Prediction of Proinflammatory Potentials of Engine Exhausts by Integrating Chemical and Biological Features

  • Chia-Chi Wang
  • Ying-Chi Lin
  • Yuan-Chung Lin
  • Syu-Ruei Jhang
  • Chun-Wei Tung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9656)

Abstract

The increasing prevalence of immune-related diseases has raised concerns about immunotoxicity of engine exhausts. The evaluation of immunotoxicity associated with engine exhausts has relied on expensive and time-consuming experiments. In this study, a computational method named CBM was developed for predicting proinflammatory potentials of engine exhausts using chemical and biological data which are routinely analyzed for toxicity evaluation. The CBM model, based on a principal component regression algorithm, performs well with high correlation coefficient values of 0.972 and 0.849 obtained from training and independent test sets, respectively. In contrast, chemical or biological features alone showed poor correlation with the toxicity. The model indicates the importance of the utilization of both chemical and biological features for developing an effective model. The proposed method could be further developed and applied to predict bioactivities of mixtures.

Keywords

Engine exhaust Genotoxicity Immunotoxicity Principal component regression Proinflammatory potential 

References

  1. 1.
    Krivoshto, I.N., Richards, J.R., Albertson, T.E., Derlet, R.W.: The toxicity of diesel exhaust: implications for primary care. J. Am. Board Family Med. 21, 55–62 (2008)CrossRefGoogle Scholar
  2. 2.
    Benbrahim-Tallaa, L., Baan, R.A., Grosse, Y., Lauby-Secretan, B., El Ghissassi, F., Bouvard, V., Guha, N., Loomis, D., Straif, K.: Carcinogenicity of diesel-engine and gasoline-engine exhausts and some nitroarenes. Lancet Oncol. 13, 663–664 (2012)CrossRefGoogle Scholar
  3. 3.
    Ris, C.: U.S. EPA health assessment for diesel engine exhaust: a review. Inhalation Toxicol. 19(Suppl. 1), 229–239 (2007)CrossRefGoogle Scholar
  4. 4.
    Claxton, L.D.: The history, genotoxicity and carcinogenicity of carbon-based fuels and their emissions: part 4-alternative fuels. Mutat. Res./Rev. Mutat. Res. 763, 86–102 (2015)CrossRefGoogle Scholar
  5. 5.
    IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, World Health Organization, International Agency for Research on Cancer: Overall evaluations of carcinogenicity: an updating of IARC monographs volumes 1 to 42. World Health Organization (1987)Google Scholar
  6. 6.
    Lin, Y.-C., Lee, W.-J., Hou, H.-C.: PAH emissions and energy efficiency of palm-biodiesel blends fueled on diesel generator. Atmos. Environ. 40, 3930–3940 (2006)CrossRefGoogle Scholar
  7. 7.
    Lin, Y.-C., Lee, W.-J., Wu, T.-S., Wang, C.-T.: Comparison of PAH and regulated harmful matter emissions from biodiesel blends and paraffinic fuel blends on engine accumulated mileage test. Fuel 85, 2516–2523 (2006)CrossRefGoogle Scholar
  8. 8.
    Lin, Y.-C., Lee, W.-J., Li, H.-W., Chen, C.-B., Fang, G.-C., Tsai, P.-J.: Impact of using fishing boat fuel with high poly aromatic content on the emission of polycyclic aromatic hydrocarbons from the diesel engine. Atmos. Environ. 40, 1601–1609 (2006)CrossRefGoogle Scholar
  9. 9.
    Lin, Y.-C., Lee, W.-J., Chen, C.-C., Chen, C.-B.: Saving energy and reducing emissions of both polycyclic aromatic hydrocarbons and particulate matter by adding bio-solution to emulsified diesel. Environ. Sci. Technol. 40, 5553–5559 (2006)CrossRefGoogle Scholar
  10. 10.
    Lin, Y.-C., Lee, W.-J., Chen, C.-B.: Characterization of Polycyclic Aromatic Hydrocarbons from the. J. Air Waste Manag. Assoc. 56, 752–758 (2006)CrossRefGoogle Scholar
  11. 11.
    Ames, B.N., Durston, W.E., Yamasaki, E., Lee, F.D.: Carcinogens are mutagens: a simple test system combining liver homogenates for activation and bacteria for detection. Proc. Nat. Acad. Sci. USA 70, 2281–2285 (1973)CrossRefGoogle Scholar
  12. 12.
    Fall, M., Haddouk, H., Loriot, S., Diouf, A., Dionnet, F., Forster, R., Morin, J.-P.: Mutagenicity of diesel engine exhaust in the Ames/ Salmonella assay using a direct exposure method. Toxicol. Environ. Chem. 93, 1971–1981 (2011)CrossRefGoogle Scholar
  13. 13.
    Bunger, J., Bunger, J.F., Krahl, J., Munack, A., Schroder, O., Bruning, T., Hallier, E., Westphal, G.A.: Combusting vegetable oils in diesel engines: the impact of unsaturated fatty acids on particle emissions and mutagenic effects of the exhaust. Archives of Toxicology (2015)Google Scholar
  14. 14.
