in de-compression of DNA; Cho et al., 2011). Additionally, histone marks like H3K4me2 are predictably re-organized throughout the neural differentiation timeline on relevant genes. A high-throughput analysis revealed that H3K4me2 marks are acquired between the stages of pluripotent embryonic stem cells (ESCs) to neural progenitors and mature neurons on cell adhesion, synaptogenic, and neural transmitter signaling pathway genes (Zhang et al., 2012). The histone mark H3K27me3, on the other hand, has been found to decrease in the intergenic regions during neural progenitor cell (NPC) differentiation (Hahn et al., 2013). DNA methylation patterns have also been characterized in developing neural systems. Mainly, it has been shown that 5-methylcytosine (5 mC) is upregulated in neuroepithelial cells (NE) and rapidly downregulated during the specification of NE cells to mature neuronal populations (Chen et al., 2014). 5-Hydroxymethylcytosine (5 hmC; a derivative of 5 mC) patterns appear alternatively enriched at regions of active maturation compared to neuroprogenitor sites. Additionally, several highthroughput DNA methylation analyses of differentiating ESCs have reported that DNA methylation is altered on multiple genes on the path to neural progenitor conversion. These methylation shifts are bi-directional and include hypermethylation and hypomethylation (Singh et al., 2009; Cortese et al., 2011). Perhaps more important than cumulative levels of DNA methylation, however, are the recent findings that genomic landscapes undergo DNA methylation shifts such that regions previously methylated become hydroxymethylated while other un-methylated regions acquire methylation during this critical time of neural predisposition (Hirabayashi et al., 2013). The observation that neural gene clusters appear to acquire 5-hydroxymethylcytosine (5 hmC) during ESC to NPC differentiation led to the hypothesis that though 5 hmC does not directly up-regulate the genes that promote neural differentiation, the methylation intermediate may act as a "priming" mechanisms for the de-methylation which will eventually allow the expression of these genes (Tan et al., 2013). Many non-coding RNA transcripts are similarly altered during ESC conversion to neural progenitors (Iyengar et al., 2014), potentially impacting a host of complimentary mRNA. Briefly, microRNAs involved in provisioning self-renewal capacity to neural stem

cells (NSCs; miR 134, 137, 25) are understandably reduced as NPCs undergo neural specification and lose their proliferative ability (Meza-Sosa et al., 2014). Conversely, miRs that support neurogenesis (miR 124, 9, let7) are upregulated during the developmental progression of NPCs to immature neurons. Finally, chromatin-remodeling proteins have been shown to undergo up to 30-fold changes during neural precursor specification (these can be entirely lineage specific; Juliandi et al., 2010; Weng et al., 2012). These protein complexes interact with and/or influence subsequent epigenetic modifications on the path to regulating neuronal transcriptomes. It is worth noting that studies of global epigenetic change during neural differentiation often come from two sources, cell populations analyzed in live developmental systems or cultured NSCs. The use of one versus the other can lead to contradictory conclusions regarding the nature of epigenetic change. As Cho et al. (2011) explains, this is not surprising given that epigenetic modifications are often products of external cues and *in vivo* extracellular environments have not yet been precisely recapitulated *in vitro*. Altogether, there is ample evidence to support that these epigenetic mechanisms (histone modification, DNA methylation, non-coding RNA elements, and chromatin remodeling proteins) contribute largely to the transcriptional reprograming that is required during stem cell commitment to neural lineages (**Figure 1**). Next we discuss particular gene targets and the integrative epigenetic modifications which guide them along the specification pathways that distinguish neural precursors.

