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Systems Biology to Support Nanomaterial Grouping

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Modelling the Toxicity of Nanoparticles

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 947))

Abstract

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

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References

  1. Adam N, Vergauwen L, Blust R, Knapen D (2015) Gene transcription patterns and energy reserves in Daphnia magna show no nanoparticle specific toxicity when exposed to ZnO and CuO nanoparticles. Environ Res 138C:82–92

    Article  CAS  Google Scholar 

  2. Alisi A, Leoni S, Piacentani A, Conti Devirgiliis L (2003) Retinoic acid modulates the cell-cycle in fetal rat hepatocytes and HepG2 cells by regulating cyclin-cdk activities. Liver Int Off J Int Assoc Study Liver 23:179–186

    Article  CAS  Google Scholar 

  3. Bajak E, Fabbri M, Ponti J, Gioria S, Ojea-Jimenez I, Collotta A, Mariani V, Gilliland D, Rossi F, Gribaldo L (2015) Changes in Caco-2 cells transcriptome profiles upon exposure to gold nanoparticles. Toxicol Lett 233:187–199

    Article  CAS  PubMed  Google Scholar 

  4. Bandara LR, Kennedy S (2002) Toxicoproteomics – a new preclinical tool. Drug Discov Today 7:411–418

    Article  CAS  PubMed  Google Scholar 

  5. Baumann S, Kalkhof S, Hackermuller J, Otto W, Tomm JM, Wissenbach DK, RK U, von Bergen M (2013) Requirements and perspectives for integrating metabolomics with other omics data. Curr Metabolomics 1:15–27

    CAS  Google Scholar 

  6. Becker RA, Ankley GT, Edwards SW, Kennedy SW, Linkov I, Meek B, Sachana M, Segner H, Van Der Burg B, Villeneuve DL, Watanabe H, Barton-Maclaren TS (2015) Increasing Scientific Confidence in Adverse Outcome Pathways: Application of Tailored Bradford-Hill Considerations for Evaluating Weight of Evidence. Regul Toxicol Pharmacol 72:514–537

    Article  PubMed  Google Scholar 

  7. Bhatia SN, Ingber DE (2014) Microfluidic organs-on-chips. Nat Biotechnol 32:760–772

    Article  CAS  PubMed  Google Scholar 

  8. Blaauboer BJ (2002) The necessity of biokinetic information in the interpretation of in vitro toxicity data. Altern Lab Anim ATLA 30(Suppl 2):85–91

    CAS  PubMed  Google Scholar 

  9. Blaauboer BJ (2003) The integration of data on physico-chemical properties, in vitro-derived toxicity data and physiologically based kinetic and dynamic as modelling a tool in hazard and risk assessment. A commentary. Toxicol Lett 138:161–171

    Article  CAS  PubMed  Google Scholar 

  10. Blaauboer BJ, Clewell HJ, Clothier R, Crespi C, Gerson B, Hawksworth G, Kedderis GL, Rozman K, Willhite C (2001) In vitro methods for assessing acute toxicity: biokinetic determinations. In: Report of the International Workshop on in vitro methods for assessing acute systemic toxicity: results of an International Workshop Organized by ICCVAM and NICEATM (NIH Publication 01–4499). NIEHS, Research Triangle Park. pp 47–60

    Google Scholar 

  11. Blazer-Yost BL, Banga A, Amos A, Chernoff E, Lai X, Li C, Mitra S, Witzmann FA (2011) Effect of carbon nanoparticles on renal epithelial cell structure, barrier function, and protein expression. Nanotoxicology 5:354–371

    Article  CAS  PubMed  Google Scholar 

  12. Bo Y, Jin C, Liu Y, Yu W, Kang H (2014) Metabolomic analysis on the toxicological effects of TiO(2) nanoparticles in mouse fibroblast cells: from the perspective of perturbations in amino acid metabolism. Toxicol Mech Methods 24:461–469

    Article  CAS  PubMed  Google Scholar 

  13. Bouhifd M, Hartung T, Hogberg HT, Kleensang A, Zhao L (2013) Review: toxicometabolomics. J Appl Toxicol JAT 33:1365–1383

    Article  CAS  PubMed  Google Scholar 

  14. Bourdon JA, Halappanavar S, Saber AT, Jacobsen NR, Williams A, Wallin H, Vogel U, Yauk CL (2012) Hepatic and pulmonary toxicogenomic profiles in mice intratracheally instilled with carbon black nanoparticles reveal pulmonary inflammation, acute phase response, and alterations in lipid homeostasis. Toxicol Sci Off J Soc Toxicol 127:474–484

    Article  CAS  Google Scholar 

  15. Bourdon JA, Williams A, Kuo B, Moffat I, White PA, Halappanavar S, Vogel U, Wallin H, Yauk CL (2013) Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure. Toxicology 303:83–93

    Article  CAS  PubMed  Google Scholar 

  16. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29:365–371

    Article  CAS  PubMed  Google Scholar 

  17. Brugger B (2014) Lipidomics: analysis of the lipid composition of cells and subcellular organelles by electrospray ionization mass spectrometry. Annu Rev Biochem 83:79–98

    Article  PubMed  CAS  Google Scholar 

  18. Bu Q, Yan G, Deng P, Peng F, Lin H, Xu Y, Cao Z, Zhou T, Xue A, Wang Y, Cen X, Zhao YL (2010) NMR-based metabonomic study of the sub-acute toxicity of titanium dioxide nanoparticles in rats after oral administration. Nanotechnology 21:125105

    Article  PubMed  CAS  Google Scholar 

  19. Buesen R, Landsiedel R, Sauer UG, Wohlleben W, Groeters S, Strauss V, Kamp H, van Ravenzwaay B (2014) Effects of SiO(2), ZrO(2), and BaSO(4) nanomaterials with or without surface functionalization upon 28-day oral exposure to rats. Arch Toxicol 88:1881–1906

