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. Author manuscript; available in PMC: 2021 Apr 15.
Published in final edited form as: Curr Opin Toxicol. 2017 Oct 1;6:18–25. doi: 10.1016/j.cotox.2017.07.001

Air Pollution and the Epigenome: A Model Relationship for the Exploration of Toxicoepigenetics

Shaun D McCullough 1,*, Radhika Dhingra 1, Marie C Fortin 2, David Diaz-Sanchez 1
PMCID: PMC8048108  NIHMSID: NIHMS1622615  PMID: 33869910

Abstract

The field of toxicoepigenetics is rapidly emerging to provide new insights into the relationship between environmental factors, the epigenome, and public health. Toxicoepigenetic data have the potential to revolutionize our understanding of environmental exposure effects and susceptibility. Studies in recent years have demonstrated that exposure to air pollution alters epigenetic modification states; however, continued advancement of the field is limited by the intrinsic complexity of the epigenome and inherent limitations of different types of studies (epidemiological, clinical, and in vitro) that are used in toxicoepigenetics. Overcoming these challenges will require a concerted and collaborative effort between molecular and cellular biologists, toxicologists, epidemiologists, and risk assessors to develop a thorough and practical understanding of the relationship between air pollution exposure, the epigenome, and health effects. Here we review the current state of air pollution epigenetics and discuss perspectives on the necessary steps to move the field forward to determine the role that the epigenome plays in air pollution exposure effects and susceptibility.

Keywords: Epigenetics, DNA methylation, histone, acetylation, methylation, air pollution, CRISPR

1. Introduction

The epigenome consists of a complex set of modifications to DNA and histone proteins that primarily serves to provide instructions for the regulation of chromatin structure and thus directs gene expression, DNA replication/repair, and other cellular functions [1–3]. These instructions are written in an alphabet that includes >130 histone modifications [4], as well as DNA methylation (5-methylcytosine) and its derivatives 5-hydroxymethylcytosine (5-hmC), 5-formylcytosine(5-fC), and 5-carboxylcytosine (5-caC), which play varying roles in the regulation of gene expression that are yet to be fully characterized [reviewed in 5]. The patterns of these modifications can be stable and inherited across mitotic and meiotic generations; however, they are also dictated by intrinsic (e.g., age, sex, and genotype) and extrinsic (e.g., stress, diet, and toxic exposures) factors, collectively referred to as “environmental factors” [reviewed in 6]. These environmental factors shape the epigenome and mediate exposure outcomes from the molecular level [7]. They can also influence short- and long-term disease susceptibility by modifying the baseline epigenetic state and “priming” cells, tissues, and organisms for a given response. Understanding the complex relationship between environmental factors, the epigenome, and the effects of air pollutant exposure has the potential to lead to the identification of transformative biomarkers of susceptibility and effect that will drive the next generation of risk assessment. While promising, moving forward will require that critical questions be addressed and that challenges be overcome.

The effects of air pollution have been widely investigated in vitro (cell lines and primary cells) and in vivo (animal models, controlled human exposures, and epidemiological studies) [8,9]. These studies have shown that pollutants such as ozone and particulate matter (PM) are important contributors to cardiopulmonary morbidity and mortality; however, only a small fraction of these studies have included epigenetic endpoints. This relatively small number of studies has demonstrated an association between air pollution exposures and epigenetic changes, and has been the subject of several reviews in recent years [10–17]. Moving forward from these founding observations will require the establishment of a causative relationship between air pollution exposures, health effects, and the epigenome; however, accomplishing that goal will require researchers to develop innovative approaches to acquire and integrate data describing how exposures influence levels of a diverse array of epigenetic modifications, determine the utility and applicability of surrogate tissues, and identify the mechanisms through which epigenetic changes/differences lead to exposure-associated disease, among others. Here we provide a brief history of studies that serve as the foundation for our understanding of the association between air pollutant exposure and the epigenome. We discuss challenges, data gaps, and strategies to examine the putative causative role for the epigenome in the adverse effects of environmental exposures, using air pollution as a model. As recommended by Burris and Baccarelli [14], the perspectives expressed here were developed through a cross-specialty collaboration between an epigeneticist/molecular toxicologist, an epidemiologist, a risk assessor, and an environmental health scientist that specializes in clinical research.

