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Risk profiling using metabolomic characteristics for susceptible individuals of drug-induced liver injury caused by Polygonum multiflorum

Zhang, Le, Niu, Ming, Wei, Ai-wu, Tang, Jin-fa, Tu, Can, Bai, Zhao-fang, Zou, Zheng-sheng, Xiao, Xiao-he, Liu, You-ping, Wang, Jia-bo·2020·Archives of Toxicology
DOI 10.1007/s00204-019-02595-3
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Abstract

Idiosyncratic drug-induced liver injury (IDILI) is a rare but potentially severe adverse drug reaction. To date, identifying individuals at risk for IDILI remains challenging. This is a prospective study, where a nested case-control (1:5) design was adopted. For six patients who had abnormalities in liver function test after Polygonum multiflorum Thunb. (PM) ingestion (susceptible group), 30 patients with normal liver function were matched (tolerant group). Based on liquid chromatography-mass spectrometry, metabolomics analysis was done on serum samples prior to PM ingestion, to screen the differential metabolites and characterize metabolomic profiles of patient serum in the two groups. Multivariate analysis showed that there were remarkable separations between susceptible and tolerant groups. A total of 25 major differential metabolites were screened out, involving glycerophospholipid metabolism, sphingolipid metabolism, fatty acid metabolism, histidine metabolism and aromatic amino acid metabolism. Wherein, the area under the curve of the receiver operating characteristic curves of metabolites PE 22:6, crotonoyl-CoA, 2E-tetradecenoyl-CoA, phenyllactic acid, indole-5,6-quinone, phosphoribosyl-ATP were all greater than 0.9. The overall serum metabolic profile comprising of 25 metabolites could clearly distinguish susceptible and tolerant groups. This proof-of-concept study used metabolomics to characterize the metabolic profile of IDILI risk individuals before drug ingestion…

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  • Several potential biomarkers associated with the risk of liver injury caused by specific drugs or herbal medicines have been reported, such as HLA-B*35:01, immune factors, and metabolic markers that may be associated with the risk of Polygonum multiflorum-induced liver injury.

    “several potential biomarkers associated with the risk of liver injury caused by specific drugs or HMs have been reported, such as HLA-B*35:01, immune factors, and metabolic markers that may be associated with the risk of Polygonum multiflorum-induced liver injury [178][179][180].”

    From Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update (2024) · cites this paper as [180]

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Research works printed but not linked: 40

