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      Review and gap analysis: molecular pathways leading to fetal alcohol spectrum disorders

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          Abstract

          Alcohol exposure during pregnancy affects the development of the fetus in various ways and may lead to Fetal Alcohol Spectrum Disorders (FASD). FASD is one of the leading preventable forms of neurodevelopmental disorders. In the light of prevention and early intervention, knowledge on how ethanol exposure induces fetal damage is urgently needed. Besides direct ethanol and acetaldehyde toxicity, alcohol increases oxidative stress, and subsequent general effects (e.g., epigenetic imprinting, gene expression, and metabolite levels). The current review provides an overview of the existing knowledge about specific downstream pathways for FASD that affects e.g., the SHH pathway, cholesterol homeostasis, neurotransmitter signaling, and effects on the cytoskeleton. Available human data vary greatly, while animal studies with controlled ethanol exposition are only to a certain limit transferable to humans. The main deficits in knowledge about FASD are the lack of pathophysiological understanding and dose–response relationships, together with the lack of reliable biomarkers for either FASD detection or estimation of susceptibility. In addition to single outcome experiments, omics data should be generated to overcome this problem. Therefore, for future studies we recommend holistic data driven analysis, which allows integrative analyses over multiple levels of genetic variation, transcriptomics and metabolomics data to investigate the whole image of FASD development and to provide insight in potential drug targets for intervention.

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          Histone acetyltransferase complexes: one size doesn't fit all.

          Over the past 10 years, the study of histone acetyltransferases (HATs) has advanced significantly, and a number of HATs have been isolated from various organisms. It emerged that HATs are highly diverse and generally contain multiple subunits. The functions of the catalytic subunit depend largely on the context of the other subunits in the complex. We are just beginning to understand the specialized roles of HAT complexes in chromosome decondensation, DNA-damage repair and the modification of non-histone substrates, as well as their role in the broader epigenetic landscape, including the role of protein domains within HAT complexes and the dynamic interplay between HAT complexes and existing histone modifications.
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            Maternal Ethanol Consumption Alters the Epigenotype and the Phenotype of Offspring in a Mouse Model

