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Integration of metabolomics and transcriptomics in nanotoxicity studies

  • Shin, Tae Hwan (Institute of Molecular Science and Technology, Ajou University) ;
  • Lee, Da Yeon (Department of Physiology, Ajou University School of Medicine) ;
  • Lee, Hyeon-Seong (College of Pharmacy, Sunchon National University) ;
  • Park, Hyung Jin (Department of Physiology, Ajou University School of Medicine) ;
  • Jin, Moon Suk (Department of Physiology, Ajou University School of Medicine) ;
  • Paik, Man-Jeong (College of Pharmacy, Sunchon National University) ;
  • Manavalan, Balachandran (Department of Physiology, Ajou University School of Medicine) ;
  • Mo, Jung-Soon (Genomic Instability Research Center, Ajou University School of Medicine) ;
  • Lee, Gwang (Institute of Molecular Science and Technology, Ajou University)
  • Received : 2017.09.02
  • Published : 2018.01.31

Abstract

Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics."

Keywords

References

  1. Stark WJ (2011) Nanoparticles in biological systems. Angew Chem Int Ed Engl 50, 1242-1258 https://doi.org/10.1002/anie.200906684
  2. Havel H, Finch G, Strode P et al (2016) Nanomedicines: From Bench to Bedside and Beyond. AAPS J 18, 1373-1378 https://doi.org/10.1208/s12248-016-9961-7
  3. Yang Y and Yu C (2016) Advances in silica based nanoparticles for targeted cancer therapy. Nanomedicine 12, 317-332 https://doi.org/10.1016/j.nano.2015.10.018
  4. Deng R, Lin D, Zhu L et al (2017) Nanoparticle interactions with co-existing contaminants: Joint toxicity, bioaccumulation and risk. Nanotoxicology, 1-56
  5. Vayssieres L, Chaneac C, Tronc E and Jolivet JP (1998) Size Tailoring of Magnetite Particles Formed by Aqueous Precipitation: An Example of Thermodynamic Stability of Nanometric Oxide Particles. J Colloid Interface Sci 205, 205-212 https://doi.org/10.1006/jcis.1998.5614
  6. Auffan M, Rose J, Bottero JY, Lowry GV, Jolivet JP and Wiesner MR (2009) Towards a definition of inorganic nanoparticles from an environmental, health and safety perspective. Nat Nanotechnol 4, 634-641 https://doi.org/10.1038/nnano.2009.242
  7. Krug HF and Wick P (2011) Nanotoxicology: an interdisciplinary challenge. Angew Chem Int Ed Engl 50, 1260-1278 https://doi.org/10.1002/anie.201001037
  8. Bouallegui Y, Ben Younes R, Bellamine H and Oueslati R (2017) Histopathology and analyses of inflammation intensity in the gills of mussels exposed to silver nanoparticles: role of nanoparticle size, exposure time, and uptake pathways. Toxicol Mech Methods, 1-10
  9. Service RF (2000) Is nanotechnology dangerous? Science 290, 1526-1527 https://doi.org/10.1126/science.290.5496.1526
  10. Boyes WK, Thornton BLM, Al-Abed SR et al (2017) A comprehensive framework for evaluating the environmental health and safety implications of engineered nanomaterials. Crit Rev Toxicol, 1-44
  11. Schnackenberg LK, Sun J and Beger RD (2012) Metabolomics techniques in nanotoxicology studies. Methods Mol Biol 926, 141-156
  12. Masoud R, Bizouarn T, Trepout S et al (2015) Titanium Dioxide Nanoparticles Increase Superoxide Anion Production by Acting on NADPH Oxidase. PLoS One 10, e0144829 https://doi.org/10.1371/journal.pone.0144829
  13. Tuncbag N, Gosline SJ, Kedaigle A, Soltis AR, Gitter A and Fraenkel E (2016) Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package. PLoS Comput Biol 12, e1004879 https://doi.org/10.1371/journal.pcbi.1004879
  14. Norris JL, Farrow MA, Gutierrez DB et al (2017) Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of Action. J Proteome Res 16, 1364-1375 https://doi.org/10.1021/acs.jproteome.6b01004
  15. Hasin Y, Seldin M and Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18, 83 https://doi.org/10.1186/s13059-017-1215-1
  16. Hu X, Li D, Gao Y, Mu L and Zhou Q (2016) Knowledge gaps between nanotoxicological research and nanomaterial safety. Environ Int 94, 8-23 https://doi.org/10.1016/j.envint.2016.05.001
