Temporal changes in DNA methylation and RNA expression in a small song bird: within- and between-tissue comparisons.
Accessible and inaccessible tissues
DNA methylation
Great tit
RNA expression
Tissue-specific and tissue-general temporal changes
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
07 Jan 2021
07 Jan 2021
Historique:
received:
17
07
2020
accepted:
15
12
2020
entrez:
8
1
2021
pubmed:
9
1
2021
medline:
15
5
2021
Statut:
epublish
Résumé
DNA methylation is likely a key mechanism regulating changes in gene transcription in traits that show temporal fluctuations in response to environmental conditions. To understand the transcriptional role of DNA methylation we need simultaneous within-individual assessment of methylation changes and gene expression changes over time. Within-individual repeated sampling of tissues, which are essential for trait expression is, however, unfeasible (e.g. specific brain regions, liver and ovary for reproductive timing). Here, we explore to what extend between-individual changes in DNA methylation in a tissue accessible for repeated sampling (red blood cells (RBCs)) reflect such patterns in a tissue unavailable for repeated sampling (liver) and how these DNA methylation patterns are associated with gene expression in such inaccessible tissues (hypothalamus, ovary and liver). For this, 18 great tit (Parus major) females were sacrificed at three time points (n = 6 per time point) throughout the pre-laying and egg-laying period and their blood, hypothalamus, ovary and liver were sampled. We simultaneously assessed DNA methylation changes (via reduced representation bisulfite sequencing) and changes in gene expression (via RNA-seq and qPCR) over time. In general, we found a positive correlation between changes in CpG site methylation in RBCs and liver across timepoints. For CpG sites in close proximity to the transcription start site, an increase in RBC methylation over time was associated with a decrease in the expression of the associated gene in the ovary. In contrast, no such association with gene expression was found for CpG site methylation within the gene body or the 10 kb up- and downstream regions adjacent to the gene body. Temporal changes in DNA methylation are largely tissue-general, indicating that changes in RBC methylation can reflect changes in DNA methylation in other, often less accessible, tissues such as the liver in our case. However, associations between temporal changes in DNA methylation with changes in gene expression are mostly tissue- and genomic location-dependent. The observation that temporal changes in DNA methylation within RBCs can relate to changes in gene expression in less accessible tissues is important for a better understanding of how environmental conditions shape traits that temporally change in expression in wild populations.
Sections du résumé
BACKGROUND
BACKGROUND
DNA methylation is likely a key mechanism regulating changes in gene transcription in traits that show temporal fluctuations in response to environmental conditions. To understand the transcriptional role of DNA methylation we need simultaneous within-individual assessment of methylation changes and gene expression changes over time. Within-individual repeated sampling of tissues, which are essential for trait expression is, however, unfeasible (e.g. specific brain regions, liver and ovary for reproductive timing). Here, we explore to what extend between-individual changes in DNA methylation in a tissue accessible for repeated sampling (red blood cells (RBCs)) reflect such patterns in a tissue unavailable for repeated sampling (liver) and how these DNA methylation patterns are associated with gene expression in such inaccessible tissues (hypothalamus, ovary and liver). For this, 18 great tit (Parus major) females were sacrificed at three time points (n = 6 per time point) throughout the pre-laying and egg-laying period and their blood, hypothalamus, ovary and liver were sampled.
RESULTS
RESULTS
We simultaneously assessed DNA methylation changes (via reduced representation bisulfite sequencing) and changes in gene expression (via RNA-seq and qPCR) over time. In general, we found a positive correlation between changes in CpG site methylation in RBCs and liver across timepoints. For CpG sites in close proximity to the transcription start site, an increase in RBC methylation over time was associated with a decrease in the expression of the associated gene in the ovary. In contrast, no such association with gene expression was found for CpG site methylation within the gene body or the 10 kb up- and downstream regions adjacent to the gene body.