# **NEURAL STEM CELL DIFFERENTIATION: GENE NETWORKS AND EPIGENETIC REGULATION**

# *(A) Cell-cycle regulation and pluripotency*

Cell cycle regulators play an important role in developing NSCs. Primarily, they allow the self-renewal of neural progenitors and are ultimately responsible for the cell cycle arrest that occurs when a progenitor becomes post-mitotic (no longer able to selfrenew). Both symmetric (self-renewing) and asymmetric cell division are important for the development of cortical cells in the CNS. The number of cell divisions of neuroprogenitors will determine the number of mature neurons in the brain. The propagation of symmetric cell division past the normal developmental schedule or, conversely, the premature arrest of the cell cycle can drastically alter the structure and, ultimately, lead to functional aberrations. To make sure that cell cycles adhere to preset schedules, epigenetic mechanisms are utilized to regulate the expression of pro-mitotic and pro-pluripotent genes. One example of epigenetic regulation at the cell cycle level can be observed during the G2 to mitosis phase. For this mitotic progression to occur, cdk2/cyclinA and cdk1/cyclinB must sequentially phosphorylate FoxM1, an important transcription factor for pro-mitotic genes (*Cycb, Cenpf*). The transcription factor SC1, which has been shown to recruit the type II arginine methyltransferase PRMT5, works to repress the pro-mitotic genes *Cyclinb* and *Bub1b*, thereby keeping cells in a proliferative state (Chittka et al., 2012). When SC1 is deleted, premature differentiation of neural precursors is observed. This is just one small part of the cell cycle at which epigenetic interference can work to influence the developmental status of a neuroprogenitor. In fact many

**FIGURE 1 | Global epigenetic trends in neural stem cell differentiation.** Schematic diagram of cellular epigenetic program during neurogenesis. The top panel shows cell states during neurogenesis, from self-renewing neural progenitor cells (NPCs) to fate-determined neuroblasts, to differentiating and maturating neurons. The epigenetic programing is mapped in the bottom panel accordingly: cells gain 5 mC at the beginning of cell specification and sequentially gain 5 hmC at the beginning of cell differentiation; both 5 mC and 5 hmC accumulate during neuronal differentiation and maturation while at later stages of neuronal maturation, 5 mC levels decrease (Chen et al., 2013). Global trends in H3 and H4 acetylation have been traced in vivo to find that

more cell cycle regulatory genes have been ousted as epigenetic

targets. Another facet of neural development that aids in the "stemness" or the self-renewal property of a neural precursor involves a network of pluripotency-promoting transcription factors. Oct4 and Nanog share an overwhelming number of target genes, most of which promote the Inner Cell Mass (ICM) conditions from which ESCs are derived. They help maintain cells in a pluripotent state by either repressing or activating the expression of associated genes. They may also form a complex with Sox2 and together, regulate neighboring Sox elements involved in embryonic development. JARID1B, the H3K4me2 demethylase, has been shown to affect the expression of Oct4 and Nanog. Specifically, JARID1B plays a hand in suppressing the expression of these transcription factors as deletion allows Oct4 and Nanog expression to continue past their normal time course in an *in vivo* developmental model (Schmitz et al., 2011). The H3K9 demethylase JMJD1C has also shown a direct binding capacity to Oct4 (Wang et al., 2014a). Additionally, the Jarid family of proteins may not be acting alone as they have shown complex-forming capacity with polycomb repressor proteins (PcGs)-chromatin remodeling proteins that act to repress gene activation (Pasini et al., 2010). Histone demethylases therefore contribute to neural differentiation dynamically by inhibiting activating histone methylation marks and by recruiting proteins that catalyze repressive histone methyl marks. In cases where PcGs overlap on target genes

mature cells such as those in the mouse cortical plate are richer for these markers than the neural progenitor cells that preceed them (Cho et al., 2011). H3K4me2 is primarily acquired in the neural progenitor cell stage and becomes pronounced in the matured brain cell stage (Zhang et al., 2012). H3K27me3 has been shown to be negatively correlated with 5 hmC on intergenic regions during NPC differentiation (Hahn et al., 2013). Finally, MicroRNAs that support proliferative gene expression are diminished as self-renewable NPCs become specified neural precursors while pro-neurogenesis non coding RNA are upregulated during NSC conversion to a mature neuronal state (Meza-Sosa et al., 2014).

with histone demethylases (reportedly 90%), it is unclear whether there exists competition between demethylases of repressive histone marks and PcGs conferring new repressive histone marks. Oct4, Nanog and Sox2 have also been identified in screens of differential methylation during neural differentiation, indicating that a DNA methylation reprogramming occurs in these genes at the onset of their quiescence (Kim et al., 2014). Nanog, for example, lost 5 hmC in the enhancer regions and gained 5 mC promoter methylation while displaying decreased expression (though it remains to be resolved whether gain of 5 hmC or loss of 5 mC is primarily responsible for the observed expression change). Finally, Sox2 has also been identified as a direct target of the long non-coding RNA RMST and the miRNA 200c (Peng et al., 2012). lncRMST misexpression can inhibit normal neural maturation by affecting the expression of pro-neural genes regulated by Sox2.

