高脂与维生素D缺乏饮食诱导的2型糖尿病小鼠血清和肝脏代谢组学研究

doi:10.7503/cjcu20180272

摘要/Abstract

摘要：

采用高脂与维生素D缺乏(VDD)饮食长期(24周)喂养小鼠, 诱导其形成2型糖尿病(T2DM), 通过小鼠血清和肝脏代谢组学分析探究了T2DM发生、发展的代谢物和代谢通路变化机制. 实验收集小鼠血清和肝脏样品, 通过气相色谱-质谱联用技术和硅烷化衍生方法分析得到血清和肝脏代谢轮廓; 利用正交偏最小二乘判别分析和非参数检验筛选血清和肝脏代谢组中具有显著性差异的代谢标志物, 发现血清样品中乳酸、丙氨酸、甘油、苏氨酸和葡萄糖含量在高脂+VDD小鼠中显著升高, 肝脏样品中乳酸、核糖、果糖、葡萄糖、油酸和棕榈酸含量在高脂+VDD小鼠中显著升高. 本文还进行了血清和肝脏代谢轮廓整体分析和代谢通路探索, 发现高脂+VDD小鼠中三羧酸循环、糖异生、氨基酸以及脂质代谢通量均显著增强, 这些代谢路径相互影响共同促进T2DM的发生和发展. 本文通过饮食诱导小鼠形成T2DM, 得到血清和肝脏代谢物及其代谢通路的变化关系, 可为T2DM诊断提供参考信息.

关键词: 2型糖尿病, 代谢组学, 高脂饮食, 维生素D缺乏, 气相色谱-质谱联用

Abstract:

This study developed mice with type 2 diabetes mellitus(T2DM) through 24 weeks high fat diet feeding with vitamin D deficiency(VDD), and analyzed metabolites in serum and liver to explore the change mechanism of metabolites and metabolic pathways in the development of T2DM. The serum and liver samples of mice were collected and analyzed through gas chromatography-mass spectrometry(GC-MS) coupled with trimethylsilyl derivatization. Orthogonal partial least squares discriminant analysis(OPLS-DA) and nonparametric tests were used to screen the metabolic biomarkers, which in serum were lactate, alanine, glycerol, threonine and glucose, and in liver were lactic acid, ribose, fructose, glucose, oleic acid and palmitic acid, and these serum and liver metabolic biomarkers were all up-regulated in high fat+VDD mice, when compared with control. Additionally, this work conducted overall comparison of metabolites in serum and liver between high fat+VDD group and control group, and further explored the metabolic pathways related to these metabolites, that TCA(tricarboxylic acid) cycle, gluconeogenesis, amino acid andlipid metabolism pathways were significantly enhanced in high fat+VDD mice. The disturbance of these pathways contributes to the development of T2DM.

Key words: Type 2 diabetes mellitus, Metabolomics, High fat diet, Vitamin D deficiency, Gas chromatography-mass spectrometry

中图分类号:

O657

TrendMD:

李雯雯, 朱爱如, 龙怡静, 王春燕, 韩源平, 段忆翔. 高脂与维生素D缺乏饮食诱导的2型糖尿病小鼠血清和肝脏代谢组学研究. 高等学校化学学报, 2018, 39(11): 2395.

LI Wenwen, ZHU Airu, LONG Yijing, WANG Chunyan, HAN Yuanping, DUAN Yixiang. Metabolomics Study of Serum and Liver in Type 2 Diabetes Mice Induced by High Fat Diet with Vitamin D Deficiency^†. Chem. J. Chinese Universities, 2018, 39(11): 2395.