CONCLUSION
CONCLUSIONS
Temporal changes in DNA methylation are largely tissue-general, indicating that changes in RBC methylation can reflect changes in DNA methylation in other, often less accessible, tissues such as the liver in our case. However, associations between temporal changes in DNA methylation with changes in gene expression are mostly tissue- and genomic location-dependent. The observation that temporal changes in DNA methylation within RBCs can relate to changes in gene expression in less accessible tissues is important for a better understanding of how environmental conditions shape traits that temporally change in expression in wild populations.
Identifiants
pubmed: 33413102
doi: 10.1186/s12864-020-07329-9
pii: 10.1186/s12864-020-07329-9
pmc: PMC7792223
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
36Subventions
Organisme : European Research Council
ID : 339092
Pays : International
Organisme : European Research Council ()
ID : 339092
Organisme : Norges Forskningsråd (NO)
ID : 239974
Organisme : Norges Forskningsråd
ID : 223257
Références
Pigliucci M. Phenotypic plasticity: beyond nature and nurture. Baltimore: John Hopkins University Press; 2001.
Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.
pubmed: 11782440
Liu L, Wylie RC, Andrews LG, Tollefsbol TO. Aging, cancer and nutrition: the DNA methylation connection. Mech Ageing Dev. 2003;124:989–98.
pubmed: 14659588
Bind M, Baccarelli A, Zanobetti A, Tarantini L, Suh H, Vokonas P, et al. Air pollution and markers of coagulation, inflammation, and endothelial function: associations and epigene-environment interactions in an elderly cohort. Epidemiology. 2012;23:332–40.
pubmed: 22237295
pmcid: 3285258
Stevenson TJ, Prendergast BJ. Reversible DNA methylation regulates seasonal photoperiodic time measurement. Proc Natl Acad Sci U S A. 2013;110:16651–6.
pubmed: 24067648
pmcid: 3799317
Viitaniemi HM, Verhagen I, Visser ME, Honkela A, van Oers K, Husby A. Seasonal variation in genome-wide DNA methylation patterns and the onset of seasonal timing of reproduction in great tits. Genome Biol Evol. 2019;11:970–83.
pubmed: 30840074
pmcid: 6447391
Sepers B, van den Heuvel K, Lindner M, Viitaniemi HM, Husby A, van Oers K. Avian ecological epigenetics: pitfalls and promises. J Ornithol. 2019;160:1183–1203.
Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, et al. Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res. 2010;20:332–40.
pubmed: 20107151
pmcid: 2840983
Slieker RC, Relton CL, Gaunt TR, Slagboom PE, Heijmans BT. Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics Chromatin. 2018;11:25.
pubmed: 29848354
pmcid: 5975493
Kang SW, Madkour M, Kuenzel WJ. Tissue-specific expression of DNA methyltransferases involved in early-life nutritional stress of chicken, Gallus gallus. Front Genet. 2017;8:204.
pubmed: 29270191
pmcid: 5723639
Alvarado S, Mak T, Liu S, Storey KB, Szyf M. Dynamic changes in global and gene-specific DNA methylation during hibernation in adult thirteen-lined ground squirrels, Ictidomys tridecemlineatus. J Exp Biol. 2015;218:1787–95. https://doi.org/10.1242/jeb.116046 .
doi: 10.1242/jeb.116046
pubmed: 25908059
Pegoraro M, Bafna A, Davies NJ, Shuker DM, Tauber E. DNA methylation changes induced by long and short photoperiods in Nasonia. Genome Res. 2016;26:203–10.
pubmed: 26672019
pmcid: 4728373
Cortijo S, Wardenaar R, Colome-Tatche M, Gilly A, Etcheverry M, Labadie K, et al. Mapping the epigenetic basis of complex traits. Science. 2014;343:1145–8.
pubmed: 24505129
Wilschut RA, Oplaat C, Snoek LB, Kirschner J, Verhoeven KJF. Natural epigenetic variation contributes to heritable flowering divergence in a widespread asexual dandelion lineage. Mol Ecol. 2016;25:1759–68.
pubmed: 26615058
Verhulst EC, Mateman AC, Zwier MV, Caro SP, Verhoeven KJF, van Oers K. Evidence from pyrosequencing indicates that natural variation in animal personality is associated with DRD4 DNA methylation. Mol Ecol. 2016;25:1801–11.