  17. Reyes VC, Li M, Hoek EM, Mahendra S and Damoiseaux R (2012) Genome-wide assessment in Escherichia coli reveals time-dependent nanotoxicity paradigms. ACS Nano 6, 9402-9415 https://doi.org/10.1021/nn302815w
  18. Bo Y, Jin C, Liu Y, Yu W and 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 https://doi.org/10.3109/15376516.2014.939321
  19. Verano-Braga T, Miethling-Graff R, Wojdyla K et al (2014) Insights into the cellular response triggered by silver nanoparticles using quantitative proteomics. ACS Nano 8, 2161-2175 https://doi.org/10.1021/nn4050744
  20. Zhao Y, Li L, Zhang PF et al (2015) Differential Regulation of Gene and Protein Expression by Zinc Oxide Nanoparticles in Hen's Ovarian Granulosa Cells: Specific Roles of Nanoparticles. PLoS One 10, e0140499 https://doi.org/10.1371/journal.pone.0140499
  21. Prohaska SJ and Stadler PF (2011) The use and abuse of -omes. Methods Mol Biol 719, 173-196
  22. Van Assche R, Broeckx V, Boonen K et al (2015) Integrating -Omics: Systems Biology as Explored Through C. elegans Research. J Mol Biol 427, 3441-3451 https://doi.org/10.1016/j.jmb.2015.03.015
  23. Sun YV and Hu YJ (2016) Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases. Adv Genet 93, 147-190
  24. Hood L (2003) Leroy Hood expounds the principles, practice and future of systems biology. Drug Discov Today 8, 436-438 https://doi.org/10.1016/S1359-6446(03)02710-7
  25. Hood L (2003) Systems biology: integrating technology, biology, and computation. Mech Ageing Dev 124, 9-16 https://doi.org/10.1016/S0047-6374(02)00164-1
  26. Palsson B and Zengler K (2010) The challenges of integrating multi-omic data sets. Nat Chem Biol 6, 787-789 https://doi.org/10.1038/nchembio.462
  27. Shim W, Paik MJ, Nguyen DT et al (2012) Analysis of changes in gene expression and metabolic profiles induced by silica-coated magnetic nanoparticles. ACS Nano 6, 7665-7680 https://doi.org/10.1021/nn301113f
  28. Phukan G, Shin TH, Shim JS et al (2016) Silica-coated magnetic nanoparticles impair proteasome activity and increase the formation of cytoplasmic inclusion bodies in vitro. Sci Rep 6, 29095 https://doi.org/10.1038/srep29095
  29. Kim JS, Yoon TJ, Yu KN et al (2006) Toxicity and tissue distribution of magnetic nanoparticles in mice. Toxicol Sci 89, 338-347 https://doi.org/10.1093/toxsci/kfj027
  30. Park KS, Tae J, Choi B et al (2010) Characterization, in vitro cytotoxicity assessment, and in vivo visualization of multimodal, RITC-labeled, silica-coated magnetic nanoparticles for labeling human cord blood-derived mesenchymal stem cells. Nanomedicine 6, 263-276 https://doi.org/10.1016/j.nano.2009.07.005
  31. Beck GR Jr, Ha SW, Camalier CE et al (2012) Bioactive silica-based nanoparticles stimulate bone-forming osteoblasts, suppress bone-resorbing osteoclasts, and enhance bone mineral density in vivo. Nanomedicine 8, 793-803 https://doi.org/10.1016/j.nano.2011.11.003
  32. Fan TW, Higashi RM and Lane AN (2006) Integrating metabolomics and transcriptomics for probing SE anticancer mechanisms. Drug Metab Rev 38, 707-732 https://doi.org/10.1080/03602530600959599
  33. Rehrauer H, Opitz L, Tan G, Sieverling L and Schlapbach R (2013) Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching. BMC Bioinformatics 14, 370 https://doi.org/10.1186/1471-2105-14-370
  34. Evans TG (2015) Considerations for the use of transcriptomics in identifying the 'genes that matter' for environmental adaptation. J Exp Biol 218, 1925-1935 https://doi.org/10.1242/jeb.114306
  35. Gibney MJ, Walsh M, Brennan L, Roche HM, German B and van Ommen B (2005) Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr 82, 497-503 https://doi.org/10.1093/ajcn/82.3.497
  36. Moseley HN (2013) Error Analysis and Propagation in Metabolomics Data Analysis. Comput Struct Biotechnol J 4