图/表 12

Table 1 Biochemical parameters for HD group and control group

Parameter	Control(mean±SE)	HD(mean±SE)
Mass/g	33.57±0.71	45.47±2.14^*
Visceral fat coefficient(%)	2.70±0.32	7.50±0.74^*
FPB/(mmol·L^-1)	5.20±0.24	8.90±0.31^*
HOMA-IR(fold)	1.0	1.7^*
Total cholesterol/(mmol·L^-1)	3.33±0.22	4.94±0.33^*
Triglyceride/(mmol·L^-1)	0.63±0.05	0.90±0.07^*
Free fatty acid/(mmol·L^-1)	0.89±0.04	1.19±0.03^*
LDL-C/(mmol·L^-1)	0.42±0.04	0.78±0.11^*
HDL-C/LDL-C	7.14±0.48	5.33±0.38^*

Fig.1 Total ion chromatogram of a serum samplePeak: 1. hexanol; 2. pyruvic acid**; 3. L-lactic acid**; 4. L-Alanine**; 5. hydroxylamine; 6. 2-hydroxybutyric acid*; 7. oxalic acid; 8. 3-hydroxybutyric acid; 9. L-valine; 10. urea**; 11. ethanol amine; 12. glycerol**; 13. L-isoleucine; 14. L-proline**; 15. glycine; 16. succinic acid*; 17. glyceric acid*; 18. L-serine**; 19. L-threonine**; 20. 2,4-dihydroxybutyric acid; 21. β-alanine; 22. malic acid; 23. erythritol; 24. L-methionine**; 25. 5-oxoproline; 26. erythronic acid; 27. creatinine*; 28. threonic acid**; 29. α-ketoglutarate; 30. L-glutamic acid*; 31. L-phenylalanine; 32. ribose; 33. ribitol; 34. L-ornithine; 35. citric acid; 36. 1-deoxyglucose*; 37. fructose**; 38. mannose; 39. glucose**; 40. glucitol; 41. L-tyrosine; 42. palmitic acid; 43. inositol*; 44. heptadecanoic acid; 45. linoleic acid; 46. oleic acid; 47. stearic acid. Metabolites confirmed with standards are in bold, statistical significance between control and HD group, * P<0.05, ** P<0.01.

Fig.2 Total ion chromatogram of a liver samplePeak: 1. hexanol; 2. L-lactic acid*; 3. L-alanine; 4. hydroxylamine*; 5. 2-hydroxybutyric acid*; 6. oxalic acid; 7. 3-hydroxybutyric acid; 8. 2-aminobutyric acid*; 9. L-valine*; 10. 4-hydroxybutyric acid; 11. urea*; 12. ethanol amine**; 13. glycerol; 14. L-isoleucine*; 15. L-proline; 16. glycine; 17. succinic acid*; 18. glyceric acid*; 19. fumaric acid**; 20. L-serine; 21. L-threonine; 22. β-alanine**; 23. malic acid**; 24. erythritol**; 25. L-methionine*; 26. 5-oxoproline; 27. 4-aminobutyric acid; 28. 2,6-ditertbutylphenol; 29. cysteine; 30. L-glutamic acid; 31. L-phenylalanine; 32. xylose**; 33. lauric acid**; 34. ribose**; 35. xylitol; 36. ribitol; 37. myristic acid; 38. fructose**; 39. mannose**; 40. glucose**; 41. glucitol; 42. L-tyrosine; 43. palmitelaidic acid*; 44. palmitoleic acid; 45. palmitic acid**; 46. inositol; 47. heptadecanoic acid; 48. linoleic acid; 49. oleic acid*; 50. stearic acid; 51. arachidonic acid. Metabolites confirmed with standards are in bold, * P<0.05, ** P<0.01.

Fig.3 Principal component analysis score plots for metabolic profiling of serum(A) and liver(B) samples in HD group and control group(A) R2X[1]=0.14, R2X[2]=0.1; (B) R2X[1]=0.181, R2X[2]=0.0884.

Fig.4 Orthogonal partial least squares-discriminant analysis(OPLS-DA) score plots for metabolic profiling of serum(A) and liver(B) samples in HD group and control group and S-plots of variablesin serum(C) and liver(D)(A) R2X[1]=0.35; R2X[X side comp. 1]=0.217; (B) R2X[1]=0.305; R2X[X side comp.1]=0.172; (C) R2X[1]=0.35; (D) R2X[1]=0.305. (C) and (D): red triangles are metabolic biomarkers with VIP>1 and statistical significance P<0.05.

Fig.5 Serum metabolic biomarkers for T2DM(** P<0.01)(A) Glyccrol; (B) lactic acid; (C) Ala; (D) Thr; (E) glucose.

