Pubertal development in healthy children is mirrored by DNA methylation patterns in peripheral blood

Kristian Almstrup, Marie Lindhardt Johansen, Alexander S Busch, Casper P Hagen, John E Nielsen, Jørgen Holm Petersen, Anders Juul, Kristian Almstrup, Marie Lindhardt Johansen, Alexander S Busch, Casper P Hagen, John E Nielsen, Jørgen Holm Petersen, Anders Juul

Abstract

Puberty marks numerous physiological processes which are initiated by central activation of the hypothalamic-pituitary-gonadal axis, followed by development of secondary sexual characteristics. To a large extent, pubertal timing is heritable, but current knowledge of genetic polymorphisms only explains few months in the large inter-individual variation in the timing of puberty. We have analysed longitudinal genome-wide changes in DNA methylation in peripheral blood samples (n = 102) obtained from 51 healthy children before and after pubertal onset. We show that changes in single methylation sites are tightly associated with physiological pubertal transition and altered reproductive hormone levels. These methylation sites cluster in and around genes enriched for biological functions related to pubertal development. Importantly, we identified that methylation of the genomic region containing the promoter of TRIP6 was co-ordinately regulated as a function of pubertal development. In accordance, immunohistochemistry identified TRIP6 in adult, but not pre-pubertal, testicular Leydig cells and circulating TRIP6 levels doubled during puberty. Using elastic net prediction models, methylation patterns predicted pubertal development more accurately than chronological age. We demonstrate for the first time that pubertal attainment of secondary sexual characteristics is mirrored by changes in DNA methylation patterns in peripheral blood. Thus, modulations of the epigenome seem involved in regulation of the individual pubertal timing.

Figures

Figure 1. Identification of CpGs associated with…
Figure 1. Identification of CpGs associated with pubertal age.
(a) From the COPENHAGEN puberty study of children followed longitudinally, selected pre- and post-pubertal samples from 31 boys and 20 girls were included in the study. Time relative to onset of puberty as measured by a testicular volume of 4 ml or breast tanner stage B2 or above was calculated and defined as the pubertal age. (b) Genomic inflation of the genome-wide DNA methylation patterns correlating with pubertal age were corrected by applying an improved surrogate variant analysis (SmartSVA) resulting in a genomic inflation factor of 1.2 and a qq-plot leaving 457 significant CpG sites at a FDR of 0.05 (red). (c) Manhattan plot of the significant CpG sites reveal a distribution across all autosomes. (d) Unsupervised hierarchical centroid clustering of CpGs associated with pubertal age. The resulting dendrograms were colour coded according to their height and divided samples into two major groups that nearly uniquely represented pre- and post-pubertal boys and girls. (e) Gene ontology analysis of all significant CpGs revealed several ontologies (p-value < 0.05) that could be related to pubertal development. Biological process gene ontologies were plotted in a sematic space, using REVIGO, that groups related ontologies together.
Figure 2. Identification of CpGs associated with…
Figure 2. Identification of CpGs associated with changes in circulating reproductive hormones in boys.
(a) Venn diagram of CpGs associated with Testosterone (n = 999), FSH (n = 492), LH (n = 403), AMH (n = 282), and Inhibin B (n = 218) at a FDR cut-off of 0.05 in boys after correcting for genomic inflation and age. (b) Binary tree obtained from a sequence of union operations showing how related the hormone CpG clusters are to each other. (c) Venn diagram of the CpGs associated with pubertal age and circulating reproductive hormones (Supplementary Tables 1, 5 and 6).
Figure 3. Identification of differentially methylated regions…
Figure 3. Identification of differentially methylated regions associated with pubertal age.
(a) The most significant differentially methylated genomic region (nine probes with a mean p-value of 1.3e–31) was found on chromosome 7 (chr7:100463206-100464771) that contains both gene body and 3′ UTR of SLC12A9 and transcription start site of TRIP6. The CpGs were located in several putative transcription factor bindings sites upstream of TRIP6. TX Factor ChIP, DNase1 clusters tracks were obtained from the ENCODE database and the CpG Islands from the UCSC database. (b) Immunohistochemical staining for TRIP6 in normal adult testicular tissue showing intense and specific staining in Leydig cells (arrow heads) producing testosterone. (c) TRIP6 staining of pre-pubertal (7.9 years old) testicular tissue. TRIP6 staining was absent from Leydig cells (arrow heads) in pre-pubertal testis. Bar equals 20 μm. Negative controls are shown in Supplementary Figure 4 together with staining of ovarian tissue. (d) Circulating levels of TRIP6 were determined by ELISA in boys and girls pre-pubertally, around pubertal onset and post-pubertally. The box plots show the distribution of the measurements (the band inside the box depicts the median) at each time point and the connected lines are drawn from the mean of each group. **Denotes a p-value below 0.01.
Figure 4. Prediction models of pubertal and…
Figure 4. Prediction models of pubertal and biological aging.
(a) Scatter plot of chronological age and pubertal age. Using an elastic net prediction model and a 10-fold cross validation, the pubertal age was predicted from DNA methylation patterns in peripheral blood from (b) boys and (c) girls. A residual error of 0.39 and 0.74 year, equalling five and nine months was observed in boys and girls, respectively. (d) Prediction of biological aging from our data using the same algorithm as applied for pubertal age revealed a residual error of 0.94 year (11 months). The dashed red line is the identity line.