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Carbo-Dorca R, Besalu E (2011) Construction of coherent nano quantitative structure-properties relationships (nano-QSPR) models and catastrophe theory. SAR QSAR Environ Res 22:661–665

    Article  CAS  PubMed  Google Scholar 

  21. Cheng LC, Jiang X, Wang J, Chen C, Liu RS (2013) Nano-bio effects: interaction of nanomaterials with cells. Nanoscale 5:3547–3569

    Article  CAS  PubMed  Google Scholar 

  22. Cunningham MJ, Shah M, Lema C, Magnuson SR, Falduto MT, Balzano L, Resasco DE (2007) An OMICs approach for assessing the safety of single-walled carbon nanotubes in human skin and lung cells. In: 2007 NSTI nanotechnology conference and trade show – NSTI Nanotech 2007, Technical Proceedings, vol 2. pp 651–654

    Google Scholar 

  23. Dailey LA, Hernandez-Prieto R, Casas-Ferreira AM, Jones MC, Riffo-Vasquez Y, Rodriguez-Gonzalo E, Spina D, Jones SA, Smith NW, Forbes B, Page C, Legido-Quigley C (2014) Adenosine monophosphate is elevated in the bronchoalveolar lavage fluid of mice with acute respiratory toxicity induced by nanoparticles with high surface hydrophobicity. Nanotoxicology 9:106–115

    Article  PubMed  CAS  Google Scholar 

  24. de Ridder D, de Ridder J, Reinders MJ (2013) Pattern recognition in bioinformatics. Brief Bioinform 14:633–647

    Article  PubMed  Google Scholar 

  25. DeJongh J, Verhaar HJ, Hermens JL (1997) A quantitative property-property relationship (QPPR) approach to estimate in vitro tissue-blood partition coefficients of organic chemicals in rats and humans. Arch Toxicol 72:17–25

    Article  CAS  PubMed  Google Scholar 

  26. Dhawan A, Sharma V (2010) Toxicity assessment of nanomaterials: methods and challenges. Anal Bioanal Chem 398:589–605

    Article  CAS  PubMed  Google Scholar 

  27. Dondrup M, Albaum SP, Griebel T, Henckel K, Junemann S, Kahlke T, Kleindt CK, Kuster H, Linke B, Mertens D, Mittard-Runte V, Neuweger H, Runte KJ, Tauch A, Tille F, Puhler A, Goesmann A (2009) EMMA 2 – a MAGE-compliant system for the collaborative analysis and integration of microarray data. BMC Bioinformatics 10:50

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Dong MS, Choi JY, Sung JH, Kim JS, Song KS, Ryu HR, Lee JH, Bang IS, An K, Park HM, Song NW, Yu IJ (2013) Gene expression profiling of kidneys from Sprague-Dawley rats following 12-week inhalation exposure to silver nanoparticles. Toxicol Mech Methods 23:437–448

    Article  CAS  PubMed  Google Scholar 

  29. Dowling P, Hughes DJ, Larkin AM, Meiller J, Henry M, Meleady P, Lynch V, Pardini B, Naccarati A, Levy M, Vodicka P, Neary P, Clynes M (2015) Elevated levels of 14-3-3 proteins, serotonin, gamma enolase and pyruvate kinase identified in clinical samples from patients diagnosed with colorectal cancer. Clin Chim Acta 441:133–141

    Article  CAS  PubMed  Google Scholar 

  30. Driessen MD, Mues S, Vennemann A, Hellack B, Bannuscher A, Vimalakanthan V, Riebeling C, Ossig R, Wiemann M, Schnekenburger J, Kuhlbusch TA, Renard B, Luch A, Haase A (2015) Proteomic analysis of protein carbonylation: a useful tool to unravel nanoparticle toxicity mechanisms. Part Fibre Toxicol 12:36

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Duranton F, Lundin U, Gayrard N, Mischak H, Aparicio M, Mourad G, Daures JP, Weinberger KM, Argiles A (2014) Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function. Clin J Am Soc Nephrol 9:37–45

    Article  CAS  PubMed  Google Scholar 

  32. Edelmann MJ, Shack LA, Naske CD, Walters KB, Nanduri B (2014) SILAC-based quantitative proteomic analysis of human lung cell response to copper oxide nanoparticles. PLoS One 9:e114390

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Feliu N, Kohonen P, Ji J, Zhang Y, Karlsson HL, Palmberg L, Nystrom A, Fadeel B (2015) Next-generation sequencing reveals low-dose effects of cationic dendrimers in primary human bronchial epithelial cells. ACS Nano 9:146–163

    Article  CAS  PubMed  Google Scholar 

  34. Feng J, Liu H, Bhakoo KK, Lu L, Chen Z (2011) A metabonomic analysis of organ specific response to USPIO administration. Biomaterials 32:6558–6569

    Article  CAS  PubMed  Google Scholar 

  35. Feng J, Liu H, Zhang L, Bhakoo K, Lu L (2010) An insight into the metabolic responses of ultra-small superparamagnetic particles of iron oxide using metabonomic analysis of biofluids. Nanotechnology 21:395101

    Article  PubMed  CAS  Google Scholar 

  36. Fisichella M, Berenguer F, Steinmetz G, Auffan M, Rose J, Prat O (2012) Intestinal toxicity evaluation of TiO2 degraded surface-treated nanoparticles: a combined physico-chemical and toxicogenomics approach in caco-2 cells. Part Fibre Toxicol 9:18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fleischer CC, Payne CK (2014) Secondary structure of corona proteins determines the cell surface receptors used by nanoparticles. J Phys Chem B 118:14017–14026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fowler SJ, Basanta-Sanchez M, Xu Y, Goodacre R, Dark PM (2015) Surveillance for lower airway pathogens in mechanically ventilated patients by metabolomic analysis of exhaled breath: a case-control study. Thorax 70:320–325

