Differentially methylated probes for each cell population in comparison to whole blood.

<p>PBMC-Peripheral blood mononuclear cells. Differentially methylated probes were defined by a linear model using the M-values. M-value is the log2 ratio of the intensities of methylated probe versus unmethylated probe, a measurement of how much more a probe is methylated compared to unmethylated <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Du2" target="_blank">[46]</a>. *To extract the probes with largest difference in methylation, a gamma fit model was applied to M-values in order to define the three calls: “unmethylated”, “marginal” and “methylated”. Significant probes sharing the same call in the two compared populations were removed. **Variation is based on the estimate of the log2-fold-change corresponding to the effect obtained from the linear model, absolute M-values are presented. The percentages are based on the call distribution.</p

Schematic presentation of the isolation protocol and purity of the cell populations as measured by flow cytometry.

<p>Peripheral blood mononuclear cells (PBMC) and granulocytes were obtained by density gradient centrifugation and seven cell populations were purified by magnetic sorting. Upper panel shows forward and side scatter which confirmed cell morphology and granularity. The lower panel shows the overlay of cell surface markers for all the six donors. The purities of the cell populations were highly similar among all the six donors. Th cells  =  CD4⁺ T cells, Tc cells  =  CD8⁺ T cells, NK cells  =  CD56⁺ NK cells, B cells  =  CD19⁺ B cells, Monocytes  =  CD14⁺ monocytes. Data analyses are based on the comparison of all cell populations to whole blood.</p

Differentially methylated CpG sites in candidate genes related to inflammatory diseases.

<p>A) Heatmap of 1865 probes representing 293 candidate genes for selected inflammatory diseases showing differential methylation in blood cell populations. Candidate genes for the diseases asthma, atopy, atopic dermatitis, inflammatory bowel disease, rheumatoid arthritis, systemic lupus erythematosus, Type 1 and Type 2 diabetes were selected from the Genome wide association study atlas (<a href="http://www.genome.gov/gwastudies/" target="_blank">http://www.genome.gov/gwastudies/</a>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Hindorff1" target="_blank">[30]</a>. The heatmap is based on median M-values. The M-value is calculated as the log2 ratio of the intensities of methylated probe versus unmethylated probe <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Du2" target="_blank">[46]</a>. Blue color indicates low DNA methylation while yellow represents high DNA methylation. WB  =  whole blood, CD4T  =  CD4⁺ T cells, CD8T  =  CD8⁺ T cells, CD56NK  =  CD56⁺ NK cells, CD19B  =  CD19⁺ B cells, CD14Mono  =  CD14⁺ monocytes. B) The genomic distribution of the differentially methylated probes associated with inflammatory complex diseases according to the UCSC RefGene group (included in the Illumina annotation data). Intergenic  =  site not annotated in a gene, TSS  =  transcription start site at 200–1500 bp, 5′ region  = 5′UTR and 1st exon, Intragenic  =  gene body including introns and exons and, 3′ region  = 3′UTR. UTR – untranslated region.</p

Gene ontology enrichment for isolated cell populations.

<p>Gene ontology was performed using DAVID (<a href="http://david.abcc.ncifcrf.gov" target="_blank">http://david.abcc.ncifcrf.gov</a>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Huangda1" target="_blank">[47]</a>. The human genome was used as background and the level of significance was set to p<0.05. The top ten enriched pathways are described for genes showing significantly differentially methylated probes in comparison to whole blood where the cell population shows unmethylated state and whole blood shows methylated state according to the gamma fit model. Red color indicates peripheral blood mononuclear cells (PBMC) and green color indicates granulocytes.</p

Differentially methylated probes based on M-values in each studied cell population in blood compared to each other.

<p>PBMC – Peripheral blood mononuclear cells. Differentially methylated probes were defined by a linear model using the M-values.</p

Differentially methylated sites are harbored by cell-specific genes but the connection between methylation status and surface expression depend on the gene and lineage.

<p>The surface expression of CD3 and CD14 according to the methylation status of the coding gene is presented. Demethylation of CpG sites was observed in T cells expressing the CD3 marker (upper panel). For CD14 the difference between positive and negative cells involved cell-restricted marginal status in a context of demethylation (lower panel). Histograms (right) represent the log fluorescence of the marker (<i>x</i>-axis) and the cell counts (<i>y</i>-axis). Peaks within the gray shaded gates represent positive populations. For CD14 two gates are presented, the “<i>low</i>” which include monocytes and a fraction of neutrophils and the “<i>high</i>” population of monocytes. m  =  membrane; e*  =  positive or negative cell expression of the marker according to the Human Leucocyte Differentiation Antigens (HLDA) Workshop and CD nomenclature (<a href="http://www.hcdm.org/Home/tabid/36/Default.aspx" target="_blank">http://www.hcdm.org/Home/tabid/36/Default.aspx</a>).</p

Clustering of cell populations in blood with regard to global DNA methylation.