  1. Aiyar N, Disa J, Ao Z et al (2007) Lysophosphatidylcholine induces inflammatory activation of human coronary artery smooth muscle cells. Mol Cell Biochem 295:113–120. https://doi.org/10.1007/s11010-006-9280-x · DOI
  2. Anderson EK, Hill AA, Hasty AH (2012) Stearic acid accumulation in macrophages induces toll-like receptor 4/2-independent inflammation leading to endoplasmic reticulum stress-mediated apoptosis. Arterioscler Thromb Vasc Biol 32:1687–1695. https://doi.org/10.1161/atvbaha.112.250142 · DOI
  3. Asghar A, Sheikh N (2017) Role of immune cells in obesity induced low grade inflammation and insulin resistance. Cell Immunol 315:18–26. https://doi.org/10.1016/j.cellimm.2017.03.001 · DOI
  4. Banarova A, Koller T, Payer J (2012) Toxic hepatitis induced by Polygonum multiflorum. Vnitr Lek 58:958–962
  5. Benesic A, Rotter I, Dragoi D et al (2018) Development and validation of a test to identify drugs that cause idiosyncratic drug-induced liver injury. Clin Gastroenterol Hepatol 16:1488–1494. https://doi.org/10.1016/j.cgh.2018.04.049 · DOI · Paper page
  6. Björnsson E (2010) Review article: drug-induced liver injury in clinical practice. Aliment Pharmacol Ther 32:3–13. https://doi.org/10.1111/j.1365-2036.2010.04320.x · DOI · Paper page
  7. Cai YC, Gu CC, He J et al (2017) An analysis of Zhu Nansun’s experience of regulating menstruation for assisting pregnancy. Henan Tradit Chin Med 37:1353–1355
  8. Chalasani NP, Hayashi PH, Bonkovsky HL et al (2014) ACG clinical guideline: the diagnosis and management of idiosyncratic drug-induced liver injury. Am J Gastroenterol 109:950–966. https://doi.org/10.1038/ajg.2014.131 (Quiz 967) · DOI · Paper page
  9. Chen M, Borlak J, Tong W (2013) High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury. Hepatology 58:388–396. https://doi.org/10.1002/hep.26208 · DOI · Paper page
  10. Chen M, Suzuki A, Borlak J et al (2015) Drug-induced liver injury: interactions between drug properties and host factors. J Hepatol 63:503–514. https://doi.org/10.1016/j.jhep.2015.04.016 · DOI
  11. Chen M, Borlak J, Tong W (2016) A model to predict severity of drug-induced liver injury in humans. Hepatology 64:931–940. https://doi.org/10.1002/hep.28678 · DOI
  12. Cheng ML, Wang CH, Shiao MS et al (2015) Metabolic disturbances identified in plasma are associated with outcomes in patients with heart failure: diagnostic and prognostic value of metabolomics. J Am Coll Cardiol 65:1509–1520. https://doi.org/10.1016/j.jacc.2015.02.018 · DOI
  13. Ci X, Song Y, Zeng F et al (2008) Ceftiofur impairs pro-inflammatory cytokine secretion through the inhibition of the activation of NF-κB and MAPK. Biochem Biophys Res Commun 372:73–77. https://doi.org/10.1016/j.bbrc.2008.04.170 · DOI
  14. Gomez-Munoz A, Presa N, Gomez-Larrauri A et al (2016) Control of inflammatory responses by ceramide, sphingosine 1-phosphate and ceramide 1-phosphate. Prog Lipid Res 61:51–62. https://doi.org/10.1016/j.plipres.2015.09.002 · DOI
  15. Hirasawa N (2019) Expression of histidine decarboxylase and its roles in inflammation. Int J Mol Sci. https://doi.org/10.3390/ijms20020376 · DOI
  16. Hirashima R, Itoh T, Tukey RH et al (2017) Prediction of drug-induced liver injury using keratinocytes. J Appl Toxicol 37(7):863–872. https://doi.org/10.1002/jat.3435 · DOI
  17. Jung KA, Min HJ, Yoo SS et al (2011) Drug-induced liver injury: twenty five cases of acute hepatitis following ingestion of Polygonum multiflorum Thunb. Gut Liver 5:493–499. https://doi.org/10.5009/gnl.2011.5.4.493 · DOI · Paper page
  18. Kim JH, Wang M, Lee J et al (2018) Prediction of hepatotoxicity for drugs using human pluripotent stem cell-derived hepatocytes. Cell Biol Toxicol 34:51–64. https://doi.org/10.1007/s10565-017-9392-y · DOI
  19. Kuc D, Rahnama M, Tomaszewski T et al (2005) Kynurenic acid in human saliva-does it influence oral microflora? Pharmacol Rep 58:393–398
  20. Lei X, Chen J, Ren J et al (2015) Liver damage associated with Polygonum multiflorum Thunb.: a systematic review of case reports and case series. Evid Based Complement Altern Med 2015:459749. https://doi.org/10.1155/2015/459749 · DOI
  21. Li CY, Li XF, Tu C et al (2015) The idiosyncratic hepatotoxicity of Polygonum multiflorum based on endotoxin model. Acta Pharm Sin 50:28–33
  22. Li B, He X, Jia W et al (2017) Novel applications of metabolomics in personalized medicine: a mini-review. Molecules. https://doi.org/10.3390/molecules22071173 · DOI
