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Integrative Omics Reveals Metabolic and Transcriptomic Alteration of Nonalcoholic Fatty Liver Disease in Catalase Knockout Mice

  • Na, Jinhyuk (College of Pharmacy, Korea University) ;
  • Choi, Soo An (College of Pharmacy, Korea University) ;
  • Khan, Adnan (College of Pharmacy, Korea University) ;
  • Huh, Joo Young (College of Pharmacy, Chonnam National University) ;
  • Piao, Lingjuan (Graduate School of Pharmaceutical Sciences, College of Pharmacy, Ewha Womans University) ;
  • Hwang, Inah (Graduate School of Pharmaceutical Sciences, College of Pharmacy, Ewha Womans University) ;
  • Ha, Hunjoo (Graduate School of Pharmaceutical Sciences, College of Pharmacy, Ewha Womans University) ;
  • Park, Youngja H (College of Pharmacy, Korea University)
  • 투고 : 2018.09.10
  • 심사 : 2018.12.17
  • 발행 : 2019.03.01

초록

The prevalence of nonalcoholic fatty liver disease (NAFLD) has increased with the incidence of obesity; however, the underlying mechanisms are unknown. In this study, high-resolution metabolomics (HRM) along with transcriptomics were applied on animal models to draw a mechanistic insight of NAFLD. Wild type (WT) and catalase knockout (CKO) mice were fed with normal fat diet (NFD) or high fat diet (HFD) to identify the changes in metabolic and transcriptomic profiles caused by catalase gene deletion in correspondence with HFD. Integrated omics analysis revealed that cholic acid and $3{\beta}$, $7{\alpha}$-dihydroxy-5-cholestenoate along with cyp7b1 gene involved in primary bile acid biosynthesis were strongly affected by HFD. The analysis also showed that CKO significantly changed all-trans-5,6-epoxy-retinoic acid or all-trans-4-hydroxy-retinoic acid and all-trans-4-oxo-retinoic acid along with cyp3a41b gene in retinol metabolism, and ${\alpha}/{\gamma}$-linolenic acid, eicosapentaenoic acid and thromboxane A2 along with ptgs1 and tbxas1 genes in linolenic acid metabolism. Our results suggest that dysregulated primary bile acid biosynthesis may contribute to liver steatohepatitis, while up-regulated retinol metabolism and linolenic acid metabolism may have contributed to oxidative stress and inflammatory phenomena in our NAFLD model created using CKO mice fed with HFD.