            Introduction While it is well-recognized that gestational exposure to environmental triggers can lead to compromised fetal development and adult disease in humans [1], the underlying molecular mechanisms remain unknown. There is increasing evidence in animal models that environmental factors can affect gene expression via epigenetic modifications such as DNA methylation [2]–[6]. One way of detecting such events is to use reporters whose expression is closely linked to their epigenetic state. Such epigenetically sensitive alleles are also known as metastable epialleles, and the best known example in the mouse is Agouti viable yellow (MGI:1855930) or Avy [7]. Avy is a dominant mutation of the murine Agouti (A) locus, caused by the insertion of an intracisternal A-particle (IAP) retrotransposon upstream of the Agouti coding exons. The activity of Avy is variable among genetically identical mice, resulting in mice with a range of coat colors; from yellow to mottled to agouti (termed pseudoagouti) [8]. The expression of Avy is known to correlate with DNA methylation at a cryptic long terminal repeat (LTR) promoter located at the 3′ end of the inserted IAP. Specifically, hypomethylation is associated with constitutive ectopic Agouti expression and a yellow coat, while hypermethylation correlates with cryptic promoter silencing and a pseudoagouti coat [9]. We have previously shown that DNA methylation at Avy is reprogrammed in early development at the same time that the rest of the genome is undergoing epigenetic reprogramming [10]. Alcohol consumption is widespread in our society, but it is also recognized as the leading preventable cause of birth defects and mental retardation [11],[12]. High levels of alcohol consumption during pregnancy can result in fetal alcohol syndrome (FAS) which is characterized by prenatal and postnatal growth restriction, craniofacial dysmorphology and structural abnormalities of the central nervous system. The clinical features of FAS are variable and include a range of other birth defects, as well as educational and behavioral problems [13]. This syndrome is the most extreme form of a range of disorders that are known as fetal alcohol spectrum disorders (FASDs) [14]. Approximately 5% of the children of mothers who have drunk heavily during pregnancy have FAS [15], and studies have shown that the dose, time and duration of ethanol exposure are critical [16],[17]. There are a number of mouse models of FAS that have reproduced some of the phenotypic characteristics of the human disorder, particularly the craniofacial abnormalities [16],[18],[19]. It should be noted that these studies used acute ethanol exposures between gestational days (GDs) 7 and 9 and high concentrations; generally two intraperitoneal injections of 0.015 ml of ∼25% (v/v) ethanol per gram of body weight over a 4 hour interval resulting in ataxia and lethargy. These studies only examined the fetal outcomes (GDs 8-18) of ethanol exposure and did not assay offspring either after birth or as adults. There are some rodent studies of the effects of gestational exposure to moderate amounts of ethanol, but these have only identified neurological and behavioral deficits [20]. The molecular mechanisms underlying FAS are unknown. Some studies have focused on the toxic effects of acetaldehyde, the first metabolite of ethanol [18],[21]. Acute ethanol exposure has also been found to result in increased cell death in the developing central nervous system and neurological anomalies in rodents and other animal models [22],[23]. The idea that epigenetic changes are involved has been raised but evidence in support of this hypothesis has, so far, been weak. Garro and colleagues [24] detected a small decrease in the level of global methylation of fetal DNA after acute ethanol administration from GDs 9-11. Bielawski et al. [25] reported decreased DNA methyltransferase 1 (Dnmt1) messenger RNA levels in rat sperm after nine weeks of paternal ethanol exposure. Haycock and Ramsey [26] studied imprinting of the H19/Igf2 in preimplantation mouse embryos after maternal ethanol exposure. Despite severe growth retardation of embryos, they did not find epigenetic changes at the H19 imprinting control region. Here we have developed a mouse model of chronic ethanol exposure (overt signs of intoxication are not observed) that produces measurable phenotypes in adults. We find that maternal ethanol consumption either before or after fertilization affects the expression of an epigenetically sensitive allele, Avy , in her offspring and that, at least in the latter case, can also impact postnatal body weight and skull size and shape in a manner consistent with FASD. Our work raises the possibility of a role for epigenetic reprogramming in the etiology of FASD and provides researchers with a relevant mouse model of the human disorder. Results In this study, Avy was used primarily as a sensitive reporter of epigenetic changes in response to maternal ethanol consumption. The C57BL/6J mouse is null (a) at the Agouti locus, so it has a black coat color. Avy is a gain-of-function, semi-dominant mutation and so the coat color of heterozygous (Avy/a) mice in the C57BL/6J background is a direct read out of Avy transcriptional activity and DNA methylation. The nature of the matings used in this study, an Avy/a male crossed with an a/a female, means that only 50% of the offspring will inherit the Avy allele and be useful for coat color phenotyping. The remaining (a/a) offspring will be black. To study the effects of gestational ethanol exposure, female a/a C57BL/6J mice were supplied with 10% (v/v) ethanol in their drink bottles for eight days after fertilization by a congenic male carrying the Avy allele (n = 46 litters, 242 total offspring, 109 Avy/a offspring). To evaluate the effects of preconceptional ethanol exposure, female a/a mice were given 10% (v/v) ethanol for four days per week for ten weeks prior to fertilization (n = 22 litters, 131 total offspring, 69 Avy/a offspring). The Avy allele was passed through the male germ line to avoid the bias associated with maternal transmission, where epigenetic marks can be incompletely cleared between generations [9]. Control mice were given water instead of ethanol (n = 37 litters, 189 total offspring, 91 Avy/a offspring). Maternal ethanol exposure during gestation did not significantly alter Mendelian inheritance of the Avy allele (data not shown) or litter size (control 5.1±0.4, ethanol exposed 5.2±0.3, mean±SEM, Student's t-test, p = 0.9). The establishment of epigenetic marks at Avy occurs during early embryogenesis and is a probabilistic event. The resulting variable expression of Avy among genetically identical mice produces individuals with a predictable range of coat colors. We found that, in the absence of any treatment, 21% of the offspring of Avy/a sires were yellow, 66% were mottled and 13% were pseudoagouti (Figure 1). Gestational ethanol exposure resulted in a higher proportion of pseudoagouti (Pearson's chi-square test, p 95% yellow), yellow/mottled (75–95% yellow), mottled (25–74% yellow or 25–74% agouti), pseudoagouti/mottled (75–95% agouti) or pseudoagouti (>95% agouti). In the final analysis these categories were combined into three classes: yellow, mottled (comprised of yellow/mottled, mottled and pseudoagouti/mottled) and pseudoagouti. Bisulfite Sequencing For bisulfite sequencing of the Avy allele, 200–400 ng of tail genomic DNA was embedded in agarose and then treated with sodium bisulfite as described previously [10]. The bisulfite-treated DNA was resuspended in 30 µl of water and 5 µl was used in the primary PCR followed by a semi-nested PCR with 2–5 µl of template (primers were forward 5′ gaaaagagagtaagaagtaagagagagag 3′, reverse 5′ aaaatttaacacataccttctaaaaccccc 3′ and semi-nested reverse 5′ actccctcttctaaaactacaaaaactc 3′) [10]. One bisulfite conversion and PCR was performed for each pseudoagouti sample, while 3–5 independent conversions and 3 PCRs/conversion were performed for each yellow sample. Global IAP LTR sequences were amplified from bisulfite-converted tail and forebrain DNA using universal IAP primers; forward 5′ ttgatagttgtgttttaagtggtaaataaa 3′ and reverse 5′ aaaacaccacaaaccaaaatcttctac 3′ [67]. An agarose-only (no template) control was always included and the experiment was only continued if the agarose control was negative at the end of the semi-nested PCR. PCR fragments were gel-isolated and subcloned into the pGEM-T vector (Promega, Madison, Wisconsin, United States). Individually sequenced clones were analyzed with BiQ Analyzer [68]. To avoid bias, clones from the same PCR were only accepted if they differed by either CpG or non-CpG methylation. Any clones with lower than 90% conversion rate were also excluded from the dataset. Gene Expression Arrays To detect possible changes in gene expression in gestational ethanol exposure mice compared to the controls, we used the MouseWG-6 v2.0 Expression BeadChips (Illumina). We extracted total liver RNA from 28 days old males from control and gestational ethanol groups, using a Qiagen RNeasy Plus-kit (Qiagen). We used a Bioanalyzer (Agilent RNA 6000 Nano, Agilent) to confirm the quality of RNA and accepted only samples with RNA Integrity Numbers (RINs) above 9. We amplified RNA using an Illumina TotalPrep RNA Amplification Kit and performed a Whole-Genome Gene Expression Direct Hybridization Assay (Illumina). The gene expression data from scanned microarray images generated by the Illumina BeadArrayTM Reader was analysed by the GenomeStudio Gene Expression Module (Illumina) by using probe information. Four control samples from two litters and three gestational ethanol exposure samples from three litters were analysed. Analysis of Skull Morphology Seventeen a/a mice (ten controls and seven ethanol exposed mice) aged between 28 and 30 days were subjected to micro-computed tomography using a SkyScan 1076 microtomograph at the Small Animal Tomographic Analysis Facility located at the University of Washington. The sex and treatment breakdown of the microCT samples is female ethanol (n = 4), female control (n = 5), male ethanol (n = 3) and male control (n = 5). Specimens were scanned at 18 micron resolution (65 kV, 150 mA, 1.0 mm Al filter) and reconstructed as series of 8-bit grayscale images. Three-dimensional models of the skulls were generated using the thresholding algorithm in Analyze 3D (Mayo Clinic, version 9.0). A grayscale value of 55 was determined to be the optimum threshold value to remove the soft tissue structures and scan noise while keeping the skull morphology intact, and was used for all specimens. Using the point measurement tool of Analyze, 35 landmarks were collected from each specimen (Text S1 and Figure S3). Specimens were digitized by the same observer (MM) to reduce inter-observer error. Visualizations showed that landmark 31 could not be accurately determined in every specimen because of the occasional fusion of the presphenoid and basisphenoid bones. Because geometric morphometrics requires homologous landmarks collected from every specimen, this landmark was omitted in subsequent analyses. Landmark data were fed into various morphometric packages. Using the R statistical package [69], linear measurements of certain common cranial dimensions were calculated from the landmark coordinates and normalized to their respective skull centroid sizes. Generalized Procrustes Analysis (GPA) was also conducted in R by using the SHAPES module. Goodall's F test was used to test for statistical significance of mean shape differences among groups. The Canonical Variates Analysis (CVA) was conducted in the MorphoJ package [70]. The loadings of the canonical variates 1 and 2 were used to visualize the cranial shape changes depicted by each axis. The WinEDMA package [71] was used to conduct Euclidean Distance Matrix Analysis. We used the FORM procedure of WinEDMA to find the landmark pairs that significantly differed between two mean forms (i.e., ethanols and controls) as measured by the form difference matrix. Following Lele and Richtsmeier [45], the 90% confidence intervals for the pairwise ratios were calculated by bootstrapping the form difference matrix 1000 times. Supporting Information Figure S1 Avy methylation in control offspring and offspring exposed to ethanol in utero in mottled mice. Only mice with 50% yellow/50% pseudoagouti coats were assayed. (1.01 MB TIF) Click here for additional data file. Figure S2 Global IAP methylation in control offspring and in offspring exposed to ethanol in utero. Methylation was analyzed by sequencing individual clones of PCR-amplified, bisulfite-converted forebrain and tail genomic DNA. (0.98 MB TIF) Click here for additional data file. Figure S3 Landmark positions. (9.62 MB TIF) Click here for additional data file. Table S1 Summary of significantly up- and down-regulated genes in liver following ethanol exposure in utero. The Diff Score is a transformation of the p-value that provides directionality to the p-value based on the difference between the average signal in the control group versus the ethanol exposed group. For p-values of 0.05, 0.01 and 0.001 the Diff Scores are ±13, ±20, and ±30, respectively. (0.03 MB XLS) Click here for additional data file. Text S1 Landmark descriptions. (0.03 MB DOC) Click here for additional data file.
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              Approaching the prevalence of the full spectrum of fetal alcohol spectrum disorders in a South African population-based study.