2. Linking Air Pollution Exposure and the Epigenome

Early studies linking air pollution exposure and epigenetic changes focused around the effect of PM exposures (black carbon, PM2.5, and PM10) on DNA methylation levels within either repetitive elements (Alu and LINE-1) – which serve as markers of broader-scale epigenetic changes throughout the genome – or the inducible nitric oxide synthase (iNOS/NOS2) gene – which plays a role in the modulation of oxidative stress and inflammation following air pollution exposure [18–21]. While still primarily focused on the effects of air pollution exposure on epigenetic endpoints in peripheral leukocytes in large study populations, more recent approaches have expanded to global profiling of site-specific DNA methylation by incorporating bead chip technology, which allows for the simultaneous interrogation of >480,000 (450k array) or >850,000 (EPIC array) individual CpG loci. When paired with large, highly documented populations (e.g., KORA and the Normative Aging Study) with matched local or regional air pollution data these studies have revealed associations between methylation of CpG sites linked to a range of genes and different air pollutants [22–25], health outcomes [26,27], and epigenetic aging [28–32]. More focused exploration of small sets of exposure- or effect-associated loci in these studies has also demonstrated an association between PM exposure and coagulation, inflammation, and endothelial function. For example, altered methylation of inflammatory markers such as, ICAM-1 and IL-6, were observed with increased exposure to black carbon [33], and showed some modification by psychological factors [34]. Further, mediation analysis found that the methylation status of ICAM-1 mediated a small portion (9%) of the association of 28-day PM2.5 with fasting blood glucose levels [35]. Overall, these studies illustrate the complexity of the interrelationship between environmental exposures, such as air pollution, and intrinsic factors such as fasting blood glucose and anxiety.

While the majority of studies examining the relationship between air pollution exposure and the epigenome focus on DNA methylation, several have linked exposures with bulk changes in histone modifications in peripheral leukocytes of exposed humans [36,37], blood and total lung tissue in rats [38], and cultured human airway epithelial cells [39,40]. To the best of our knowledge, only one study to date has explored the air pollution exposure-associated changes in histone modifications at specific loci. Liu et al. [41] associated high levels of PM2.5 exposure with changes in H3K27ac within the regulatory regions of genes associated with immune function in peripheral leukocytes; however, the small study population (n=4; with 2 high- and 2 low-exposed individuals) may limit the broader interpretation of their findings. Studies such as those described above serve as proof-of-principle that exposure to a range of air pollutants is associated with changes in both DNA methylation and histone modifications; however, the field has yet to address key questions that must be answered to provide the depth of understanding required for the use of epigenetic data in practical applications, such as risk assessment and adverse outcome pathway development (Box 1). Doing so will involve coordinated effort between a broad range of disciplines including: molecular biologists/toxicologists, who can demonstrate biological plausibility and supply mechanistically relevant epigenetic profiles, clinical researchers, who can validate the putative biomarkers in humans and infer causality, and epidemiologists, who can assess relationships between the candidate biomarkers and exposures in real-world scenarios. Finally, risk assessors will be needed to integrate all these data into a weight of evidence framework.

Box 1:

Box 1:

3. Moving the Field Forward

3.1. Incorporating the Diversity of Epigenetic Modifications in Air Pollution Research

A recent epigenetics work group has proposed that small-magnitude effect sizes in DNA methylation are important in environmental health studies, although the authors state that it, “is reasonable and necessary that we question the relevance of such small effects” [42]. While further research will help to clarify this point, regardless it should be appreciated that DNA methylation is only one of a myriad of epigenetic modifications and is unlikely to be a primary driver of exposure effects on its own. Examining the diversity of epigenetic modifications (>130 histone modifications as well as DNA methylation and its derivatives at millions of loci) in a large number of samples comes with a host of challenges and is not readily feasible with relatively limited sample material using the technologies currently available. This is further confounded by relatively recent description of divergent roles of DNA methylation derivatives in the regulation of gene expression [43–46]. Unfortunately, these DNA methylation derivatives cannot be differentiated by standard bisulfite conversion methods, which serve as an integral component of DNA methylation analysis protocols, including site-specific PCR, bisulfite-seq, and bead arrays. The bisulfite conversion products of 5-mC and 5-hmC, which have opposing effects on the regulation of gene expression, are indistinguishable in downstream assays and thus both are interpreted at “methylated” DNA [47]. In contrast, 5-fC and 5-caC are indistinguishable from unmodified cytosine and are interpreted as “unmethylated” DNA [48].

Recent improvements in epigenome mapping have facilitated the production of publicly-available epigenome browsers [49], which allow for the visualization of the global distribution of histone modifications in a range of cell types thus facilitating the prioritization of target histone modifications. When paired with improvements in the efficiency and reproducibility of the chromatin immunoprecipitation procedure [e.g., 50], the ability to multiplex ChIP-seq [51], and strategies for normalizing ChIP-seq data [52] the examination of histone modifications in toxicoepigenetic studies is more practical and accessible. Further, the application of techniques to distinguish these DNA methylation derivatives will help mitigate the potential for ambiguity in detection [48,53]; however, doing so currently requires that 5-methylcytosine and its derivatives be assayed separately by methods such as oxidative bisulfite on an array (oxBS-Array) [54], leading to increased costs and demand on sample material.

Regardless of the study approach, the observation of an exposure/susceptibility-related epigenetic or epigenomic change is only the first step toward determining whether there is a causal relationship between air pollution, the epigenome, and adverse health outcomes, and more fundamental research will allow to support a transition from associative observations to the examination of molecular determinants of susceptibility/effects.

