DNA Methylation; Epigenetics; PTSD; Female; Humans; Monocytes
Abstract :
[en] DNA methylation patterns can be responsive to environmental influences. This observation has sparked interest in the potential for psychological interventions to influence epigenetic processes. Recent studies have observed correlations between DNA methylation changes and therapy outcome. However, most did not control for changes in cell composition. This study had two aims: first, we sought to replicate therapy-associated changes in DNA methylation of commonly assessed candidate genes in isolated monocytes from 60 female patients with post-traumatic stress disorder (PTSD). Our second, exploratory goal was to identify novel genomic regions with substantial pre-to-post intervention DNA methylation changes by performing whole-genome bisulfite sequencing (WGBS) in two patients with PTSD. Equivalence testing and Bayesian analyses provided evidence against physiologically meaningful intervention-associated DNA methylation changes in monocytes of PTSD patients in commonly investigated target genes (NR3C1, FKBP5, SLC6A4, OXTR). Furthermore, WGBS yielded only a limited set of candidate regions with suggestive evidence of differential DNA methylation pre- to post-therapy. These differential DNA methylation patterns did not prove replicable when investigated in the entire cohort. We conclude that there is no evidence for major, recurrent intervention-associated DNA methylation changes in the investigated genes in monocytes of patients with PTSD.
Disciplines :
Neurosciences & behavior
Author, co-author :
Hummel, Elisabeth
Elgizouli, Magdeldin
Sicorello, Maurizio
Leitão, Elsa
Beygo, Jasmin
Schröder, Christopher
Zeschnigk, Michael
Müller, Svenja
Herpertz, Stephan
Moser, Dirk
Kessler, Henrik
Horsthemke, Bernhard
Kumsta, Robert ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)
Szyf, M. The early life social environment and DNA methylation: DNA methylation mediating the long-term impact of social environments early in life. Epigenetics 6, 971–978 (2011). DOI: 10.4161/epi.6.8.16793
Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016). DOI: 10.1038/nrg.2016.59
Godfrey, K. M., Costello, P. M. & Lillycrop, K. A. The developmental environment, epigenetic biomarkers and long-term health. J. Dev. Orig. Health Dis. 6, 399–406 (2015). DOI: 10.1017/S204017441500121X
McGowan, P. O. & Szyf, M. The epigenetics of social adversity in early life: Implications for mental health outcomes. Neurobiol. Dis. 39, 66–72 (2010). DOI: 10.1016/j.nbd.2009.12.026
Tobi, E. W. et al. DNA methylation as a mediator of the association between prenatal adversity and risk factors for metabolic disease in adulthood. Sci. Adv. 10.1126/sciadv.aao4364 (2018). DOI: 10.1126/sciadv.aao4364
Weaver, I. C. et al. Epigenetic programming by maternal behavior. Nat. Neurosci. 7, 847–854 (2004). DOI: 10.1038/nn1276
Jones, P. A. & Liang, G. Rethinking how DNA methylation patterns are maintained. Nat. Rev. Genet. 10, 805–811 (2009). DOI: 10.1038/nrg2651
Morgan, H. D. et al. Epigenetic reprogramming in mammals. Hum. Mol. Genet. 14(supp_1), R47–R58 (2005). DOI: 10.1093/hmg/ddi114
Talens, R. P. et al. Variation, patterns and temporal stability of DNA methylation: Considerations for epigenetic epidemiology. FASEB J. 24, 3135–3144 (2010). DOI: 10.1096/fj.09-150490
Dekkers, K. F. et al. Blood lipids influence DNA methylation in circulating cells. Genome Biol. 17, 138 (2016). DOI: 10.1186/s13059-016-1000-6
Emeny, R. T. et al. Anxiety associated increased CpG methylation in the promoter of Asb1: A translational approach evidenced by epidemiological and clinical studies and a Murine model. Neuropsychopharmacology 43, 342–353 (2018). DOI: 10.1038/npp.2017.102
Joehanes, R. et al. Epigenetic signatures of cigarette smoking. Circ. Cardiovasc. Genet. 9, 436–447 (2016). DOI: 10.1161/CIRCGENETICS.116.001506
Saunderson, E. A. et al. Stress-induced gene expression and behavior are controlled by DNA methylation and methyl donor availability in the dentate gyrus. Proc. Natl. Acad. Sci. U. S. A. 113, 4830–4835 (2016). DOI: 10.1073/pnas.1524857113
Wong, C. C. et al. A longitudinal study of epigenetic variation in twins. Epigenetics 5, 516–526 (2010). DOI: 10.4161/epi.5.6.12226
Kumsta, R. The role of epigenetics for understanding mental health difficulties and its implications for psychotherapy research. Psychol. Psychother 92, 190–207 (2019). DOI: 10.1111/papt.12227
Roberts, S. et al. Hpa axis related genes and response to psychological therapies: Genetics and epigenetics. Depress Anxiety 32, 861–870 (2015). DOI: 10.1002/da.22430
Roberts, S. et al. DNA methylation of FKBP5 and response to exposure-based psychological therapy. Am. J. Med. Genet. B Neuropsychiatr. Genet. 180, 150–158 (2019). DOI: 10.1002/ajmg.b.32650
Yehuda, R. et al. Epigenetic biomarkers as predictors and correlates of symptom improvement following psychotherapy in combat veterans with PTSD. Front. Psych. 4, 118 (2013).