    Article  PubMed  Google Scholar 

  39. Frohlich E, Meindl C, Wagner K, Leitinger G, Roblegg E (2014) Use of whole genome expression analysis in the toxicity screening of nanoparticles. Toxicol Appl Pharmacol 280:272–284

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Gagne F, Andre C, Skirrow R, Gelinas M, Auclair J, van Aggelen G, Turcotte P, Gagnon C (2012) Toxicity of silver nanoparticles to rainbow trout: a toxicogenomic approach. Chemosphere 89:615–622

    Article  CAS  PubMed  Google Scholar 

  41. Ganter B, Zidek N, Hewitt PR, Muller D, Vladimirova A (2008) Pathway analysis tools and toxicogenomics reference databases for risk assessment. Pharmacogenomics 9:35–54

    Article  CAS  PubMed  Google Scholar 

  42. Garcia-Contreras R, Sugimoto M, Umemura N, Kaneko M, Hatakeyama Y, Soga T, Tomita M, Scougall-Vilchis RJ, Contreras-Bulnes R, Nakajima H, Sakagami H (2015) Alteration of metabolomic profiles by titanium dioxide nanoparticles in human gingivitis model. Biomaterials 57:33–40

    Article  CAS  PubMed  Google Scholar 

  43. Gattiker A, Hermida L, Liechti R, Xenarios I, Collin O, Rougemont J, Primig M (2009) MIMAS 3.0 is a Multiomics Information Management and Annotation System. BMC Bioinformatics 10:151

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Giskeodegard GF, Bertilsson H, Selnaes KM, Wright AJ, Bathen TF, Viset T, Halgunset J, Angelsen A, Gribbestad IS, Tessem MB (2013) Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness. PLoS One 8:e62375

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Gomes T, Pereira CG, Cardoso C, Bebianno MJ (2013) Differential protein expression in mussels Mytilus galloprovincialis exposed to nano and ionic Ag. Aquat Toxicol 136:79–90

    Article  PubMed  CAS  Google Scholar 

  46. Gomez-Cabrero D, Abugessaisa I, Maier D, Teschendorff A, Merkenschlager M, Gisel A, Ballestar E, Bongcam-Rudloff E, Conesa A, Tegner J (2014) Data integration in the era of omics: current and future challenges. BMC Syst Biol 8(Suppl 2):I1

    Article  PubMed  PubMed Central  Google Scholar 

  47. Goni FM (2014) The basic structure and dynamics of cell membranes: an update of the Singer-Nicolson model. Biochim Biophys Acta 1838:1467–1476

    Article  CAS  PubMed  Google Scholar 

  48. Grosch S, Schiffmann S, Geisslinger G (2012) Chain length-specific properties of ceramides. Prog Lipid Res 51:50–62

    Article  PubMed  CAS  Google Scholar 

  49. Guo L, Panderi I, Yan DD, Szulak K, Li Y, Chen YT, Ma H, Niesen DB, Seeram N, Ahmed A, Yan B, Pantazatos D, Lu W (2013) A comparative study of hollow copper sulfide nanoparticles and hollow gold nanospheres on degradability and toxicity. ACS Nano 7:8780–8793

    Article  CAS  PubMed  Google Scholar 

  50. Hadrup N, Lam HR, Loeschner K, Mortensen A, Larsen EH, Frandsen H (2012) Nanoparticulate silver increases uric acid and allantoin excretion in rats, as identified by metabolomics. J Appl Toxicol JAT 32:929–933

    Article  CAS  PubMed  Google Scholar 

  51. Halappanavar S, Jackson P, Williams A, Jensen KA, Hougaard KS, Vogel U, Yauk CL, Wallin H (2011) Pulmonary response to surface-coated nanotitanium dioxide particles includes induction of acute phase response genes, inflammatory cascades, and changes in microRNAs: a toxicogenomic study. Environ Mol Mutagen 52:425–439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Halappanavar S, Saber AT, Decan N, Jensen KA, Wu D, Jacobsen NR, Guo C, Rogowski J, Koponen IK, Levin M, Madsen AM, Atluri R, Snitka V, Birkedal RK, Rickerby D, Williams A, Wallin H, Yauk CL, Vogel U (2015) Transcriptional profiling identifies physicochemical properties of nanomaterials that are determinants of the in vivo pulmonary response. Environ Mol Mutagen 56:245–264

    Article  CAS  PubMed  Google Scholar 

  53. Haniu H, Matsuda Y, Takeuchi K, Kim YA, Hayashi T, Endo M (2010) Proteomics-based safety evaluation of multi-walled carbon nanotubes. Toxicol Appl Pharmacol 242:256–262

    Article  CAS  PubMed  Google Scholar 

  54. Hilton GM, Taylor AJ, McClure CD, Parsons GN, Bonner JC, Bereman MS (2015) Toxicoproteomic analysis of pulmonary carbon nanotube exposure using LC-MS/MS. Toxicology 329:80–87

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hla T, Dannenberg AJ (2012) Sphingolipid signaling in metabolic disorders. Cell Metab 16:420–434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Holthuis JC, Menon AK (2014) Lipid landscapes and pipelines in membrane homeostasis. Nature 510:48–57

    Article  CAS  PubMed  Google Scholar 

  57. Horev-Azaria L, Baldi G, Beno D, Bonacchi D, Golla-Schindler U, Kirkpatrick JC, Kolle S, Landsiedel R, Maimon O, Marche PN, Ponti J, Romano R, Rossi F, Sommer D, Uboldi C, Unger RE, Villiers C, Korenstein R (2013) Predictive toxicology of cobalt ferrite nanoparticles: comparative in-vitro study of different cellular models using methods of knowledge discovery from data. Part Fibre Toxicol 10:32

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huang Y, Lü X, Ma J (2014) Toxicity of silver nanoparticles to human dermal fibroblasts on MicroRNA level. J Biomed Nanotechnol 10:3304–3317

