Research Article

miRNA processing gene polymorphisms, blood DNA methylation age and long-term ambient PM_2.5 exposure in elderly men

Jamaji C Nwanaji-Enwerem

*Author for correspondence: Tel.: +1 617 432 2050; Fax: +1 617 384 8745;

E-mail Address: jnwanajienwerem@g.harvard.edu

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Elena Colicino

Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, NY, USA

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Lingzhen Dai

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Qian Di

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Allan C Just

Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

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Lifang Hou

Center for Population Epigenetics, Department of Preventive Medicine, Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

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Pantel Vokonas

VA Normative Aging Study, Veterans Affairs Boston Healthcare System & the Department of Medicine, Boston University School of Medicine, Boston, MA, USA

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Immaculata De Vivo

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Bernardo Lemos

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Quan Lu

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Marc G Weisskopf

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Andrea A Baccarelli

Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, NY, USA

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Joel D Schwartz

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Published Online:6 Nov 2017https://doi.org/10.2217/epi-2017-0094

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Abstract

Aim: We tested whether genetic variation in miRNA processing genes modified the association of PM_2.5 with DNA methylation (DNAm) age. Patients & methods: We conducted a repeated measures study based on 552 participants from the Normative Aging Study with multiple visits between 2000 and 2011 (n = 940 visits). Address-level 1-year PM_2.5 exposures were estimated using the GEOS-chem model. DNAm-age and a panel of 14 SNPs in miRNA processing genes were measured from participant blood samples. Results & conclusion: In fully adjusted linear mixed-effects models, having at least one copy of the minor rs4961280 [AGO2] allele was associated with a lower DNAm-age (β = -1.13; 95% CI: -2.26 to -0.002). However, the association of PM_2.5 with DNAm-age was significantly (P_interaction = 0.01) weaker in homozygous carriers of the major rs4961280 [AGO2] allele (β = 0.38; 95% CI: -0.20 to 0.96) when compared with all other participants (β = 1.58; 95% CI: 0.76 to 2.39). Our results suggest that miRNA processing impacts DNAm-age relationships.

Graphical abstract:

miRNA processing AGO2 polymorphism (rs4961280) modifies the association of long-term ambient fine particle exposure with blood DNA methylation age

The graph depicts lines from a fully adjusted linear regression model with fine particle exposure levels ranging from the tenth to the ninetieth percentile, all other continuous variables held constant at their means, and all other categorical variables held at their most frequent level.

Keywords:

Papers of special note have been highlighted as: • of interest; •• of considerable interest

