Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context

Brock C Christensen, E Andres Houseman, Carmen J Marsit, Shichun Zheng, Margaret R Wrensch, Joseph L Wiemels, Heather H Nelson, Margaret R Karagas, James F Padbury, Raphael Bueno, David J Sugarbaker, Ru-Fang Yeh, John K Wiencke, Karl T Kelsey, Brock C Christensen, E Andres Houseman, Carmen J Marsit, Shichun Zheng, Margaret R Wrensch, Joseph L Wiemels, Heather H Nelson, Margaret R Karagas, James F Padbury, Raphael Bueno, David J Sugarbaker, Ru-Fang Yeh, John K Wiencke, Karl T Kelsey

Abstract

Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P<0.0001) and were significant predictors of tissue origin (P<0.0001). In solid tissues (n = 119) we found striking, highly significant CpG island-dependent correlations between age and methylation; loci in CpG islands gained methylation with age, loci not in CpG islands lost methylation with age (P<0.001), and this pattern was consistent across tissues and in an analysis of blood-derived DNA. Our data clearly demonstrate age- and exposure-related differences in tissue-specific methylation and significant age-associated methylation patterns which are CpG island context-dependent. This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations. Collectively we reveal key issues to consider both in the construction of reference and disease-related epigenomes and in the interpretation of potentially pathologically important alterations.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1. Unsupervised clustering of average beta…
Figure 1. Unsupervised clustering of average beta values in normal human tissues.
Normal tissue sample average beta values were subjected to unsupervised hierarchical clustering based on Manhattan distance and average linkage. Each column represents a sample and each row represents a CpG locus (500 most variable autosomal loci). Above the heatmap, colors indicate tissue type as in key. In the heat map blue = average β of one, or methylated, and yellow = average β of zero, or unmethylated.
Figure 2. Recursive partitioning mixture models (RPMM)…
Figure 2. Recursive partitioning mixture models (RPMM) of methylation profiles in normal tissuses.
Methylation average β is yellow for unmethylated and blue for methylated. Methylation profile classes are stacked in rows separated by red lines, and class height corresponds to the number of samples in each class. Class methylation at each CpG locus is a mean of methylation for all samples within a class. (A) Methylation profile classes significantly differentiate all normal tissue types (n = 217, P<0.0001). (B) Methylation profile classes from RPMM of adult bloods, class membership is significantly associated with age (P<0.005, n = 30). (C) Methylation profile classes from RPMM of lung tissue samples (n = 53). (D) Methylation profile classes from RPMM of pleural samples (n = 18).
Figure 3. The direction of correlations for…
Figure 3. The direction of correlations for age associated methylation alterations differ dependent upon CpG island status.
For (A–F), the top plot is the estimate of mean regression coefficients for age associated methylation (by decade), and its 95% confidence interval from GEE for each CpG RPMM class. The middle plot is of CpGs clustered with RPMM into eight classes for each group of samples. The bottom plot indicates the CpG island status for each locus (where magenta = CpG island locus, green = non-CpG island locus). (A) Estimates of class-specific age-associated methylation among all solid tissues (n = 119), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly increased among classes with a high prevalence of CpG-island loci (P = 2.3E-08). (B) Estimates of class-specific age-associated methylation among blood samples (n = 29), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly decreased among classes with a high prevalence of non-CpG-island loci (P = 6.3E-06). (C) Estimates of class-specific age associated methylation among pleural tissues (n = 18), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly increased among classes with a high prevalence of CpG-island loci (P = 2.3E-08). (D) Estimates of class-specific age-associated methylation among lung tissues (n = 52), RPMM clustering of CpGs, and CpG island status. (E) Estimates of class-specific age-associated methylation among brain tissues (n = 11), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly increased for the predominantly CpG island loci in class 3, and significantly decreased among classes with a high prevalence of non-CpG-island loci (P = 7.0E-04). (F) Estimates of class-specific age associated methylation among head and neck tissues (n = 10), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly decreased among classes with a high prevalence of non-CpG-island loci (P = 5.2E-08).
