Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Background: Exposure in utero to particulate matter (PM2.5 and PM10) is associated with maladaptive health outcomes. Although exposure to prenatal PM2.5 and PM10 has cord blood DNA methylation signatures at birth, signature persistence into childhood and saliva cross-tissue applicability has not been tested. Methods: In the Fragile Families and Child Wellbeing Study, a United States 20-city birth cohort, average residential PM2.5 and PM10 during the three months prior to birth was estimated using air quality monitors with inverse distance weighting. Saliva DNA methylation at ages 9 (n = 749) and 15 (n = 793) was measured using the Illumina HumanMethylation 450 k BeadArray. Cumulative DNA methylation scores for particulate matter were estimated by weighting participant DNA methylation at each site by independent meta-analysis effect estimates and standardizing the sums. Using a mixed-effects regression analysis, we tested the associations between cumulative DNA methylation scores at ages 9 and 15 and PM exposure during pregnancy, adjusted for child sex, age, race/ethnicity, maternal income-to-needs ratio, nonmartial birth status, and saliva cell-type proportions. Results: Our study sample was 50.5% male, 56.3% non-Hispanic Black, and 19.8% Hispanic, with a median income-to-needs ratio of 1.4. Mean exposure levels for PM2.5 were 27.9 μg/m3/day (standard deviation: 7.0; 23.7% of observations exceeded safety standards) and for PM10 were 15.0 μg/m3/day (standard deviation: 3.1). An interquartile range increase in PM2.5 exposure (10.73 μg/m3/day) was associated with a −0.0287 standard deviation lower cumulative DNA methylation score for PM2.5 (95% CI: −0.0732, 0.0158, p = 0.20) across all participants. An interquartile range increase in PM10 exposure (3.20 μg/m3/day) was associated with a −0.1472 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.3038, 0.0095, p = 0.06) across all participants. The PM10 findings were driven by the age 15 subset where an interquartile range increase in PM10 exposure was associated with a −0.024 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.043, −0.005, p = 0.012). Findings were robust to adjustment for PM exposure at ages 1 and 3. Conclusion: In utero PM10-associated DNA methylation differences were identified at age 15 in saliva. Benchmarking the timing and cell-type generalizability is critical for epigenetic exposure biomarker assessment.

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Particulate matter exposure in utero is associated with childhood saliva DNA methylation at ages 9 and 15

ISEE Conference Abstracts ◽

10.1289/isee.2020.virtual.p-0939 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

K.M. Bakulski ◽

J.F. Dou ◽

J. Fisher ◽

A.M. Gard ◽

E.B. Ware ◽

...

Keyword(s):

Dna Methylation ◽

Particulate Matter ◽

In Utero ◽

Particulate Matter Exposure

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DNA methylation and gene expression integration in cardiovascular disease

Clinical Epigenetics ◽

10.1186/s13148-021-01064-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Guillermo Palou-Márquez ◽

Isaac Subirana ◽

Lara Nonell ◽

Alba Fernández-Sanlés ◽

Roberto Elosua

Keyword(s):

Gene Expression ◽

Cardiovascular Disease ◽

Dna Methylation ◽

Cardiovascular Diseases ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Risk Function ◽

Predictive Biomarkers ◽

Independent Study ◽

Cell Type

Abstract Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.

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Particulate matter (PM2.5 and PM10) generation map using MODIS Level-1 satellite images and deep neural network

Journal of Environmental Management ◽

10.1016/j.jenvman.2020.111888 ◽

2021 ◽

Vol 281 ◽

pp. 111888

Author(s):

Maryam Imani

Keyword(s):

Neural Network ◽

Particulate Matter ◽

Deep Neural Network ◽

Satellite Images ◽

Level 1 ◽

Pm2.5 And Pm10

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Analysis of the Particulate Matter Pollution in the Urban Areas of Croatia, EU

Environmental Sciences Proceedings ◽

10.3390/ecas2020-08145 ◽

2020 ◽

Vol 4 (1) ◽

pp. 9

Author(s):

Martina Habulan ◽

Bojan Đurin ◽

Anita Ptiček Siročić ◽

Nikola Sakač

Keyword(s):

Particulate Matter ◽

Urban Areas ◽

Fossil Fuels ◽

Negative Impact ◽

Electric Energy ◽

Two Cities ◽

Adriatic Coast ◽

Pm2.5 And Pm10 ◽

Autumn And Winter ◽

Particulate Matter Pollution

Particulate matter (PM) comprises a mixture of chemical compounds and water particles found in the air. The size of suspended particles is directly related to the negative impact on human health and the environment. In this paper, we present an analysis of the PM pollution in urban areas of Croatia. Data on PM10 and PM2.5 concentrations were measured with nine instruments at seven stationary measuring units located in three continental cities, namely Zagreb (the capital), Slavonski Brod, and Osijek, and two cities on the Adriatic coast, namely Rijeka and Dubrovnik. We analyzed an hourly course of PM2.5 and PM10 concentrations and average seasonal PM2.5 and PM10 concentrations from 2017 to 2019. At most measuring stations, maximum concentrations were recorded during autumn and winter, which can be explained by the intensive use of fossil fuels and traffic. Increases in PM concentrations during the summer months at measuring stations in Rijeka and Dubrovnik may be associated with the intensive arrival of tourists by air during the tourist season, and lower PM concentrations during the winter periods may be caused by a milder climate consequently resulting in lower consumption of fossil fuels and use of electric energy for heating.

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EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

Scientific Reports ◽

10.1038/s41598-021-84864-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hanyu Zhang ◽

Ruoyi Cai ◽

James Dai ◽

Wei Sun

Keyword(s):

Dna Methylation ◽

Tumor Cells ◽

T Regulatory Cells ◽

Simulated Data ◽

Cell Types ◽

Computational Method ◽

Methylation Data ◽

Cell Type ◽

A Cell ◽

Type Decomposition

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

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