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참고문헌

  1. Arguello, G., Balboa, E., Arrese, M. and Zanlungo, S. (2015) Recent insights on the role of cholesterol in non-alcoholic fatty liver disease. Biochim. Biophys. Acta 1852, 1765-1778. https://doi.org/10.1016/j.bbadis.2015.05.015
  2. Cani, P. D., Bibiloni, R., Knauf, C., Waget, A., Neyrinck, A. M., Delzenne, N. M. and Burcelin, R. (2008) Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice. Diabetes 57, 1470-1481. https://doi.org/10.2337/db07-1403
  3. Cavill, R., Jennen, D., Kleinjans, J. and Briede, J. J. (2016) Transcriptomic and metabolomic data integration. Brief. Bioinform. 17, 891-901. https://doi.org/10.1093/bib/bbv090
  4. Clarke, G. M., Anderson, C. A., Pettersson, F. H., Cardon, L. R., Morris, A. P. and Zondervan, K. T. (2011) Basic statistical analysis in genetic case-control studies. Nat. Protoc. 6, 121-133. https://doi.org/10.1038/nprot.2010.182
  5. Darwish, R. S., Amiridze, N. and Aarabi, B. (2007) Nitrotyrosine as an oxidative stress marker: evidence for involvement in neurologic outcome in human traumatic brain injury. J. Trauma 63, 439-442. https://doi.org/10.1097/TA.0b013e318069178a
  6. Gao, Y., Lu, Y., Huang, S., Gao, L., Liang, X., Wu, Y., Wang, J., Huang, Q., Tang, L., Wang, G., Yang, F., Hu, S., Chen, Z., Wang, P., Jiang, Q., Huang, R., Xu, Y., Yang, X. and Ong, C. N. (2014) Identifying early urinary metabolic changes with long-term environmental exposure to cadmium by mass-spectrometry-based metabolomics. Environ. Sci. Technol. 48, 6409-6418. https://doi.org/10.1021/es500750w
  7. Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam-Rudloff, E., Conesa, A. and Tegner, J. (2014) Data integration in the era of omics: current and future challenges. BMC Syst. Biol. 8 Suppl 2, I1.
  8. Hariri, N. and Thibault, L. (2010) High-fat diet-induced obesity in animal models. Nutr. Res. Rev. 23, 270-299. https://doi.org/10.1017/S0954422410000168
  9. Hassan, K., Bhalla, V., El Regal, M. E. and A-Kader, H. H. (2014) Nonalcoholic fatty liver disease: a comprehensive review of a growing epidemic. World J. Gastroenterol. 20, 12082-12101. https://doi.org/10.3748/wjg.v20.i34.12082
  10. Horgan, R. P. and Kenny, L. C. (2011) 'Omic' technologies: genomics, transcriptomics, proteomics and metabolomics. TOG 13, 189-195. https://doi.org/10.1576/toag.13.3.189.27672
  11. Hwang, I., Lee, J., Huh, J. Y., Park, J., Lee, H. B., Ho, Y. S. and Ha, H. (2012) Catalase deficiency accelerates diabetic renal injury through peroxisomal dysfunction. Diabetes 61, 728-738. https://doi.org/10.2337/db11-0584
  12. Kuehl, F. A., Jr. and Egan, R. W. (1980) Prostaglandins, arachidonic acid, and inflammation. Science 210, 978-984. https://doi.org/10.1126/science.6254151
  13. Li, T., Francl, J. M., Boehme, S. and Chiang, J. Y. (2013) Regulation of cholesterol and bile acid homeostasis by the cholesterol 7alphahydroxylase/steroid response element-binding protein 2/microRNA-33a axis in mice. Hepatology 58, 1111-1121. https://doi.org/10.1002/hep.26427
  14. Lin, S., Thomas, T., Storlien, L. and Huang, X. (2000) Development of high fat diet-induced obesity and leptin resistance in C 57 Bl/6 J mice. Int. J. Obes. Relat. Metab. Disord. 24, 639-646. https://doi.org/10.1038/sj.ijo.0801209
  15. Machado, M. V., Michelotti, G. A., Xie, G., Almeida Pereira, T., Boursier, J., Bohnic, B., Guy, C. D. and Diehl, A. M. (2015) Mouse models of diet-induced nonalcoholic steatohepatitis reproduce the heterogeneity of the human disease. PLoS ONE 10, e0127991. https://doi.org/10.1371/journal.pone.0127991
  16. Mouzaki, M., Wang, A. Y., Bandsma, R., Comelli, E. M., Arendt, B. M., Zhang, L., Fung, S., Fischer, S. E., McGilvray, I. G. and Allard, J. P. (2016) Bile acids and dysbiosis in non-alcoholic fatty liver disease. PLoS ONE 11, e0151829. https://doi.org/10.1371/journal.pone.0151829
  17. Nakahata, N. (2008) Thromboxane A2: physiology/pathophysiology, cellular signal transduction and pharmacology. Pharmacol. Ther. 118, 18-35. https://doi.org/10.1016/j.pharmthera.2008.01.001
  18. Norris, A. W., Chen, L., Fisher, S. J., Szanto, I., Ristow, M., Jozsi, A. C., Hirshman, M. F., Rosen, E. D., Goodyear, L. J., Gonzalez, F. J., Spiegelman, B. M. and Kahn, C. R. (2003) Muscle-specific PPARgamma-deficient mice develop increased adiposity and insulin resistance but respond to thiazolidinediones. J. Clin. Invest. 112, 608-618. https://doi.org/10.1172/JCI17305
  19. Obuchowski, N. A. and Bullen, J. A. (2018) Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys. Med. Biol. 63, 07TR01. https://doi.org/10.1088/1361-6560/aab4b1