Figure 4. Bisulfite pyrosequencing confirmation of age…
Figure 4. Bisulfite pyrosequencing confirmation of age and environmental exposure–related methylation alterations observed in array results.
Each plot displays the gene transcription start site and surrounding CpGs are indicated by tick marks where the array CpG tick mark has a filled circle on top, and the sequenced CpGs are surrounded by a box. (A) Distribution of mean bisulfite pyrosequencing percent methylation for RARA_P176 and five downstream CpGs (79 bases total) stratified by known exposure to asbestos in DNA from pleural samples (n = 16) confirms the observation from array results of increased methylation at this locus among individuals with a known asbestos exposure (P = 0.10). (B) Mean bisulfite pyrosequencing percent methylation of DNMT3B_P352 and two downstream CpGs (30 bases total) in adult bloods run on the array and an independent set of controls plotted versus age (P = 0.03, rho = ––0.18, n = 112). (C) Mean bisulfite pyrosequencing percent methylation of LIF_P383 and two downstream CpGs (41 bases total) in adult bloods run on the array and an independent set of controls plotted versus smoking packyears (P = 0.01, rho = 0.24, n = 112). (D) Mean bisulfite pyrosequencing percent methylation of FZD9_E458 and five downstream CpGs (25 bases total) in adult bloods run on the array and an independent set of controls plotted versus age (P = 7.0E-04, rho = 0.29, n = 112).

References

    1. Russo V, Martienssen RA, Riggs AD. Cold Spring Harbor Laboratory Press; 1996. Epigenetic mechanisms of gene regulation.
    1. Feinberg AP, Tycko B. The history of cancer epigenetics. Nat Rev Cancer. 2004;4:143–153.
    1. Amir RE, Van den Veyver IB, Wan M, Tran CQ, Francke U, et al. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat Genet. 1999;23:185–188.
    1. Xu GL, Bestor TH, Bourc'his D, Hsieh CL, Tommerup N, et al. Chromosome instability and immunodeficiency syndrome caused by mutations in a DNA methyltransferase gene. Nature. 1999;402:187–191.
    1. DeBaun MR, Niemitz EL, McNeil DE, Brandenburg SA, Lee MP, et al. Epigenetic alterations of H19 and LIT1 distinguish patients with Beckwith-Wiedemann syndrome with cancer and birth defects. Am J Hum Genet. 2002;70:604–611.
    1. Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nat Rev Genet. 2002;3:415–428.
    1. Shiota K. DNA methylation profiles of CpG islands for cellular differentiation and development in mammals. Cytogenet Genome Res. 2004;105:325–334.
    1. Eckhardt F, Lewin J, Cortese R, Rakyan VK, Attwood J, et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet. 2006;38:1378–1385.
    1. Illingworth R, Kerr A, Desousa D, Jorgensen H, Ellis P, et al. A novel CpG island set identifies tissue-specific methylation at developmental gene loci. PLoS Biol. 2008;6:e22. doi: .
    1. Rakyan VK, Down TA, Thorne NP, Flicek P, Kulesha E, et al. An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res. 2008;18:1518–1529.
    1. McCarthy MI, Hirschhorn JN. Genome-wide association studies: potential next steps on a genetic journey. Hum Mol Genet. 2008;17:R156–165.
    1. Christensen BC, Houseman EA, Godleski JJ, Marsit CJ, Longacker JL, et al. Epigenetic profiles distinguish pleural mesothelioma from normal pleura and predict lung asbestos burden and clinical outcome. Cancer Res. 2009;69:227–234.
    1. Marsit CJ, Christensen BC, Houseman EA, Karagas MR, Wrensch MR, et al. Epigenetic profiling reveals etiologically distinct patterns of DNA methylation in head and neck squamous cell carcinoma. Carcinogenesis. 2009;30:416–422.