3.2. Strengthening the Relationship Between Air Pollution, the Epigenome, and Exposure Effects

Environmental epigenetics studies, primarily designed using simple epidemiological conceptual models (Figure 1A), have shown associations between air pollution exposures and alterations to the epigenome, which have been instrumental in raising public awareness and giving traction to this emerging field. Despite these accomplishments, the path toward determining whether the epigenome plays a causal role in mediating air pollution health effects will require the integration of data from mechanistic, clinical, and epidemiologic studies. As our understanding of epigenetic mechanisms of gene regulation deepens, and as initially posited in the Seed and Soil model [6, 55], the relationship between exposure and epigenome is so complex that a more sophisticated conceptual model may be required to better inform study design (Figure 1B) [56]. In Figure 1B, an epigenetic state, referring to either a single epigenetic modification, the set of relevant epigenetic modifications, or the entire epigenome occupy the multiple simultaneous functions (e.g., effect, effect modifier, or mediator) through time, as compared to Figure 1A where it is conceived to occupy a single role. For example, Gregory et al. [57] showed that maternal exposure to diesel at time point t=1, might affect the epigenetic state after birth (t=2) and further demonstrated that the resulting epigenetic state (t=2) after birth may interact with an environmental insult to cause an allergic response at t=3. While both individual findings (t=1 to t=2 and t=2 to t=3) are somewhat interesting, the assembly of these findings offers a more insight into risk assessment, future research questions, and potential health interventions. Following this conceptual model, an ideal air pollution epigenetic epidemiologic study will capture and appropriately analyze exposure data, exposure effects (e.g., markers of inflammation, pulmonary function testing, or biomarkers of cardiovascular disease) as well as epigenetic modifications in a time-course appropriate longitudinal manner. Anticipating such complex relationships in the data may, of course, require more sophisticated statistical techniques, such as marginal structural modeling or system dynamics modeling, and the associated study design would likely benefit greatly by engaging a biostatistician or even a systems biologist.

Figure 1: Conceptual diagram of hypothesized roles of epigenetic modification in the exposure to effect relationship.

Figure 1:

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In preliminary studies of available cohorts, epigenetic epidemiology has necessarily hypothesized (A) simplified roles for epigenetic modifications as mediator, effect modifier and biomarker of effect. It is possible, however, that a given epigenomic state may play (B) multiple simultaneous roles through time. In (B), the subscript t=1,2,3, indicate the time point for the condition of exposure, epigenomic state (i.e., soil) or effect. Here, epigenomic state refers to either a single epigenetic modification, the set of relevant epigenetic modifications, or the entire epigenome, as appropriate to the exposure-effect relationship in question.

While epidemiologic studies are the most direct investigation in public health, their utility can be limited by their intrinsic complexity and relative lack of granularity with respect to the exploration of the molecular mechanisms at the heart of pollutant exposure effects and susceptibility, and by the difficulties associated with adequately documenting the exposure of interest and concurrent environmental factors. These challenges can be overcome by complementary clinical and in vitro studies, which allow for the careful control of exposure scenario and relative isolation of affected cell types, and provide a system to identify and screen candidate epigenetic modifications to narrow the scope an increase the practicality of larger, higher-impact studies. Further, both in vitro and clinical studies allow for the comparison of pre- and post-exposure epigenetic states with biologically relevant endpoints.

Traditional approaches to modulating levels of epigenetic modifications have involved either the inactivation of epigenetic enzymes with small molecule inhibitors or depletion by small interfering RNAs; however, these approaches typically have profound effects on global epigenetic states and are not ideal for unambiguously linking changes in a specific epigenetic modification at an individual genomic locus with a discrete exposure effect. These limitations can be overcome through the application of locus-specific epigenetic editing using clustered regularly interspaced short palindromic repeat (CRISPR) technology. Traditional CRISPR-based genomic editing directs the CRISPR-associated protein 9 (Cas9) nuclease with a guide RNA (gRNA) that is complementary to the target genomic locus [58,59]. The subsequent development of deactivated Cas9 (dCas9), which can be directed to a target genomic locus, but lacks nuclease activity [60], has provided a platform for the targeted delivery of transcription factors [61,62], histone modifying enzymes [63], and both DNA methyltransferases [64,65] and demethylases [66,67] to defined genomic loci. These tools and approaches can be used in cell-based or animal models to selectively control epigenetic modification levels at specific loci to determine the functional role that epigenetic changes observed in clinical and epidemiological studies play in regulating the expression of associated genes. Such studies will be valuable in establishing biological plausibility to the associations between exposure and epigenetic changes that are observed in clinical and epidemiology studies. The further development and use of tools such as these in the context of well-designed in vitro and in vivo models will reinforce and expand the findings of larger-scale studies by providing data linking epigenetic changes/differences with exposure effects and susceptibility. In doing so they will support the biological plausibility of cause and effect relationships between exposure-associated epigenetic changes and health outcomes.

