Maternal-fetal stress and DNA methylation signatures in neonatal saliva: an epigenome-wide association study

Ritika Sharma, Martin G Frasch, Camila Zelgert, Peter Zimmermann, Bibiana Fabre, Rory Wilson, Melanie Waldenberger, James W MacDonald, Theo K Bammler, Silvia M Lobmaier, Marta C Antonelli, Ritika Sharma, Martin G Frasch, Camila Zelgert, Peter Zimmermann, Bibiana Fabre, Rory Wilson, Melanie Waldenberger, James W MacDonald, Theo K Bammler, Silvia M Lobmaier, Marta C Antonelli

Abstract

Background: Maternal stress before, during and after pregnancy has profound effects on the development and lifelong function of the infant's neurocognitive development. We hypothesized that the programming of the central nervous system (CNS), hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS) induced by prenatal stress (PS) is reflected in electrophysiological and epigenetic biomarkers. In this study, we aimed to find noninvasive epigenetic biomarkers of PS in the newborn salivary DNA.

Results: A total of 728 pregnant women were screened for stress exposure using Cohen Perceived Stress Scale (PSS), 164 women were enrolled, and 114 dyads were analyzed. Prenatal Distress Questionnaire (PDQ) was also administered to assess specific pregnancy worries. Transabdominal fetal electrocardiograms (taECG) were recorded to derive coupling between maternal and fetal heart rates resulting in a 'Fetal Stress Index' (FSI). Upon delivery, we collected maternal hair strands for cortisol measurements and newborn's saliva for epigenetic analyses. DNA was extracted from saliva samples, and DNA methylation was measured using EPIC BeadChip array (850 k CpG sites). Linear regression was used to identify associations between PSS/PDQ/FSI/Cortisol and DNA methylation. We found epigenome-wide significant associations for 5 CpG with PDQ and cortisol at FDR < 5%. Three CpGs were annotated to genes (Illumina Gene annotation file): YAP1, TOMM20 and CSMD1, and two CpGs were located approximately lay at 50 kb from SSBP4 and SCAMP1. In addition, two differentiated methylation regions (DMR) related to maternal stress measures PDQ and cortisol were found: DAXX and ARL4D.

Conclusions: Genes annotated to these CpGs were found to be involved in secretion and transportation, nuclear signaling, Hippo signaling pathways, apoptosis, intracellular trafficking and neuronal signaling. Moreover, some CpGs are annotated to genes related to autism, post-traumatic stress disorder (PTSD) and schizophrenia. However, our results should be viewed as hypothesis generating until replicated in a larger sample. Early assessment of such noninvasive PS biomarkers will allow timelier detection of babies at risk and a more effective allocation of resources for early intervention programs to improve child development. A biomarker-guided early intervention strategy is the first step in the prevention of future health problems, reducing their personal and societal impact.

Trial registration: ClinicalTrials.gov NCT03389178.

Keywords: Biomarkers; Cortisol; DNA methylation; EWAS; Epigenetics; Newborn saliva; Perceived stress; Pregnancy; Prenatal stress.

Conflict of interest statement

MGF holds a pending US patent on fetal ECG technology (20210330236); US patent on fetal EEG (9215999), equity in Delfina and Vitalink AI. The remaining 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.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Manhattan plot and Q–Q plot of salivary DNA methylation associated with PSS. Manhattan plots of salivary DNA methylation associated with PSS. The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 2

Manhattan plot and Q–Q plot…

Fig. 2

Manhattan plot and Q–Q plot of the association between PDQ and salivary DNA…

Fig. 2
Manhattan plot and Q–Q plot of the association between PDQ and salivary DNA methylation. Manhattan plots of salivary DNA methylation associated with PDQ. The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 3

Manhattan plot and Q–Q plot…

Fig. 3

Manhattan plot and Q–Q plot of the association between cortisol and salivary DNA…

Fig. 3
Manhattan plot and Q–Q plot of the association between cortisol and salivary DNA methylation. The lambda value for the Q–Q plot is 1.08. Manhattan plots of salivary DNA methylation associated with cortisol. The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 4

Manhattan plot and Q–Q plot…

Fig. 4

Manhattan plot and Q–Q plot of the association between FSI and salivary DNA…

Fig. 4
Manhattan plot and Q–Q plot of the association between FSI and salivary DNA methylation. Manhattan plots of salivary DNA methylation associated with FSI (Fetal Stress Index). The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 5

Network plot of significant hits…

Fig. 5

Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for…

Fig. 5
Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for significant hits from the association for PDQ and cortisol. Protein–protein interaction (PPI) enrichment p value: 3.47e-06. PPI legend by string-db.org. The permanent link is: https://version-11-5.string-db.org/cgi/network?taskId=bvfqNrZYaHe6&sessionId=bjK7XvqNxMXe

Fig. 6

Direct acyclic graph (DAG) displaying…

Fig. 6

Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress…

Fig. 6
Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress and infant salivatory DNA methylation