    Article  CAS  PubMed  Google Scholar 

  59. Irfan A, Cauchi M, Edmands W, Gooderham NJ, Njuguna J, Zhu H (2014) Assessment of temporal dose-toxicity relationship of fumed silica nanoparticle in human lung A549 cells by conventional cytotoxicity and (1)H-NMR-based extracellular metabonomic assays. Toxicol Sci Off J Soc Toxicol 138:354–364

    Article  CAS  Google Scholar 

  60. Jackson P, Hougaard KS, Vogel U, Wu D, Casavant L, Williams A, Wade M, Yauk CL, Wallin H, Halappanavar S (2012) Exposure of pregnant mice to carbon black by intratracheal instillation: toxicogenomic effects in dams and offspring. Mutat Res 745:73–83

    Article  CAS  PubMed  Google Scholar 

  61. Jung HR, Sylvanne T, Koistinen KM, Tarasov K, Kauhanen D, Ekroos K (2011) High throughput quantitative molecular lipidomics. Biochim Biophys Acta 1811:925–934

    Article  CAS  PubMed  Google Scholar 

  62. Jungnickel H, Potratz S, Baumann S, Tarnow P, von Bergen M, Luch A (2014) Identification of lipidomic biomarkers for coexposure to subtoxic doses of benzo[a]pyrene and cadmium: the toxicological cascade biomarker approach. Environ Sci Technol 48:10423–10431

    Article  CAS  PubMed  Google Scholar 

  63. Kalkhof S, Dautel F, Loguercio S, Baumann S, Trump S, Jungnickel H, Otto W, Rudzok S, Potratz S, Luch A, Lehmann I, Beyer A, von Bergen M (2015) Pathway and time-resolved benzo[a]pyrene toxicity on Hepa1c1c7 cells at toxic and subtoxic exposure. J Proteome Res 14:164–182

    Article  CAS  PubMed  Google Scholar 

  64. Kim KB, Um SY, Chung MW, Jung SC, Oh JS, Kim SH, Na HS, Lee BM, Choi KH (2010) Toxicometabolomics approach to urinary biomarkers for mercuric chloride (HgCl(2))-induced nephrotoxicity using proton nuclear magnetic resonance ((1)H NMR) in rats. Toxicol Appl Pharmacol 249:114–126

    Article  CAS  PubMed  Google Scholar 

  65. Kinter CS, Lundie JM, Patel H, Rindler PM, Szweda LI, Kinter M (2012) A quantitative proteomic profile of the Nrf2-mediated antioxidant response of macrophages to oxidized LDL determined by multiplexed selected reaction monitoring. PLoS One 7:e50016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kitchin KT, Grulke E, Robinette BL, Castellon BT (2014) Metabolomic effects in HepG2 cells exposed to four TiO2 and two CeO2 nanomaterials. Environ Sci Nano 1:466–477

    Article  CAS  Google Scholar 

  67. Kreyling WG, Fertsch-Gapp S, Schaffler M, Johnston BD, Haberl N, Pfeiffer C, Diendorf J, Schleh C, Hirn S, Semmler-Behnke M, Epple M, Parak WJ (2014) In vitro and in vivo interactions of selected nanoparticles with rodent serum proteins and their consequences in biokinetics. Beilstein J Nanotechnol 5:1699–1711

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Kusama M, Toshimoto K, Maeda K, Hirai Y, Imai S, Chiba K, Akiyama Y, Sugiyama Y (2010) In silico classification of major clearance pathways of drugs with their physiochemical parameters. Drug Metab Dispos Biol Fate Chem 38:1362–1370

    Article  CAS  PubMed  Google Scholar 

  69. Kuznetsova GP, Larina OV, Petushkova NA, Kisrieva YS, Samenkova NF, Trifonova OP, Karuzina II, Ipatova OM, Zolotaryov KV, Romashova YA, Lisitsa AV (2014) Effects of fullerene C60 on proteomic profile of Danio rerio fish embryos. Bull Exp Biol Med 156:694–698

    Article  CAS  PubMed  Google Scholar 

  70. Lai ZW, Yan Y, Caruso F, Nice EC (2012) Emerging techniques in proteomics for probing nano-bio interactions. ACS Nano 6:10438–10448

    Article  CAS  PubMed  Google Scholar 

  71. Lan J, Gou N, Gao C, He M, Gu AZ (2014) Comparative and mechanistic genotoxicity assessment of nanomaterials via a quantitative toxicogenomics approach across multiple species. Environ Sci Technol 48:12937–12945

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 4:222

    Article  PubMed  PubMed Central  Google Scholar 

  73. Li J, Zhao Z, Feng J, Gao J, Chen Z (2013) Understanding the metabolic fate and assessing the biosafety of MnO nanoparticles by metabonomic analysis. Nanotechnology 24:455102

    Article  PubMed  CAS  Google Scholar 

  74. Li JJ, Biggin MD (2015) Gene expression. Statistics requantitates the central dogma. Science 347:1066–1067

    Article  CAS  PubMed  Google Scholar 

  75. Liao M, Liu H (2012) Gene expression profiling of nephrotoxicity from copper nanoparticles in rats after repeated oral administration. Environ Toxicol Pharmacol 34:67–80

    Article  CAS  PubMed  Google Scholar 

  76. Lin BC, Zhang HS, Lin ZQ, Fang YJ, Tian L, Yang HL, Yan J, Liu HL, Zhang W, Xi ZG (2013) Studies of single-walled carbon nanotubes-induced hepatotoxicity by NMR-based metabonomics of rat blood plasma and liver extracts. Nanoscale Res Lett 8:236

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Lin ZQ, Ma L, X ZG, Zhang, HS, Lin BC (2013) A comparative study of lung toxicity in rats induced by three types of nanomaterials. Nanoscale Res Lett 8:521.