References

1 WhO. Ageing and health (2015). www.who.int/ageing/events/world-report-2015-launch/en/.Google Scholar
2 Association As. 2017 Alzheimer's disease facts and figures. In: Alzheimer's & Dementia. 13325–13373 (2017). www.alz.org/documents_custom/2017-facts-and-figures.pdf.Google Scholar
3 WHO. Burden of disease from household air pollution for 2012. WHO, Geneva (2014). www.who.int/phe/health_topics/outdoorair/databases/FINAL_HAP_AAP_BoD_24March2014.pdf.Google Scholar
4 Zhong J, Cayir A, Trevisi L et al. Traffic-related air pollution, blood pressure, and adaptive response of mitochondrial abundance. Circulation 133(4), 378–387 (2016).Crossref, Medline, CAS, Google Scholar
5 Schikowski T, Vossoughi M, Vierkotter A et al. Association of air pollution with cognitive functions and its modification by APOE gene variants in elderly women. Environ. Res. 142, 10–16 (2015).Crossref, Medline, CAS, Google Scholar
6 Raaschou-Nielsen O, Beelen R, Wang M et al. Particulate matter air pollution components and risk for lung cancer. Environ. Int. 87, 66–73 (2016).Crossref, Medline, CAS, Google Scholar
7 Nwanaji-Enwerem JC, Colicino E, Trevisi L et al. Long-term ambient particle exposures and blood DNA methylation age: findings from the VA normative aging study. Environ. Epigenet. 2(2), dvw006 (2016). •• The first published work reporting associations of PM2.5 with DNA methylation (DNAm) age.Crossref, Medline, Google Scholar
8 Nwanaji-Enwerem JC, Dai L, Colicino E et al. Associations between long-term exposure to PM_2.5 component species and blood DNA methylation age in the elderly: the VA Normative Aging Study. Environ. Int. 102, 57–65 (2017).Crossref, Medline, CAS, Google Scholar
9 Chen BH, Marioni RE, Colicino E et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging (Albany NY). 8(9), 1844–1865 (2016). •• A key study that presents data suggesting that DNAm-age is an important measure of mortality risk.Crossref, Medline, CAS, Google Scholar
10 Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients. Aging (Albany NY). 7(12), 1130–1142 (2015).Crossref, Medline, CAS, Google Scholar
11 Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 14(10), R115 (2013). •• The first report of the DNAm-age measure that we are using and it discusses many of the basic properties of the measure.Crossref, Medline, Google Scholar
12 Edwards JR, Yarychkivska O, Boulard M, Bestor TH. DNA methylation and DNA methyltransferases. Epigenetics Chromatin. 10, 23 (2017).Crossref, Medline, Google Scholar
13 Rodosthenous RS, Coull BA, Lu Q et al. Ambient particulate matter and microRNAs in extracellular vesicles: a pilot study of older individuals. Part Fibre Toxicol. 13, 13 (2016).Crossref, Medline, Google Scholar
14 Kumar S, Vijayan M, Bhatti JS, Reddy PH. MicroRNAs as peripheral biomarkers in aging and age-related diseases. Prog. Mol. Biol. Transl. Sci. 146, 47–94 (2017).Crossref, Medline, CAS, Google Scholar
15 Parodi F, Carosio R, Ragusa M et al. Epigenetic dysregulation in neuroblastoma: a tale of miRNAs and DNA methylation. Biochim. Biophys. Acta 1859(12), 1502–1514 (2016).Crossref, Medline, CAS, Google Scholar
16 Morita S, Horii T, Kimura M et al. One Argonaute family member, Eif2c2 (Ago2), is essential for development and appears not to be involved in DNA methylation. Genomics 89(6), 687–696 (2007).Crossref, Medline, CAS, Google Scholar
17 Hodjat M, Rahmani S, Khan F et al. Environmental toxicants, incidence of degenerative diseases, and therapies from the epigenetic point of view. Arch. Toxicol. 91(7), 2577–2597 (2017).Crossref, Medline, CAS, Google Scholar
18 Bell B, Rose CL, Damon A. The veterans administration longitudinal study of healthy aging. Gerontologist 6(4), 179–184 (1966). • Important for understanding the Normative Aging Study cohort.Crossref, Medline, CAS, Google Scholar
19 DNA methylation age and theepigenetic clock. http://labs.genetics.ucla.edu/horvath/dnamage/.Google Scholar
20 GEOS-Chem Model. www.geos-chem.org.Google Scholar
21 Di Q, Koutrakis P, Schwartz J. A hybrid prediction model for PM_2.5 mass and components using a chemical transport model and land use regression. Atmospheric Environment. 131, 390–399 (2016).Crossref, CAS, Google Scholar
22 Yang H, Dinney CP, Ye Y et al. Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer. Cancer Res. 68(7), 2530–2537 (2008).Crossref, Medline, CAS, Google Scholar
23 Horikawa Y, Wood CG, Yang H et al. Single nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinoma. Clin. Cancer Res. 14(23), 7956–7962 (2008).Crossref, Medline, CAS, Google Scholar
24 Colicino E, Giuliano G, Power MC et al. Long-term exposure to black carbon, cognition and single nucleotide polymorphisms in microRNA processing genes in older men. Environ. Int. 88, 86–93 (2016).Crossref, Medline, CAS, Google Scholar
25 Wilker EH, Baccarelli A, Suh H et al. Black carbon exposures, blood pressure, and interactions with single nucleotide polymorphisms in microRNA processing genes. Environ. Health Perspect. 118(7), 943–948 (2010).Crossref, Medline, CAS, Google Scholar
26 PhAT. Pharmacogenetics of Asthma Treatment. www.pharmgat.org/Tools/pbtoldplotform.Google Scholar
27 Lenters V, Portengen L, Rignell-Hydbom A et al. Prenatal phthalate, perfluoroalkyl acid, and organochlorine exposures and term birth weight in three birth cohorts: multi-pollutant models based on elastic net regression. Environ. Health Perspect. 124(3), 365–372 (2016).Crossref, Medline, CAS, Google Scholar
28 glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. https://cran.r-project.org/web/packages/glmnet/index.html.Google Scholar