    1. Bock C, Paulsen M, Tierling S, Mikeska T, Lengauer T, et al. CpG island methylation in human lymphocytes is highly correlated with DNA sequence, repeats, and predicted DNA structure. PLoS Genet. 2006;2:e26. doi: .
    1. Rakyan VK, Hildmann T, Novik KL, Lewin J, Tost J, et al. DNA methylation profiling of the human major histocompatibility complex: a pilot study for the human epigenome project. PLoS Biol. 2004;2:e405. doi: .
    1. Schilling E, Rehli M. Global, comparative analysis of tissue-specific promoter CpG methylation. Genomics. 2007;90:314–323.
    1. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A. 2005;102:10604–10609.
    1. Kwabi-Addo B, Chung W, Shen L, Ittmann M, Wheeler T, et al. Age-related DNA methylation changes in normal human prostate tissues. Clin Cancer Res. 2007;13:3796–3802.
    1. Shen L, Kondo Y, Rosner GL, Xiao L, Hernandez NS, et al. MGMT promoter methylation and field defect in sporadic colorectal cancer. J Natl Cancer Inst. 2005;97:1330–1338.
    1. Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, et al. Intra-individual change over time in DNA methylation with familial clustering. JAMA. 2008;299:2877–2883.
    1. Houseman EA, Christensen BC, Marsit CJ, Karagas MR, Wrensch MR, et al. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics. 2008;9
    1. Issa JP. Age-related epigenetic changes and the immune system. Clin Immunol. 2003;109:103–108.
    1. Taddei A, Hediger F, Neumann FR, Gasser SM. The function of nuclear architecture: a genetic approach. Annu Rev Genet. 2004;38:305–345.
    1. Straussman R, Nejman D, Roberts D, Steinfeld I, Blum B, et al. Developmental programming of CpG island methylation profiles in the human genome. Nat Struct Mol Biol. 2009;16:564–571.
    1. Esnault G, Majocchi S, Martinet D, Besuchet-Schmutz N, Beckmann JS, et al. Transcription factor CTF1 acts as a chromatin domain boundary that shields human telomeric genes from silencing. Mol Cell Biol. 2009;29:2409–2418.
    1. Takai D, Jones PA. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci U S A. 2002;99:3740–3745.
    1. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–130.
    1. Christensen BC, Godleski JJ, Marsit CJ, Houseman EA, Lopez-Fagundo CY, et al. Asbestos exposure predicts cell cycle control gene promoter methylation in pleural mesothelioma. Carcinogenesis. 2008;29:1555–1559.
    1. Marsit CJ, Houseman EA, Schned AR, Karagas MR, Kelsey KT. Promoter hypermethylation is associated with current smoking, age, gender and survival in bladder cancer. Carcinogenesis. 2007;28:1745–1751.
    1. Marsit CJ, McClean MD, Furniss CS, Kelsey KT. Epigenetic inactivation of the SFRP genes is associated with drinking, smoking and HPV in head and neck squamous cell carcinoma. Int J Cancer. 2006;119:1761–1766.
    1. Toyooka S, Maruyama R, Toyooka KO, McLerran D, Feng Z, et al. Smoke exposure, histologic type and geography-related differences in the methylation profiles of non-small cell lung cancer. Int J Cancer. 2003;103:153–160.
    1. Lyon CM, Klinge DM, Liechty KC, Gentry FD, March TH, et al. Radiation-induced lung adenocarcinoma is associated with increased frequency of genes inactivated by promoter hypermethylation. Radiat Res. 2007;168:409–414.
    1. Christensen BC, Houseman EA, Godleski JJ, Marsit CJ, Longacker JL, et al. Epigenetic profiles distinguish pleural mesothelioma from normal pleura and predict lung asbestos burden and clinical outcome. Cancer Res. 2008. (Accepted)
    1. Richardson B. Impact of aging on DNA methylation. Ageing Res Rev. 2003;2:245–261.
    1. Issa JP, Ottaviano YL, Celano P, Hamilton SR, Davidson NE, et al. Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nat Genet. 1994;7:536–540.
