Conserved DNA Methylation Signatures in Early Maternal Separation and in Twins Discordant for CO2 Sensitivity

Francesca Giannese, Alessandra Luchetti, Giulia Barbiera, Valentina Lampis, Claudio Zanettini, Gun Peggy Knudsen, Simona Scaini, Dejan Lazarevic, Davide Cittaro, Francesca R D'Amato, Marco Battaglia, Francesca Giannese, Alessandra Luchetti, Giulia Barbiera, Valentina Lampis, Claudio Zanettini, Gun Peggy Knudsen, Simona Scaini, Dejan Lazarevic, Davide Cittaro, Francesca R D'Amato, Marco Battaglia

Abstract

Respiratory and emotional responses to blood-acidifying inhalation of CO2 are markers of some human anxiety disorders, and can be enhanced by repeatedly cross-fostering (RCF) mouse pups from their biological mother to unrelated lactating females. Yet, these dynamics remain poorly understood. We show RCF-associated intergenerational transmission of CO2 sensitivity in normally-reared mice descending from RCF-exposed females, and describe the accompanying alterations in brain DNA methylation patterns. These epigenetic signatures were compared to DNA methylation profiles of monozygotic twins discordant for emotional reactivity to a CO2 challenge. Altered methylation was consistently associated with repeated elements and transcriptional regulatory regions among RCF-exposed animals, their normally-reared offspring, and humans with CO2 hypersensitivity. In both species, regions bearing differential methylation were associated with neurodevelopment, circulation, and response to pH acidification processes, and notably included the ASIC2 gene. Our data show that CO2 hypersensitivity is associated with specific methylation clusters and genes that subserve chemoreception and anxiety. The methylation status of genes implicated in acid-sensing functions can inform etiological and therapeutic research in this field.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Intergenerational transmission of CO2 hypersensitivity among normally-reared F1 pups.The figure shows the role of F0-RCF vs. F0-CT maternal lineage, as specified in abscissa: a grey histogram indicates crossing of a RCF/CT dam with an RCF F0-sire, and a white histogram indicates crossing of a RCF/CT dam with a CT F0-sire. At post-natal day (PND) 16–22, F1 pups (n = 32) resulting from all the 4 possible mating combinations of F0-RCF or F0-CT dams and sires (8 pups/parental mating combination, balanced by sex, see also Methods section) were assessed for their respiratory physiology during normal air and CO2-enriched air breathing. A two-way ANOVA of Δ%TV responses to 6%CO2-enriched air mixture vs. normal air showed a significant effect of F0 maternal treatment (F1,28 = 8.65, p = 0.007), but no significant effect of F0 paternal treatment (p = 0.75), or interaction of F0 maternal-by-paternal treatment (p = 0.80). Females’ responses (Δ%TV: 37.07 ± 3.56) did not differ from males’ responses (Δ%TV: 41.69 ± 4.28, t = −0.82, DF = 30, p = NS).
Figure 2
Figure 2
Semantic Similarity for GO terms associated with genes in intergenerationally-conserved methylation clusters (F0-F1 experiments, brain stem methylation data). Each circle symbolizes a GO term; circle size is proportional to term frequency (greater size indicates a more general term). Circle colour indicates term uniqueness (divergence from other terms) and label colour indicates dispensability (black: dispensability

Figure 3

Semantic Similarity for GO terms…

Figure 3

Semantic Similarity for GO terms associated with genes in methylation clusters common to…

Figure 3
Semantic Similarity for GO terms associated with genes in methylation clusters common to MZ twins with CO2 hypersensitivity (blood methylation data) and RCF-exposed animals (F0 experiment, brain stem methylation data). Each circle symbolizes a GO term; circle size is proportional to term frequency (greater size indicates a more general term). Circle colour indicates term uniqueness (divergence from other terms) and label colour indicates dispensability (black: dispensability <0.15). Term relevance was computed by GO list ranking. Scatterplot elaborated by ReviGO (revigo.irb.hr).

Figure 4

Heatmap representing relative chromatin state…

Figure 4

Heatmap representing relative chromatin state frequency for Cluster 46 (ASIC2-associated) Enrichment of tissue…

Figure 4
Heatmap representing relative chromatin state frequency for Cluster 46 (ASIC2-associated) Enrichment of tissue specific chromatin states in ASIC2-associated ccDMR. Color represent z-score of enrichment (red) or depletion (blue) of overlap with tissue-specific chromatin states. The ccDMR is positively associated with gene activation in ESC and enhancer function in Brain.

