Digestive Diseases and Sciences

, Volume 63, Issue 6, pp 1485–1496 | Cite as

Alterations in Docosahexaenoic Acid-Related Lipid Cascades in Inflammatory Bowel Disease Model Mice

  • Shin NishiumiEmail author
  • Yoshihiro Izumi
  • Masaru YoshidaEmail author
Original Article



Inflammatory bowel disease (IBD) is an intestinal disorder, involving chronic and relapsing inflammation of the digestive tract. Dysregulation of the immune system based on genetic, environmental, and other factors seems to be involved in the onset of IBD, but its exact pathogenesis remains unclear. Therefore, radical treatments for ulcerative colitis and Crohn’s disease remain to be found, and IBD is considered to be a refractory disease.


The aim of this study is to obtain novel insights into IBD via metabolite profiling of interleukin (IL)-10 knockout mice (an IBD animal model that exhibits a dysregulated immune system).


In this study, the metabolites in the large intestine and plasma of IL-10 knockout mice were analyzed. In our analytical system, two kinds of analysis (gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry) were used to detect a broader range of metabolites, including both hydrophilic and hydrophobic metabolites. In addition, an analysis of lipid mediators in the large intestine and ascites of IL-10 knockout mice was carried out.


The levels of a variety of metabolites, including lipid mediators, were altered in IL-10 knockout mice. For example, high large intestinal and plasma levels of docosahexaenoic acid (DHA) were observed. In addition, arachidonic acid- and DHA-related lipid cascades were upregulated in the ascites of the IL-10 knockout mice.


Our findings based on metabolite profiles including lipid mediators must contribute to development of researches about IBD.


IBD IL-10 knockout mice Metabolite profiling Mass spectrometry 



This study was supported by a Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science (JSPS) (16H05227) [M.Y.]; a Grant-in-Aid for Scientific Research (C) from the JSPS (26350960) [S.N.]; the AMED-CREST by the Japan Agency for Medical Research and Development (AMED) (17gm0710013h0004) [S.N., M.Y.]; and the Special Coordination Funds for Promoting Science and Technology, Creation of Innovation Centers for Advanced Interdisciplinary Research Areas (Innovative Bioproduction Kobe) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan [M.Y.].

Compliance with ethical standards

Conflict of interest

All authors have no conflict to declare.

Supplementary material

10620_2018_5025_MOESM1_ESM.pdf (89 kb)
Supplementary material 1 (PDF 89 kb)
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Supplementary material 2 (PDF 91 kb)
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Supplementary material 3 (PDF 95 kb)
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Supplementary material 4 (PDF 102 kb)
10620_2018_5025_MOESM5_ESM.pdf (110 kb)
Supplementary material 5 (PDF 109 kb)