As stated earlier, these are only small fractions of the cellular and genomic cascades that govern the replication of a neural precursor. Many other genes and factors are at work beyond what is presented here. Also, it is likely that, as in the case of Sox2, genes governing cellular stemness are actually affected by a myriad of epigenetic factors, both direct and indirect. This suggests that the ultimate expression of the target cannot merely be attributed to one epigenetic mechanism, as is customarily investigated and described, but rather the sum of all their interactions. Such epigenetic–genetic mapping would be a welcome and useful undertaking toward a

more complete understanding of the epigenetic governance of an entire cellular property.

### *(B) Neurogenesis/gliogenesis and cell survival*

Of course the path to neuronal maturation does not end upon exit of the cell cycle. For a neuronal precursor to mature to a final state it must follow a pathway of neural specification. Since there are many different mature neuronal fates, each with unique morphological and functional specificities, it only makes sense that there would exist many distinct neural pathways driving each neuronal subtype. Here we will only focus on a few of the many cascades of genetic profiles that ultimately drive a mature neuron into existence. These have been selected to showcase the interplay of pro-neural genes and epigenetic mechanisms.

The initiation of transcriptional drivers of neuronal maturation often comes from an escape from a repressive action. These inhibitory signals must first be lifted in order for pro-neural genes to activate the maturation schemes of the neural progenitor. A major inhibitory signaling cascade that exemplifies this is the Notch1 pathway. The Notch1 pathway plays a big role in CNS development and, depending on the timing of its activation, can heavily influence the fate of multipotent CNS precursors (Yoon and Gaiano, 2005). Notch1 activation signals the upregulation of Hes family genes. Hes1 and Hes5 specifically, can act to repress the pro-neural genes *Mash1 (Ascl1)* and *Ngn1/2*. These pro-neural factors typically form complexes and act as transcriptional activators of downstream genes important for neuronal specification (more of which will be covered later). While Notch1 signaling is active, neuronal precursors are pushed toward self-renewal (itself necessary to maintain an appropriate progenitor pool size). Meanwhile, cells that manage to become post-mitotic upregulate Notch1 ligands which, through lateral inhibition, restrict neighboring cells from undergoing their own post-mitotic specification (Kageyama et al., 2008). This negative feedback loop, however, is in competition with the negative regulator(s) of Notch signalingat least one of which has also been found downstream of postmitotic gene cascades (Kaltezioti et al., 2010). In other words, neuronal differentiation both inhibits and partly promotes Notch activation/silencing in neighboring cells.

Another factor in the neurogenesis/stemness equation is the presence of the transcription factor Pax6 – a regulator of NSC maintenance and neurogenesis genes. Pax6 interjects with Notch signaling by pairing with Ngn2 to promote neurogenesis. Together, they override the inhibitory action of Notch-mediated Hes1 (Sansom et al., 2009). Pax6 also plays a Notch-independent role in neurogenesis by inhibiting the expression of the pluripotency genes *Oct4* and *Nanog* (Zhang et al., 2010). Finally, several important neurodevelopmental transcription factors such as Sox2 and Pax6 exhibit ZEB-dependent expression (Du et al., 2013). ZEB family proteins act as transcriptional repressors for competing signaling cascades which seek to drive stem cells away from ectodermal (neural) lineages, namely BMP signaling (Postigo, 2003).

