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The HEALS Project

  • D. A. Sarigiannis
Chapter

Abstract

Exposome appears as a very promising tool for better understanding the complexity of interactions between genome and environment, especially when investigating large population studies. The HEALS project aims at identifying the complex links among genes, environment, and human disease on allergies and asthma, neurodevelopmental/neurodegenerative and metabolic disorders based on individual exposome characterization and how should this be implemented in large cohorts. HEALS relies on the re-analysis of existing cohort studies and the deployment of a Pilot European Exposure and Health Examination Survey. Although the analysis will start from the collection of biomonitoring data, a wide array of omics technologies (completed by confirmatory in vitro testing) will be employed. Lifetime exposure assessment will involve novel technologies such as sensors and agent- based modelling. Mapping the different omics responses onto regulatory networks and disease pathways will allow understanding the intermediate stages from exposure to disease at individual as well as population level. HEALS is expected to provide additional insights into the way to synthesize different data and methodological tools for assessing the internal and external exposome overall aiming to a better understanding of both the potential mechanisms and the origin of disease. This includes (1) how different environmental factors contribute cumulatively to disease and (2) the common nodes of exposure and molecular events resulting in phenomenally different health outcomes. HEALS is a comprehensive methodological advance aiming to provide the way of linking interdisciplinary research towards the understanding of genome and lifetime environmental interaction at individual and population level.

