Short-term exposure to fine particulate matter and genome-wide DNA methylation in chronic obstructive pulmonary disease: A panel study conducted in Beijing, China

Abstract

BackgroundFine particulate matter (PM2.5) is a crucial risk factor for chronic obstructive pulmonary disease (COPD). However, the mechanisms whereby PM2.5 contribute to COPD risk have not been fully elucidated. Accumulating evidence suggests that epigenetics, including DNA methylation, play an important role in this process; however, the association between PM2.5 exposure and genome-wide DNA methylation in patients with COPD has not been studied.ObjectiveTo evaluate the association of personal exposure to PM2.5 and genome-wide DNA methylation changes in the peripheral blood of patients with COPD.MethodsA panel study was conducted in Beijing, China. We repeatedly measured and collected personal PM2.5 data for 72 h. Genome-wide DNA-methylation of peripheral blood was analyzed using the Illumina Infinium Human Methylation BeadChip (850 k). A linear-mixed effect model was used to identify the differentially methylated probe (DMP) associated with PM2.5. Finally, we performed a functional enrichment analysis of the DMPs that were significantly associated with PM2.5.ResultsA total of 24 COPD patients were enrolled and 48 repeated DNA methylation measurements were associated in this study. When the false discovery rate was < 0.05, 19 DMPs were significantly associated with PM2.5 and were annotated to corresponding genes. Functional enrichment analysis of these genes showed that they were related to the response to toxic substances, regulation of tumor necrosis factor superfamily cytokine production, regulation of photosensitivity 3-kinase signaling, and other pathways.ConclusionThis study provided evidence for a significant relationship between personal PM2.5 exposure and DNA methylation in patients with COPD. Our research also revealed a new biological pathway explaining the adverse effects of PM2.5 exposure on COPD risk.

Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes

“…Although we recommend the use of the most comprehensive technology available, the contribution of groundbreaking studies using older arrays to the current body of knowledge should not be understated [160,161]. Similarly, for the bioinformatics analyses of the methylomics results, researchers took advantage of the rapidly evolving tools such as KEGG for pathway analysis [39,43,114,133,157,158], the National Institutes of Health Databases for Annotation, Visualization and Integrated Discovery (NIH-DAVID) [39,53,62,152,153], Ingenuity Pathway Analysis (IPA) [40,63,126,148,154,162], Mummichog [15,92–94,101–103,105,106,163,164], and MetaboAnalyst [95–97,112,130,165–168].…”

“…There were no metabolomic markers of this pathway identified as significantly associated with TRAP (Table 2). Short-term exposure to PM 2.5 was associated with hypomethylation of the genes that encode plasminogen activator inhibitor type I ( SERPINE1 ), coagulation factor III ( F3 ), and coagulation factor II receptor ( F2R ), as well as hypermethylation of coagulation factor II ( F2 ) [38,46,120,142,157]. Within the proteomics literature, short-term exposure to PM 10 and PM 2.5-10 were associated with lower levels of the protein plasminogen activator inhibitor type 1, whereas long-term exposure to PM 2.5 , NO 2 , CO, and O 3 were associated with higher levels of this protein [69,71,73].…”

“…The PCR sequencing papers focused on candidate gene approaches (primarily for inflammatory and immune-related proteins, as well as genes related to circadian rhythm and epigenetic age) [38,47,116,117,120,[140][141][142][143][144][145][146][147]. Analyses using arrays took advantage of the evolving technology to capture the most comprehensive set of biomarkers possible: one paper utilized a 385K array [43], 24 utilized a 450K array [39][40][41][42][44][45][46]48,[50][51][52][53][54][55]57,[62][63][64][148][149][150][151][152][153], and 12 utilized an 850K array [58][59][60][61]114,115,[154][155][156]…”

Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis

“…The PCR sequencing papers focused on candidate gene approaches (primarily for inflammatory and immune-related proteins, as well as genes related to circadian rhythm and epigenetic age) [ 41 , 50 , 118 , 119 , 120 , 125 , 146 , 147 , 148 , 149 , 150 , 151 , 152 ]. Analyses using arrays took advantage of the evolving technology to capture the most comprehensive set of biomarkers possible: one paper utilized a 385 K array [ 46 ], twenty-four utilized a 450 K array [ 42 , 43 , 44 , 45 , 47 , 48 , 49 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 65 , 66 , 67 , 153 , 154 , 155 , 156 ], and twelve utilized an 850 K array [ 61 , 62 , 63 , 64 , 116 , 117 , 157 , 158 , 159 , 160 , 161 , …”

“…Although we recommend the use of the most comprehensive technology available, the contribution of groundbreaking studies using older arrays to the current body of knowledge should not be understated [ 163 , 164 ]. Similarly, for the bioinformatics analyses of the methylomics results, researchers took advantage of the rapidly evolving tools such as KEGG for pathway analysis [ 42 , 46 , 116 , 140 , 160 , 161 ], the National Institutes of Health Databases for Annotation, Visualization, and Integrated Discovery (NIH-DAVID) [ 42 , 56 , 65 , 155 , 156 ], Ingenuity Pathway Analysis (IPA) [ 43 , 66 , 130 , 153 , 157 , 165 ], Mummichog [ 14 , 95 , 96 , 97 , 104 , 105 , 106 , 107 , 108 , 166 , 167 ], and MetaboAnalyst [ 98 , 99 , 100 , 102 , 136 , 168 , 169 , 170 , 171 ].…”