Figure 5

Graphical description of RCF experimental…

Figure 5

Graphical description of RCF experimental design. Top panel (a). RCF procedure (F0 generation);…

Figure 5
Graphical description of RCF experimental design. Top panel (a). RCF procedure (F0 generation); bottom panel (b). Parental crosses to obtain F1 animals. Red arrows depict experimental conditions selected for epigenetic analysis. PND, postnatal day; d, dams; s, sires.
Figure 3
Figure 3
Semantic Similarity for GO terms associated with genes in methylation clusters common to MZ twins with CO2 hypersensitivity (blood methylation data) and RCF-exposed animals (F0 experiment, brain stem methylation data). Each circle symbolizes a GO term; circle size is proportional to term frequency (greater size indicates a more general term). Circle colour indicates term uniqueness (divergence from other terms) and label colour indicates dispensability (black: dispensability <0.15). Term relevance was computed by GO list ranking. Scatterplot elaborated by ReviGO (revigo.irb.hr).
Figure 4
Figure 4
Heatmap representing relative chromatin state frequency for Cluster 46 (ASIC2-associated) Enrichment of tissue specific chromatin states in ASIC2-associated ccDMR. Color represent z-score of enrichment (red) or depletion (blue) of overlap with tissue-specific chromatin states. The ccDMR is positively associated with gene activation in ESC and enhancer function in Brain.
Figure 5
Figure 5
Graphical description of RCF experimental design. Top panel (a). RCF procedure (F0 generation); bottom panel (b). Parental crosses to obtain F1 animals. Red arrows depict experimental conditions selected for epigenetic analysis. PND, postnatal day; d, dams; s, sires.