              The prevalence and characteristics of fetal alcohol spectrum disorders (FASD) were determined in this fourth study of first-grade children in a South African community. Active case ascertainment methods were employed among 747 first-grade pupils. The detailed characteristics of children within the continuum of FASD are contrasted with randomly selected, normal controls on (i) physical growth and dysmorphology; (ii) cognitive/behavioral characteristics; and (iii) maternal risk factors. The rates of specific diagnoses within the FASD spectrum continue to be among the highest reported in any community in the world. The prevalence (per 1,000) is as follows: fetal alcohol syndrome (FAS)-59.3 to 91.0; partial fetal alcohol syndrome (PFAS)-45.3 to 69.6; and alcohol-related neurodevelopmental disorder (ARND)-30.5 to 46.8. The overall rate of FASD is therefore 135.1 to 207.5 per 1,000 (or 13.6 to 20.9%). Clinical profiles of the physical and cognitive/behavioral traits of children with a specific FASD diagnosis and controls are provided for understanding the full spectrum of FASD in a community. The spectral effect is evident in the characteristics of the diagnostic groups and summarized by the total (mean) dysmorphology scores of the children: FAS = 18.9; PFAS = 14.3; ARND = 12.2; and normal controls, alcohol exposed = 8.2 and unexposed = 7.1. Documented drinking during pregnancy is significantly correlated with verbal (r = -0.253) and nonverbal ability (r = -0.265), negative behaviors (r = 0.203), and total dysmorphology score (r = 0.431). Other measures of drinking during pregnancy are significantly associated with FASD, including binge drinking as low as 3 drinks per episode on 2 days of the week. High rates of specific diagnoses within FASD were well documented in this new cohort of children. FASD persists in this community. The data reflect an increased ability to provide accurate and discriminating diagnoses throughout the continuum of FASD. Copyright © 2012 by the Research Society on Alcoholism.
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                Author and article information