Fig.6 Liver metabolic biomarkers for T2DM(* P<0.05, ** P<0.01)(A) Ethanolamine; (B) lactic acid; (C) palmitic acid; (D) oleic acid; (E) ribose; (F) fructose; (G) glucose.

Fig.7 Heat map for serum and liver metabolites in HD group and control groupA, B, C and D represent amino acids, fatty acids, organic acids, and carbohydrates and alditol, respectively. Red color and blue color indicate up-regulated and down-regulated, grey color represents absence.

Fig.8 Metabo analyst pathway analysis for serum(A) and liver(B) metabolitesNumbers in the plots are metabolic pathways listed in Table 2 and Table 3, respectively.

Table 2 Significant pathways for serum metabolites

No.	Pathway name	P	Impact	KEGG
1	Aminoacyl-tRNA biosynthesis^*	0	0.113	map00970
2	Alanine, aspartate and glutamate metabolism^*	0	0.234	map00250
3	Arginine and proline metabolism^*	0	0.175	map00330
4	Glycine, serine and threonine metabolism^*	0	0.233	map00260
5	Cysteine and methionine metabolism^*	0.001	0.067	map00270
6	Glycolysis or Gluconeogenesis^*	0.002	0.095	map00010
7	Pentose phosphate pathway	0.002	0.022	map00030
8	Propanoate metabolism	0.002	0.001	map00640
9	Butanoate metabolism^*	0.003	0.103	map00650
10	Galactose metabolism^*	0.004	0.003	map00052
11	Ascorbate and aldarate metabolism	0.005	0.024	map00053
12	Glyoxylate and dicarboxylate metabolism^*	0.006	0.033	map00630
13	Taurine and hypotaurine metabolism	0.010	0.054	map00430
14	Citrate cycle(TCA cycle)^*	0.010	0.105	map00020
15	Valine, leucine and isoleucine biosynthesis	0.018	0.022	map00290
16	Glycerolipid metabolism^*	0.025	0.209	map00561
17	Pyruvate metabolism^*	0.025	0.320	map00620
18	Phenylalanine metabolism	0.048	0	map00360

Table 3 Significant pathways for liver metabolites

No.	Pathway name	P	Impact	KEGG
1	Propanoate metabolism^#	0	0.086	map00640
2	Pentose phosphate pathway	0.003	0.022	map00030
3	Amino sugar and nucleotide sugar metabolism^*	0.009	0	map00520
4	Fatty acid biosynthesis	0.011	0	map00061
5	Starch and sucrose metabolism^*	0.011	0.017	map00500
6	Citrate cycle(TCA cycle)^*	0.015	0.031	map00020
7	Alanine, aspartate and glutamate metabolism	0.021	0.003	map00250
8	Valine, leucine and isoleucine biosynthesis^#	0.027	0.027	map00290
9	Pantothenate and CoA biosynthesis^#	0.027	0.073	map00770
10	Aminoacyl-tRNA biosynthesis^#	0.032	0	map00970
11	Glycolysis or Gluconeogenesis	0.034	0	map00010

Fig.9 Network of metabolites and pathways in this workRed is TCA cycle; green is glycolysis; blue is amino acids into TCA cycle; pink is pentose phosphate pathway; purple is fatty acid synthesis.