Fig. 7

Overall methodological study design. Illumina…

Fig. 7

Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray…

Fig. 7
Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray platform. The raw data were processed and quality-controlled using array-specific algorithms in R studio. Data visualization and statistical analysis identified relevant associations and derived a list of differentially methylated positions and regions
All figures (7)
Similar articles
Cited by
References
    1. Baier CJ, Katunar MR, Adrover E, Pallarés ME, Antonelli MC. Gestational restraint stress and the developing dopaminergic system: an overview. Neurotox Res. 2012;22(1):16–32. doi: 10.1007/s12640-011-9305-4. - DOI - PubMed
    1. Bale TL, Baram TZ, Brown AS, Goldstein JM, Insel TR, McCarthy MM, et al. Early life programming and neurodevelopmental disorders. Biol Psychiat. 2010;68(4):314–319. doi: 10.1016/j.biopsych.2010.05.028. - DOI - PMC - PubMed
    1. Boersma GJ, Tamashiro KL. Individual differences in the effects of prenatal stress exposure in rodents. Neurobiol Stress. 2015;1:100–108. doi: 10.1016/j.ynstr.2014.10.006. - DOI - PMC - PubMed
    1. Brannigan R, Cannon M, Tanskanen A, Huttunen M, Leacy F, Clarke M. The association between subjective maternal stress during pregnancy and offspring clinically diagnosed psychiatric disorders. Acta Psychiatr Scand. 2019;139(4):304–310. doi: 10.1111/acps.12996. - DOI - PubMed
    1. Charil A, Laplante DP, Vaillancourt C, King S. Prenatal stress and brain development. Brain Res Rev. 2010;65(1):56–79. doi: 10.1016/j.brainresrev.2010.06.002. - DOI - PubMed
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Fig. 2
Fig. 2
Manhattan plot and Q–Q plot of the association between PDQ and salivary DNA methylation. Manhattan plots of salivary DNA methylation associated with PDQ. The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 3

Manhattan plot and Q–Q plot…

Fig. 3

Manhattan plot and Q–Q plot of the association between cortisol and salivary DNA…

Fig. 3
Manhattan plot and Q–Q plot of the association between cortisol and salivary DNA methylation. The lambda value for the Q–Q plot is 1.08. Manhattan plots of salivary DNA methylation associated with cortisol. The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 4

Manhattan plot and Q–Q plot…

Fig. 4

Manhattan plot and Q–Q plot of the association between FSI and salivary DNA…

Fig. 4
Manhattan plot and Q–Q plot of the association between FSI and salivary DNA methylation. Manhattan plots of salivary DNA methylation associated with FSI (Fetal Stress Index). The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 5

Network plot of significant hits…

Fig. 5

Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for…

Fig. 5
Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for significant hits from the association for PDQ and cortisol. Protein–protein interaction (PPI) enrichment p value: 3.47e-06. PPI legend by string-db.org. The permanent link is: https://version-11-5.string-db.org/cgi/network?taskId=bvfqNrZYaHe6&sessionId=bjK7XvqNxMXe

Fig. 6

Direct acyclic graph (DAG) displaying…

Fig. 6

Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress…

Fig. 6
Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress and infant salivatory DNA methylation

Fig. 7

Overall methodological study design. Illumina…

Fig. 7

Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray…

Fig. 7
Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray platform. The raw data were processed and quality-controlled using array-specific algorithms in R studio. Data visualization and statistical analysis identified relevant associations and derived a list of differentially methylated positions and regions
All figures (7)
Similar articles
Cited by
References
    1. Baier CJ, Katunar MR, Adrover E, Pallarés ME, Antonelli MC. Gestational restraint stress and the developing dopaminergic system: an overview. Neurotox Res. 2012;22(1):16–32. doi: 10.1007/s12640-011-9305-4. - DOI - PubMed
    1. Bale TL, Baram TZ, Brown AS, Goldstein JM, Insel TR, McCarthy MM, et al. Early life programming and neurodevelopmental disorders. Biol Psychiat. 2010;68(4):314–319. doi: 10.1016/j.biopsych.2010.05.028. - DOI - PMC - PubMed
    1. Boersma GJ, Tamashiro KL. Individual differences in the effects of prenatal stress exposure in rodents. Neurobiol Stress. 2015;1:100–108. doi: 10.1016/j.ynstr.2014.10.006. - DOI - PMC - PubMed
    1. Brannigan R, Cannon M, Tanskanen A, Huttunen M, Leacy F, Clarke M. The association between subjective maternal stress during pregnancy and offspring clinically diagnosed psychiatric disorders. Acta Psychiatr Scand. 2019;139(4):304–310. doi: 10.1111/acps.12996. - DOI - PubMed
    1. Charil A, Laplante DP, Vaillancourt C, King S. Prenatal stress and brain development. Brain Res Rev. 2010;65(1):56–79. doi: 10.1016/j.brainresrev.2010.06.002. - DOI - PubMed
Show all 105 references
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MeSH terms
Associated data
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Format: AMA APA MLA NLM

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The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

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Fig. 3
Fig. 3
Manhattan plot and Q–Q plot of the association between cortisol and salivary DNA methylation. The lambda value for the Q–Q plot is 1.08. Manhattan plots of salivary DNA methylation associated with cortisol. The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 4