References

    1. Sorensen K. et al.. Birth size and age at menarche: a twin perspective. Hum Reprod 28, 2865–2871, 10.1093/humrep/det283 (2013).
    1. Abreu A. P. et al.. Central Precocious Puberty Caused by Mutations in the Imprinted Gene MKRN3. New England Journal of Medicine 368, 2467–2475, 10.1056/NEJMoa1302160 (2013).
    1. Boehm U. et al.. Expert consensus document: European Consensus Statement on congenital hypogonadotropic hypogonadism–pathogenesis, diagnosis and treatment. Nature reviews. Endocrinology 11, 547–564, 10.1038/nrendo.2015.112 (2015).
    1. Perry J. R. et al.. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514, 92–97, 10.1038/nature13545 (2014).
    1. Elks C. E. et al.. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat Genet 42, 1077–1085, 10.1038/ng.714 (2010).
    1. Hagen C. P. et al.. Pubertal onset in girls is strongly influenced by genetic variation affecting FSH action. Scientific reports 4, 6412, 10.1038/srep06412 (2014).
    1. Hannum G. et al.. Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular cell 49, 359–367, 10.1016/j.molcel.2012.10.016 (2013).
    1. Horvath S. DNA methylation age of human tissues and cell types. Genome biology 14, R115, 10.1186/gb-2013-14-10-r115 (2013).
    1. Bekaert B., Kamalandua A., Zapico S. C., Van de Voorde W. & Decorte R. Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. Epigenetics , 1–9, 10.1080/15592294.2015.1080413 (2015).
    1. Bell J. T. et al.. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet 8, e1002629, 10.1371/journal.pgen.1002629 (2012).
    1. Marioni R. E. et al.. DNA methylation age of blood predicts all-cause mortality in later life. Genome biology 16, 25, 10.1186/s13059-015-0584-6 (2015).
    1. Weidner C. I. et al.. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome biology 15, R24, 10.1186/gb-2014-15-2-r24 (2014).
    1. Richmond R. C. et al.. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum Mol Genet 24, 2201–2217, 10.1093/hmg/ddu739 (2015).
    1. Zhang Y. et al.. Smoking-Associated DNA Methylation Biomarkers and Their Predictive Value for All-Cause and Cardiovascular Mortality. Environ Health Perspect , 10.1289/ehp.1409020 (2015).
    1. Aslibekyan S. et al.. Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity (Silver Spring, Md.) 23, 1493–1501, 10.1002/oby.21111 (2015).
    1. Horvath S. et al.. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci USA 111, 15538–15543, 10.1073/pnas.1412759111 (2014).
    1. Heijmans B. T. et al.. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA 105, 17046–17049, 10.1073/pnas.0806560105 (2008).
    1. Tobi E. W. et al.. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nature communications 5, 5592, 10.1038/ncomms6592 (2014).
    1. Demetriou C. A. et al.. Methylome analysis and epigenetic changes associated with menarcheal age. PLoS One 8, e79391, 10.1371/journal.pone.0079391 (2013).
    1. Lunetta K. L. et al.. Rare coding variants and X-linked loci associated with age at menarche. Nature communications 6, 7756, 10.1038/ncomms8756 (2015).
    1. Lee J. W., Choi H. S., Gyuris J., Brent R. & Moore D. D. Two classes of proteins dependent on either the presence or absence of thyroid hormone for interaction with the thyroid hormone receptor. Molecular endocrinology 9, 243–254, 10.1210/mend.9.2.7776974 (1995).
    1. Lv K. et al.. Trip6 promotes dendritic morphogenesis through dephosphorylated GRIP1-dependent myosin VI and F-actin organization. J Neurosci 35, 2559–2571, 10.1523/JNEUROSCI.2125-14.2015 (2015).