3.3. The Use of Surrogate Tissues and Biological Matrices

The study of target tissue such as the bronchial epithelium is impractical in large populations, which has led to the use of leukocytes isolated from peripheral blood as a surrogate tissue. Even though all cells contain the same genetic information, cellular fate and function is dictated by specific epigenetic patterns within each cell type. For example, the epigenome of a neutrophil is different than that of an airway epithelial cell. It remains unclear whether the effect of air pollutant exposure on the airway epithelial cell epigenome, or that of any other impacted cell type, would be accurately reflected in peripheral leukocytes. Further, the relationship between the pre-exposure epigenome within the regulatory regions of genes of interest in peripheral leukocytes and tissues affected by air pollutant exposure remains elusive. The relationship between the epigenomes of peripheral leukocytes and cell types within structural tissues is currently being investigated by the NIH-funded consortium “TaRGET II: Environmental Epigenomic Analysis in Tissue Surrogates.”

As a further complication, peripheral blood is comprised of a complex and variable mixture of distinct cell populations, each of which has a divergent epigenome that is also subject to change as function of the immune response. Post hoc computational methods can be applied to ensure that statistically observed differential methylation in peripheral blood samples is not an artifact resulting from the over- or under-representation of one or more leukocyte sub-types in the sample [68–70]. While useful for deconvolution of mixed cell populations, these methods are not able to prevent epigenetic changes/differences in minor cell populations from being obscured by the abundance of a difference the same locus in a more abundant cell type within the mixture. Given these complications, buccal cells have recently been proposed as a more informative surrogate tissue for epigenetic studies [71], with limited use in assessing the epigenetic effects of tobacco smoke exposure on childhood asthma [reviewed in 72], but have yet to be used broadly in air pollution epigenetics studies. Further, induced sputum is an accepted representative of airway inflammation [73], which may also serve as a readily-accessible surrogate for epigenetic analysis of the airway. While peripheral leukocytes still have great utility as a practical surrogate marker, it will be imperative to develop an understanding of how their epigenome relates to that in target tissues and verify both the extent and persistence of epigenetic changes indicated in these studies in the exposed cell type in a controlled exposure (i.e., clinical) setting.

4. Conclusions

Founding observations have demonstrated that air pollutant exposure influences the epigenome; however, several key questions must be answered before we can effectively use epigenetic endpoints as biomarkers or in air pollution risk assessment (Box 1). While challenging, these questions can be answered through the concerted and collaborative efforts of molecular/cellular biologists, epidemiologists, clinical researchers, toxicologists, and risk assessors. These collaborations will produce the information that is necessary to support the identification of susceptible populations through the application of novel epigenetic biomarkers and will lead to the generation of data from all the realms of research to inform risk assessment. Instead of single epigenetic modifications at discrete loci, these biomarkers will reflect the fertility of the “epigenetic soils” within the regulatory regions of one or more genes that play key roles in the induction, severity, or persistence of exposure effects. The integration of a broad range of epigenetic modifications in the Seed and Soil model together with the measurement of adverse effects will provide an approach toward identifying epigenetic biomarkers of susceptibility and exposure effects that is biologically comprehensive and useful for risk assessment.

Acknowledgements

The authors would like to thank Dr. Cavin K. Ward-Caviness and Dr. Ilona Jaspers for their expert opinions during critical review of the manuscript. The contents of this article have been reviewed by the Environmental Protection Agency and approved for publication, and do not necessarily represent Agency policy, nor does mention of trade names or commercial products constitute endorsement or recommendations for use.

Footnotes

The authors declare no competing interests.