Manhattan plot and Q–Q plot…

Fig. 4

Manhattan plot and Q–Q plot of the association between FSI and salivary DNA…

Fig. 4
Manhattan plot and Q–Q plot of the association between FSI and salivary DNA methylation. Manhattan plots of salivary DNA methylation associated with FSI (Fetal Stress Index). The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 5

Network plot of significant hits…

Fig. 5

Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for…

Fig. 5
Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for significant hits from the association for PDQ and cortisol. Protein–protein interaction (PPI) enrichment p value: 3.47e-06. PPI legend by string-db.org. The permanent link is: https://version-11-5.string-db.org/cgi/network?taskId=bvfqNrZYaHe6&sessionId=bjK7XvqNxMXe

Fig. 6

Direct acyclic graph (DAG) displaying…

Fig. 6

Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress…

Fig. 6
Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress and infant salivatory DNA methylation

Fig. 7

Overall methodological study design. Illumina…

Fig. 7

Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray…

Fig. 7
Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray platform. The raw data were processed and quality-controlled using array-specific algorithms in R studio. Data visualization and statistical analysis identified relevant associations and derived a list of differentially methylated positions and regions
All figures (7)
Similar articles
Cited by
References
    1. Baier CJ, Katunar MR, Adrover E, Pallarés ME, Antonelli MC. Gestational restraint stress and the developing dopaminergic system: an overview. Neurotox Res. 2012;22(1):16–32. doi: 10.1007/s12640-011-9305-4. - DOI - PubMed
    1. Bale TL, Baram TZ, Brown AS, Goldstein JM, Insel TR, McCarthy MM, et al. Early life programming and neurodevelopmental disorders. Biol Psychiat. 2010;68(4):314–319. doi: 10.1016/j.biopsych.2010.05.028. - DOI - PMC - PubMed
    1. Boersma GJ, Tamashiro KL. Individual differences in the effects of prenatal stress exposure in rodents. Neurobiol Stress. 2015;1:100–108. doi: 10.1016/j.ynstr.2014.10.006. - DOI - PMC - PubMed
    1. Brannigan R, Cannon M, Tanskanen A, Huttunen M, Leacy F, Clarke M. The association between subjective maternal stress during pregnancy and offspring clinically diagnosed psychiatric disorders. Acta Psychiatr Scand. 2019;139(4):304–310. doi: 10.1111/acps.12996. - DOI - PubMed
    1. Charil A, Laplante DP, Vaillancourt C, King S. Prenatal stress and brain development. Brain Res Rev. 2010;65(1):56–79. doi: 10.1016/j.brainresrev.2010.06.002. - DOI - PubMed
Show all 105 references
Publication types
MeSH terms
Associated data
[x]
Cite
Copy Download .nbib
Format: AMA APA MLA NLM
Fig. 4
Fig. 4
Manhattan plot and Q–Q plot of the association between FSI and salivary DNA methylation. Manhattan plots of salivary DNA methylation associated with FSI (Fetal Stress Index). The x-axis represents the genomic loci of the individual CpGs and the y-axis represents the –log10 (p value). Black line: Bonferroni threshold (p = 6.183879e-08) and the dotted line: Multiple testing correction threshold (FDR 

Fig. 5

Network plot of significant hits…

Fig. 5

Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for…

Fig. 5
Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for significant hits from the association for PDQ and cortisol. Protein–protein interaction (PPI) enrichment p value: 3.47e-06. PPI legend by string-db.org. The permanent link is: https://version-11-5.string-db.org/cgi/network?taskId=bvfqNrZYaHe6&sessionId=bjK7XvqNxMXe

Fig. 6

Direct acyclic graph (DAG) displaying…

Fig. 6

Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress…

Fig. 6
Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress and infant salivatory DNA methylation

Fig. 7

Overall methodological study design. Illumina…

Fig. 7

Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray…

Fig. 7
Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray platform. The raw data were processed and quality-controlled using array-specific algorithms in R studio. Data visualization and statistical analysis identified relevant associations and derived a list of differentially methylated positions and regions
All figures (7)
Fig. 5
Fig. 5
Network plot of significant hits from the EWAS analysis. STRING-Db network analysis for significant hits from the association for PDQ and cortisol. Protein–protein interaction (PPI) enrichment p value: 3.47e-06. PPI legend by string-db.org. The permanent link is: https://version-11-5.string-db.org/cgi/network?taskId=bvfqNrZYaHe6&sessionId=bjK7XvqNxMXe
Fig. 6
Fig. 6
Direct acyclic graph (DAG) displaying the hypothesized associations between maternal and fetal stress and infant salivatory DNA methylation
Fig. 7
Fig. 7
Overall methodological study design. Illumina measured salivary DNA methylation using the EPIC microarray platform. The raw data were processed and quality-controlled using array-specific algorithms in R studio. Data visualization and statistical analysis identified relevant associations and derived a list of differentially methylated positions and regions