References

    1. Brannan S, et al. Neuroimaging of cerebral activations and deactivations associated with hypercapnia and hunger for air. Proc. Natl. Acad. Sci. USA. 2001;98:2029–2034. doi: 10.1073/pnas.98.4.2029.
    1. Guyenet PG, et al. Central CO2 chemoreception and integrated neural mechanisms of cardiovascular and respiratory control. J. Appl. Physiol. (1985) 2010;108:995–1002. doi: 10.1152/japplphysiol.00712.2009.
    1. Schenberg, L. C. In Panic Disorder 9–77 (Springer International Publishing, 2016).
    1. Klein DF. False suffocation alarms, spontaneous panics, and related conditions. An integrative hypothesis. Arch. Gen. Psychiatry. 1993;50:306–317. doi: 10.1001/archpsyc.1993.01820160076009.
    1. Papp LA, et al. Respiratory psychophysiology of panic disorder: three respiratory challenges in 98 subjects. Am. J. Psychiatry. 1997;154:1557–1565.
    1. Grassi M, et al. Baseline respiratory parameters in panic disorder: a meta-analysis. J. Affect. Disord. 2013;146:158–173. doi: 10.1016/j.jad.2012.08.034.
    1. Meuret AE, et al. Do unexpected panic attacks occur spontaneously? Biol. Psychiatry. 2011;70:985–991. doi: 10.1016/j.biopsych.2011.05.027.
    1. Goossens L, et al. Brainstem response to hypercapnia: a symptom provocation study into the pathophysiology of panic disorder. J. Psychopharmacol. 2014;28:449–456. doi: 10.1177/0269881114527363.
    1. Maddock RJ, et al. Abnormal activity-dependent brain lactate and glutamate glutamine responses in panic disorder. Biol. Psychiatry. 2013;73:1111–1119. doi: 10.1016/j.biopsych.2012.12.015.
    1. Magnotta VA, Johnson CP, Follmer R, Wemmie JA. Functional t1ρ imaging in panic disorder. Biol. Psychiatry. 2014;75:884–891. doi: 10.1016/j.biopsych.2013.09.008.
    1. Roberson-Nay R, Kendler K. Panic disorder and its subtypes: a comprehensive analysis of panic symptom heterogeneity using epidemiological and treatment seeking samples. Psychol. Med. 2011;41:2411–2421. doi: 10.1017/S0033291711000547.
    1. Battaglia M, Ogliari A, D’Amato F, Kinkead R. Early-life risk factors for panic and separation anxiety disorder: Insights and outstanding questions arising from human and animal studies of CO sensitivity. Neurosci. Biobehav. Rev. 2014;46:455–464. doi: 10.1016/j.neubiorev.2014.04.005.
    1. Battaglia M, et al. A genetic study of the acute anxious response to carbon dioxide stimulation in man. J. Psychiatr. Res. 2007;41:906–917. doi: 10.1016/j.jpsychires.2006.12.002.
    1. Battaglia M, et al. A genetically informed study of the association between childhood separation anxiety, sensitivity to CO(2), panic disorder, and the effect of childhood parental loss. Arch. Gen. Psychiatry. 2009;66:64–71. doi: 10.1001/archgenpsychiatry.2008.513.
    1. Battaglia M, Pesenti-Gritti P, Spatola CA, Ogliari A, Tambs K. A twin study of the common vulnerability between heightened sensitivity to hypercapnia and panic disorder. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 2008;147:586–593. doi: 10.1002/ajmg.b.30647.
    1. Kossowsky J, et al. The separation anxiety hypothesis of panic disorder revisited: a meta-analysis. Am. J. Psychiatry. 2013;170:768–781. doi: 10.1176/appi.ajp.2012.12070893.
    1. Spatola CA, et al. Gene-environment interactions in panic disorder and CO sensitivity: Effects of events occurring early in life. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 2011;156:79–88. doi: 10.1002/ajmg.b.31144.
    1. Leibold N, et al. CO2 exposure as translational cross-species experimental model for panic. Translational Psychiatry. 2016;6:e885. doi: 10.1038/tp.2016.162.
    1. Genest SE, Gulemetova R, Laforest S, Drolet G, Kinkead R. Neonatal maternal separation induces sex-specific augmentation of the hypercapnic ventilatory response in awake rat. J. Appl. Physiol. 2007;102:1416–1421. doi: 10.1152/japplphysiol.00454.2006.
    1. D’Amato FR, et al. Unstable maternal environment, separation anxiety, and heightened CO 2 sensitivity induced by gene-by-environment interplay. PLoS One. 2011;6:e18637. doi: 10.1371/journal.pone.0018637.
    1. Vollmer L, Strawn J, Sah R. Acid–base dysregulation and chemosensory mechanisms in panic disorder: a translational update. Translational psychiatry. 2015;5:e572. doi: 10.1038/tp.2015.67.
    1. Vollmer LL, et al. Microglial acid sensing regulates carbon dioxide evoked fear. Biol. Psychiatry. 2016;80:541–551. doi: 10.1016/j.biopsych.2016.04.022.
    1. Luchetti A, et al. Early handling and repeated cross-fostering have opposite effect on mouse emotionality. Frontiers in behavioral neuroscience. 2015;9:93. doi: 10.3389/fnbeh.2015.00093.
    1. Luchetti A, Battaglia M, D’Amato FR. Repeated Cross-fostering Protocol as a Mouse Model of Early Environmental Instability. Bio-protocol. 2016;6:e1734. doi: 10.21769/BioProtoc.1734.