  23. Li C, Rao T, Chen X et al (2019) HLA-B*35:01 allele is a potential biomarker for predicting Polygonum multiflorum-induced liver injury in humans. Hepatology 70:346–357. https://doi.org/10.1002/hep.30660 · DOI · Paper page
  24. Lin L, Ni B, Lin H et al (2015) Traditional usages, botany, phytochemistry, pharmacology and toxicology of Polygonum multiflorum Thunb.: a review. J Ethnopharmacol 159:158–183. https://doi.org/10.1016/j.jep.2014.11.009 · DOI
  25. Maceyka M, Spiegel S (2014) Sphingolipid metabolites in inflammatory disease. Nature 510:58–67. https://doi.org/10.1038/nature13475 · DOI
  26. Metushi IG, Hayes MA, Uetrecht J (2015) Treatment of PD-1(−/−) mice with amodiaquine and anti-CTLA4 leads to liver injury similar to idiosyncratic liver injury in patients. Hepatology 61:1332–1342. https://doi.org/10.1002/hep.27549 · DOI
  27. Miao H, Chen L, Hao L et al (2015) Stearic acid induces proinflammatory cytokine production partly through activation of lactate-HIF1alpha pathway in chondrocytes. Sci Rep 5:13092. https://doi.org/10.1038/srep13092 · DOI
  28. Milajerdi A, Larijani B, Esmaillzadeh A (2019) Statins influence biomarkers of low grade inflammation in apparently healthy people or patients with chronic diseases: a systematic review and meta-analysis of randomized clinical trials. Cytokine 123:154752. https://doi.org/10.1016/j.cyto.2019.154752 · DOI
  29. Mucke VT, Thomas D, Mucke MM et al (2019) Serum sphingolipids predict de novo hepatocellular carcinoma in hepatitis C cirrhotic patients with sustained virologic response. Liver Int. https://doi.org/10.1111/liv.14178 · DOI
  30. Ooi JD, Petersen J, Tan YH et al (2017) Dominant protection from HLA-linked autoimmunity by antigen-specific regulatory T cells. Nature 545:243–247. https://doi.org/10.1038/nature22329 · DOI · Paper page
  31. Pagadala M, Kasumov T, McCullough AJ et al (2012) Role of ceramides in nonalcoholic fatty liver disease. Trends Endocrinol Metab 23:365–371. https://doi.org/10.1016/j.tem.2012.04.005 · DOI
  32. Ruiz M, Jove M, Schluter A et al (2015) Altered glycolipid and glycerophospholipid signaling drive inflammatory cascades in adrenomyeloneuropathy. Hum Mol Genet 24:6861–6876. https://doi.org/10.1093/hmg/ddv375 · DOI
  33. Sreekumar A, Poisson LM, Rajendiran TM et al (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457:910–914. https://doi.org/10.1038/nature07762 · DOI
  34. Stephens C, Lucena MI, Andrade RJ (2018) Host risk modifiers in idiosyncratic drug-induced liver injury (DILI) and its interplay with drug properties. In: Chen M, Will Y (eds) Drug-induced liver toxicity. Springer, New York, pp 477–496. https://doi.org/10.1007/978-1-4939-7677-5_23 · DOI
  35. Tu C, He Q, Li CY et al (2019) Susceptibility-Related factor and biomarkers of dietary supplement Polygonum multiflorum-induced liver injury in rats. Front Pharmacol 10:335. https://doi.org/10.3389/fphar.2019.00335 · DOI
  36. Tujios S, Fontana RJ (2011) Mechanisms of drug-induced liver injury: from bedside to bench. Nat Rev Gastroenterol Hepatol 8:202. https://doi.org/10.1038/nrgastro.2011.22 · DOI
  37. Wirthgen E, Hoeflich A, Rebl A et al (2017) Kynurenic acid: the Janus-faced role of an immunomodulatory tryptophan metabolite and its link to pathological conditions. Front Immunol 8:1957. https://doi.org/10.3389/fimmu.2017.01957 · DOI
  38. Zeng Q, Wang PL, Wang WH et al (2017) Brief analysis of professor HAN bing using drug compatibility to treat infertility. Tianjin J Tradit Chin Med 34:433–435
  39. Zhang L, Liu X, Tu C et al (2019) Components synergy between stilbenes and emodin derivatives contributes to hepatotoxicity induced by Polygonum multiflorum. Xenobiotica. https://doi.org/10.1080/00498254.2019.1658138 · DOI
  40. Zhu Y, Liu SH, Wang JB et al (2015) Clinical analysis of drug-induced liver injury caused by Polygonum multiflorum and its preparations. Chin J Integr Tradit Western Med 35:1442–1447

Cited by

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Metadata

Authors
Zhang, L., Niu, M., Wei, A.-W., Tang, J.-F., Tu, C., Bai, Z.-F., Zou, Z.-S., Xiao, X.-H., Liu, Y.-P., & Wang, J.-B.
Venue
Archives of Toxicology
Publisher
Springer Science and Business Media LLC
Published
2019-10-19
Volume
94
Issue
1
Pages
245-256
ISSN
  • 0340-5761
  • 1432-0738
DOI
10.1007/s00204-019-02595-3
Cited by
49
References
4
Language
en
Licence
Funders
National Natural Science Foundation of China (81630100, 81721002, 81503350)

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