References

    1. Baier CJ, Katunar MR, Adrover E, Pallarés ME, Antonelli MC. Gestational restraint stress and the developing dopaminergic system: an overview. Neurotox Res. 2012;22(1):16–32. doi: 10.1007/s12640-011-9305-4.
    1. Bale TL, Baram TZ, Brown AS, Goldstein JM, Insel TR, McCarthy MM, et al. Early life programming and neurodevelopmental disorders. Biol Psychiat. 2010;68(4):314–319. doi: 10.1016/j.biopsych.2010.05.028.
    1. Boersma GJ, Tamashiro KL. Individual differences in the effects of prenatal stress exposure in rodents. Neurobiol Stress. 2015;1:100–108. doi: 10.1016/j.ynstr.2014.10.006.
    1. Brannigan R, Cannon M, Tanskanen A, Huttunen M, Leacy F, Clarke M. The association between subjective maternal stress during pregnancy and offspring clinically diagnosed psychiatric disorders. Acta Psychiatr Scand. 2019;139(4):304–310. doi: 10.1111/acps.12996.
    1. Charil A, Laplante DP, Vaillancourt C, King S. Prenatal stress and brain development. Brain Res Rev. 2010;65(1):56–79. doi: 10.1016/j.brainresrev.2010.06.002.
    1. Dong E, Pandey SC. Prenatal stress induced chromatin remodeling and risk of psychopathology in adulthood. Int Rev Neurobiol. 2021;156:185. doi: 10.1016/bs.irn.2020.08.004.
    1. Frasch MG, Lobmaier SM, Stampalija T, Desplats P, Pallarés ME, Pastor V, et al. Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis. Neurosci Biobehav Rev. 2020;117:165–183. doi: 10.1016/j.neubiorev.2018.05.026.
    1. Rakers F, Rupprecht S, Dreiling M, Bergmeier C, Witte OW, Schwab M. Transfer of maternal psychosocial stress to the fetus. Neurosci Biobehav Rev. 2020;117:185–197. doi: 10.1016/j.neubiorev.2017.02.019.
    1. Monk C, Lugo-Candelas C, Trumpff C. Prenatal developmental origins of future psychopathology: mechanisms and pathways. Annu Rev Clin Psychol. 2019;15:317–344. doi: 10.1146/annurev-clinpsy-050718-095539.
    1. Van den Bergh BR, van den Heuvel MI, Lahti M, Braeken M, de Rooij SR, Entringer S, et al. Prenatal developmental origins of behavior and mental health: The influence of maternal stress in pregnancy. Neurosci Biobehav Rev. 2020;117:26–64. doi: 10.1016/j.neubiorev.2017.07.003.
    1. Beversdorf DQ, Manning S, Hillier A, Anderson S, Nordgren R, Walters S, et al. Timing of prenatal stressors and autism. J Autism Dev Disord. 2005;35(4):471–478. doi: 10.1007/s10803-005-5037-8.
    1. Entringer S, Kumsta R, Hellhammer DH, Wadhwa PD, Wüst S. Prenatal exposure to maternal psychosocial stress and HPA axis regulation in young adults. Horm Behav. 2009;55(2):292–298. doi: 10.1016/j.yhbeh.2008.11.006.
    1. Müller JJ, Antonow-Schlorke I, Kroegel N, Rupprecht S, Rakers F, Witte OW, et al. Cardiovascular effects of prenatal stress—are there implications for cerebrovascular, cognitive and mental health outcome? Neurosci Biobehav Rev. 2020;117:78–97. doi: 10.1016/j.neubiorev.2018.05.024.
    1. Moisiadis VG, Matthews SG. Glucocorticoids and fetal programming part 1: outcomes. Nat Rev Endocrinol. 2014;10(7):391–402. doi: 10.1038/nrendo.2014.73.
    1. Monk C, Myers MM, Sloan RP, Ellman LM, Fifer WP. Effects of women’s stress-elicited physiological activity and chronic anxiety on fetal heart rate. J Dev Behav Pediatr. 2003;24(1):32–38. doi: 10.1097/00004703-200302000-00008.
    1. Gao Y, Huang Y, Li X. Interaction between prenatal maternal stress and autonomic arousal in predicting conduct problems and psychopathic traits in children. J Psychopathol Behav Assess. 2017;39(1):1–14. doi: 10.1007/s10862-016-9556-8.
    1. Kinsella MT, Monk C. Impact of maternal stress, depression & anxiety on fetal neurobehavioral development. Clin Obstet Gynecol. 2009;52(3):425. doi: 10.1097/GRF.0b013e3181b52df1.
    1. Cao-Lei L, De Rooij S, King S, Matthews S, Metz G, Roseboom T, et al. Prenatal stress and epigenetics. Neurosci Biobehav Rev. 2020;117:198–210. doi: 10.1016/j.neubiorev.2017.05.016.
    1. Kundakovic M, Jaric I. The epigenetic link between prenatal adverse environments and neurodevelopmental disorders. Genes. 2017;8(3):104. doi: 10.3390/genes8030104.
    1. Bredy TW, Sun YE, Kobor MS. How the epigenome contributes to the development of psychiatric disorders. Dev Psychobiol. 2010;52(4):331–342. doi: 10.1002/dev.20424.
    1. Weaver IC, Cervoni N, Champagne FA, D'Alessio AC, Sharma S, Seckl JR, et al. Epigenetic programming by maternal behavior. Nat Neurosci. 2004;7(8):847–854. doi: 10.1038/nn1276.