Some of the same pathways that direct neurogenesis play a hand in gliogenesis as well. Notch1 for example (likely through Hes1/5 and the downstream effector Dll1) interacts with the FGFmediated Sox9 to promote astroglial fates (Grandbarbe et al., 2003; Wu et al., 2003; Bani-Yaghoub et al., 2006; Esain et al., 2010). The FGF signaling pathway, alternatively, can contribute to the formation of oligodendrocyte progenitors (OLP). This specification is probably due to the expression of the *Olig2* gene, which is expressed under the combined signaling of FGF and SHH (Esain et al., 2010). Additionally, FGF-regulated Sox9, when paired with Sox10, has been shown to aid in OLP survival and migration (Finzsch et al., 2008). Having presented just a faction of the many developmental signaling pathways that interact to regulate neuronal and glial fates, it becomes apparent that these signals are carefully poised to converge in space and time to drive a specific lineage. As such, deviations from these thresholds, no matter how small, can alter a signaling network enough to change the trajectory of a neural precursor. In other words, even small disturbances in these delicate signaling networks can produce a sort of "domino" effect by which lasting neurodevelopmental changes are propagated in an organism. In this vein, let us next consider the opportunities or "windows" that exist in these networks for epigenetic regulation and indirectly, for external input to propel developmental change.

Beginning with the Notch pathway, NSC differentiation analysis has identified both *Notch1 and Hes5* (along with a handful of other downstream Notch1 genes) as targets of differential DNA methylation (Kim et al., 2014). Specifically, the expression of these Notch-related genes displays some dependency on the methylation status of their promoter and/or gene body. Additionally, the histone modifiers SIRT1 and JMJD2B have been shown to affect the expression of Hes1 and Notch1, respectively, in models of neural progenitor differentiation (Hisahara et al., 2008; Das et al., 2013). At least in the case of Notch1, the histone demethylase JMJD2B acts on Notch1 expression by regulating the presence of the repressive histone mark H3K9me3 on the gene promoter. The pro-neural genes *Mash1* and *Ngn1* are not only repressed through active Notch signaling but also serve as direct targets of the Sox2-regulated miRNA let-7i (Cimadamore et al., 2013). This miRNA sequestration results in decreased neuroprogenitor proliferation and neurogenesis similar to that of Sox2-defficient precursors (recall that Mash1 and Ngn1 are downstream targets of Sox2). Ngn1 also serves as a target of the polycomb repressive complex (PRC) 1 and 2 (Hirabayashi et al., 2013). As discussed above, the transcription factor Sox2 is susceptible to both lncRNA and miRNA in addition to exhibiting differential methylation patterns during neural commitment. Additionally, the transcription factor has a promoter binding capacity for the histone 2B ubiquitinylase USP22, which in turn alters the recruitment of histone 3 methylation marks and ultimately leads to Sox2 repression, a function necessary for stem cell differentiation (Sussman et al., 2013). USP22 can also form a complex with the histone deacetylase SIRT1, which serves a similar repressive action on Sox2. There is evidence that some of the transcriptional regulators (inhibitors) of pluripotency factors also exist under the regulation of epigenetic machinery. The master transcription factor Pax6 which suppresses the stemness factors Oct4 and Nanog during ESC conversion to neural progenitor, is a target of the miRNA 96 family (Du et al., 2013). The repression of Pax6 by miR-26 members was experimentally confirmed and inhibited

the differentiation of stem cells into neural precursory lineages exclusively. Finally, the ZEB transcription factor family, which is critical for repression of the competitive epidermal BMP signaling, has been isolated as a target of the miRNA 200 family (Du et al., 2013). This miRNA-mediated repression of ZEB is likely poised at the commitment of ectodermal precursors to either neural or epidermal fates. BMP (epidermal) repression via this epigenetic mechanism indeed swayed ESC populations toward neuroectodermal fates. Interestingly, early attempts to reshape the epigenetic landscape of important fate-determining pathways like Notch1 with epigenetic modifiers have proved unsuccessful (Reichrath and Reichrath, 2012). It is likely that as further investigation with more targeted approaches and diverse cell populations will yield promising results that will strengthen our understanding of these signaling pathways and their vulnerability to epigenetic influence.