    1. Fraga MF, Agrelo R, Esteller M. Cross-talk between aging and cancer: the epigenetic language. Ann N Y Acad Sci. 2007;1100:60–74.
    1. Tra J, Kondo T, Lu Q, Kuick R, Hanash S, et al. Infrequent occurrence of age-dependent changes in CpG island methylation as detected by restriction landmark genome scanning. Mech Ageing Dev. 2002;123:1487–1503.
    1. Wiemels JL, Wiencke JK, Sison JD, Miike R, McMillan A, et al. History of allergies among adults with glioma and controls. Int J Cancer. 2002;98:609–615.
    1. Wiencke JK, Kelsey KT, Varkonyi A, Semey K, Wain JC, et al. Correlation of DNA adducts in blood mononuclear cells with tobacco carcinogen-induced damage in human lung. Cancer Res. 1995;55:4910–4914.
    1. Karagas MR, Tosteson TD, Blum J, Morris JS, Baron JA, et al. Design of an epidemiologic study of drinking water arsenic exposure and skin and bladder cancer risk in a U.S. population. Environ Health Perspect. 1998;106(Suppl 4):1047–1050.
    1. Peters ES, McClean MD, Liu M, Eisen EA, Mueller N, et al. The ADH1C polymorphism modifies the risk of squamous cell carcinoma of the head and neck associated with alcohol and tobacco use. Cancer Epidemiol Biomarkers Prev. 2005;14:476–482.
    1. Urayama KY, Wiencke JK, Buffler PA, Chokkalingam AP, Metayer C, et al. MDR1 gene variants, indoor insecticide exposure, and the risk of childhood acute lymphoblastic leukemia. Cancer Epidemiol Biomarkers Prev. 2007;16:1172–1177.
    1. Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW, et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res. 2006;16:383–393.
    1. R Development CT. Vienna, Austria: R Foundation for Statistical Computing; 2007. R: A Language and Environment for Statistical Computing.
    1. van der Laan M, Pollard K. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. Journal of Statistical Planning and Inference. 2003;117:275–303.
    1. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008;24:719–720.
    1. Shen L, Toyota M, Kondo Y, Lin E, Zhang L, et al. Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci U S A. 2007;104:18654–18659.
    1. Siegmund KD, Connor CM, Campan M, Long TI, Weisenberger DJ, et al. DNA methylation in the human cerebral cortex is dynamically regulated throughout the life span and involves differentiated neurons. PLoS ONE. 2007;2:e895. doi: .
    1. Siegmund KD, Laird PW, Laird-Offringa IA. A comparison of cluster analysis methods using DNA methylation data. Bioinformatics. 2004;20:1896–1904.
    1. Ji Y, Wu C, Liu P, Wang J, Coombes KR. Applications of beta-mixture models in bioinformatics. Bioinformatics. 2005;21:2118–2122.
    1. Fraley F, Raftery A. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002;97:611–631.
    1. Houseman EA, Coull BA, Betensky RA. Feature-specific penalized latent class analysis for genomic data. Biometrics. 2006;62:1062–1070.
    1. Breiman L. Random Forests. Machine Learning. 2001;45:5–32.
    1. Pico ASI, Chang JS, Yeh RF, Williamson DW, Wiemels JL, Wiencke JL, Tihan T, Conklin BR, Wrensch M. SNPLogic: an interactive web resource for the pathway-based selection and prioritization of SNPs for genotyping studies; 2007; Santa Ana Pueblo, New Mexico.
    1. Hsiung DT, Marsit CJ, Houseman EA, Eddy K, Furniss CS, et al. Global DNA methylation level in whole blood as a biomarker in head and neck squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev. 2007;16:108–114.
    1. Storey J, Taylor J, Siegmund D. Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. J Royal Stat Soc Series B. 2004:187–205.
    1. Yang AS, Estecio MR, Doshi K, Kondo Y, Tajara EH, et al. A simple method for estimating global DNA methylation using bisulfite PCR of repetitive DNA elements. Nucleic Acids Res. 2004;32:e38.

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