Keywords

Life-time exposure assessment Agent-based modelling 

References

  1. Andra SS, Charisiadis P, Karakitsios S, Sarigiannis DA, Makris KC (2015) Passive exposures of children to volatile trihalomethanes during domestic cleaning activities of their parents. Environ Res 136(0):187–195.  https://doi.org/10.1016/j.envres.2014.10.018CrossRefGoogle Scholar
  2. Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, Liquet B, Vermeulen RCH (2013) Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers. Environ Mol Mutagen 54(7):542–557.  https://doi.org/10.1002/em.21797CrossRefGoogle Scholar
  3. Eissing T, Kuepfer L, Becker C, Block M, Coboeken K, Gaub T, Goerlitz L, Jaeger J, Loosen R, Ludewig B, Meyer M, Niederalt C, Sevestre M, Siegmund HU, Solodenko J, Thelen K, Telle U, Weiss W, Wendl T, Willmann S, Lippert J (2011) A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol 2:4CrossRefGoogle Scholar
  4. Exarchos TP, Papaloukas C, Fotiadis DI, Michalis LK (2006) An association rule mining-based methodology for automated detection of ischemic ECG beats. IEEE Trans Biomed Eng 53(8):1531–1540CrossRefGoogle Scholar
  5. Exarchos TP, Tsipouras MG, Papaloukas C, Fotiadis DI (2009) An optimized sequential pattern matching methodology for sequence classification. Knowl Inf Syst 19(2):249–264CrossRefGoogle Scholar
  6. Fleischer NL, Diez Roux AV (2008) Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction. J Epidemiol Community Health 62(9):842–846CrossRefGoogle Scholar
  7. Georgopoulos PG, Sasso AF, Isukapalli SS, Lioy PJ, Vallero DA, Okino M, Reiter L (2008) Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities. J Expo Sci Environ Epidemiol 19(2):149–171CrossRefGoogle Scholar
  8. Greenland S (2000) When should epidemiologic regressions use random coefficients? Biometrics 56(3):915–921CrossRefGoogle Scholar
  9. Gustafson P, McCandless LC, Levy AR, Richardson S (2010) Simplified Bayesian sensitivity analysis for mismeasured and unobserved confounders. Biometrics 66(4):1129–1137CrossRefGoogle Scholar
  10. Gutsell S, Russell P (2013) The role of chemistry in developing understanding of adverse outcome pathways and their application in risk assessment. Toxicol Res 2(5):299–307CrossRefGoogle Scholar
  11. Hossain S, Gustafson P (2009) Bayesian adjustment for covariate measurement errors: a flexible parametric approach. Stat Med 28(11):1580–1600CrossRefGoogle Scholar
  12. Judson RS, Kavlock RJ, Setzer RW, Cohen Hubal EA, Martin MT, Knudsen TB, Houck KA, Thomas RS, Wetmore BA, Dix DJ (2011) Estimating toxicity-related biological pathway altering doses for high-throughput chemical risk assessment. Chem Res Toxicol 24(4):451–462CrossRefGoogle Scholar
  13. Krauss M, Schaller S, Borchers S, Findeisen R, Lippert J, Kuepfer L (2012) Integrating cellular metabolism into a multiscale whole-body model. PLoS Comput Biol 8(10):e1002750CrossRefGoogle Scholar
  14. Loh M, Sarigiannis D, Gotti A, Karakitsios S, Pronk A, Kuijpers E, Annesi-Maesano I, Baiz N, Madureira J, Oliveira Fernandes E, Jerrett M, Cherrie J (2017) How sensors might help define the external exposome. Int J Environ Res Public Health 14(4):434CrossRefGoogle Scholar
  15. Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S, Mattingly CJ, Ritchie M, Schmitt C, Sarigiannis DA, Thomas DC, Wishart D, Balshaw DM, Patel CJ (2017) Informatics and data analytics to support exposome-based discovery for public health. Annu Rev Public Health 38:279–294.  https://doi.org/10.1146/annurev-publhealth-082516-012737CrossRefGoogle Scholar
  16. Mosquin PL, Licata AC, Liu B, Sumner SCJ, Okino MS (2009) Reconstructing exposures from small samples using physiologically based pharmacokinetic models and multiple biomarkers. J Expo Sci Environ Epidemiol 19(3):284–297CrossRefGoogle Scholar
  17. Papadaki K, Sarigiannis DA, Karakitsios SP (2017) Modeling of adipose/blood partition coefficient for environmental chemicals. Food Chem Toxicol 110c:274–285CrossRefGoogle Scholar
  18. Rappaport SM, Smith MT (2010) Environment and disease risks. Science 330(6003):460–461CrossRefGoogle Scholar
  19. Roede JR, Uppal K, Park Y, Tran V, Jones DP (2014) Transcriptome–metabolome wide association study (TMWAS) of maneb and paraquat neurotoxicity reveals network level interactions in toxicologic mechanism. Toxicol Rep 1(0):435–444.  https://doi.org/10.1016/j.toxrep.2014.07.006CrossRefGoogle Scholar
  20. Sabel CE, Boyle P, Raab G, Löytönen M, Maasilta P (2009) Modelling individual space-time exposure opportunities: a novel approach to unravelling the genetic or environment disease causation debate. Spat Spatiotemporal Epidemiol 1(1):85–94CrossRefGoogle Scholar
  21. Sarigiannis D, Marafante E, Gotti A, Reale GC (2009) Reflections on new directions for risk assessment of environmental chemical mixtures. Int J Risk Assess Manag 13(3–4):216–241CrossRefGoogle Scholar
  22. Sarigiannis D, Karakitsios S, Handakas E, Simou K, Solomou E, Gotti A (2016) Integrated exposure and risk characterization of bisphenol-a in Europe. Food Chem Toxicol 98:134–147.  https://doi.org/10.1016/j.fct.2016.10.017CrossRefGoogle Scholar
  23. Sarigiannis D, Papadaki K, Kontoroupis P, Karakitsios SP (2017) Development of QSARs for parameterizing physiology based ToxicoKinetic models. Food Chem Toxicol 106(Pt A):114–124.  https://doi.org/10.1016/j.fct.2017.05.029CrossRefGoogle Scholar
  24. Sarigiannis DA, Gotti A (2008) Biology-based dose-response models for health risk assessment of chemical mixtures. Fresenius Environ Bull 17(9 B):1439–1451Google Scholar
  25. Sarigiannis DΑ, Karakitsios SP, Zikopoulos D, Nikolaki S, Kermenidou M (2015) Lung cancer risk from PAHs emitted from biomass combustion. Environ Res 137:147–156.  https://doi.org/10.1016/j.envres.2014.12.009CrossRefGoogle Scholar
  26. Schmutz J, Wheeler J, Grimwood J, Dickson M, Yang J, Caoile C, Bajorek E, Black S, Chan YM, Denys M, Escobar J, Flowers D, Fotopulos D, Garcia C, Gomez M, Gonzales E, Haydu L, Lopez F, Ramirez L, Retterer J, Rodriguez A, Rogers S, Salazar A, Tsai M, Myers RM (2004) Quality assessment of the human genome sequence. Nature 429(6990):365–368CrossRefGoogle Scholar
  27. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342.  https://doi.org/10.1038/nature10098CrossRefGoogle Scholar
  28. Shahar E (2010) Metabolic syndrome? A critical look from the viewpoints of causal diagrams and statistics. J Cardiovasc Med 11(10):772–779CrossRefGoogle Scholar
  29. Su L, Hogan JW (2008) Bayesian semiparametric regression for longitudinal binary processes with missing data. Stat Med 27(17):3247–3268CrossRefGoogle Scholar
  30. VanderWeele TJ, Hernán MA, Robins JM (2008) Causal directed acyclic graphs and the direction of unmeasured confounding bias. Epidemiology 19(5):720–728CrossRefGoogle Scholar
  31. Vineis P, Wild CP (2014) Global cancer patterns: causes and prevention. Lancet 383(9916):549–557.  https://doi.org/10.1016/s0140-6736(13)62224-2CrossRefGoogle Scholar
  32. Vineis P, van Veldhoven K, Chadeau-Hyam M, Athersuch TJ (2013) Advancing the application of omics-based biomarkers in environmental epidemiology. Environ Mol Mutagen 54(7):461–467.  https://doi.org/10.1002/em.21764CrossRefGoogle Scholar
  33. Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagaña X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Gražuleviciene˙ R, Gutzkow KB, Julvez J, Keun HC, Kogevinas M, McEachan RRC, Meltzer HM, Sabidó E, Schwarze PE, Siroux V, Sunyer J, Want EJ, Zeman F, Nieuwenhuijsen MJ (2014) The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect 122(6):535–544.  https://doi.org/10.1289/ehp.1307204CrossRefGoogle Scholar
  34. Weinberg CR (2007) Can DAGs clarify effect modification? Epidemiology 18(5):569–572CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and InnovationThessalonikiGreece
  2. 2.Environmental Engineering LaboratorySchool of Chemical Engineering, Aristotle University of ThessalonikiThessalonikiGreece
  3. 3.University School for Advanced Study (IUSS)PaviaItaly

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