References

  • 1.).Strahl BD, Allis CD. 2000. The language of covalent histone modifications. Nature 403: 41–45. [DOI] [PubMed] [Google Scholar]
  • 2.).Jenuwein T, Allis CD. 2001. Translating the histone code. Science 293: 1074–1080. [DOI] [PubMed] [Google Scholar]
  • 3.).Cedar H, Bergman Y. 2009. Linking DNA methylation and histone modification: patterns and paradigms. Nat Rev Genet 10: 295–304. [DOI] [PubMed] [Google Scholar]
  • 4.).Tan M, Luo H, Lee S, Jin F, Yang JS, Montellier E, et al. , 2011. Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell 146: 1016–1028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.).Schübeler D 2015. Function and information content of DNA methylation. Nature 517: 321–326. [DOI] [PubMed] [Google Scholar]
  • 6.).Bowers EC, McCullough SD. 2017. Linking the Epigenome with Exposure Effects and Susceptibility: The Epigenetic Seed and Soil Model. Toxicol Sci 155: 302–314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.).Baccarelli AA, Bollati V. 2009. Epigenetics and environmental chemicals. Curr Opin Pediatr 21: 243–251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.).U.S. EPA. 2009. Final Report: Integrated Science Assessment for Particulate Matter. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-08/139F, 2009. [PubMed] [Google Scholar]
  • 9.).U.S. EPA. 2013. Final Report: Integrated Science Assessment of Ozone and Related Photochemical Oxidants. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-10/076F, 2013. [Google Scholar]
  • 10.).Bollati V, Baccarelli AA. 2010. Environmental epigenetics. Heredity 105: 105–112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.).Ji H, Hershey GKK. 2012. Genetic and epigenetic influence on the response to environmental particulate matter. J Allergy Clin Immunol 129: 33–41. Doi: 10.1016/j.jaci.2011.11.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.).Wang T, Garcia JGN, Zhang W. 2012. Epigenetic regulation in particulate matter-mediated cardiopulmonary toxicities: a systems biology perspective. Curr Pharmacogenomics Person Med 10: 314–321. PMCID: PMC3505120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.).Breton CV, Marutani AN. 2014. Air pollution and epigenetics: recent findings. Curr Envir Health Rpt 1: 35–45. Doi: 10.1007/s40572-013-0001-9. [DOI] [Google Scholar]
  • 14.).Burris HH, Baccarelli AA. 2014. Environmental epigenetics: from novelty to scientific discipline. J Appl Toxicol 34: 113–116. Doi: 10.1002/jat.2904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.).Li J, Li WX, Bai C, Song Y. 2015. Particulate matter-induced epigenetic changes and lung cancer. Clin Respir J. doi: 10.111/crj.12389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.).Ji H, Myers JMB, Brandt EB, Brokamp C, Ryan PH, Hershey GKK. 2016. Air pollution, epigenetics, and asthma. Allergy Asthma Clin Immunol 12:51. Doi: 10.1186/s13223-016-0159-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.).Burris HH, Baccarelli AA. 2017. Air pollution and in utero programming of poor fetal growth. Epigenomics 9: 213–216. Doi: 10.2217/epi-2017-0008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.).Baccarelli AA, Wright RO, Bollait V, Tarantini L, Litonjua AA, Suh HH, Zanobetti A, Sparrow D, Vokonas PS, Schwartz J. 2009. Rapid DNA methylation changes after exposure to traffic particles. Am J Respir Crit Care Med 179: 572–578. Doi: 10.1164/rccm.200807-1097OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.).Tarantini L, Bonzini M, Apostoli P, Pegoraro V, Bollati V, Marinelli B, Cantone L, Rizzo G, Hou L, Schwartz J, Bertazzi PA, Baccarelli AA. 2009. Effects of particulate matter on genomic DNA methylation content and iNOS promoter methylation. Environ Health Perspect 117: 217–222. Doi: 10.1289/ehp.11898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.).Breton CV, Salam MT, Wang X, Byun H-M, Siegmund KD, Gilliland FD. 2012. Particulate matter, DNA methylation in nitric oxide synthase, and childhood respiratory disease. Environ Health Perspect 120: 1320–1326. Doi: 10.1289/ehp.1104439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.).Kile ML, Fang S, Baccarelli AA, Tarantini L, Cavallari J, Christiani DC. 2013. A panel study of occupational exposure to fine particulate matter and changes in DNA methylation over a single workday and years worked in boilermaker welders. Environmental Health 12: 47. Doi: 10.1186/1476-069X-12-47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.).Bind M-A, Coull BA, Peters A, Baccarelli AA, Tarantini L, Cantone L, Vokonas PS, Koutrakis P, Schwartz JD. 2015. Beyond the mean: quantile regression to explore the association of air pollution with gene-specific methylation in the normative aging study. Environ Health Perspect 123: 759–765. Doi: 10-1289/ehp.1307824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.).Panni T, Mehta AJ, Schwartz JD, Baccarelli AA, Just AC, Wolf K, Wahl S, Cyrys J, Kunze S, Strauch K, Waldenberger M, Peters A. 2016. A 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: 983–990. Doi: 10.1289/ehp.1509966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.).Somineni KH, Zhang X, Myers JMB, Kovacic MB, Ulm A, Jurcak N, Ryan PH, Hershey GKK, Ji H. 2016. TET1 methylation is associated with childhood asthma and traffic-related air pollution. J Allergy Clin Immunol 137: 797–805. Doi: 10.1016/j.jaci.2015.10.