Metabolomics Reveals that Momordica charantia Attenuates Metabolic Changes in Experimental Obesity
Zhi-gang Gong
Key Lab of Training, Monitoring and Intervention of Aquatic Sports of General Administration of Sport of China, Faculty of Physical Education, Jiangxi Normal University, Nanchang, China
Search for more papers by this authorJianbing Zhang
Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
Search for more papers by this authorCorresponding Author
Yong-Jiang Xu
Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
Department of Medicine, University of California San Diego, La Jolla, CA, USA
Correspondence to: Yong-Jiang Xu, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, No 300 Fenglin Road, Shanghai 201203, China.
E-mail: [email protected]
Search for more papers by this authorZhi-gang Gong
Key Lab of Training, Monitoring and Intervention of Aquatic Sports of General Administration of Sport of China, Faculty of Physical Education, Jiangxi Normal University, Nanchang, China
Search for more papers by this authorJianbing Zhang
Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
Search for more papers by this authorCorresponding Author
Yong-Jiang Xu
Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
Department of Medicine, University of California San Diego, La Jolla, CA, USA
Correspondence to: Yong-Jiang Xu, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, No 300 Fenglin Road, Shanghai 201203, China.
E-mail: [email protected]
Search for more papers by this authorAbstract
Momordica charantia L., also known as bitter melon, has been shown to ameliorate obesity and insulin resistance. However, metabolic changes regulated by M. charantia in obesity are not clearly understood. In this study, serums obtained from obese and M. charantia-treated mice were analyzed by using gas and liquid chromatography-mass spectrometry, and multivariate statistical analysis was performed by Orthogonal partial least squares discriminant analysis. The results from this study indicated that body weight fat and insulin levels of obese mice are dramatically suppressed by 8 weeks of dietary supplementation of M. charantia. Metabolomic data revealed that overproductions of energy and nutrient metabolism in obese mice were restored by M. charantia treatment. The antiinflammatory and inhibition of insulin resistance effect of M. charantia in obesity was illustrated with the restoration of free fatty acids and eicosanoids. The findings achieved in this study further strengthen the therapeutic value of using M. charantia to treat obesity. Copyright © 2016 John Wiley & Sons, Ltd.
Supporting Information
Figure E1. The TIC chromatography of LC-MS analysis of aqueous extract of M. charantia
Figure E2. Phosphochlines (PCs), diglycerides (DGs) and triglycerides (TGs) changes in HFD mice and Effects of Momordica charantia treatment. Heatmap depicting significant metabolome changes in serum in all treatment groups. Green squares indicate a reduction of up to 2 folds; White squares indicate no significant fold changes; Red square indicates an increase of up to 2 folds.
Figure E3. Momordica charantia does not alter serum metabolic profiles in NCD animals. PCA of global metabolite profiles in the serum of NCD mice treated with (NCD/MCAE) or without M. charantia (NCD), detected by (A) GC-MS analysis or (B) LC-MS analysis. OPLS-DA of global metabolite profiles in the serum of NCD treated with (NCD/MCAE) or without M. charantia (NCD), detected by (C) GC/MS or (D) LC-MS analysis. In PCA and OPLS-DA analysis, the value of R2X and R2Y describes how well the data in the training set are mathematically reproduced, ranging between 0 and 1, where 1 indicates a model with a perfect fit. Models with a Q2 value greater than or equal to 0.5 are generally considered to have good predictive capability. Green open circles, NCD (n = 10); blue diamonds, NCD/MCAE (n = 10). The x axis, t[1], and y axis, t[2], indicate the first and second principle components, respectively
Table E1 The retention time, m/z and relative concentration of main peaks in LC-MS analysis of aqueous extract of M. charantia.
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References
- Alam MA, Uddin R, Subhan N, et al. 2015. Beneficial role of bitter melon supplementation in obesity and related complications in metabolic syndrome. J lipids 2015: 496169.
- Balazs A. 2010. Role of phytotherapy in the prevention and treatment of obesity. Orv Hetil 151: 763–773.
- Bao B, Chen YG, Zhang L, et al.. 2013. Momordica charantia (bitter melon) reduces obesity-associated macrophage and mast cell infiltration as well as inflammatory cytokine expression in adipose tissues. PLoS One 8: e84075.
- Boden G. 2008. Obesity and free fatty acids. Endocrinol Metab Clin North Am 37: 635–646, viii-ix.
- Boden G. 2011. Obesity, insulin resistance and free fatty acids. Curr Opin Endocrinol 18: 139–143.
- Ding W, Mak RH. 2015. Early markers of obesity-related renal injury in childhood. Pediatr Nephrol 30: 1–4.
- Duggan GE, Hittel DS, Hughey CC, Weljie A, Vogel HJ, Shearer J. 2011. Differentiating short- and long-term effects of diet in the obese mouse using (1) H-nuclear magnetic resonance metabolomics. Diabetes Obes Metab 13: 859–862.
- Farook VS, Reddivari L, Chittoor G, et al. 2015. Metabolites as novel biomarkers for childhood obesity-related traits in Mexican–American children. Pediatr Obes 10: 320–327.
- Fuangchan A, Sonthisombat P, Seubnukarn T, et al. 2011. Hypoglycemic effect of bitter melon compared with metformin in newly diagnosed type 2 diabetes patients. J Ethnopharmacol 134: 422–428.
- Gil AM, de Pinho PG, Monteiro MS, Duarte IF. 2015. NMR metabolomics of renal cancer: an overview. Bioanalysis 7: 2361–2374.