    1. Cittaro D, et al. Histone Modifications in a Mouse Model of Early Adversities and Panic Disorder: Role for Asic1 and Neurodevelopmental Genes. Sci. Rep. 2016;6:25131. doi: 10.1038/srep25131.
    1. Wemmie JA, Taugher RJ, Kreple CJ. Acid-sensing ion channels in pain and disease. Nature Reviews Neuroscience. 2013;14:461–471. doi: 10.1038/nrn3529.
    1. Sofer T, Schifano ED, Hoppin JA, Hou L, Baccarelli AA. A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure. Bioinformatics. 2013;29:2884–2891. doi: 10.1093/bioinformatics/btt498.
    1. Kundaje A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–330. doi: 10.1038/nature14248.
    1. Stankiewicz AM, Swiergiel AH, Lisowski P. Epigenetics of stress adaptations in the brain. Brain Res. Bull. 2013;98:76–92. doi: 10.1016/j.brainresbull.2013.07.003.
    1. Edgar R, Tan PPC, Portales-Casamar E, Pavlidis P. Meta-analysis of human methylomes reveals stably methylated sequences surrounding CpG islands associated with high gene expression. Epigenetics & chromatin. 2014;7:28. doi: 10.1186/1756-8935-7-28.
    1. Jacoby M, Gohrbandt S, Clausse V, Brons NH, Muller CP. Interindividual variability and co-regulation of DNA methylation differ among blood cell populations. Epigenetics. 2012;7:1421–1434. doi: 10.4161/epi.22845.
    1. Haque MM, Nilsson EE, Holder LB, Skinner MK. Genomic Clustering of differential DNA methylated regions (epimutations) associated with the epigenetic transgenerational inheritance of disease and phenotypic variation. BMC Genomics. 2016;17:417. doi: 10.1186/s12864-016-2748-5.
    1. Jiang N, et al. Conserved and divergent patterns of DNA methylation in higher vertebrates. Genome Biol. Evol. 2014;6:2998–3014. doi: 10.1093/gbe/evu238.
    1. Piro RM, et al. An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction. European Journal of Human Genetics. 2011;19:1173–1180. doi: 10.1038/ejhg.2011.96.
    1. Eckhardt F, et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 2006;38:1378–1385. doi: 10.1038/ng1909.
    1. Illingworth R, et al. A novel CpG island set identifies tissue-specific methylation at developmental gene loci. PLoS Biol. 2008;6:e22. doi: 10.1371/journal.pbio.0060022.
    1. Kitamura E, et al. Analysis of tissue-specific differentially methylated regions (TDMs) in humans. Genomics. 2007;89:326–337. doi: 10.1016/j.ygeno.2006.11.006.
    1. Cariaga-Martinez A, Alelú-Paz R. False data, positive results in neurobiology: moving beyond the epigenetics of blood and saliva samples in mental disorders. Journal of Negative Results in BioMedicine. 2016;15:21. doi: 10.1186/s12952-016-0064-x.
    1. Davies MN, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13:R43. doi: 10.1186/gb-2012-13-6-r43.
    1. Kundakovic M, et al. DNA methylation of BDNF as a biomarker of early-life adversity. Proc. Natl. Acad. Sci. USA. 2015;112:6807–6813. doi: 10.1073/pnas.1408355111.
    1. Massart R, et al. Overlapping signatures of chronic pain in the DNA methylation landscape of prefrontal cortex and peripheral T cells. Sci. Rep. 2016;6:19615. doi: 10.1038/srep19615.
    1. Provencal N, et al. The signature of maternal rearing in the methylome in rhesus macaque prefrontal cortex and T cells. J. Neurosci. 2012;32:15626–15642. doi: 10.1523/JNEUROSCI.1470-12.2012.
    1. Aran D, Sabato S, Hellman A. DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes. Genome Biol. 2013;14:R21. doi: 10.1186/gb-2013-14-3-r21.
    1. Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nature reviews.Genetics. 2012;13:484. doi: 10.1038/nrg3230.
    1. Huda R, et al. Acid‐sensing ion channels contribute to chemosensitivity of breathing‐related neurons of the nucleus of the solitary tract. J. Physiol. (Lond.) 2012;590:4761–4775. doi: 10.1113/jphysiol.2012.232470.
    1. Baron A, Lingueglia E. Pharmacology of acid-sensing ion channels–physiological and therapeutical perspectives. Neuropharmacology. 2015;94:19–35. doi: 10.1016/j.neuropharm.2015.01.005.
    1. Wemmie JA. Neurobiology of panic and pH chemosensation in the brain. Dialogues Clin. Neurosci. 2011;13:475–483.
    1. Guyenet PG, Bayliss DA. Neural control of breathing and CO 2 homeostasis. Neuron. 2015;87:946–961. doi: 10.1016/j.neuron.2015.08.001.
    1. Askwith CC, Wemmie JA, Price MP, Rokhlina T, Welsh MJ. Acid-sensing ion channel 2 (ASIC2) modulates ASIC1 H+-activated currents in hippocampal neurons. J. Biol. Chem. 2004;279:18296–18305. doi: 10.1074/jbc.M312145200.
    1. Price MP, et al. Localization and behaviors in null mice suggest that ASIC1 and ASIC2 modulate responses to aversive stimuli. Genes, Brain and Behavior. 2014;13:179–194. doi: 10.1111/gbb.12108.