Roberts, S. et al. Serotonin transporter [corrected] methylation and response to cognitive behaviour therapy in children with anxiety disorders. Transl. Psychiatry 4, e444 (2014). DOI: 10.1038/tp.2014.83
Ziegler, C. et al. MAOA gene hypomethylation in panic disorder-reversibility of an epigenetic risk pattern by psychotherapy. Transl. Psychiatry 6, e773 (2016). DOI: 10.1038/tp.2016.41
Perroud, N. et al. Response to psychotherapy in borderline personality disorder and methylation status of the BDNF gene. Transl. Psychiatry 3, e207 (2013). DOI: 10.1038/tp.2012.140
Vinkers, C. H. et al. Successful treatment of post-traumatic stress disorder reverses DNA methylation marks. Mol. Psychiatry 10.1038/s41380-019-0549-3 (2019). DOI: 10.1038/s41380-019-0549-3
Farre, P. et al. Concordant and discordant DNA methylation signatures of aging in human blood and brain. Epigenetics Chromatin 8, 19 (2015). DOI: 10.1186/s13072-015-0011-y
Nowak, J., Borkowska, B. & Pawlowski, B. Leukocyte changes across menstruation, ovulation, and mid-luteal phase and association with sex hormone variation. Am. J. Hum. Biol. 28, 721–728 (2016). DOI: 10.1002/ajhb.22856
Cole, S. W. Elevating the perspective on human stress genomics. Psychoneuroendocrinology 35, 955–962 (2010). DOI: 10.1016/j.psyneuen.2010.06.008
Jones, M. J., Moore, S. R. & Kobor, M. S. Principles and challenges of applying epigenetic epidemiology to psychology. Annu. Rev. Psychol. 69, 459–485 (2018). DOI: 10.1146/annurev-psych-122414-033653
Cole, S. W. et al. Transcript origin analysis identifies antigen-presenting cells as primary targets of socially regulated gene expression in leukocytes. Proc. Natl. Acad. Sci. U. S. A. 108, 3080–3085 (2011). DOI: 10.1073/pnas.1014218108
Kuan, P. F. et al. Cell type-specific gene expression patterns associated with posttraumatic stress disorder in World Trade Center responders. Transl. Psychiatry 9, 1 (2019). DOI: 10.1038/s41398-018-0355-8
Schiweck, C. et al. Childhood trauma, suicide risk and inflammatory phenotypes of depression: Insights from monocyte gene expression. Transl. Psychiatry 10, 296 (2020). DOI: 10.1038/s41398-020-00979-z
Zhu, Y. et al. Genome-wide profiling of DNA methylome and transcriptome in peripheral blood monocytes for major depression: A monozygotic discordant twin study. Transl. Psychiatry 9, 215 (2019). DOI: 10.1038/s41398-019-0550-2
Jones, K. A. & Thomsen, C. The role of the innate immune system in psychiatric disorders. Mol. Cell Neurosci. 53, 52–62 (2013). DOI: 10.1016/j.mcn.2012.10.002
Miller, A. H. & Raison, C. L. The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nat. Rev. Immunol. 16, 22–34 (2016). DOI: 10.1038/nri.2015.5
Wohleb, E. S. et al. Monocyte trafficking to the brain with stress and inflammation: A novel axis of immune-to-brain communication that influences mood and behavior. Front. Neurosci. 8, 447 (2014).
Weathers, F. W., Litz, B. T., Keane, T. M. et al. The PTSD Checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov., (2013).