  1. 1.
    Baumgart DC, Sandborn WJ. Inflammatory bowel disease: clinical aspects and established and evolving therapies. Lancet. 2007;369:1641–1657.CrossRefPubMedGoogle Scholar
  2. 2.
    Jumhawan U, Putri SP, Yusianto, Bamba T, Fukusaki E. Quantification of coffee blends for authentication of Asian palm civet coffee (Kopi Luwak) via metabolomics: a proof of concept. J Biosci Bioeng. 2016;122:79–84.CrossRefPubMedGoogle Scholar
  3. 3.
    Tianniam S, Bamba T, Fukusaki E. Pyrolysis GC-MS-based metabolite fingerprinting for quality evaluation of commercial Angelica acutiloba roots. J Biosci Bioeng. 2010;109:89–93.CrossRefPubMedGoogle Scholar
  4. 4.
    Ohta E, Nakayama Y, Mukai Y, Bamba T, Fukusaki E. Metabolomic approach for improving ethanol stress tolerance in Saccharomyces cerevisiae. J Biosci Bioeng. 2016;121:399–405.CrossRefPubMedGoogle Scholar
  5. 5.
    Suzuki M, Nishiumi S, Matsubara A, Azuma T, Yoshida M. Metabolome analysis for discovering biomarkers of gastroenterological cancer. J Chromatogr B Anal Technol Biomed Life Sci. 2014;966:59–69.CrossRefGoogle Scholar
  6. 6.
    Nishiumi S, Suzuki M, Kobayashi T, Matsubara A, Azuma T, Yoshida M. Metabolomics for biomarker discovery in gastroenterological cancer. Metabolites. 2014;4:547–571.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Ooi M, Nishiumi S, Yoshie T, et al. GC/MS-based profiling of amino acids and TCA cycle-related molecules in ulcerative colitis. Inflamm Res. 2011;60:831–840.CrossRefPubMedGoogle Scholar
  8. 8.
    Hisamatsu T, Okamoto S, Hashimoto M, et al. Novel, objective, multivariate biomarkers composed of plasma amino acid profiles for the diagnosis and assessment of inflammatory bowel disease. PLoS ONE. 2012;7:e31131.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Hisamatsu T, Ono N, Imaizumi A, et al. Decreased plasma histidine level predicts risk of relapse in patients with ulcerative colitis in remission. PLoS ONE. 2015;10:e0140716.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Dawiskiba T, Deja S, Mulak A, et al. Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases. World J Gastroenterol. 2014;20:163–174.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Shiomi Y, Nishiumi S, Ooi M, et al. GCMS-based metabolomic study in mice with colitis induced by dextran sulfate sodium. Inflamm Bowel Dis. 2011;17:2261–2274.CrossRefPubMedGoogle Scholar
  12. 12.
    Yoshie T, Nishiumi S, Izumi Y, et al. Regulation of the metabolite profile by an APC gene mutation in colorectal cancer. Cancer Sci. 2012;103:1010–1021.CrossRefPubMedGoogle Scholar
  13. 13.
    Nishiumi S, Kobayashi T, Ikeda A, et al. A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS ONE. 2012;7:e40459.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kobayashi T, Nishiumi S, Ikeda A, et al. A novel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiol Biomark Prev. 2013;22:571–579.CrossRefGoogle Scholar
  15. 15.
    Tsugawa H, Bamba T, Shinohara M, Nishiumi S, Yoshida M, Fukusaki E. Practical non-targeted gas chromatography/mass spectrometry-based metabolomics platform for metabolic phenotype analysis. J Biosci Bioeng. 2011;112:292–298.CrossRefPubMedGoogle Scholar
  16. 16.
    Tsugawa H, Tsujimoto Y, Arita M, Bamba T, Fukusaki E. GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA). BMC Bioinform. 2011;12:131.CrossRefGoogle Scholar
  17. 17.
    Yamada T, Uchikata T, Sakamoto S, Yokoi Y, Fukusaki E, Bamba T. Development of a lipid profiling system using reverse-phase liquid chromatography coupled to high-resolution mass spectrometry with rapid polarity switching and an automated lipid identification software. J Chromatogr A. 2013;1292:211–218.CrossRefPubMedGoogle Scholar
  18. 18.
    Matsubara A, Izumi Y, Nishiumi S, et al. Supercritical fluid extraction as a preparation method for mass spectrometry of dried blood spots. J Chromatogr B Anal Technol Biomed Life Sci. 2014;969:199–204.CrossRefGoogle Scholar
  19. 19.
    Tsugawa H, Ohta E, Izumi Y, et al. MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies. Front Genet. 2015;5:471.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Blaho VA, Buczynski MW, Brown CR, Dennis EA. Lipidomic analysis of dynamic eicosanoid responses during the induction and resolution of Lyme arthritis. J Biol Chem. 2009;284:21599–21612.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Izumi Y, Aritake K, Urade Y, Fukusaki E. Practical evaluation of liquid chromatography/tandem mass spectrometry and enzyme immunoassay method for the accurate quantitative analysis of prostaglandins. J Biosci Bioeng. 2014;118:116–118.CrossRefPubMedGoogle Scholar
  22. 22.
    Hou JK, Abraham B, El-Serag H. Dietary intake and risk of developing inflammatory bowel disease: a systematic review of the literature. Am J Gastroenterol. 2011;106:563–573.CrossRefPubMedGoogle Scholar
  23. 23.
    Marion-Letellier R, Savoye G, Ghosh S. Polyunsaturated fatty acids and inflammation. IUBMB Life. 2015;67:659–667.CrossRefPubMedGoogle Scholar
  24. 24.
    Nikolaus S, Schulte B, Al-Massad N, et al. Increased tryptophan metabolism is associated with activity of inflammatory bowel diseases. Gastroenterology. 2017;153:1504.e2–1516.e2.CrossRefGoogle Scholar
  25. 25.
    Coburn LA, Gong X, Singh K, et al. L-arginine supplementation improves responses to injury and inflammation in dextran sulfate sodium colitis. PLoS ONE. 2012;7:e33546.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Sung MK, Park MY. Nutritional modulators of ulcerative colitis: clinical efficacies and mechanistic view. World J Gastroenterol. 2013;19:994–1004.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Zhao J, Shi P, Sun Y, et al. DHA protects against experimental colitis in IL-10-deficient mice associated with the modulation of intestinal epithelial barrier function. Br J Nutr. 2015;114:181–188.CrossRefPubMedGoogle Scholar
  28. 28.
    Tapia G, Valenzuela R, Espinosa A, et al. n-3 long-chain PUFA supplementation prevents high fat diet induced mouse liver steatosis and inflammation in relation to PPAR-α upregulation and NF-κB DNA binding abrogation. Mol Nutr Food Res. 2014;58:1333–1341.CrossRefPubMedGoogle Scholar
  29. 29.
    Adkins Y, Kelley DS. Mechanisms underlying the cardioprotective effects of omega-3 polyunsaturated fatty acids. J Nutr Biochem. 2010;21:781–792.CrossRefPubMedGoogle Scholar
  30. 30.
    Wang X, Pan L, Lu J, Li N, Li J. n-3 PUFAs attenuate ischemia/reperfusion induced intestinal barrier injury by activating I-FABP-PPARγ pathway. Clin Nutr. 2012;31:951–957.CrossRefPubMedGoogle Scholar
  31. 31.
    Gobbetti T, Dalli J, Colas RA, et al. Protectin D1n-3 DPA and resolvin D5n-3 DPA are effectors of intestinal protection. Proc Natl Acad Sci USA. 2017;114:3963–3968.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Gastroenterology, Department of Internal MedicineKobe University Graduate School of MedicineKobeJapan
  2. 2.Division of Metabolomics, Medical Institute of BioregulationKyushu UniversityFukuokaJapan
  3. 3.Division of Metabolomics Research, Department of Internal RelatedKobe University Graduate School of MedicineKobeJapan
  4. 4.AMED-CREST, AMEDKobeJapan

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