    Google Scholar 

  78. Linkov I, Massey O, Keisler J, Rusyn I, Hartung T (2015) From “weight of evidence” to quantitative data integration using multicriteria decision analysis and Bayesian methods. ALTEX 32:3–8

    Article  PubMed  Google Scholar 

  79. Lipscomb JC, Meek ME, Krishnan K, Kedderis GL, Clewell H, Haber L (2004) Incorporation of pharmacokinetic and pharmacodynamic data into risk assessments. Toxicol Mech Methods 14:145–158

    Article  CAS  PubMed  Google Scholar 

  80. Lu X, Tian Y, Zhao Q, Jin T, Xiao S, Fan X (2011) Integrated metabonomics analysis of the size-response relationship of silica nanoparticles-induced toxicity in mice. Nanotechnology 22:055101

    Article  PubMed  CAS  Google Scholar 

  81. Lubinski L, Urbaszek P, Gajewicz A, Cronin MT, Enoch SJ, Madden JC, Leszczynska D, Leszczynski J, Puzyn T (2013) Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling. SAR QSAR Environ Res 24:995–1008

    Article  CAS  PubMed  Google Scholar 

  82. Malone JH, Oliver B (2011) Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol 9:34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Mandell JW (2003) Phosphorylation state-specific antibodies: applications in investigative and diagnostic pathology. Am J Pathol 163:1687–1698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Miethling-Graff R, Rumpker R, Richter M, Verano-Braga T, Kjeldsen F, Brewer J, Hoyland J, Rubahn HG, Erdmann H (2014) Exposure to silver nanoparticles induces size- and dose-dependent oxidative stress and cytotoxicity in human colon carcinoma cells. Toxicol In Vitro Int J Publ Assoc BIBRA 28:1280–1289

    Article  CAS  Google Scholar 

  85. Mikut R, Dickmeis T, Driever W, Geurts P, Hamprecht FA, Kausler BX, Ledesma-Carbayo MJ, Maree R, Mikula K, Pantazis P, Ronneberger O, Santos A, Stotzka R, Strahle U, Peyrieras N (2013) Automated processing of zebrafish imaging data: a survey. Zebrafish 10:401–421

    Article  PubMed  PubMed Central  Google Scholar 

  86. Nazari P, Dowlatabadi-Bazaz R, Mofid MR, Pourmand MR, Daryani NE, Faramarzi MA, Sepehrizadeh Z, Shahverdi AR (2014) The antimicrobial effects and metabolomic footprinting of carboxyl-capped bismuth nanoparticles against Helicobacter pylori. Appl Biochem Biotechnol 172:570–579

    Article  CAS  PubMed  Google Scholar 

  87. Nel AE (2013) Implementation of alternative test strategies for the safety assessment of engineered nanomaterials. J Intern Med 274:561–577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Newman RH, Zhang J, Zhu H (2014) Toward a systems-level view of dynamic phosphorylation networks. Front Genet 5:263

    PubMed  PubMed Central  Google Scholar 

  89. Ng CT, Yung LY, Swa HL, Poh RW, Gunaratne J, Bay BH (2015) Altered protein expression profile associated with phenotypic changes in lung fibroblasts co-cultured with gold nanoparticle-treated small airway epithelial cells. Biomaterials 39:31–38

    Article  CAS  PubMed  Google Scholar 

  90. NRC (2007) Applications of toxicogenomic technologies to predictive toxicology and risk assessment. Washington, DC

    Google Scholar 

  91. Nuwaysir EF, Bittner M, Trent J, Barrett JC, Afshari CA (1999) Microarrays and toxicology: the advent of toxicogenomics. Mol Carcinog 24:153–159

    Article  CAS  PubMed  Google Scholar 

  92. O’Brien PJ (2014) High-content analysis in toxicology: screening substances for human toxicity potential, elucidating subcellular mechanisms and in vivo use as translational safety biomarkers. Basic Clin Pharmacol Toxicol 115:4–17

    Article  PubMed  CAS  Google Scholar 

  93. Oberbach A, Bluher M, Wirth H, Till H, Kovacs P, Kullnick Y, Schlichting N, Tomm JM, Rolle-Kampczyk U, Murugaiyan J, Binder H, Dietrich A, von Bergen M (2011) Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes. J Proteome Res 10:4769–4788

    Article  CAS  PubMed  Google Scholar 

  94. OECD (2013) Guidance document on developing and assessing adverse outcome pathways. In: Series on testing and assessment. Environment Directorate of the OECD, Paris

    Google Scholar 

  95. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127:635–648

    Article  CAS  PubMed  Google Scholar 

  96. Overmyer KA, Thonusin C, Qi NR, Burant CF, Evans CR (2015) Impact of anesthesia and euthanasia on metabolomics of mammalian tissues: studies in a C57BL/6 J mouse model. PLoS One 10:e0117232

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98

    Article  CAS  PubMed  Google Scholar 

  98. Palomaki J, Sund J, Vippola M, Kinaret P, Greco D, Savolainen K, Puustinen A, Alenius H (2015) A secretomics analysis reveals major differences in the macrophage responses towards different types of carbon nanotubes. Nanotoxicology 9:719–728

    Article  PubMed  CAS  Google Scholar 

  99. Parveen A, Rizvi SH, Gupta A, Singh R, Ahmad I, Mahdi F, Mahdi AA (2012) NMR-based metabonomics study of sub-acute hepatotoxicity induced by silica nanoparticles in rats after intranasal exposure. Cell Mol Biol 58:196–203

    CAS  PubMed  Google Scholar 

  100. Paterson S, Mackay D (1989) Correlation of tissue, blood, and air partition coefficients of volatile organic chemicals. Br J Ind Med 46:321–328

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Patlewicz G, Simon TW, Rowlands JC, Budinsky RA, Becker RA (2015) Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes. Regul Toxicol Pharmacol 71:463–477