pubmed: 26678756
Mäkinen H, Viitaniemi HM, Visser ME, Verhagen I, van Oers K, Husby A. Temporally replicated DNA methylation patterns in great tit using reduced representation bisulfite sequencing. Sci Data. 2019;6:136.
pubmed: 31341168
pmcid: 6656709
Meissner A, Gnirke A, Bell GW, Ramsahoye B, Lander ES, Jaenisch R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 2005;33:5868–77.
pubmed: 16224102
pmcid: 1258174
Lindner M, Laine VN, Verhagen I, Viitaniemi HM, Visser ME, van Oers K, et al. Epigenetic mediation of the onset of reproduction in a songbird. bioRxiv. 2020;2020.02.01.929968.
Williams TD. Physiological adaptations for breeding in birds; 2012.
McKay JA, Xie L, Harris S, Wong YK, Ford D, Mathers JC. Blood as a surrogate marker for tissue-specific DNA methylation and changes due to folate depletion in post-partum female mice. Mol Nutr Food Res. 2011;55:1026–35.
pubmed: 21520493
Derks MFL, Schachtschneider KM, Madsen O, Schijlen E, Verhoeven KJF, van Oers K. Gene and transposable element methylation in great tit (Parus major) brain and blood. BMC Genomics. 2016;17:332.
pubmed: 27146629
pmcid: 4855439
Laine VN, Verhagen I, Mateman AC, Pijl A, Williams TD, Gienapp P, et al. Exploration of tissue-specific gene expression patterns underlying timing of breeding in contrasting temperature environments in a song bird. BMC Genomics. 2019;20:693.
pubmed: 31477015
pmcid: 6720064
Husby A. On the use of blood samples for measuring DNA methylation in ecological epigenetic studies. Integr Comp Biol. 2020;60:1558–66.
Laine VN, Gossmann TI, Schachtschneider KM, Garroway CJ, Madsen O, Verhoeven KJFF, et al. Evolutionary signals of selection on cognition from the great tit genome and methylome. Nat Commun. 2016;7:10474.
pubmed: 26805030
pmcid: 4737754
Lokk K, Modhukur V, Rajashekar B, Märtens K, Mägi R, Kolde R, et al. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol. 2014;15:3248.
Wang J, Duan Y, Meng Q, Gong R, Guo C, Zhao Y, et al. Integrated analysis of DNA methylation profiling and gene expression profiling identifies novel markers in lung cancer in Xuanwei, China. PLoS One. 2018;13:e0203155.
pubmed: 30286088
pmcid: 6171826
Xie F, Deng F, Wu L, Mo X, Zhu H, Wu J, et al. Multiple correlation analyses revealed complex relationship between DNA methylation and mRNA expression in human peripheral blood mononuclear cells. Funct Integr Genomics. 2018;18:1–10.
pubmed: 28735351
Zhu T, Zheng SC, Paul DS, Horvath S, Teschendorff AE. Cell and tissue type independent age-associated DNA methylation changes are not rare but common. Aging (Albany NY). 2018;10:3541–57.
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;168B:36–44.
pubmed: 25355443
Dmitrijeva M, Ossowski S, Serrano L, Schaefer MH. Tissue-specific DNA methylation loss during ageing and carcinogenesis is linked to chromosome structure, replication timing and cell division rates. Nucleic Acids Res. 2018;46:7022–39.
pubmed: 29893918
pmcid: 6101545
Klengel T, Binder EB. Epigenetics of stress-related psychiatric disorders and gene x environment interactions. Neuron. 2015;86:1343–57.
pubmed: 26087162
John S, Sabo PJ, Thurman RE, Sung M, Biddie SC, Johnson TA, et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat Genet. 2011;43:264.
pubmed: 21258342
pmcid: 6386452
Ewald ER, Wand GS, Seifuddin F, Yang X, Tamashiro KL, Potash JB, et al. Alterations in DNA methylation of Fkbp5 as a determinant of blood-brain correlation of glucocorticoid exposure. Psychoneuroendocrinology. 2014;44:112–22.