                Contributors
                +31 43 38 82913 , friederike.ehrhart@maastrichtuniversity.nl
                Journal
                Mol Psychiatry
                Mol. Psychiatry
                Molecular Psychiatry
                Nature Publishing Group UK (London )
                1359-4184
                1476-5578
                11 June 2018
                11 June 2018
                2019
                : 24
                : 1
                : 10-17
                Affiliations
                [1 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Governor Kremers Centre, , Maastricht University Medical Centre+, ; Maastricht, The Netherlands
                [2 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Bioinformatics, NUTRIM School of Nutrition and Translational Research in Metabolism, , Maastricht University, ; Maastricht, The Netherlands
                [3 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Work and Social Psychology, , Maastricht University, ; Maastricht, The Netherlands
                [4 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Department of Internal Medicine, Division of gastroenterology and hepatology, , Maastricht University Medical Centre+, ; Maastricht, The Netherlands
                [5 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Institute for Public Health Genomics, , Maastricht University, ; Maastricht, The Netherlands
                [6 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Department of Genetics, , Maastricht University Medical Centre+, ; Maastricht, The Netherlands
                Author information
                http://orcid.org/0000-0002-7770-620X
                Article
                95
                10.1038/s41380-018-0095-4
                6325721
                29892052
                a8e3d13c-7824-4b49-9a09-9a30ac97a546
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 September 2017
                : 17 November 2017
                : 23 April 2018
                Funding
                Funded by: Dutch Governement of the Netherlands (ZonMw)
                Categories
                Review Article
                Custom metadata
                © Springer Nature Limited 2019

                Molecular medicine
                Molecular medicine

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