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.).Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Antó JM, Auffray C, Ballereau S, Bellander T, Bousquet J, Bustamante M, Charles MA, de Kluizenaar Y, den Dekker HT, Duijts L, Felix JF, Gehring U, Guxens M, Jaddoe VV, Jankipersadsing SA, Merid SK, Kere J, Kumar A, Lemonnier N, Lepeule J, Nystad W, Page CM, Panasevich S, Postma D, Slama R, Sunyer J, Söderhäll C, Yao J, London SJ, Pershagen G, Koppelman GH, Melén E. 2017. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect 125: 104–110. Doi: 10.1289/EHP36 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.).Lepeule J, Bind MAC, Baccarelli AA, Koutrakis P, Tarantini L, Litonjua A, Sparrow D, Vokonas P, Schwartz JD. 2014. Epigenetic influences on associations between air pollutants and lung function in elderly men: the Normative Aging Study. Environ Health Perspect 122: 566–572. Doi: 10.1289/ehp.1206458 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.).Zhong J, Colicino E, Lin X, Mehta A, Kloog I, Zanobetti A, Byun H-M, Bind M-A, Cantone L, Prada D, Tarantini L, Trevisi L, Sparrow D, Vokonas P, Schwartz J, Baccarelli AA. 2015. Cardiac autonomic dysfunction: particulate air pollution effects are modulated by epigenetic immunoregulation of Toll-like receptor 2 and dietary flavonoid intake. J Am Heart Assoc 4: e001423. Doi: 10.1161/JAHA.114.001423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.).Nwanaji-Enwerem JC, Colicino E, Trevisi L, Kloog I, Just AC, Shen J, et al. 2016. Long-term ambient particle exposures and blood DNA methylation age: findings from the VA normative aging study. Environ Epigenet 2: 1–10. Doi: 10.1093/eep/dvw006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.).Nwanaji-Enwerem JC, Bind MA, Dai L, Oulhote Y, Colicino E, Di Q, Just AC, Hou L, Vokonas P, Coull BA, Weisskopf MG, Baccarelli AA, Schwartz JD. 2017. Modifying role of endothelial function gene variants on the association of long-term PM2.5 exposure with blood DNA methylation age: the VA normative aging study. Toxicol Sci epub. Doi: 10.1093/toxsci/kfx077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.).Nwanaji-Enwerem JC, Dai L, Colicino E, Oulhote Y, Di Q, Kloog I, Just AC, Hou L, Vokonas P, Baccarelli AA, Weisskopf MG, Schwartz JD. 2017. Associations between long-term exposure to PM2.5 component species and blood DNA methylation age in the elderly: the VA normative aging study. Environ Int 102: 57–65. Doi: 10.1016/j.envint.2016.12.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.).Ward-Caviness CK, Nwanaji-Enwerem JC, Wolf K, Wahl S, Colicino E, Trevisi L, Kloog I, Just AC, Vokonas P, Cyrys J, Gieger C, Schwartz J, Baccarelli AA, Schneider A, Peters A. 2016. Oncotarget 7: 74510–74525.Doi: 10.18632/oncotarget.12903. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.).Liu J, Xie K, Chen W, Zhu M, Shen W, Yuan J, Cheng Y, Geng L, Wang Y, Li Z, Zhang J, Jin G, Dai J, Ma H, Du J, Wang M, Zhang Z, Hu Z, Wu T, Shen H. 2017. Genetic variants, PM2.5 exposure level and global DNA methylation level: a multi-center population-based study in Chinese. Toxicol Lett 269: 77–82. Doi: 10.1016/j.toxlet.2017.02.003. [DOI] [PubMed] [Google Scholar]
  • 33.).Bind M-A, Lepeule J, Zanobetti A, Gasparrini A, Baccarelli AA, Coull BA, Tarantini L, Vokonas PS, Koutrakis P, Schwartz J. 2014. Air pollution and gene-specific methylation in the normative aging study. Epigenetics 9: 448–458. Doi: 10.4161/epi27584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.).Madrigano J, Baccarelli AA, Mittleman MA, Sparrow D, Spiro A III, Vokonas PS, Cantone L, Kubzansky L, Schwartz J. 2012. Air pollution and DNA methylation: interaction by psychological factors in the VA normative aging study. Am J Epidemiol 176: 224–232. Doi: 10.1093/aje/kwr523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.).Peng C, Bind MA, Colicino E, Kloog I, Byun HM, Cantone L, Trevisi L, Zhong J, Brennan K, Dereix AE, Vokonas PS, Coull BA, Schwartz JD, Baccarelli AA. 2016. Particulate air pollution and fasting blood glucose in nondiabetic individuals: associations and epigenetic mediation in the Normative Aging Study, 2000–2011. Environ Health Perspect 124: 1715–1721. Doi: 10.1289/EHP183 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.).Cantone L, Nordio F, Hou L, Apostoli P, Bonzini M, Tarantini L, Angelici L, Bollati V, Zanobetti A, Schwartz J, Bertazzi PA, Baccarelli AA. 2011. Inhalable metal-ricj air particles and histone H3K4 dimethylation and H3K9 acetylation in a cross-sectional study of steel workers. Environ Health Perspect 119: 964–969. Doi: 10.1289/ehp.1002955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.).Zheng Y, Sanchez-Guerra M, Zhang Z, Joyce BT, Zhong J, Kresovich JK, Liu L, Zhang W, Gao T, Chang D, Osorio-Yanez C, Carmona JJ, Wang S, McCracken JP, Zhang X, Chernova Y, Diaz A, Bertazzi PA, Koutrakis P, Kang C-M, Schwartz J, Baccarelli AA, Hou L. 2017. Traffic-derived particulate matter exposure and histone H3 modification: a repeated measures study. Environ Res 153: 112–119. Doi: 10.1016/j.envres.2016.11.