- Gomes IC, Santos VR, Christofaro DG, Santos LL, Freitas Junior IF. 2013. The most frequent cardiovascular risk factors in Brazilian aged 80 years or older. J Appl Gerontol 32: 408–421.
- Goran MI. 2000. Energy metabolism and obesity. Med Clin N Am 84: 347–362.
- Grover JK, Yadav SP. 2004. Pharmacological actions and potential uses of Momordica charantia: a review. J Ethnopharmacol 93: 123–132.
- Hasani-Ranjbar S, Nayebi N, Larijani B, Abdollahi M. 2009. A systematic review of the efficacy and safety of herbal medicines used in the treatment of obesity. World J Gastroenterol 15: 3073–3085.
- Hjartaker A, Langseth H, Weiderpass E. 2008. Obesity and diabetes epidemics: cancer repercussions. Adv Exp Med Biol 630: 72–93.
- Ho WE, Xu YJ, Xu F, et al. 2013. Metabolomics reveals altered metabolic pathways in experimental asthma. Am J Respir Cell Mol Biol 48: 204–211.
- Izzo AA, Hoon-Kim S, Radhakrishnan R, Williamson EM. 2016. A critical approach to evaluating clinical efficacy, adverse events and drug interactions of herbal remedies. Phytother Res 30: 691–700.
- Jung JY, Kim IY, Kim YN, et al. 2012. 1H NMR-based metabolite profiling of diet-induced obesity in a mouse mode. BMB Rep 45: 419–424.
- Kim HJ, Kim JH, Noh S, et al. 2011. Metabolomic analysis of livers and serum from high-fat diet induced obese mice. J Proteome Res 10: 722–731.
- Kim JY, Park JY, Kim OY, et al. 2010. Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res 9: 4368–4375.
- Kim SH, Yang SO, Kim HS, Kim Y, Park T, Choi HK. 2009. 1H-nuclear magnetic resonance spectroscopy-based metabolic assessment in a rat model of obesity induced by a high-fat diet. Anal Bioanal Chem 395: 1117–1124.
- Kopelman PG. 2000. Obesity as a medical problem. Nature 404: 635–643.
- Krawinkel MB, Keding GB. 2006. Bitter gourd (Momordica charantia): a dietary approach to hyperglycemia. Nutr Rev 64: 331–337.
- Loprinzi PD, Crespo CJ, Andersen RE, Smit E. 2015. Association of body mass index with cardiovascular disease biomarkers. Am J Prev Med 48: 338–344.
- Madsen R, Lundstedt T, Trygg J. 2010. Chemometrics in metabolomics—a review in human disease diagnosis. Anal Chim Acta 659: 23–33.
- Matsuda M, Shimomura I. 2014. Roles of adiponectin and oxidative stress in obesity-associated metabolic and cardiovascular diseases. Rev Endocr Metab Dis 15: 1–10.
- McCormack SE, Shaham O, McCarthy MA, et al. 2013. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr Obes 8: 52–61.
- Newgard CB, An J, Bain JR, et al. 2009. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9: 311–326.
- Oberbach A, Bluher M, Wirth H, et al. 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.
- Parekh S, Anania FA. 2007. Abnormal lipid and glucose metabolism in obesity: implications for nonalcoholic fatty liver disease. Gastroenterology 132: 2191–2207.
- Pickens C, Sordillo L, Comstock S, Fenton J. 2015. Obesity is associated with changes in plasma oxylipids. FASEB J 29: 389–381.
- Pluskal T, Castillo S, Villar-Briones A, Oresic M. 2010. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11: 395.
- Reddy BS, Engle A, Simi B, et al. 1988. Effect of low-fat, high-carbohydrate, high-fiber diet on fecal bile acids and neutral sterols. Prev Med 17: 432–439.
- Singh J, Cumming E, Manoharan G, Kalasz H, Adeghate E. 2011. Medicinal chemistry of the anti-diabetic effects of Momordica charantia: active constituents and modes of actions. Open Med Chem J 5: 70–77.
- Tataranni PA, Ortega E. 2005. A burning question: does an adipokine-induced activation of the immune system mediate the effect of overnutrition on type 2 diabetes? Diabetes 54: 917–927.
- van der Veen JN, Lingrell S, Vance DE. 2012. The membrane lipid phosphatidylcholine is an unexpected source of triacylglycerol in the liver. J Biol Chem 287: 23418–23426.
- Viru A, Litvinova L, Viru M, Smirnova T. 1994. Glucocorticoids in metabolic control during exercise: alanine metabolism. J Appl Physiol 76: 801–805.
- Winder CL, Dunn WB, Goodacre R. 2011. TARDIS-based microbial metabolomics: time and relative differences in systems. Trends Microbiol 19: 315–322.
- Xu X, Shan B, Liao CH, Xie JH, Wen PW, Shi JY. 2015. Anti-diabetic properties of Momordica charantia L. polysaccharide in alloxan-induced diabetic mice. Int J Biol Macromol 81: 538–543.
- Xu YJ, Luo F, Gao Q, Shang Y, Wang C. 2015. Metabolomics reveals insect metabolic responses associated with fungal infection. Anal Bioanal Chem 407: 4815–4821.
- Yilmaz Y, Younossi ZM. 2014. Obesity-associated nonalcoholic fatty liver disease. Clin Liver Dis 18: 19–31.
- Zhang A, Sun H, Wang X. 2012. Serum metabolomics as a novel diagnostic approach for disease: a systematic review. Anal Bioanal Chem 404: 1239–1245.
- Zhang A, Sun H, Wang X. 2013. Power of metabolomics in biomarker discovery and mining mechanisms of obesity. Obesity Rev 14: 344–349.