    Article  PubMed  Google Scholar 

  102. Peirce V, Carobbio S, Vidal-Puig A (2014) The different shades of fat. Nature 510:76–83

    Article  CAS  PubMed  Google Scholar 

  103. Peters R, Kramer E, Oomen AG, Rivera ZE, Oegema G, Tromp PC, Fokkink R, Rietveld A, Marvin HJ, Weigel S, Peijnenburg AA, Bouwmeester H (2012) Presence of nano-sized silica during in vitro digestion of foods containing silica as a food additive. ACS Nano 6:2441–2451

    Article  CAS  PubMed  Google Scholar 

  104. Pettit S, des Etages SA, Mylecraine L, Snyder R, Fostel J, Dunn RT 2nd, Haymes K, Duval M, Stevens J, Afshari C, Vickers A (2010) Current and future applications of toxicogenomics: results summary of a survey from the HESI Genomics State of Science Subcommittee. Environ Health Perspect 118:992–997

    Google Scholar 

  105. Plant NJ (2015) An introduction to systems toxicology. Toxicol Res Uk 4:9–22

    Article  CAS  Google Scholar 

  106. Platt FM (2014) Sphingolipid lysosomal storage disorders. Nature 510:68–75

    Article  CAS  PubMed  Google Scholar 

  107. Poulsen SS, Saber AT, Williams A, Andersen O, Kobler C, Atluri R, Pozzebon ME, Mucelli SP, Simion M, Rickerby D, Mortensen A, Jackson P, Kyjovska ZO, Molhave K, Jacobsen NR, Jensen KA, Yauk CL, Wallin H, Halappanavar S, Vogel U (2014) MWCNTs of different physicochemical properties cause similar inflammatory responses, but differences in transcriptional and histological markers of fibrosis in mouse lungs. Toxicol Appl Pharmacol 284:16–32

    Article  PubMed  CAS  Google Scholar 

  108. Poynton HC, Lazorchak JM, Impellitteri CA, Blalock BJ, Rogers K, Allen HJ, Loguinov A, Heckman JL, Govindasmawy S (2012) Toxicogenomic responses of nanotoxicity in Daphnia magna exposed to silver nitrate and coated silver nanoparticles. Environ Sci Technol 46:6288–6296

    Article  CAS  PubMed  Google Scholar 

  109. Rabilloud T, Lescuyer P (2015) Proteomics in mechanistic toxicology: History, concepts, achievements, caveats, and potential. Proteomics 15:1051–1074

    Article  CAS  PubMed  Google Scholar 

  110. Rainville LC, Carolan D, Varela AC, Doyle H, Sheehan D (2014) Proteomic evaluation of citrate-coated silver nanoparticles toxicity in Daphnia magna. Analyst 139:1678–1686

    Article  CAS  PubMed  Google Scholar 

  111. Rappaport SM, Li H, Grigoryan H, Funk WE, Williams ER (2012) Adductomics: characterizing exposures to reactive electrophiles. Toxicol Lett 213:83–90

    Article  CAS  PubMed  Google Scholar 

  112. Ratnasekhar C, Sonane M, Satish A, Mudiam MK (2015) Metabolomics reveals the perturbations in the metabolome of Caenorhabditis elegans exposed to titanium dioxide nanoparticles. Nanotoxicology 9:1–11

    Article  CAS  Google Scholar 

  113. Rayner TF, Rocca-Serra P, Spellman PT, Causton HC, Farne A, Holloway E, Irizarry RA, Liu J, Maier DS, Miller M, Petersen K, Quackenbush J, Sherlock G, Stoeckert CJ Jr, White J, Whetzel PL, Wymore F, Parkinson H, Sarkans U, Ball CA, Brazma A (2006) A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinformatics 7:489

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Rocheleau S, Arbour M, Elias M, Sunahara GI, Masson L (2014) Toxicogenomic effects of nano- and bulk-TiO particles in the soil nematode Caenorhabditis elegans. Nanotoxicology 9(4):502–512

    Google Scholar 

  115. Rushton EK, Jiang J, Leonard SS, Eberly S, Castranova V, Biswas P, Elder A, Han X, Gelein R, Finkelstein J, Oberdorster G (2010) Concept of assessing nanoparticle hazards considering nanoparticle dosemetric and chemical/biological response metrics. J Toxicol Environ Health Part A 73:445–461

    Article  CAS  PubMed  Google Scholar 

  116. Russell WMS, Burch RL (1959) The principles of humane experimental technique. Methuen, London, 238 pp

    Google Scholar 

  117. Sarkar A, Ghosh M, Sil PC (2014) Nanotoxicity: oxidative stress mediated toxicity of metal and metal oxide nanoparticles. J Nanosci Nanotechnol 14:730–743

    Article  CAS  PubMed  Google Scholar 

  118. Schmitt W (2008) General approach for the calculation of tissue to plasma partition coefficients. Toxicol In Vitro Int J Publ Assoc BIBRA 22:457–467

    Article  CAS  Google Scholar 

  119. Schnackenberg LK, Sun J, Beger RD (2012) Metabolomics techniques in nanotoxicology studies. Methods Mol Biol 926:141–156

    Article  CAS  PubMed  Google Scholar 

  120. Shim W, Paik MJ, Nguyen DT, Lee JK, Lee Y, Kim JH, Shin EH, Kang JS, Jung HS, Choi S, Park S, Shim JS, Lee G (2012) Analysis of changes in gene expression and metabolic profiles induced by silica-coated magnetic nanoparticles. ACS Nano 6:7665–7680

    Article  CAS  PubMed  Google Scholar 

  121. Sohm B, Immel F, Bauda P, Pagnout C (2015) Insight into the primary mode of action of TiO2 nanoparticles on Escherichia coli in the dark. Proteomics 15:98–113