<p>A) Hierarchical tree presenting the relationship between cell populations based on median M-values from six donors. B) Principal component analysis of each individual sample showing specific clustering based on cell population.</p

DNA methylation levels across the gene regions in purified cell populations for candidate genes.

<p>DNA methylation is defined by the M-values for each cell population at a given CpG site. The M-value is calculated as the log2 ratio of the intensities of methylated probe versus unmethylated probe and describes a measurement of how much more a probe is methylated compared to unmethylated <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Du2" target="_blank">[46]</a>. Negative numbers represent unmethylated and positive numbers represent methylated. Every cell population correspond to a vertical bar which is listed from left to right as peripheral blood mononuclear cells (PBMC), CD4⁺ T cells, CD8⁺ T cells, CD56⁺ NK cells, CD19⁺ B cells, CD14⁺ monocytes, granulocytes, neutrophils, eosinophils and whole blood. Lymphoid cells are colored in red bars, myeloid cells are colored in green bars and whole blood is represented by black bars. A) the asthma candidate genes lymphotoxin alpha (<i>LTA</i>) and tumor necrosis factor (<i>TNF</i>), and B) the Type 2 diabetes candidate gene transcription factor 7-like 2 (<i>TCF7L2</i>), black arrows indicate regions with cell type specific pattern for monocytes. TSS  =  transcription start site at 200–1500 bp; UTR  =  untranslated region, gene body including introns and exons.</p

Electric Mobility Shift Assay (EMSA) for four sites in the promoter for Neuropeptide S Receptor 1 gene (<i>NPSR1</i>).

<p>The experiment was performed three separate times using nuclear protein extracts from two different cell types (Colo205 and HEK293). Data presented here is a representative gel using nuclear extract from Colo205. Data was similar for nuclear cell extracts from both cell lines. The sites studied included CpG site 2 coinciding with rs2168890 in a predicted HMX2 binding site, CpG site 3 in a predicted STAT1 binding site, CpG site 8 coinciding with rs2530547 in a predicted binding site for MYB, and CpG site 9 coinciding with rs887020 in a predicted binding site for AP1. Arrows indicates sites showing differential binding.</p

The role of aircraft noise annoyance and noise sensitivity in the association between aircraft noise levels and medication use: results of a pooled-analysis from seven European countries

BackgroundFew studies have considered aircraft noise annoyance and noise sensitivity in analyses of the health effects of aircraft noise, especially in relation to medication use. This study aims to investigate the moderating and mediating role of these two factors in the relationship between aircraft noise levels and medication use among 5860 residents of ten European airports included in the HYENA and DEBATS studies.MethodsInformation on aircraft noise annoyance, noise sensitivity, medication use, and demographic, socio-economic and lifestyle factors was collected during a face-to-face interview at home. Medication was coded according to the Anatomical Therapeutic Chemical (ATC) classification. Outdoor aircraft noise exposure was estimated by linking the participant’s home address to noise contours using Geographical Information Systems (GIS) methods. Logistic regressions with adjustment for potential confounding factors were used. In addition, Baron and Kenny’s recommendations were followed to investigate the moderating and mediating effects of aircraft noise annoyance and noise sensitivity.ResultsA significant association was found between aircraft noise levels at night and antihypertensive medication only in the UK (OR = 1.43, 95%CI 1.19–1.73 for a 10 dB(A)-increase in Lnight). No association was found with other medications. Aircraft noise annoyance was significantly associated with the use of antihypertensive medication (OR = 1.33, 95%CI 1.14–1.56), anxiolytics (OR = 1.48, 95%CI 1.08–2.05), hypnotics and sedatives (OR = 1.60, 95%CI 1.07–2.39), and antasthmatics (OR = 1.44, 95%CI 1.07–1.96), with no difference between countries. Noise sensitivity was significantly associated with almost all medications, with the exception of the use of antasthmatics, showing an increase in ORs with the level of noise sensitivity, with differences in ORs among countries only for the use of antihypertensive medication. The results also suggested a mediating role of aircraft noise annoyance and a modifying role of both aircraft noise annoyance and noise sensitivity in the association between aircraft noise levels and medication use.ConclusionsThe present study is consistent with the results of the small number of studies available to date suggesting that both aircraft noise annoyance and noise sensitivity should be taken into account in analyses of the health effects of exposure to aircraft noise.</div