    1. Wakefield C, Yao L, Self S, Frasch MG. Wearable technology for health monitoring during pregnancy: an observational cross-sectional survey study. medRxiv. 2022:2022.01.26.22269158.
    1. Antonelli MC, Frasch MG, Rumi M, Sharma R, Zimmermann P, Molinet MS, et al. Early biomarkers and intervention programs for the infant exposed to prenatal stress. Curr Neuropharmacol. 2020;20(1):94–106. doi: 10.2174/1570159X19666210125150955.
    1. Lobmaier SM, Müller A, Zelgert C, Shen C, Su PC, Schmidt G, et al. Fetal heart rate variability responsiveness to maternal stress, non-invasively detected from maternal transabdominal ECG. Arch Gynecol Obstet. 2020;301(2):405–414. doi: 10.1007/s00404-019-05390-8.
    1. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–D613. doi: 10.1093/nar/gky1131.
    1. Abrahams BS, Arking DE, Campbell DB, Mefford HC, Morrow EM, Weiss LA, et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs) Mol Autism. 2013;4(1):1–3. doi: 10.1186/2040-2392-4-36.
    1. MG Frasch GS, MC Antonelli. Autism spectrum disorder: a neuro-immunometabolic hypothesis of the developmental origins” Journal of Developmental Origins of Health and Disease 2019.
    1. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–541. doi: 10.1038/nrg3000.
    1. Monk C, Feng T, Lee S, Krupska I, Champagne FA, Tycko B. Distress during pregnancy: epigenetic regulation of placenta glucocorticoid-related genes and fetal neurobehavior. Am J Psychiatry. 2016;173(7):705–713. doi: 10.1176/appi.ajp.2015.15091171.
    1. Oberlander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008;3(2):97–106. doi: 10.4161/epi.3.2.6034.
    1. Cao-Lei L, Massart R, Suderman MJ, Machnes Z, Elgbeili G, Laplante DP, et al. DNA methylation signatures triggered by prenatal maternal stress exposure to a natural disaster: Project Ice Storm. PLoS ONE. 2014;9(9):e107653. doi: 10.1371/journal.pone.0107653.
    1. Mehta D, Klengel T, Conneely KN, Smith AK, Altmann A, Pace TW, et al. Childhood maltreatment is associated with distinct genomic and epigenetic profiles in posttraumatic stress disorder. Proc Natl Acad Sci. 2013;110(20):8302–8307. doi: 10.1073/pnas.1217750110.
    1. Non AL, Binder AM, Kubzansky LD, Michels KB. Genome-wide DNA methylation in neonates exposed to maternal depression, anxiety, or SSRI medication during pregnancy. Epigenetics. 2014;9(7):964–972. doi: 10.4161/epi.28853.
    1. Tobi EW, Slieker RC, Stein AD, Suchiman HED, Slagboom PE, Van Zwet EW, et al. Early gestation as the critical time-window for changes in the prenatal environment to affect the adult human blood methylome. Int J Epidemiol. 2015;44(4):1211–1223. doi: 10.1093/ije/dyv043.
    1. Sammallahti S, Hidalgo APC, Tuominen S, Malmberg A, Mulder RH, Brunst KJ, et al. Maternal anxiety during pregnancy and newborn epigenome-wide DNA methylation. Mol Psychiatry. 2021;2021:1–14.
    1. Sosnowski DW, Booth C, York TP, Amstadter AB, Kliewer W. Maternal prenatal stress and infant DNA methylation: a systematic review. Dev Psychobiol. 2018;60(2):127–139. doi: 10.1002/dev.21604.
    1. Ma S, Meng Z, Chen R, Guan K-L. The Hippo pathway: biology and pathophysiology. Annu Rev Biochem. 2019;88:577–604. doi: 10.1146/annurev-biochem-013118-111829.
    1. Kandilya D, Shyamasundar S, Singh DK, Banik A, Hande MP, Stünkel W, et al. High glucose alters the DNA methylation pattern of neurodevelopment associated genes in human neural progenitor cells in vitro. Sci Rep. 2020;10(1):1–14. doi: 10.1038/s41598-020-72485-7.
    1. Huang Z, Wang Y, Hu G, Zhou J, Mei L, Xiong W-C. YAP is a critical inducer of SOCS3, preventing reactive astrogliosis. Cereb Cortex. 2016;26(5):2299–2310. doi: 10.1093/cercor/bhv292.
    1. Passaro F, De Martino I, Zambelli F, Di Benedetto G, Barbato M, D’Erchia AM, et al. YAP contributes to DNA methylation remodeling upon mouse embryonic stem cell differentiation. J Biol Chem. 2021;296:100138. doi: 10.1074/jbc.RA120.015896.
    1. Vohra M, Sharma AR, Rai PS. SNPs in sites for DNA methylation, transcription factor binding, and miRNA targets leading to allele-specific gene expression and contributing to complex disease risk: a systematic review. Public Health Genom. 2020;23:155–170. doi: 10.1159/000510253.