  37. Johnson CH, Ivanisevic J and Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17, 451-459 https://doi.org/10.1038/nrm.2016.25
  38. Fukusaki E (2014) Application of Metabolomics for High Resolution Phenotype Analysis. Mass Spectrom (Tokyo) 3, S0045 https://doi.org/10.5702/massspectrometry.S0045
  39. Zlatkis A, Brazell RS and Poole CF (1981) The role of organic volatile profiles in clinical diagnosis. Clin Chem 27, 789-797
  40. Karlic H, Thaler R, Gerner C et al (2015) Inhibition of the mevalonate pathway affects epigenetic regulation in cancer cells. Cancer Genet 208, 241-252 https://doi.org/10.1016/j.cancergen.2015.03.008
  41. Zabala-Letona A, Arruabarrena-Aristorena A, Martin-Martin N et al (2017) mTORC1-dependent AMD1 regulation sustains polyamine metabolism in prostate cancer. Nature 547, 109-113 https://doi.org/10.1038/nature22964
  42. Robertson DG, Watkins PB and Reily MD (2011) Metabolomics in toxicology: preclinical and clinical applications. Toxicol Sci 120 Suppl 1, S146-170 https://doi.org/10.1093/toxsci/kfq358
  43. Pan Z and Raftery D (2007) Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem 387, 525-527 https://doi.org/10.1007/s00216-006-0687-8
  44. Shah SH, Kraus WE and Newgard CB (2012) Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 126, 1110-1120 https://doi.org/10.1161/CIRCULATIONAHA.111.060368
  45. Smith CA, Want EJ, O'Maille G, Abagyan R and Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78, 779-787 https://doi.org/10.1021/ac051437y
  46. Dudley E, Yousef M, Wang Y and Griffiths WJ (2010) Targeted metabolomics and mass spectrometry. Adv Protein Chem Struct Biol 80, 45-83
  47. Paik MJ, Lee KA, Park CS et al (2007) Pattern recognition analysis of polyamines in the plasma of rat models with adenovirus infection. Clin Chim Acta 380, 228-231 https://doi.org/10.1016/j.cca.2007.02.003
  48. Paik MJ, Li WY, Ahn YH et al (2009) The free fatty acid metabolome in cerebral ischemia following human mesenchymal stem cell transplantation in rats. Clin Chim Acta 402, 25-30 https://doi.org/10.1016/j.cca.2008.12.022
  49. Paik MJ, Ahn YH, Lee PH et al (2010) Polyamine patterns in the cerebrospinal fluid of patients with Parkinson's disease and multiple system atrophy. Clin Chim Acta 411, 1532-1535 https://doi.org/10.1016/j.cca.2010.05.034
  50. Shin TH, Phukan G, Shim JS et al (2016) Restoration of Polyamine Metabolic Patterns in In Vivo and In Vitro Model of Ischemic Stroke following Human Mesenchymal Stem Cell Treatment. Stem Cells Int 2016, 4612531
  51. Shin TH, Lee S, Choi KR et al (2017) Quality and freshness of human bone marrow-derived mesenchymal stem cells decrease over time after trypsinization and storage in phosphate-buffered saline. Sci Rep 7, 1106 https://doi.org/10.1038/s41598-017-01315-0
  52. Lv M, Huang W, Chen Z et al (2015) Metabolomics techniques for nanotoxicity investigations. Bioanalysis 7, 1527-1544 https://doi.org/10.4155/bio.15.83
  53. Vatakuti S, Pennings JL, Gore E, Olinga P and Groothuis GM (2016) Classification of Cholestatic and Necrotic Hepatotoxicants Using Transcriptomics on Human Precision-Cut Liver Slices. Chem Res Toxicol 29, 342-351 https://doi.org/10.1021/acs.chemrestox.5b00491
  54. Kohonen P, Parkkinen JA, Willighagen EL et al (2017) A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nat Commun 8, 15932 https://doi.org/10.1038/ncomms15932
  55. Dix DJ, Gallagher K, Benson WH et al (2006) A framework for the use of genomics data at the EPA. Nat Biotechnol 24, 1108-1111 https://doi.org/10.1038/nbt0906-1108
  56. Williams TD, Mirbahai L and Chipman JK (2014) The toxicological application of transcriptomics and epigenomics in zebrafish and other teleosts. Brief Funct Genomics 13, 157-171 https://doi.org/10.1093/bfgp/elt053
  57. Cheng F, Theodorescu D, Schulman IG and Lee JK (2011) In vitro transcriptomic prediction of hepatotoxicity for early drug discovery. J Theor Biol 290, 27-36 https://doi.org/10.1016/j.jtbi.2011.08.009
