TY - JOUR
T1 - Impact of long-term exposure to PM2.5 on peripheral blood gene expression pathways involved in cell signaling and immune response
AU - Vlaanderen, Jelle
AU - Vermeulen, Roel
AU - Whitaker, Matthew
AU - Chadeau-Hyam, Marc
AU - Hottenga, Jouke Jan
AU - de Geus, Eco
AU - Willemsen, Gonneke
AU - Penninx, Brenda W.J.H.
AU - Jansen, Rick
AU - Boomsma, Dorret I.
N1 - Funding Information: Funding: This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874627 (EXPANSE) and by the US National Institute of Mental Health (RC2 MH089951, PI Sullivan) as part of the American Recovery and Reinvestment Act of 2009. The Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Twin Register (NTR) acknowledge funding by the Netherlands Organization for Scientific Research (MagW/ZonMW grants 904-61-090, 985-10-002,904-61-193,480-04-004, 400-05-717, 912-100-20; Spinozapremie 56-464-14192; Geestkracht program grant 10-000-1002); Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL); the European Science Foundation (EU/QLRT-2001-01254); the European Community's Seventh Framework Program (FP7/2007-2013); ENGAGE (HEALTH-F4-2007-201413); and the European Research Council (ERC, 230374). Funding Information: Funding: This work was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 874627 (EXPANSE) and by the US National Institute of Mental Health (RC2 MH089951, PI Sullivan) as part of the American Recovery and Reinvestment Act of 2009. The Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Twin Register (NTR) acknowledge funding by the Netherlands Organization for Scientific Research (MagW/ZonMW grants 904-61-090, 985-10-002,904-61-193,480-04-004, 400-05-717, 912-100-20; Spinozapremie 56-464-14192; Geestkracht program grant 10-000-1002); Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL); the European Science Foundation (EU/QLRT-2001-01254); the European Community's Seventh Framework Program (FP7/2007-2013); ENGAGE (HEALTH-F4-2007-201413); and the European Research Council (ERC, 230374). Publisher Copyright: © 2022 The Author(s)
PY - 2022/10
Y1 - 2022/10
N2 - Background: Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts. Methods: In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO2, NOx, particulate matter (PM2.5, PM2.5abs, PM10, PMcoarse), and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID. Results: In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors. Conclusion: Our results indicate that PM2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures.
AB - Background: Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts. Methods: In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO2, NOx, particulate matter (PM2.5, PM2.5abs, PM10, PMcoarse), and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID. Results: In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors. Conclusion: Our results indicate that PM2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures.
KW - Air pollution
KW - Biological pathways
KW - Cell signaling
KW - Immune system
KW - Molecular epidemiology
KW - Transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85137304876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137304876&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.envint.2022.107491
DO - https://doi.org/10.1016/j.envint.2022.107491
M3 - Article
C2 - 36081220
SN - 0160-4120
VL - 168
SP - 107491
JO - Environment International
JF - Environment International
M1 - 107491
ER -