    1. Lin F.-T., Lin V. Y., Lin V. T. G. & Lin W.-C. TRIP6 antagonizes the recruitment of A20 and CYLD to TRAF6 to promote the LPA2 receptor-mediated TRAF6 activation. Cell Discovery 2, 15048 (2016).
    1. Diefenbacher M. E., Litfin M., Herrlich P. & Kassel O. The nuclear isoform of the LIM domain protein Trip6 integrates activating and repressing signals at the promoter-bound glucocorticoid receptor. Molecular and Cellular Endocrinology 320, 58–66 (2010).
    1. McBryan J., Howlin J., Kenny P. A., Shioda T. & Martin F. ERalpha-CITED1 co-regulated genes expressed during pubertal mammary gland development: implications for breast cancer prognosis. Oncogene 26, 6406–6419, 10.1038/sj.onc.1210468 (2007).
    1. Ron M. et al.. Combining mouse mammary gland gene expression and comparative mapping for the identification of candidate genes for QTL of milk production traits in cattle. BMC Genomics 8, 183, 10.1186/1471-2164-8-183 (2007).
    1. Tsutsumi M. et al.. Screening of genes involved in chromosome segregation during meiosis I: in vitro gene transfer to mouse fetal oocytes. J Hum Genet 57, 515–522, 10.1038/jhg.2012.61 (2012).
    1. Parent A. S. et al.. Gene expression profiling of hypothalamic hamartomas: a search for genes associated with central precocious puberty. Horm Res 69, 114–123, 10.1159/000111815 (2008).
    1. Arambepola N. K., Bunick D. & Cooke P. S. Thyroid hormone effects on androgen receptor messenger RNA expression in rat Sertoli and peritubular cells. J Endocrinol 156, 43–50 (1998).
    1. Manna P. R. et al.. Assessment of mechanisms of thyroid hormone action in mouse Leydig cells: regulation of the steroidogenic acute regulatory protein, steroidogenesis, and luteinizing hormone receptor function. Endocrinology 142, 319–331, 10.1210/endo.142.1.7900 (2001).
    1. Zhang C. et al.. Effects of 3, 5, 3′-triiodothyronine (t3) and follicle stimulating hormone on apoptosis and proliferation of rat ovarian granulosa cells. The Chinese journal of physiology 56, 298–305, 10.4077/cjp.2013.bab186 (2013).
    1. Zhang C., Xia G. & Tsang B. K. Interactions of thyroid hormone and FSH in the regulation of rat granulosa cell apoptosis. Frontiers in bioscience (Elite edition) 3, 1401–1413 (2011).
    1. Vervenne H. B. et al.. Targeted disruption of the mouse Lipoma Preferred Partner gene. Biochemical and biophysical research communications 379, 368–373, 10.1016/j.bbrc.2008.12.074 (2009).
    1. Hoffman L. M. et al.. Genetic ablation of zyxin causes Mena/VASP mislocalization, increased motility, and deficits in actin remodeling. J Cell Biol 172, 771–782, 10.1083/jcb.200512115 (2006).
    1. Renfranz P. J., Blankman E. & Beckerle M. C. The cytoskeletal regulator zyxin is required for viability in Drosophila melanogaster. Anat Rec (Hoboken) 293, 1455–1469, 10.1002/ar.21193 (2010).
    1. Leicher T., Bahring R., Isbrandt D. & Pongs O. Coexpression of the KCNA3B gene product with Kv1.5 leads to a novel A-type potassium channel. J Biol Chem 273, 35095–35101 (1998).
    1. Miller J. A. et al.. Conserved molecular signatures of neurogenesis in the hippocampal subgranular zone of rodents and primates. Development 140, 4633–4644 (2013).
    1. Xu J. et al.. The voltage-gated potassium channel Kv1.3 regulates energy homeostasis and body weight. Human Molecular Genetics 12, 551–559, 10.1093/hmg/ddg049 (2003).
    1. Yousefi P. et al.. Sex differences in DNA methylation assessed by 450 K BeadChip in newborns. BMC Genomics 16, 911, 10.1186/s12864-015-2034-y (2015).