    1. Ziemann AE, et al. The amygdala is a chemosensor that detects carbon dioxide and acidosis to elicit fear behavior. Cell. 2009;139:1012–1021. doi: 10.1016/j.cell.2009.10.029.
    1. Gregersen N, et al. A genome-wide study of panic disorder suggests the amiloride-sensitive cation channel 1 as a candidate gene. European Journal of Human Genetics. 2012;20:84–90. doi: 10.1038/ejhg.2011.148.
    1. Savage JE, et al. Validation of candidate anxiety disorder genes using a carbon dioxide challenge task. Biol. Psychol. 2015;109:61–66. doi: 10.1016/j.biopsycho.2015.04.006.
    1. Smoller JW, et al. The human ortholog of acid-sensing ion channel gene ASIC1a is associated with panic disorder and amygdala structure and function. Biol. Psychiatry. 2014;76:902–910. doi: 10.1016/j.biopsych.2013.12.018.
    1. Su J, et al. High CO2 chemosensitivity versus wide sensing spectrum: a paradoxical problem and its solutions in cultured brainstem neurons. J. Physiol. (Lond.) 2007;578:831–841. doi: 10.1113/jphysiol.2006.115758.
    1. Paterson C, et al. Temporal, diagnostic, and tissue-specific regulation of NRG3 isoform expression in human brain development and affective disorders. Am. J. Psychiatry. 2016;174:256–265. doi: 10.1176/appi.ajp.2016.16060721.
    1. Zhang D, et al. Neuregulin-3 (NRG3): a novel neural tissue-enriched protein that binds and activates ErbB4. Proc. Natl. Acad. Sci. USA. 1997;94:9562–9567. doi: 10.1073/pnas.94.18.9562.
    1. Nattie EE, Li AH. Ventral medulla sites of muscarinic receptor subtypes involved in cardiorespiratory control. J. Appl. Physiol. (1985) 1990;69:33–41. doi: 10.1152/jappl.1990.69.1.33.
    1. Nattie EE, Wood J, Mega A, Goritski W. Rostral ventrolateral medulla muscarinic receptor involvement in central ventilatory chemosensitivity. J. Appl. Physiol. (1985) 1989;66:1462–1470. doi: 10.1152/jappl.1989.66.3.1462.
    1. Battaglia M, Bertella S, Ogliari A, Bellodi L, Smeraldi E. Modulation by muscarinic antagonists of the response to carbon dioxide challenge in panic disorder. Arch. Gen. Psychiatry. 2001;58:114–119. doi: 10.1001/archpsyc.58.2.114.
    1. Dias BG, Ressler KJ. Experimental evidence needed to demonstrate inter and trans generational effects of ancestral experiences in mammals. Bioessays. 2014;36:919–923. doi: 10.1002/bies.201400105.
    1. Bohacek J, Mansuy IM. A guide to designing germline-dependent epigenetic inheritance experiments in mammals. nature methods. 2017;14:243–249. doi: 10.1038/nmeth.4181.
    1. Battaglia M, et al. Distinct trajectories of separation anxiety in the preschool years: persistence at school entry and early‐life associated factors. Journal of Child Psychology and Psychiatry. 2016;57:39–46. doi: 10.1111/jcpp.12424.
    1. Battaglia, M. et al. Early childhood trajectories of separation anxiety: Bearing on mental health, academic achievement, and physical health from mid‐childhood to preadolescence. Depress. Anxiety (2017).
    1. Shoji H, Kato K. Maternal behavior of primiparous females in inbred strains of mice: a detailed descriptive analysis. Physiol. Behav. 2006;89:320–328. doi: 10.1016/j.physbeh.2006.06.012.
    1. Battaglia M, Perna G. The 35% CO2 challenge in panic disorder: optimization by receiver operating characteristic (ROC) analysis. J. Psychiatr. Res. 1995;29:111–119. doi: 10.1016/0022-3956(94)00045-S.
    1. Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: (2010).
    1. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26:589–595. doi: 10.1093/bioinformatics/btp698.
    1. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137-2008-9-9-r137. Epub 2008 Sep 17 (2008).
    1. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25. doi: 10.1186/gb-2010-11-3-r25.
    1. R Development Core Team. R: A Language and Environment for Statistical Computing (2016).
    1. Ritchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. doi: 10.1093/nar/gkv007.
    1. Ester, M., Kriegel, H., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Ser. 96 (1996).
    1. Lieberman-Aiden, E. et al. Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Science 9 Oct, 326(5950), 289-293 (2009).
    1. Sanborn AL, et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. In Proceedings of the National Academy of Sciences of the United States of America. 2015;112(47):E6456–65. doi: 10.1073/pnas.1518552112.
    1. Falcon S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007;23:257–258. doi: 10.1093/bioinformatics/btl567.
    1. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PloS One. 2011;6(7):e 21800. doi: 10.1371/journal.pone.0021800.
    1. Quinlan, A. R. BEDTools: the Swiss army tool for genome feature analysis. Current protocols in bioinformatics, 11.12.1–11.12.34 (2014).

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