  20. Panchal, S. K., Poudyal, H., Iyer, A., Nazer, R., Alam, A., Diwan, V., Kauter, K., Sernia, C., Campbell, F. and Ward, L. (2011) High-carbohydrate, high-fat diet-induced metabolic syndrome and cardiovascular remodeling in rats. J. Cardiovasc. Pharmacol. 57, 611-624. https://doi.org/10.1097/FJC.0b013e3181feb90a
  21. Park, Y. H., Fitzpatrick, A. M., Medriano, C. A. and Jones, D. P. (2016) High-resolution metabolomics to identify urine biomarkers in corticosteroid-resistant asthmatic children. J. Allergy Clin. Immunol. 139, 1518-1524.e4. https://doi.org/10.1016/j.jaci.2016.08.018
  22. Park, Y. H., Shi, Y. P., Liang, B., Medriano, C. A. D., Jeon, Y. H., Torres, E., Uppal, K., Slutsker, L. and Jones, D. P. (2015) High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system. Malar. J. 14, 122. https://doi.org/10.1186/s12936-015-0651-1
  23. Pasquali, M. A., Gelain, D. P., Zanotto-Filho, A., de Souza, L. F., de Oliveira, R. B., Klamt, F. and Moreira, J. C. (2008) Retinol and retinoic acid modulate catalase activity in Sertoli cells by distinct and gene expression-independent mechanisms. Toxicol. In Vitro 22, 1177-1183. https://doi.org/10.1016/j.tiv.2008.03.007
  24. Piao, L., Choi, J., Kwon, G. and Ha, H. (2017) Endogenous catalase delays high-fat diet-induced liver injury in mice. Korean J. Physiol. Pharmacol. 21, 317-325. https://doi.org/10.4196/kjpp.2017.21.3.317
  25. Rajasundaram, D. and Selbig, J. (2016) More effort - more results: recent advances in integrative 'omics' data analysis. Curr. Opin. Plant Biol. 30, 57-61. https://doi.org/10.1016/j.pbi.2015.12.010
  26. Ruhl, C. E. and Everhart, J. E. (2015) Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey. Aliment. Pharmacol. Ther. 41, 65-76. https://doi.org/10.1111/apt.13012
  27. Singh, S., Allen, A. M., Wang, Z., Prokop, L. J., Murad, M. H. and Loomba, R. (2015) Fibrosis progression in nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and metaanalysis of paired-biopsy studies. Clin. Gastroenterol. Hepatol. 13, 643-54.e1-9; quiz e39-e40. https://doi.org/10.1016/j.cgh.2014.04.014
  28. Staels, B. and Fonseca, V. A. (2009) Bile acids and metabolic regulation: mechanisms and clinical responses to bile acid sequestration. Diabetes Care 32 Suppl 2, S237-S245. https://doi.org/10.2337/dc09-S355
  29. Stewart, J. D. and Bolt, H. M. (2011) Metabolomics: biomarkers of disease and drug toxicity. Arch. Toxicol. 85, 3-4. https://doi.org/10.1007/s00204-010-0635-4
  30. Stiles, A. R., McDonald, J. G., Bauman, D. R. and Russell, D. W. (2009) CYP7B1: one cytochrome P450, two human genetic diseases, and multiple physiological functions. J. Biol. Chem. 284, 28485-28489. https://doi.org/10.1074/jbc.R109.042168
  31. Videla, L. A., Rodrigo, R., Orellana, M., Fernandez, V., Tapia, G., Quinones, L., Varela, N., Contreras, J., Lazarte, R., Csendes, A., Rojas, J., Maluenda, F., Burdiles, P., Diaz, J. C., Smok, G., Thielemann, L. and Poniachik, J. (2004) Oxidative stress-related parameters in the liver of non-alcoholic fatty liver disease patients. Clin. Sci. (Lond.) 106, 261-268. https://doi.org/10.1042/CS20030285
  32. Want, E. J., O'Maille, G., Smith, C. A., Brandon, T. R., Uritboonthai, W., Qin, C., Trauger, S. A. and Siuzdak, G. (2006) Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal. Chem. 78, 743-752. https://doi.org/10.1021/ac051312t
  33. Wolf, S., Schmidt, S., Muller-Hannemann, M. and Neumann, S. (2010) In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics 11, 148. https://doi.org/10.1186/1471-2105-11-148
  34. Yamato, M., Shiba, T., Ide, T., Seri, N., Kudo, W., Ando, M., Yamada, K., Kinugawa, S. and Tsutsui, H. (2012) High-fat diet-induced obesity and insulin resistance were ameliorated via enhanced fecal bile acid excretion in tumor necrosis factor-alpha receptor knockout mice. Mol. Cell. Biochem. 359, 161-167. https://doi.org/10.1007/s11010-011-1010-3
  35. Younossi, Z. M., Stepanova, M., Afendy, M., Fang, Y., Younossi, Y., Mir, H. and Srishord, M. (2011) Changes in the prevalence of the most common causes of chronic liver diseases in the United States from 1988 to 2008. Clin. Gastroenterol. Hepatol. 9, 524-530.e1; quiz e60. https://doi.org/10.1016/j.cgh.2011.03.020
  36. Yu, T., Park, Y., Johnson, J. M. and Jones, D. P. (2009) apLCMS-adaptive processing of high-resolution LC/MS data. Bioinformatics 25, 1930-1936. https://doi.org/10.1093/bioinformatics/btp291
  37. Yuan, L. and Bambha, K. (2015) Bile acid receptors and nonalcoholic fatty liver disease. World J. Hepatol. 7, 2811-2818. https://doi.org/10.4254/wjh.v7.i28.2811

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