29 Nwanaji-Enwerem JC, Bind MA, Dai L et al. Modifying role of endothelial function gene variants on the association of long-term PM_2.5 exposure with blood DNA methylation age: the VA Normative Aging Study. Toxicol. Sci. 158(1), 116–126 (2017).Crossref, Medline, CAS, Google Scholar
30 Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010).Crossref, Medline, Google Scholar
31 Zou H, Hastie T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. 67(2), 301–320 (2005).Crossref, Google Scholar
32 Houseman EA, Accomando WP, Koestler DC et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).Crossref, Medline, Google Scholar
33 Panni T, Mehta AJ, Schwartz JD et al. Genome-wide analysis of DNA methylation and fine particulate matter air pollution in three study populations: KORA F3, KORA F4, and the Normative Aging Study. Environ. Health Perspect. 124(7), 983–990 (2016).Crossref, Medline, CAS, Google Scholar
34 Dai L, Mehta A, Mordukhovich I et al. Differential DNA methylation and PM_2.5 species in a 450 K epigenome-wide association study. Epigenetics 12(2), 139–148 (2016).Crossref, Medline, Google Scholar
35 GeneMANIA. https://genemania.org.Google Scholar
36 Daugaard I, Hansen TB. Biogenesis and function of Ago-associated RNAs. Trends Genet. 33(3), 208–219 (2017).Crossref, Medline, CAS, Google Scholar
37 Nikolic Z, Savic Pavicevic D, Vucic N et al. Genetic variants in RNA-induced silencing complex genes and prostate cancer. World J. Urol. 35(4), 613–624 (2017).Crossref, Medline, CAS, Google Scholar
38 Mullany LE, Herrick JS, Wolff RK, Buas MF, Slattery ML. Impact of polymorphisms in microRNA biogenesis genes on colon cancer risk and microRNA expression levels: a population-based, case–control study. BMC Med. Genomics. 9(1), 21 (2016).Crossref, Medline, Google Scholar
39 An J, Rao A, Ko M. TET family dioxygenases and DNA demethylation in stem cells and cancers. Exp. Mol. Med. 49(4), e323 (2017).Crossref, Medline, CAS, Google Scholar
40 Meister G, Landthaler M, Patkaniowska A et al. Human Argonaute2 mediates RNA cleavage targeted by miRNAs and siRNAs. Mol. Cell. 15(2), 185–197 (2004).Crossref, Medline, CAS, Google Scholar
41 Zhang X, Azhar G, Wei JY. The expression of microRNA and microRNA clusters in the aging heart. PLoS ONE 7(4), e34688 (2012).Crossref, Medline, CAS, Google Scholar
42 Unnikrishnan R, Almassi N, Fareed K. Benign prostatic hyperplasia: evaluation and medical management in primary care. Cleve Clin. J. Med. 84(1), 53–64 (2017).Crossref, Medline, Google Scholar
43 Kim BS, Im YB, Jung SJ, Park CH, Kang SK. Argonaute2 regulation for K+ channel-mediated human adipose tissue-derived stromal cells self-renewal and survival in nucleus. Stem Cells Dev. 21(10), 1736–1748 (2012).Crossref, Medline, CAS, Google Scholar
44 Benhamed M, Herbig U, Ye T, Dejean A, Bischof O. Senescence is an endogenous trigger for microRNA-directed transcriptional gene silencing in human cells. Nat. Cell Biol. 14(3), 266–275 (2012).Crossref, Medline, CAS, Google Scholar
45 Lopez P, Wagner KD, Hofman P, Van Obberghen E. RNA activation of the vascular endothelial growth factor gene (VEGF) promoter by double-stranded RNA and hypoxia: role of noncoding VEGF promoter transcripts. Mol. Cell Biol. 36(10), 1480–1493 (2016).Crossref, Medline, CAS, Google Scholar
46 Abe M, Naqvi A, Hendriks GJ et al. Impact of age-associated increase in 2′-O-methylation of miRNAs on aging and neurodegeneration in Drosophila. Genes Dev. 28(1), 44–57 (2014).Crossref, Medline, CAS, Google Scholar
47 Kogure A, Uno M, Ikeda T, Nishida E. The microRNA machinery regulates fasting-induced changes in gene expression and longevity in Caenorhabditis elegans. J. Biol. Chem. 292(27), 11300–11309 (2017).Crossref, Medline, CAS, Google Scholar
48 Weinmann L, Hock J, Ivacevic T et al. Importin 8 is a gene silencing factor that targets argonaute proteins to distinct mRNAs. Cell 136(3), 496–507 (2009).Crossref, Medline, CAS, Google Scholar
49 Nguewa PA, Agorreta J, Blanco D et al. Identification of importin 8 (IPO8) as the most accurate reference gene for the clinicopathological analysis of lung specimens. BMC Mol. Biol. 9, 103 (2008).Crossref, Medline, Google Scholar
50 Kyriakopoulou CB, Nordvarg H, Virtanen A. A novel nuclear human poly(A) polymerase (PAP), PAP gamma. J. Biol. Chem. 276(36), 33504–33511 (2001).Crossref, Medline, CAS, Google Scholar
51 Armah CN, Traka MH, Dainty JR et al. A diet rich in high-glucoraphanin broccoli interacts with genotype to reduce discordance in plasma metabolite profiles by modulating mitochondrial function. Am. J. Clin. Nutr. 98(3), 712–722 (2013).Crossref, Medline, CAS, Google Scholar
52 Katoh M, Katoh M. Identification and characterization of human TIPARP gene within the CCNL amplicon at human chromosome 3q25.31. Int. J. Oncol. 23(2), 541–547 (2003).Medline, CAS, Google Scholar
53 Aung HH, Lame MW, Gohil K et al. Comparative gene responses to collected ambient particles in vitro: endothelial responses. Physiol. Genomics 43(15), 917–929 (2011).Crossref, Medline, CAS, Google Scholar
54 Gualtieri M, Longhin E, Mattioli M et al. Gene expression profiling of A549 cells exposed to Milan PM_2.5. Toxicol. Lett. 209(2), 136–145 (2012).Crossref, Medline, CAS, Google Scholar
55 Kioumourtzoglou MA, Spiegelman D, Szpiro AA et al. Exposure measurement error in PM_2.5 health effects studies: a pooled analysis of eight personal exposure validation studies. Environ. Health 13(1), 2 (2014).Crossref, Medline, Google Scholar