  58. Zhang M, Chen M and Tong W (2012) Is toxicogenomics a more reliable and sensitive biomarker than conventional indicators from rats to predict drug-induced liver injury in humans? Chem Res Toxicol 25, 122-129 https://doi.org/10.1021/tx200320e
  59. Chen M, Bisgin H, Tong L et al (2014) Toward predictive models for drug-induced liver injury in humans: are we there yet? Biomark Med 8, 201-213 https://doi.org/10.2217/bmm.13.146
  60. Chen S, Xuan J, Couch L et al (2014) Sertraline induces endoplasmic reticulum stress in hepatic cells. Toxicology 322, 78-88 https://doi.org/10.1016/j.tox.2014.05.007
  61. Otava M, Shkedy Z, Talloen W, Verheyen GR and Kasim A (2015) Identification of in vitro and in vivo disconnects using transcriptomic data. BMC Genomics 16, 615 https://doi.org/10.1186/s12864-015-1726-7
  62. Lorscheidt S and Lamprecht A (2016) Safety assessment of nanoparticles for drug delivery by means of classic in vitro assays and beyond. Expert Opin Drug Deliv 13, 1545-1558 https://doi.org/10.1080/17425247.2016.1198773
  63. Hood L, Rowen L, Galas DJ and Aitchison JD (2008) Systems biology at the Institute for Systems Biology. Brief Funct Genomic Proteomic 7, 239-248 https://doi.org/10.1093/bfgp/eln027
  64. Shah SH and Newgard CB (2015) Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ Cardiovasc Genet 8, 410-419 https://doi.org/10.1161/CIRCGENETICS.114.000223
  65. Rebollar EA, Antwis RE, Becker MH et al (2016) Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases. Front Microbiol 7, 68
  66. Hartiala JA, Tang WH, Wang Z et al (2016) Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat Commun 7, 10558 https://doi.org/10.1038/ncomms10558
  67. Kim J, Woo HR and Nam HG (2016) Toward Systems Understanding of Leaf Senescence: An Integrated Multi-Omics Perspective on Leaf Senescence Research. Mol Plant 9, 813-825 https://doi.org/10.1016/j.molp.2016.04.017
  68. Berg KCG, Eide PW, Eilertsen IA et al (2017) Multi-omics of 34 colorectal cancer cell lines - a resource for biomedical studies. Mol Cancer 16, 116
  69. Thakor AS and Gambhir SS (2013) Nanooncology: the future of cancer diagnosis and therapy. CA Cancer J Clin 63, 395-418 https://doi.org/10.3322/caac.21199
  70. Chen R, Huo L, Shi X et al (2014) Endoplasmic reticulum stress induced by zinc oxide nanoparticles is an earlier biomarker for nanotoxicological evaluation. ACS Nano 8, 2562-2574 https://doi.org/10.1021/nn406184r
  71. Matysiak M, Kapka-Skrzypczak L, Brzoska K, Gutleb AC and Kruszewski M (2016) Proteomic approach to nanotoxicity. J Proteomics 137, 35-44 https://doi.org/10.1016/j.jprot.2015.10.025
  72. Amemiya Y, Tanaka T, Yoza B and Matsunaga T (2005) Novel detection system for biomolecules using nano-sized bacterial magnetic particles and magnetic force microscopy. J Biotechnol 120, 308-314 https://doi.org/10.1016/j.jbiotec.2005.06.028
  73. Jun YW, Seo JW and Cheon J (2008) Nanoscaling laws of magnetic nanoparticles and their applicabilities in biomedical sciences. Acc Chem Res 41, 179-189 https://doi.org/10.1021/ar700121f
  74. Larsen BA, Haag MA, Serkova NJ, Shroyer KR and Stoldt CR (2008) Controlled aggregation of superparamagnetic iron oxide nanoparticles for the development of molecular magnetic resonance imaging probes. Nanotechnology 19, 265102 https://doi.org/10.1088/0957-4484/19/26/265102
  75. Kang T, Li F, Baik S, Shao W, Ling D and Hyeon T (2017) Surface design of magnetic nanoparticles for stimuliresponsive cancer imaging and therapy. Biomaterials 136, 98-114 https://doi.org/10.1016/j.biomaterials.2017.05.013
  76. Yoon TJ, Kim JS, Kim BG, Yu KN, Cho MH and Lee JK (2005) Multifunctional nanoparticles possessing a "magnetic motor effect" for drug or gene delivery. Angew Chem Int Ed Engl 44, 1068-1071 https://doi.org/10.1002/anie.200461910

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