    Article  CAS  PubMed  Google Scholar 

  122. Sturla SJ, Boobis AR, FitzGerald RE, Hoeng J, Kavlock RJ, Schirmer K, Whelan M, Wilks MF, Peitsch MC (2014) Systems toxicology: from basic research to risk assessment. Chem Res Toxicol 27:314–329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabe de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Tang M, Zhang T, Xue Y, Wang S, Huang M, Yang Y, Lu M, Lei H, Kong L, Wang Y, Pu Y (2011) Metabonomic studies of biochemical changes in the serum of rats by intratracheally instilled TiO2 nanoparticles. J Nanosci Nanotechnol 11:3065–3074

    Article  CAS  PubMed  Google Scholar 

  125. Taskinen MR, Boren J (2015) New insights into the pathophysiology of dyslipidemia in type 2 diabetes. Atherosclerosis 239:483–495

    Article  CAS  PubMed  Google Scholar 

  126. Taylor CF, Paton NW, Lilley KS, Binz PA, Julian RK Jr, Jones AR, Zhu W, Apweiler R, Aebersold R, Deutsch EW, Dunn MJ, Heck AJ, Leitner A, Macht M, Mann M, Martens L, Neubert TA, Patterson SD, Ping P, Seymour SL, Souda P, Tsugita A, Vandekerckhove J, Vondriska TM, Whitelegge JP, Wilkins MR, Xenarios I, Yates JR 3rd, Hermjakob H (2007) The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25:887–893

    Article  CAS  PubMed  Google Scholar 

  127. Taylor NS, Merrifield R, Williams TD, Chipman JK, Lead JR, Viant MR (2015) Molecular toxicity of cerium oxide nanoparticles to the freshwater alga Chlamydomonas reinhardtii is associated with supra-environmental exposure concentrations. Nanotoxicology 1–10. doi: 10.3109/17435390.2014.948941

  128. Tedesco S, Bayat N, Danielsson G, Buque X, Aspichueta P, Fresnedo O, Cristobal S (2015) Proteomic and lipidomic analysis of primary mouse hepatocytes exposed to metal and metal oxide nanoparticles. JIOMICS 5:44–57

    Google Scholar 

  129. Teeguarden JG, Barton HA (2004) Computational modeling of serum-binding proteins and clearance in extrapolations across life stages and species for endocrine active compounds. Risk Anal Off Publ Soc Risk Anal 24:751–770

    Article  Google Scholar 

  130. Teeguarden JG, Webb-Robertson BJ, Waters KM, Murray AR, Kisin ER, Varnum SM, Jacobs JM, Pounds JG, Zanger RC, Shvedova AA (2011) Comparative proteomics and pulmonary toxicity of instilled single-walled carbon nanotubes, crocidolite asbestos, and ultrafine carbon black in mice. Toxicol Sci Off J Soc Toxicol 120:123–135

    Article  CAS  Google Scholar 

  131. Thingholm TE, Jensen ON, Larsen MR (2009) Analytical strategies for phosphoproteomics. Proteomics 9:1451–1468

    Article  CAS  PubMed  Google Scholar 

  132. Tilton SC, Karin NJ, Tolic A, Xie Y, Lai X, Hamilton RF Jr, Waters KM, Holian A, Witzmann FA, Orr G (2014) Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns. Nanotoxicology 8:533–548

    Article  CAS  PubMed  Google Scholar 

  133. Toropova AP, Toropov AA, Benfenati E, Korenstein R, Leszczynska D, Leszczynski J (2015) Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides. Environ Sci Pollut Res Int 22:745–757

    Article  CAS  PubMed  Google Scholar 

  134. Tralau T, Riebeling C, Pirow R, Oelgeschlager M, Seiler A, Liebsch M, Luch A (2012) Wind of change challenges toxicological regulators. Environ Health Perspect 120:1489–1494

    Article  PubMed  PubMed Central  Google Scholar 

  135. Treuel L, Jiang X, Nienhaus GU (2013) New views on cellular uptake and trafficking of manufactured nanoparticles. J R Soc Interface R Soc 10:20120939

    Article  CAS  Google Scholar 

  136. Tsyusko OV, Unrine JM, Spurgeon D, Blalock E, Starnes D, Tseng M, Joice G, Bertsch PM (2012) Toxicogenomic responses of the model organism Caenorhabditis elegans to gold nanoparticles. Environ Sci Technol 46:4115–4124

    Article  CAS  PubMed  Google Scholar 

  137. Tucci P, Porta G, Agostini M, Dinsdale D, Iavicoli I, Cain K, Finazzi-Agro A, Melino G, Willis A (2013) Metabolic effects of TiO2 nanoparticles, a common component of sunscreens and cosmetics, on human keratinocytes. Cell Death Dis 4:e549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Tyurina YY, Kisin ER, Murray A, Tyurin VA, Kapralova VI, Sparvero LJ, Amoscato AA, Samhan-Arias AK, Swedin L, Lahesmaa R, Fadeel B, Shvedova AA, Kagan VE (2011) Global phospholipidomics analysis reveals selective pulmonary peroxidation profiles upon inhalation of single-walled carbon nanotubes. ACS Nano 5:7342–7353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Valerio LG Jr, Choudhuri S (2012) Chemoinformatics and chemical genomics: potential utility of in silico methods. J Appl Toxicol JAT 32:880–889

    Article  CAS  PubMed  Google Scholar 

  140. van Ravenzwaay B, Montoya GA, Fabian E, Herold M, Krennrich G, Looser R, Mellert W, Peter E, Strauss V, Walk T, Kamp H (2014) The sensitivity of metabolomics versus classical regulatory toxicology from a NOAEL perspective. Toxicol Lett 227:20–28

    Article  PubMed  CAS  Google Scholar 

  141. Vance DE (2008) In: Vance DE, editor. Biochemistry of lipids, lipoproteins and membranes Elektronische Ressource, 5th edn. Elsevier, Amsterdam. 1st ed