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.).Ding R, Jin Y, Liu Z, Ye H, Zhu Z, Zhang Y, Wang T, Xu Y. 2017. Dose- and time-effect responses of DNA methylation and histone H3K9 acetylation changes induced by traffic-related air pollution. Sci Rep 7: 43737. Doi: 10.1038/srep43737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.).Borgie M, Ledoux F, Verdin A, Cazier F, Greige H, Shirali P, Courcot D, Dagher Z. 2015. Genotoxic and epigenotoxic effects of fine particulate matter from rural and urban sites in Lebanon n human bronchial epithelial cells. Environ Res 136: 352–362. Doi: 10.1016/j.envres.2014.10.010. [DOI] [PubMed] [Google Scholar]
  • 40.).Leclercq B, Happillon M, Antherieu S, Hardy EM, Alleman LY, Grova N, Perdix E, Appenzeller BM, Guidice J-ML, Coddeville P, Garcon G. 2016. Differential responses of healthy and chronic obstructive pulmonary diseases human bronchial epithelial cells repeatedly exposure to air pollution-derived PM4. Environ Pollut 218: 1074–1088. Doi: 10.1016/j.envpol.2016.08.059. [DOI] [PubMed] [Google Scholar]
  • 41.).Liu C, Xu J, Chen Y, Guo X, Zheng Y, Wang Q, Chen Y, Ni Y, Zhu Y, Joyce BT, Baccarelli AA, Deng F, Zhang W, Hou L. 2015. Characterization of genome-wide H3K27ac profiles reveals a distinct PM2.5-associated histone modification signature. Environ Health 14: 65. Doi: 10.1186/s12940-015-0052-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.).Breton CV, Marsit CJ, Faustman E, Nadeau K, Goodrich JM, Dolinoy DC, Herbstman J, Holland N, LaSalle JM, Schmidt R, Yousefi P, Perera F, Joubert BR, Wiemels J, Taylor M, Yang IV, Chen R, Hew KM, Freeland DM, Miller R, Murphy SK. 2017. Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: the Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group. Environ Health Perspect 125: 511–526. Doi: 10.1289/EHP595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.).Ficz G, Branco MR, Seisenberger S, Santos F, Krueger F, Hore TA, et al. 2011. Dynamic regulation of 5-hydroxymethylcytosine in mouse ES cells and during differentiation. Nature 473: 398–402. Doi: 10.1038/nature10008. [DOI] [PubMed] [Google Scholar]
  • 44.).Ito S, Shen L, Dai Q, Wu SC, Collins LB, Swenberg JA, He C, Zhang Y. 2011. Tet proteins can convert 5-methylcytosine to form 5-formylcytosince and 5-carboxylcytosine. Science. 333: 1300–1303. Doi: 10.1126/science.1210597. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.).Wu H, Zhang Y. 2011. Mechanisms and functions of Tet protein-mediated 5-methylcytosine oxidation. Genes & Dev 25: 2436–2452. Doi: 10.1101/gad.179184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.).Bachman M, Uribe-Lewis S, Yang X, Williams M, Murrell A, Balasubramanian S. 2014. 5-hydroxymethylcytosine is a predominantly stable DNA modification. Nat Chem 6:1049–1055. Doi: 10.1038/nchem.2064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.).Huang Y, Pastor WA, Shen Y, Tahiliani M, Liu DR, Rao A. 2010. The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing. PLoS One 5: e8888. Doi: 10.1371/journal.pone.0008888. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.).Neri N, Incarnato D, Krepelova A, Parlato C, Oliviero S. 2016. Methylation-assisted bisulfite sequencing to simultaneously map 5fC and 5caC on a genome-wide scale for DNA demethylation analysis. Nat Protoc 11: 1191–1205. Doi: 10.1038/nprot.2016.063. [DOI] [PubMed] [Google Scholar]
  • 49.).Roadmap Epigenomics Consortium. (2015). Integrative analysis of 111 reference human epigenomes. Nature 518(7539), 317–330. Doi: 10.1038/nature14248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.).McCullough SD, On DM, Bowers EC. 2017. Using chromatin immunoprecipitation in toxicology: a step-by-step guide to increasing efficiency, reducing variability, and expanding applications. Curr Protoc Toxicol 72: 3.14.1–3.14.28. Doi: 10.1002/cptx.22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.).Van Galen P, Viny AD, Ram O, Ryan RJH, Cotton MJ, Donohue L, Sievers C, Drier Y, Liau BB, Gillespie SM, Carrol KM, Cross MB, Levine RL, Bernstein BE. 2015. A multiplexed system for quantitative comparisons of chromatin landscapes. Mol Cell 61: 170–180. Doi: 10.1016/j.molcel.2015.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.).Grzybowski AT, Chen Z, Ruthenburg AJ. 2016. Calibrating ChIP-seq with nucleosomal internal standards to measure histone modification density genome wide. Mol Cell 58: 886–899. Doi: 10.1010/j.molcel.2015.04.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.).Stewart SK, Morris TJ, Guilhamon P, Bulstrode H, Bachman M, Balasubramanian S, Beck S. 2015. oxBS-450K: a method for analyzing hydroxymethylation using 450K BeadChips. Methods 15: 9–15. Doi: 10.1016/j.ymeth.2014.08.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.).Field SF, Beraldi D, Bachman M, Stewart SK, Beck S, Balasubramanian S. 2015. Accurate measurement of 5-methylcytosine and 5-hydroxymethylcytosine in human cerebellum DNA by oxidative bisulfite on an array (oxBS-array). PLoS One 10: e0118202. Doi: 10.1371/journal.pone.0118202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.).McCullough SD, Bowers EC, On DM, Morgan DS, Dailey LA, Hines RN, et al. 2016. Baseline Chromatin Modification Levels May Predict Interindividual Variability in Ozone-Induced Gene Expression. Toxicol Sci 150: 216–224. Doi: 10.1093/toxsci/kfv324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.).Ladd-Acosta C 2015. Epigenetic signatures as biomarkers of exposure. Environ Epigenetics 2: 117–125. Doi: 10.1007/s40572-015-0051-2. [DOI] [PubMed] [Google Scholar]