参考文献 29

[1]	American Diabetes Association, Diabetes Care, 2014, 37(Suppl.1), S81—S90
[2]	Taskinen M. R., Curr. Mol. Med., 2005, 5(3), 297—308
[3]	Mathers C. D., Loncar D., PLoS Med., 2006, 3(11), e442
[4]	International Diabetes Federation. IDF Diabetes Atlas: Eighth Edition., 2017,
[5]	Alberti K.G. M. M. Zimmet P. Z., Diabet Med., 1998, 15(7), 539—553
[6]	de Lusignan S., Khunti K., Belsey J., Hattersley A., van Vlymen J., Gallagher H., Millett C., Hague N. J., Tomson C., Harris K., Majeed A., Diabet Med., 2010, 27(2), 203—209
[7]	Zhang A. H., Qiu S., Xu H. Y., Sun H., Wang X. J., Clin. Chim. Acta, 2014, 429, 106—110
[8]	Meiss E., Werner P., John C., Scheja L., Herbach N., Heeren J., Fischer M., Metabolomics, 2016, 12(3), 52
[9]	Fiehn O., Plant Mol .Biol., 2002, 48, 155—171
[10]	Vinayavekhin N., Homan E. A., Saghatelian A., ACS Chem. Biol., 2010, 5(1), 91—103
[11]	Johnson C. H., Ivanisevic J., Benton H. P., Siuzdak G., Anal. Chem., 2015, 87(1), 147—156
[12]	Oakes N. D., Cooney G. J., Camilleri S., Chisholm D. J., Kraegen E. W., Diabetes, 1997, 46(11), 1768—1774
[13]	Lottenberg A. M., Afonso Mda S., Lavrador M. S., Machado R. M., Nakandakare E. R., J. Nutr. Biochem., 2012, 23(9), 1027—1040
[14]	Kratz M., Baars T., Guyenet S., Eur. J. Nutr., 2013, 52(1), 1—24
[15]	Flores M., Nutr. Res. Rev., 2007, 18(2), 175—182
[16]	Cao L. J., Jiang P., Li H. D., He X., Zhu W. Y., Central South Pharmacy, 2014, 12(9), 844—847
	(曹玲娟, 江沛, 李焕德, 何昕, 朱文叶. 中南医学, 2014, 12(9), 844—847)
[17]	Chiu K. C., Chu A., Go V. L. W., Saad M. F., Am. J. Clin. Nutr., 2004, 79(5), 820—825
[18]	McCarty M. F., Thomas C. A., Med. Hypotheses, 2003, 61(5/6), 535—542
[19]	Chagas C. E., Borges M. C., Martini L. A., Rogero M. M., Nutrients, 2012, 4(1), 52—67
[20]	Wu S. M., Feng B., Cheng J. H., Li H. J., Fang J. J., Yan X. Z., Wei L., Dong F. T., Chem J Chinese Universities, 2012, 33(6), 1188—1194
	(吴胜明, 封波, 程建华, 李海静, 方均建, 颜贤忠, 魏来, 董方霆. 高等学校化学学报, 2012, 33(6), 1188—1194)
[21]	Styczynski M. P., Moxley J. F., Tong L. V., Walther J. L., Jensen K. L., Stephanopoulos G. N., Anal. Chem., 2007, 79(3), 966—973
[22]	Valenza F., Aletti G., Fossali T., Chevallard G., Sacconi F., Irace M., Gattinoni L., Crit. Care, 2005, 9(6), 588—593
[23]	Felig P., Metabolism, 1973, 22(2), 179—207
[24]	Attie A. D., Krauss R. M., Gray-Keller M. P., Brownlie A., Miyazaki M., Kastelein J. J., Lusis A. J., Stalenhoef A. F. H., Stoehr J. P., Hayden M. R., Ntambi J. M., J. Lipid. Res., 2002, 43(11), 1899—1907
[25]	Lee J. J., Lambert J. E., Hovhannisyan Y., Ramos-Roman M. A., Trombold J. R., Wagner D. A., Parks E. J., Am. J. Clin. Nutr., 2015, 101(1), 34—43
[26]	Wang T. J., Larson M. G., Vasan R. S., Cheng S., Rhee E. P., McCabe E., Lewis G. D., Fox C. S., Jacques P. F., Fernandez C., O’Donnell C. J., Carr S. A., Mootha V. K., Florez J. C., Souza A., Melander O., Clish C. B., Gerszten R. E., Nat. Med., 2011, 17(4), 448—453
[27]	Huffman K. M., Shah S. H., Stevens R. D., Bain J. R., Muehlbauer M., Slentz C. A., Tanner C. J., Kuchibhatla M., Houmard J. A., Newgard C. B., Kraus W. E., Diabetes Care, 2009, 32(9), 1678—1683
[28]	Satapati S., Sunny N. E., Kucejova B., Fu X., He T. T., Mendez-Lucas A., Shelton J. M., Perales J. C., Browning J. D., Burgess S. C., J. Lipid. Res., 2012, 53(6), 1080—1092
[29]	Vessby B., Curr. Opin Lipidol, 2003, 14(1), 15—19