    Google Scholar 

  142. Vannini C, Domingo G, Onelli E, Prinsi B, Marsoni M, Espen L, Bracale M (2013) Morphological and proteomic responses of Eruca sativa exposed to silver nanoparticles or silver nitrate. PLoS One 8:e68752

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Verano-Braga T, Miethling-Graff R, Wojdyla K, Rogowska-Wrzesinska A, Brewer JR, Erdmann H, Kjeldsen F (2014) Insights into the cellular response triggered by silver nanoparticles using quantitative proteomics. ACS Nano 8:2161–2175

    Article  CAS  PubMed  Google Scholar 

  144. Vidanapathirana AK, Lai X, Hilderbrand SC, Pitzer JE, Podila R, Sumner SJ, Fennell TR, Wingard CJ, Witzmann FA, Brown JM (2012) Multi-walled carbon nanotube directed gene and protein expression in cultured human aortic endothelial cells is influenced by suspension medium. Toxicology 302:114–122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Vihervaara T, Suoniemi M, Laaksonen R (2014) Lipidomics in drug discovery. Drug Discov Today 19:164–170

    Article  CAS  PubMed  Google Scholar 

  146. Vinken M (2013) The adverse outcome pathway concept: a pragmatic tool in toxicology. Toxicology 312:158–165

    Article  CAS  PubMed  Google Scholar 

  147. Wagner SA, Beli P, Weinert BT, Nielsen ML, Cox J, Mann M, Choudhary C (2011) A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol Cell Proteomics 10(M111):013284

    PubMed  Google Scholar 

  148. Waters M, Boorman G, Bushel P, Cunningham M, Irwin R, Merrick A, Olden K, Paules R, Selkirk J, Stasiewicz S, Weis B, Van Houten B, Walker N, Tennant R (2003) Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base. EHP Toxicogenomics J Natl Inst Environ Health Sci 111:15–28

    CAS  Google Scholar 

  149. Wenk MR (2010) Lipidomics: new tools and applications. Cell 143:888–895

    Article  CAS  PubMed  Google Scholar 

  150. Wilson VS, Keshava N, Hester S, Segal D, Chiu W, Thompson CM, Euling SY (2013) Utilizing toxicogenomic data to understand chemical mechanism of action in risk assessment. Toxicol Appl Pharmacol 271:299–308

    Article  CAS  PubMed  Google Scholar 

  151. Wruck W, Peuker M, Regenbrecht CR (2014) Data management strategies for multinational large-scale systems biology projects. Brief Bioinform 15:65–78

    Article  PubMed  Google Scholar 

  152. Xiao X, Lee JH (2010) Systems analysis of alternative splicing and its regulation. Wiley Interdiscip Rev Syst Biol Med 2:550–565

    Article  CAS  PubMed  Google Scholar 

  153. Yan LJ, Forster MJ (2011) Chemical probes for analysis of carbonylated proteins: a review. J Chromatogr B Analyt Technol Biomed Life Sci 879:1308–1315

    Article  CAS  PubMed  Google Scholar 

  154. Yang M, Soga T, Pollard PJ (2013) Oncometabolites: linking altered metabolism with cancer. J Clin Invest 123:3652–3658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Yuan J, Gao H, Sui J, Duan H, Chen WN, Ching CB (2012) Cytotoxicity evaluation of oxidized single-walled carbon nanotubes and graphene oxide on human hepatoma HepG2 cells: an iTRAQ-coupled 2D LC-MS/MS proteome analysis. Toxicol Sci Off J Soc Toxicol 126:149–161

    Article  CAS  Google Scholar 

  156. Yuan JF, Gao HC, Ching CB (2011) Comparative protein profile of human hepatoma HepG2 cells treated with graphene and single-walled carbon nanotubes: An iTRAQ-coupled 2D LC-MS/MS proteome analysis. Toxicol Lett 207:213–221

    Article  CAS  PubMed  Google Scholar 

  157. Zhang H, Ji Z, Xia T, Meng H, Low-Kam C, Liu R, Pokhrel S, Lin S, Wang X, Liao YP, Wang M, Li L, Rallo R, Damoiseaux R, Telesca D, Madler L, Cohen Y, Zink JI, Nel AE (2012) Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 6:4349–4368

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Zhang Y, Zhao F, Deng Y, Zhao Y, Ren H (2015) Metagenomic and metabolomic analysis of the toxic effects of trichloroacetamide-induced gut microbiome and urine metabolome perturbations in mice. J Proteome Res 14:1752–1761

    Article  CAS  PubMed  Google Scholar 

  159. Zhao Y, Jensen ON (2009) Modification-specific proteomics: strategies for characterization of post-translational modifications using enrichment techniques. Proteomics 9:4632–4641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Zuin S, Micheletti C, Critto A, Pojana G, Johnston H, Stone V, Tran L, Marcomini A (2011) Weight of evidence approach for the relative hazard ranking of nanomaterials. Nanotoxicology 5:445–458

    Article  CAS  PubMed  Google Scholar 

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  1. German Federal Institute for Risk Assessment, Department of Chemical and Product Safety, Berlin, Germany

    Christian Riebeling, Harald Jungnickel, Andreas Luch & Andrea Haase

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  1. Institute of Occupational Medicine, Edinburgh, United Kingdom

    Lang Tran

  2. Institute of Catalysis & Petrochemistry, CSIC, Madrid, Spain

    Miguel A. Bañares

  3. Universitat Rovira i Virgili, Departament d’Enginyeria Informatica i Matematiques, Tarragona, Spain

    Robert Rallo

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Riebeling, C., Jungnickel, H., Luch, A., Haase, A. (2017). Systems Biology to Support Nanomaterial Grouping. In: Tran, L., Bañares, M., Rallo, R. (eds) Modelling the Toxicity of Nanoparticles. Advances in Experimental Medicine and Biology, vol 947. Springer, Cham. https://doi.org/10.1007/978-3-319-47754-1_6

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