    1. Hernández JM, Giner P, Hernández-Yago J. Gene structure of the human mitochondrial outer membrane receptor Tom20 and evolutionary study of its family of processed pseudogenes. Gene. 1999;239(2):283–291. doi: 10.1016/S0378-1119(99)00409-6.
    1. Swie Goping I, Millar DG, Shore GC. Identification of the human mitochondrial protein import receptor, huMas20p Complementation of Δmas20 in yeast. FEBS Lett. 1995;373(1):45–50. doi: 10.1016/0014-5793(95)01010-C.
    1. Abd El Gayed EM, Rizk MS, Ramadan AN, Bayomy NR. mRNA expression of the CUB and sushi multiple domains 1 (CSMD1) and its serum protein level as predictors for psychosis in the familial high-risk children and young adults. ACS Omega. 2021;6(37):24128–38. doi: 10.1021/acsomega.1c03637.
    1. Liu Y, Fu X, Tang Z, Li C, Xu Y, Zhang F, et al. Altered expression of the CSMD1 gene in the peripheral blood of schizophrenia patients. BMC Psychiatry. 2019;19(1):1–5. doi: 10.1186/s12888-018-1996-0.
    1. Kraus DM, Elliott GS, Chute H, Horan T, Pfenninger KH, Sanford SD, et al. CSMD1 is a novel multiple domain complement-regulatory protein highly expressed in the central nervous system and epithelial tissues. J Immunol. 2006;176(7):4419–4430. doi: 10.4049/jimmunol.176.7.4419.
    1. Cukier HN, Dueker ND, Slifer SH, Lee JM, Whitehead PL, Lalanne E, et al. Exome sequencing of extended families with autism reveals genes shared across neurodevelopmental and neuropsychiatric disorders. Mol Autism. 2014;5(1):1–10. doi: 10.1186/2040-2392-5-1.
    1. Guo H, Peng Y, Hu Z, Li Y, Xun G, Ou J, et al. Genome-wide copy number variation analysis in a Chinese autism spectrum disorder cohort. Sci Rep. 2017;7(1):1–9. doi: 10.1038/s41598-016-0028-x.
    1. Melroy-Greif WE, Wilhelmsen KC, Yehuda R, Ehlers CL. Genome-wide association study of post-traumatic stress disorder in two high-risk populations. Twin Res Hum Genet. 2017;20(3):197–207. doi: 10.1017/thg.2017.12.
    1. Nievergelt CM, Maihofer AX, Mustapic M, Yurgil KA, Schork NJ, Miller MW, et al. Genomic predictors of combat stress vulnerability and resilience in US Marines: a genome-wide association study across multiple ancestries implicates PRTFDC1 as a potential PTSD gene. Psychoneuroendocrinology. 2015;51:459–471. doi: 10.1016/j.psyneuen.2014.10.017.
    1. Consortium SPG-WAS. Genome-wide association study identifies five new schizophrenia loci. Nature genetics. 2011;43(10):969.
    1. Håvik B, Le Hellard S, Rietschel M, Lybæk H, Djurovic S, Mattheisen M, et al. The complement control-related genes CSMD1 and CSMD2 associate to schizophrenia. Biol Psychiat. 2011;70(1):35–42. doi: 10.1016/j.biopsych.2011.01.030.
    1. Xu W, Cohen-Woods S, Chen Q, Noor A, Knight J, Hosang G, et al. Genome-wide association study of bipolar disorder in Canadian and UK populations corroborates disease loci including SYNE1 and CSMD1. BMC Med Genet. 2014;15(1):1–13.
    1. Kinney DK, Munir KM, Crowley DJ, Miller AM. Prenatal stress and risk for autism. Neurosci Biobehav Rev. 2008;32(8):1519–1532. doi: 10.1016/j.neubiorev.2008.06.004.
    1. Angel P, Karin M. The role of Jun, Fos and the AP-1 complex in cell-proliferation and transformation. Biochim et Biophys Acta (BBA) Rev Cancer. 1991;1072(2–3):129–57. doi: 10.1016/0304-419X(91)90011-9.
    1. Ameyar M, Wisniewska M, Weitzman J. A role for AP-1 in apoptosis: the case for and against. Biochimie. 2003;85(8):747–752. doi: 10.1016/j.biochi.2003.09.006.
    1. Seckl JR, Holmes MC. Mechanisms of disease: glucocorticoids, their placental metabolism and fetal'programming'of adult pathophysiology. Nat Clin Pract Endocrinol Metab. 2007;3(6):479–488. doi: 10.1038/ncpendmet0515.
    1. Aye IL, Keelan JA. Placental ABC transporters, cellular toxicity and stress in pregnancy. Chem Biol Interact. 2013;203(2):456–466. doi: 10.1016/j.cbi.2013.03.007.
    1. Jensen Peña C, Monk C, Champagne FA. Epigenetic effects of prenatal stress on 11β-hydroxysteroid dehydrogenase-2 in the placenta and fetal brain. PLoS ONE. 2012;7(6):e39791. doi: 10.1371/journal.pone.0039791.
    1. O’Donnell KJ, Jensen AB, Freeman L, Khalife N, O’Connor TG, Glover V. Maternal prenatal anxiety and downregulation of placental 11β-HSD2. Psychoneuroendocrinology. 2012;37(6):818–826. doi: 10.1016/j.psyneuen.2011.09.014.