    Bisig, C., Steiner, S., Comte, P., Czerwinski, J., Mayer, A., Petri-Fink, A., Rothen-Rutishauser, B.: Biological Effects in Lung Cells In Vitro of Exhaust Aerosols from a Gasoline Passenger Car With and Without Particle Filter. Emission Control Sci. Technol. 1, 237–246 (2015)CrossRefGoogle Scholar
  15. 15.
    Che, W., Liu, G., Qiu, H., Zhang, H., Ran, Y., Zeng, X., Wen, W., Shu, Y.: Comparison of immunotoxic effects induced by the extracts from methanol and gasoline engine exhausts in vitro. Toxicol. Vitro 24, 1119–1125 (2010)CrossRefGoogle Scholar
  16. 16.
    Kabatkova, M., Svobodova, J., Pencikova, K., Mohatad, D.S., Smerdova, L., Kozubik, A., Machala, M., Vondracek, J.: Interactive effects of inflammatory cytokine and abundant low-molecular-weight PAHs on inhibition of gap junctional intercellular communication, disruption of cell proliferation control, and the AhR-dependent transcription. Toxicol. Lett. 232, 113–121 (2014)CrossRefGoogle Scholar
  17. 17.
    Lundblad, L.K., Thompson-Figueroa, J., Leclair, T., Sullivan, M.J., Poynter, M.E., Irvin, C.G., Bates, J.H.: Tumor necrosis factor-alpha overexpression in lung disease: a single cause behind a complex phenotype. Am. J. Respir. Crit. Care Med. 171, 1363–1370 (2005)CrossRefGoogle Scholar
  18. 18.
    Lee, W.L., Downey, G.P.: Neutrophil activation and acute lung injury. Curr. Opin. Crit. Care 7, 1–7 (2001)CrossRefGoogle Scholar
  19. 19.
    Mukhopadhyay, S., Hoidal, J.R., Mukherjee, T.K.: Role of TNF alpha in pulmonary pathophysiology. Respir. Res. 7, 125 (2006)CrossRefGoogle Scholar
  20. 20.
    Marcho, Z., White, J.E., Higgins, P.J., Tsan, M.F.: Tumor necrosis factor enhances endothelial cell susceptibility to oxygen toxicity: role of glutathione. Am. J. Respir. Cell Mol. Biol. 5, 556–562 (1991)CrossRefGoogle Scholar
  21. 21.
    Gao, J., Burchiel, S.W.: Genotoxic mechanisms of PAH-induced immunotoxicity. In: Molecular Immunotoxicology, pp. 245–262. Wiley-VCH Verlag GmbH & Co. KGaA (2014)Google Scholar
  22. 22.
    Wang, C.C., Lin, H.L., Wey, S.P., Jan, T.R.: Areca-nut extract modulates antigen-specific immunity and augments inflammation in ovalbumin-sensitized mice. Immunopharmacol. Immunotoxicol. 33, 315–322 (2011)CrossRefGoogle Scholar
  23. 23.
    Adusumilli, S., Bhatt, D., Wang, H., Devabhaktuni, V., Bhattacharya, P.: A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages. Neurocomputing 166, 185–192 (2015)CrossRefGoogle Scholar
  24. 24.
    Dadousis, C., Veerkamp, R., Heringstad, B., Pszczola, M., Calus, M.: A comparison of principal component regression and genomic REML for genomic prediction across populations. Genet. Sel. Evol. 46, 60 (2014)CrossRefGoogle Scholar
  25. 25.
    Mahesh, S., Jayas, D.S., Paliwal, J., White, N.D.G.: Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat. Food Bioprocess Technol. 8, 31–40 (2015)CrossRefGoogle Scholar
  26. 26.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRefGoogle Scholar
  27. 27.
    Huang, S.H., Tung, C.W., Fulop, F., Li, J.H.: Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines. Food Chem. Toxicol. 78, 71–77 (2015)CrossRefGoogle Scholar
  28. 28.
    Liaw, C., Tung, C.W., Ho, S.Y.: Prediction and analysis of antibody amyloidogenesis from sequences. PLoS ONE 8, e53235 (2013)CrossRefGoogle Scholar
  29. 29.
    Tung, C.W., Wu, M.T., Chen, Y.K., Wu, C.C., Chen, W.C., Li, H.P., Chou, S.H., Wu, D.C., Wu, I.C.: Identification of biomarkers for esophageal squamous cell carcinoma using feature selection and decision tree methods. Sci. World J. 2013, 782031 (2013)CrossRefGoogle Scholar
  30. 30.
    Topinka, J., Milcova, A., Schmuczerova, J., Mazac, M., Pechout, M., Vojtisek-Lom, M.: Genotoxic potential of organic extracts from particle emissions of diesel and rapeseed oil powered engines. Toxicol. Lett. 212, 11–17 (2012)CrossRefGoogle Scholar
  31. 31.
    Tung, C.W., Ho, S.Y.: POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties. Bioinformatics 23, 942–949 (2007)CrossRefGoogle Scholar
  32. 32.
    Tung, C.W., Ho, S.Y.: Computational identification of ubiquitylation sites from protein sequences. BMC Bioinform. 9, 310 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chia-Chi Wang
    • 1
    • 2
    • 3
    • 4
  • Ying-Chi Lin
    • 1
    • 2
  • Yuan-Chung Lin
    • 2
    • 3
  • Syu-Ruei Jhang
    • 3
  • Chun-Wei Tung
    • 1
    • 2
    • 4
  1. 1.School of PharmacyKaohsiung Medical UniversityKaohsiungTaiwan
  2. 2.Ph.D. Program in ToxicologyKaohsiung Medical UniversityKaohsiungTaiwan
  3. 3.Institute of Environmental EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan
  4. 4.National Institute of Environmental Health SciencesNational Health Research InstitutesMiaoli CountyTaiwan

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