Ziller, M. J. et al. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat. Methods 12, 230–232 (2015). DOI: 10.1038/nmeth.3152
Juhling, F. et al. metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 26, 256–262 (2016). DOI: 10.1101/gr.196394.115
Schröder, C. Bioinformatics from genetic variants to methylation, TU Dortmund, (2018).
Klengel, T. et al. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat. Neurosci. 16, 33–41 (2013). DOI: 10.1038/nn.3275
Michels, K. B. et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods 10, 949–955 (2013). DOI: 10.1038/nmeth.2632
Leenen, F. A., Muller, C. P. & Turner, J. D. DNA methylation: Conducting the orchestra from exposure to phenotype?. Clin. Epigenetics 8, 92 (2016). DOI: 10.1186/s13148-016-0256-8
Zannas, A. S. & Chrousos, G. P. Epigenetic programming by stress and glucocorticoids along the human lifespan. Mol. Psychiatry 22, 640–646 (2017). DOI: 10.1038/mp.2017.35
Holbrook, J. D. et al. Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies?. Epigenomics 9, 1143–1150 (2017). DOI: 10.2217/epi-2017-0032
Cole, S. W. et al. Transcriptional modulation of the developing immune system by early life social adversity. Proc. Natl. Acad. Sci. U. S. A. 109, 20578–20583 (2012). DOI: 10.1073/pnas.1218253109
O’Donovan, A. et al. Transcriptional control of monocyte gene expression in post-traumatic stress disorder. Dis. Markers 30, 123–132 (2011). DOI: 10.1155/2011/560572
Powell, N. D. et al. Social stress up-regulates inflammatory gene expression in the leukocyte transcriptome via beta-adrenergic induction of myelopoiesis. Proc. Natl. Acad. Sci. U. S. A. 110, 16574–16579 (2013). DOI: 10.1073/pnas.1310655110
Wendland, J. R. et al. Simultaneous genotyping of four functional loci of human SLC6A4, with a reappraisal of 5-HTTLPR and rs25531. Mol. Psychiatry 11, 224–226 (2006). DOI: 10.1038/sj.mp.4001789
Moser, D. A. et al. Targeted bisulfite sequencing: A novel tool for the assessment of DNA methylation with high sensitivity and increased coverage. Psychoneuroendocrinology 120, 104784 (2020). DOI: 10.1016/j.psyneuen.2020.104784
Rahmann, S., Beygo, J., Kanber, D. et al. Amplikyzer: Automated methylation analysis of amplicons from bisulfite flowgram sequencing. PeerJ Preprints, (2013).
Leitão, E. et al. The sperm epigenome does not display recurrent epimutations in patients with severely impaired spermatogenesis. Clin. Epigenetics 12(1), 1–15 (2020). DOI: 10.1186/s13148-020-00854-0
Rademacher, K. et al. Evolutionary origin and methylation status of human intronic CpG islands that are not present in mouse. Genome Biol. Evol. 6, 1579–1588 (2014). DOI: 10.1093/gbe/evu125
Wallner, S. et al. Epigenetic dynamics of monocyte-to-macrophage differentiation. Epigenetics Chromatin 9, 33 (2016). DOI: 10.1186/s13072-016-0079-z
Pedersen, B. S., Eyring, K., De, S. et al. Fast and accurate alignment of long bisulfite-seq reads. Preprint at http://arXiv.org/arXiv:1401.1129 (2014).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10.1093/gigascience/giab008 (2021). DOI: 10.1093/gigascience/giab008
Tarasov, A. et al. Sambamba: Fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015). DOI: 10.1093/bioinformatics/btv098
Okonechnikov, K., Conesa, A. & Garcia-Alcalde, F. Qualimap 2: Advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292–294 (2016).
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). DOI: 10.1038/s41592-019-0686-2
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007). DOI: 10.1109/MCSE.2007.55
Faul, F. et al. G*Power 3: A flexible statistical power analysis program for the social, behavioral and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007). DOI: 10.3758/BF03193146
Rouder, J. N. et al. Default Bayes factors for ANOVA designs. J. Math. Psychol. 56, 356–374 (2012). DOI: 10.1016/j.jmp.2012.08.001
Jarosz, A. F. & Wiley, J. What are the odds? A practical guide to computing and reporting bayes factors. J. Probl. Solving 7, 1037–1040 (2014).
Lakens, D. Equivalence tests: A practical primer for t tests, correlations and meta-analyses. Soc. Psychol. Personal. Sci. 8, 355–362 (2017). DOI: 10.1177/1948550617697177