    1. Shams M, Kilby M, Somerset D, Howie A, Gupta A, Wood P, et al. 11Beta-hydroxysteroid dehydrogenase type 2 in human pregnancy and reduced expression in intrauterine growth restriction. Human Reprod. 1998;13(4):799–804. doi: 10.1093/humrep/13.4.799.
    1. Aushev VN, Li Q, Deyssenroth M, Zhang W, Finik J, Hurd YL, et al. Placental gene network modules are associated with maternal stress during pregnancy and infant temperament. FASEB J. 2021;35(10):e21922. doi: 10.1096/fj.202100144RRR.
    1. Yang X, Khosravi-Far R, Chang HY, Baltimore D. Daxx, a novel Fas-binding protein that activates JNK and apoptosis. Cell. 1997;89(7):1067–1076. doi: 10.1016/S0092-8674(00)80294-9.
    1. Fitzgerald T, Gerety S, Jones W, Van Kogelenberg M, King D, McRae J, et al. Large-scale discovery of novel genetic causes of developmental disorders. Nature. 2015;519(7542):223. doi: 10.1038/nature14135.
    1. Tremblay MW, Jiang Y-H. DNA methylation and susceptibility to autism spectrum disorder. Annu Rev Med. 2019;70:151–66. doi: 10.1146/annurev-med-120417-091431.
    1. Hoelper D, Huang H, Jain AY, Patel DJ, Lewis PW. Structural and mechanistic insights into ATRX-dependent and-independent functions of the histone chaperone DAXX. Nat Commun. 2017;8(1):1–13. doi: 10.1038/s41467-017-01206-y.
    1. Torres AR, Westover JB, Rosenspire AJ. HLA immune function genes in autism. Autism Res Treatment. 2012;2012:1–13. doi: 10.1155/2012/959073.
    1. D'Souza-Schorey C, Chavrier P. ARF proteins: roles in membrane traffic and beyond. Nat Rev Mol Cell Biol. 2006;7(5):347–358. doi: 10.1038/nrm1910.
    1. Juszczak GR, Stankiewicz AM. Glucocorticoids, genes and brain function. Prog Neuropsychopharmacol Biol Psychiatry. 2018;82:136–168. doi: 10.1016/j.pnpbp.2017.11.020.
    1. Yamauchi J, Miyamoto Y, Torii T, Mizutani R, Nakamura K, Sanbe A, et al. Valproic acid-inducible Arl4D and cytohesin-2/ARNO, acting through the downstream Arf6, regulate neurite outgrowth in N1E–115 cells. Exp Cell Res. 2009;315(12):2043–2052. doi: 10.1016/j.yexcr.2009.03.012.
    1. Yu J, Ka S-O, Kwon K-B, Lee S-M, Park J-W, Park B-H. Overexpression of the small GTPase Arl4D suppresses adipogenesis. Int J Mol Med. 2011;28(5):793–798.
    1. Li C-C, Chiang T-C, Wu T-S, Pacheco-Rodriguez G, Moss J, Lee F-JS. ARL4D recruits cytohesin-2/ARNO to modulate actin remodeling. Mol Biol Cell. 2007;18(11):4420–37. doi: 10.1091/mbc.e07-02-0149.
    1. Rubin AN, Malik R, Cho KK, Lim KJ, Lindtner S, Schwartz SER, et al. Regulatory elements inserted into AAVs confer preferential activity in cortical interneurons. ENeuro. 2020;7(6):ENEURO.0211-20.2020. doi: 10.1523/ENEURO.0211-20.2020.
    1. Drzymalla E, Gladish N, Koen N, Epstein MP, Kobor MS, Zar HJ, et al. Association between maternal depression during pregnancy and newborn DNA methylation. Transl Psychiatry. 2021;11(1):1–8. doi: 10.1038/s41398-021-01697-w.
    1. Kallak TK, Bränn E, Fransson E, Johansson Å, Lager S, Comasco E, et al. DNA methylation in cord blood in association with prenatal depressive symptoms. Clin Epigenet. 2021;13(1):1–14. doi: 10.1186/s13148-021-01054-0.
    1. Sun Y, Yao X, March ME, Meng X, Li J, Wei Z, et al. Target genes of autism risk loci in brain frontal cortex. Front Genet. 2019;10:707. doi: 10.3389/fgene.2019.00707.
    1. Rijlaarsdam J, Pappa I, Walton E, Bakermans-Kranenburg MJ, Mileva-Seitz VR, Rippe RC, et al. An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: a model approach for replication. Epigenetics. 2016;11(2):140–149. doi: 10.1080/15592294.2016.1145329.
    1. Wikenius E, Myhre AM, Page CM, Moe V, Smith L, Heiervang ER, et al. Prenatal maternal depressive symptoms and infant DNA methylation: a longitudinal epigenome-wide study. Nord J Psychiatry. 2019;73(4–5):257–263. doi: 10.1080/08039488.2019.1613446.
    1. Chovatiya R, Medzhitov R. Stress, inflammation, and defense of homeostasis. Mol Cell. 2014;54(2):281–288. doi: 10.1016/j.molcel.2014.03.030.
    1. Tylee DS, Kawaguchi DM, Glatt SJ. On the outside, looking in: a review and evaluation of the comparability of blood and brain “-omes”. Am J Med Genet Part B: Neuropsychiatr Genet. 2013;162(7):595–603. doi: 10.1002/ajmg.b.32150.
    1. Smith AK, Kilaru V, Klengel T, Mercer KB, Bradley B, Conneely KN, et al. DNA extracted from saliva for methylation studies of psychiatric traits: evidence tissue specificity and relatedness to brain. Am J Med Genet B Neuropsychiatr Genet. 2015;168(1):36–44. doi: 10.1002/ajmg.b.32278.