  • 57.).Gregory DJ, Kobzik L, Yang Z, McGuire CC, Fedulov AV. 2017. Transgeneration transmission of asthma risk after exposure to environmental particles during pregnancy. Am J Physiol Lung Cell Mol Physiol. May 2017 epub. DOI: 10.1152/ajplung.00035.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.).Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. 2012. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337: 816–821. Doi: 10.1126/science.1225829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.).Charpentier E, Doudna JA. 2013. Biotechnology: rewriting a genome. Nature 495: 50–51. Doi: 10.1038/495050a. [DOI] [PubMed] [Google Scholar]
  • 60.).Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS, Arkin AP, Lim WA. 2013. Repurposing CRISPR as a RNA-guided platform for sequence-specific control of gene expression. Cell 152: 1173–1183. Doi: 10.1016/j.cell2013.02.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.).Gilbert LA, Larson MH, Morsut L, Liu Z, Brar GA, Torres SE, Stern-Gionssar N, Brandman O, Whitehead EH, Doudna JA, Lim WA, Weissman JS, Qi LS. 2013. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154: 442–451. Doi: 10.1016/j.cell.2013.06.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.).Perez-Pinera P, Kocak DD, Vockley CM, Adler AF, Kabadi AM, Polstein LR, Thakore PI, Glass KA, Ousterout DG, Leong KW, Guilak F, Crawford GE, Reddy TE, Gersbach CA. 2013. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat Methods 10: 973–978. Doi: 10.1038/nmeth.2666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.).Hilton IB, D’Ippolito AM, Vockley CM, Thakore PI, Crawford GE, Reddy TE, Gersbach CA. 2015. Epigenome editing by a CRISPR/Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat Biotechnol 33: 510–517. Doi: 10.1038/nbt.3199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.).McDonald JI, Celik H, Rois LE, Fishberger G, Fowler T, Rees R, Kramer A, Matens A, Edwards JR, Challen GA. 2016. Reprogrammable CRISPR/Cas9-based system for inducing site-specific DNA methylation. Biol Open 5: 866–874. Doi: 10.1242/bio.019067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.).Stepper P, Kungulovski G, Jurkowska RZ, Chandra T, Kruegar F, Reinhardt R, Reik W, Jeltsch A, Jurkowski TP. 2016. Efficient targeted DNA methylation with chimeric dCas9-Dnmt3a-Dnmt3L methyltransferase. Nuc Acids Res 45: 1703–1713. Doi: 10.1093/nar/gkw1112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.).Liu XS, Wu H, Ji X, Stelzer Y, Wu X, Czauderna S, Shu J, Dadon D, Young RA, Jaenisch R. 2016. Editing DNA methylation in the mammalian genome. Cell 167: 233–247. Doi: 10.1016/j.cell.2016.08.056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.).Xu X, Tao Y, Gao X, Zhang L, Li X, Zou W, Ruan K, Wang F, Xu G-L, Hu R. 2016. A CRISPR-based approach for targeted DNA demethylation. Cell Discovery 2: 16009. Doi: 10.1038/celldisc.2016.9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.).Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. 2012. DNA methylation arrays as surrogate measures of cell mixture distribuition. BMC Bioinformatics 13: 86. Doi: 10.1186/1471-2105-13-86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.).Koestler DC, Christensen BC, Karagas MR, Marsit CJ, Langevin SM, Kelsey KT, Wiencke JK, Housman EA. 2013. Blood-based profiles of DNA methylation predict the underlying distribution of cell types. Epigenetics 8: 816–826. Doi: 10.4161/epi.25430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.).Houseman EA, Kile ML, Christiani DC, Ince TA, Kelsey KT, Marsit CJ. 2016. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects. BMC Bioinformatics 17:259. Doi: 10.1186/s12859-016-1140-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.).Lowe R, Gemma C, Beyan H, Hawa MI, Bazeos A, Leslie RD, Montpetit A, Rakyan VK, Ramagopalan SV. 2013. Buccals are likely to be a more informative surrogate tissue than blood for epigenome-wide association studies. Epigenetics 8: 445–454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.).Salam MT, Zhang Y, Begum K. 2012. Epigenetic and childhood asthma: current evident and future research directions. Epigenomics 4: 415–429. Doi: 10.2217/epi.12.32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.).Pin I, Gibson PG, Kolendowicz R, Girgis-Gabardo A, Denburg JA, Hargrave FE, Dolovish J. 1992. Use of induced sputum cell counts to investigate airway inflammation in asthma. Thorax 47: 25–29. [DOI] [PMC free article] [PubMed] [Google Scholar]

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