Vol. 9, No. 12

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History

Received 1 August 2017

Accepted 6 September 2017

Published online 6 November 2017

Published in print December 2017

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Keywords

Data availability

Data are from the Normative Aging Study, from which restricted data are available for researchers who meet the criteria. Methylation data are deposited at NCBI dbGaP (study accession number: phs000853.v1.p1).

Financial & competing interests disclosure

This work was supported by grants from the National Institute of Environmental Health Sciences (NIEHS) (R01ES021733 and R01ES025225). Other support comes from NIEHS grants (ES015172, ES014663, ES020010, P30ES009089 and P30ES000002); Environmental Protection Agency grants (RD832416 and RD83587201); and a National Heart, Lung, and Blood Institute grant (2T32HL007118-41). Additional support was provided by the US Department of Agriculture, Agricultural Research Service (contract 53-K06–510). The US Department of Veterans Affairs Normative Aging Study is supported by the Cooperative Studies Program/ERIC, US Department of Veterans Affairs, and is a research component of the Massachusetts Veterans Epidemiology Research and Information Center. The views expressed in this paper are those of the authors and do not necessarily represent the views of the US Department of Veterans Affairs. JC Nwanaji-Enwerem is also supported by an NIH/NIA Ruth L Kirschstein National Research Service Award (1 F31AG056124–01A1). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethics approval

Boston VA Medical Center, Harvard TH Chan School of Public Health (protocol 14027–102).

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miRNA processing gene polymorphisms, blood DNA methylation age and long-term ambient PM2.5 exposure in elderly men

Abstract

References

miRNA processing gene polymorphisms, blood DNA methylation age and long-term ambient PM_2.5 exposure in elderly men