    1. Zhu P, Sun MS, Hao JH, Chen YJ, Jiang XM, Tao RX, et al. Does prenatal maternal stress impair cognitive development and alter temperament characteristics in toddlers with healthy birth outcomes? Dev Med Child Neurol. 2014;56(3):283–289. doi: 10.1111/dmcn.12378.
    1. Bennett HA, Einarson A, Taddio A, Koren G, Einarson TR. Prevalence of depression during pregnancy: systematic review. Obstet Gynecol. 2004;103(4):698–709. doi: 10.1097/01.AOG.0000116689.75396.5f.
    1. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24:385–96. doi: 10.2307/2136404.
    1. Klein EM, Brähler E, Dreier M, Reinecke L, Müller KW, Schmutzer G, et al. The German version of the Perceived Stress Scale–psychometric characteristics in a representative German community sample. BMC Psychiatry. 2016;16(1):1–10. doi: 10.1186/s12888-016-0875-9.
    1. Alderdice F, Lynn F. Factor structure of the prenatal distress questionnaire. Midwifery. 2011;27(4):553–559. doi: 10.1016/j.midw.2010.05.003.
    1. Caparros-Gonzalez RA, Perra O, Alderdice F, Lynn F, Lobel M, García-García I, et al. Psychometric validation of the Prenatal Distress Questionnaire (PDQ) in pregnant women in Spain. Women Health. 2019;59(8):937–952. doi: 10.1080/03630242.2019.1584143.
    1. Lobmaier SM, Müller A, Zelgert C, Shen C, Su P, Schmidt G, et al. Fetal heart rate variability responsiveness to maternal stress, non-invasively detected from maternal transabdominal ECG. Arch Gynecol Obstet. 2020;301(2):405–414. doi: 10.1007/s00404-019-05390-8.
    1. Zimmermann P, Antonelli MC, Sharma R, Müller A, Zelgert C, Fabre B, et al. Prenatal stress perturbs neonatal iron homeostasis in a sex-specific manner. arXiv preprint . 2021.
    1. Gonzalez D, Jacobsen D, Ibar C, Pavan C, Monti J, Machulsky NF, et al. Hair cortisol measurement by an automated method. Sci Rep. 2019;9(1):1–6. doi: 10.1038/s41598-018-37186-2.
    1. Iglesias S, Jacobsen D, Gonzalez D, Azzara S, Repetto EM, Jamardo J, et al. Hair cortisol: a new tool for evaluating stress in programs of stress management. Life Sci. 2015;141:188–192. doi: 10.1016/j.lfs.2015.10.006.
    1. Li R, Frasch MG, Wu H-T. Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection. Front Physiol. 2017;8:277. doi: 10.3389/fphys.2017.00277.
    1. Theda C, Hwang SH, Czajko A, Loke YJ, Leong P, Craig JM. Quantitation of the cellular content of saliva and buccal swab samples. Sci Rep. 2018;8(1):1–8. doi: 10.1038/s41598-018-25311-0.
    1. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–1369. doi: 10.1093/bioinformatics/btu049.
    1. Triche Jr TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA methylation beadarrays. Nucleic acids research. 2013;41(7):e90-e.
    1. Fortin J-P, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ, et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15(11):1–17. doi: 10.1186/s13059-014-0503-2.
    1. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–883. doi: 10.1093/bioinformatics/bts034.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc: Ser B (Methodol) 1995;57(1):289–300.
    1. Van Dongen J, Ehli EA, Jansen R, Van Beijsterveldt CE, Willemsen G, Hottenga JJ, et al. Genome-wide analysis of DNA methylation in buccal cells: a study of monozygotic twins and mQTLs. Epigenet Chromatin. 2018;11(1):1–14. doi: 10.1186/s13072-017-0171-z.
    1. Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics. 2014;30(10):1431–1439. doi: 10.1093/bioinformatics/btu029.
    1. Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):e161. doi: 10.1371/journal.pgen.0030161.
    1. van Iterson M, van Zwet EW, Heijmans BT. Controlling bias and inflation in epigenome-and transcriptome-wide association studies using the empirical null distribution. Genome Biol. 2017;18(1):1–13. doi: 10.1186/s13059-016-1139-1.
    1. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, et al. De novo identification of differentially methylated regions in the human genome. Epigenet Chromatin. 2015;8(1):1–16. doi: 10.1186/1756-8935-8-6.
    1. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28(22):2986–2988. doi: 10.1093/bioinformatics/bts545.
    1. Mallik S, Odom GJ, Gao Z, Gomez L, Chen X, Wang L. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform. 2019;20(6):2224–2235. doi: 10.1093/bib/bby085.

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