    1. Finucane H. K. et al.. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47, 1228–1235, 10.1038/ng.3404 (2015).
    1. Heyn H. et al.. Distinct DNA methylomes of newborns and centenarians. Proc Natl Acad Sci USA 109, 10522–10527, 10.1073/pnas.1120658109 (2012).
    1. Aksglaede L., Sorensen K., Petersen J. H., Skakkebaek N. E. & Juul A. Recent decline in age at breast development: the Copenhagen Puberty Study. Pediatrics 123, e932–939, 10.1542/peds.2008-2491 (2009).
    1. Hagen C. P. et al.. Individual serum levels of anti-Mullerian hormone in healthy girls persist through childhood and adolescence: a longitudinal cohort study. Hum Reprod 27, 861–866, 10.1093/humrep/der435 (2012).
    1. Sorensen K., Aksglaede L., Petersen J. H. & Juul A. Recent changes in pubertal timing in healthy Danish boys: associations with body mass index. The Journal of clinical endocrinology and metabolism 95, 263–270, 10.1210/jc.2009-1478 (2010).
    1. Marshall W. A. & Tanner J. M. Variations in pattern of pubertal changes in girls. Archives of disease in childhood 44, 291–303 (1969).
    1. Sandoval J. et al.. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6, 692–702 (2011).
    1. Aryee M. J. et al.. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369, 10.1093/bioinformatics/btu049 (2014).
    1. Le S., Josse J. & Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw 25, 1–18 (2008).
    1. Maksimovic J., Gordon L. & Oshlack A. SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome biology 13, R44, 10.1186/gb-2012-13-6-r44 (2012).
    1. Jaffe A. E. & Irizarry R. A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome biology 15, R31, 10.1186/gb-2014-15-2-r31 (2014).
    1. Zou J., Lippert C., Heckerman D., Aryee M. & Listgarten J. Epigenome-wide association studies without the need for cell-type composition. Nature methods 11, 309–311, 10.1038/nmeth.2815 (2014).
    1. Leek J. T. & Storey J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3, 1724–1735, 10.1371/journal.pgen.0030161 (2007).
    1. Aulchenko Y. S., Ripke S., Isaacs A. & van Duijn C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296, 10.1093/bioinformatics/btm108 (2007).
    1. Barfield R. T., Kilaru V., Smith A. K. & Conneely K. N. CpGassoc: an R function for analysis of DNA methylation microarray data. Bioinformatics 28, 1280–1281, 10.1093/bioinformatics/bts124 (2012).
    1. Benjamini Y., Drai D., Elmer G., Kafkafi N. & Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125, 279–284 (2001).
    1. Peters T. J. et al.. De novo identification of differentially methylated regions in the human genome. Epigenetics & chromatin 8, 6, 10.1186/1756-8935-8-6 (2015).
    1. Heberle H., Meirelles G. V., da Silva F. R., Telles G. P. & Minghim R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC bioinformatics 16, 169, 10.1186/s12859-015-0611-3 (2015).
    1. Young M. D., Wakefield M. J., Smyth G. K. & Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome biology 11, R14, 10.1186/gb-2010-11-2-r14 (2010).
    1. Supek F., Bosnjak M., Skunca N. & Smuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800, 10.1371/journal.pone.0021800 (2011).
    1. Friedman J., Hastie T. & Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33, 1–22 (2010).
    1. Blomberg Jensen M. et al.. Vitamin D receptor and vitamin D metabolizing enzymes are expressed in the human male reproductive tract. Hum Reprod 25, 1303–1311, 10.1093/humrep/deq024 (2010).

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