pubmed: 24767625
pmcid: 4047971
Zhang B, Zhou Y, Lin N, Lowdon RF, Hong C, Nagarajan RP, et al. Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res. 2013;23:1522–40.
pubmed: 23804400
pmcid: 3759728
Campbell DEK, Langlois VS. Thyroid hormones and androgens di ff erentially regulate gene expression in testes and ovaries of sexually mature Silurana tropicalis. Gen Comp Endocrinol. 2018;267:172–82. https://doi.org/10.1016/j.ygcen.2018.07.001 .
doi: 10.1016/j.ygcen.2018.07.001
pubmed: 29990494
Kassam I, Wu Y, Yang J, Visscher PM, AF MR. Tissue-specific sex-differences in human gene expression. Hum Mol Genet. 2019;28:2976–86.
Miragaia RJ, Gomes T, Chomka A, Jardine L, Riedel A, Hegazy AN, et al. Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation. Immunity. 2019;50:493–504 e7.
pubmed: 30737144
pmcid: 6382439
Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13:R43.
pubmed: 22703893
pmcid: 3446315
Verhagen I, Laine VN, Mateman AC, Pijl A, de Wit R, van Lith B, et al. Fine-tuning of seasonal timing of breeding is regulated downstream in the underlying neuro-endocrine system in a small songbird. J Exp Biol. 2019;222:jeb.202481.
Deaton AM, Bird A. CpG islands and the regulation of transcription. Genes Dev. 2011;25:1010–22.
pubmed: 21576262
pmcid: 3093116
Höglund A, Henriksen R, Fogelholm J, Churcher AM, Guerrero-Bosagna CM, Martinez-Barrio A, et al. The methylation landscape and its role in domestication and gene regulation in the chicken. Nat Ecol Evol. 2020;4:1713–24.
Gienapp PG, Calus MPL, Laine VN, Visser ME. Genomic selection on breeding time in a wild bird population. Evol Lett. 2019:3:142–51.
Verhagen I, Gienapp P, Laine VN, van Grevenhof EM, Mateman AC, van Oers K, et al. Genetic and phenotypic responses to genomic selection for timing of breeding in a wild songbird. Funct Ecol. 2019;33:1708–21.
Visser ME, Schaper SV, Holleman LJM, Dawson A, Sharp P, Gienapp P, et al. Genetic variation in cue sensitivity involved in avian timing of reproduction. Funct Ecol. 2011;25:868–77.
Silverin B, Massa R, Stokkan KA. Photoperiodic adaptation to breeding at different latitudes in great tits. Gen Comp Endocrinol. 1993;90:14–22.
pubmed: 8504918
Boyle P, Clement K, Gu H, Smith ZD, Ziller M, Fostel JL, et al. Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol. 2012;13.
Andrews S. FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ . 2016.
Krueger F. TrimGalore! Available at: https://github.com/FelixKrueger/TrimGalore . 2016.
Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, et al. Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013;9.
Lawrence M, Gentleman R, Carey V. Rtracklayer: an R package for interfacing with genome browsers. Bioinformatics. 2009;25:1841–2.
pubmed: 19468054
pmcid: 2705236
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.
pubmed: 2832824
pmcid: 2832824
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017.
Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13:R87.
pubmed: 23034086
pmcid: 3491415
Gaudet P, Dessimoz C. In: Dessimoz C, Škunca N, editors. Gene ontology: pitfalls, biases, and remedies BT - the gene ontology handbook. New York: Springer New York; 2017. p. 189–205.
Primmer CR, Papakostas S, Leder EH, Davis MJ, Ragan MA. Annotated genes and nonannotated genomes: cross-species use of gene ontology in ecology and evolution research. Mol Ecol. 2013;22:3216–41.
pubmed: 23763602
Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25:1091–3.
pubmed: 19237447
pmcid: 2666812
Rivals I, Personnaz L, Taing L, Potier M. Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics. 2007;23:401–7.
pubmed: 17182697
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.
pubmed: 10592173
pmcid: 102409
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.