Endocrinol Metab.  2023 Dec;38(6):619-630. 10.3803/EnM.2023.1814.

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

• Lakhani A
• Kang DH
• Kang YE
• Park JO

• KMID: 2549256
• DOI: http://doi.org/10.3803/EnM.2023.1814

Abstract

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing’s syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing’s syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

Keyword

Metabolism; Endocrinology; Diabetes mellitus; Obesity; Thyroid neoplasms; Pheochromocytoma; Paraganglioma; Cushing syndrome; Aging

Figure

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  Fig. 1. Systems-level metabolic analysis in the context of endocrine disorders and cancer. By integrating advanced analytical techniques such as liquid chromatography-mass spectrometry, magnetic resonance spectroscopy, and flux analyzers and omics methods, we gain a deeper understanding of the complex metabolic alterations underlying endocrine disorders and cancer, paving the way for more effective diagnostic tools and targeted therapies. Created with BioRender.com.

Reference

1. J Ryan P, Riechman SE, Fluckey JD, Wu G. Interorgan metabolism of amino acids in human health and disease. Adv Exp Med Biol. 2021; 1332:129–49.

2. Flynn NE, Shaw MH, Becker JT. Amino acids in health and endocrine function. Adv Exp Med Biol. 2020; 1265:97–109.

3. Martinez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nat Rev Cancer. 2021; 21:669–80.

4. Xu X, Peng Q, Jiang X, Tan S, Yang Y, Yang W, et al. Metabolic reprogramming and epigenetic modifications in cancer: from the impacts and mechanisms to the treatment potential. Exp Mol Med. 2023; 55:1357–70.

5. Wei Q, Qian Y, Yu J, Wong CC. Metabolic rewiring in the promotion of cancer metastasis: mechanisms and therapeutic implications. Oncogene. 2020; 39:6139–56.

6. Moreno-Fernandez S, Garces-Rimon M, Vera G, Astier J, Landrier JF, Miguel M. High fat/high glucose diet induces metabolic syndrome in an experimental rat model. Nutrients. 2018; 10:1502.

7. Uematsu S, Ohno S, Tanaka KY, Hatano A, Kokaji T, Ito Y, et al. Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism. iScience. 2022; 25:103787.

8. O’Donovan SD, Lenz M, Vink RG, Roumans NJ, de Kok TM, Mariman EC, et al. A computational model of postprandial adipose tissue lipid metabolism derived using human arteriovenous stable isotope tracer data. PLoS Comput Biol. 2019; 15:e1007400.

9. Neinast MD, Jang C, Hui S, Murashige DS, Chu Q, Morscher RJ, et al. Quantitative analysis of the whole-body metabolic fate of branched-chain amino acids. Cell Metab. 2019; 29:417–29.

10. Maahs DM, West NA, Lawrence JM, Mayer-Davis EJ. Epidemiology of type 1 diabetes. Endocrinol Metab Clin North Am. 2010; 39:481–97.

11. Petersen MC, Vatner DF, Shulman GI. Regulation of hepatic glucose metabolism in health and disease. Nat Rev Endocrinol. 2017; 13:572–87.

12. Savolainen O, Fagerberg B, Vendelbo Lind M, Sandberg AS, Ross AB, Bergstrom G. Biomarkers for predicting type 2 diabetes development: can metabolomics improve on existing biomarkers? PLoS One. 2017; 12:e0177738.

13. Siddik MA, Shin AC. Recent progress on branched-chain amino acids in obesity, diabetes, and beyond. Endocrinol Metab (Seoul). 2019; 34:234–46.

14. Li X, Wang X, Liu R, Ma Y, Guo H, Hao L, et al. Chronic leucine supplementation increases body weight and insulin sensitivity in rats on high-fat diet likely by promoting insulin signaling in insulin-target tissues. Mol Nutr Food Res. 2013; 57:1067–79.

15. Guasch-Ferre M, Hruby A, Toledo E, Clish CB, MartinezGonzalez MA, Salas-Salvado J, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016; 39:833–46.

16. Jang C, Oh SF, Wada S, Rowe GC, Liu L, Chan MC, et al. A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance. Nat Med. 2016; 22:421–6.

17. Blair MC, Neinast MD, Jang C, Chu Q, Jung JW, Axsom J, et al. Branched-chain amino acid catabolism in muscle affects systemic BCAA levels but not insulin resistance. Nat Metab. 2023; 5:589–606.

18. Schaller S, Willmann S, Lippert J, Schaupp L, Pieber TR, Schuppert A, et al. A generic integrated physiologically based whole-body model of the glucose-insulin-glucagon regulatory system. CPT Pharmacometrics Syst Pharmacol. 2013; 2:e65.

19. Lahoz-Beneytez J, Schaller S, Macallan D, Eissing T, Niederalt C, Asquith B. Physiologically based simulations of deuterated glucose for quantifying cell turnover in humans. Front Immunol. 2017; 8:474.

20. Thiele I, Sahoo S, Heinken A, Hertel J, Heirendt L, Aurich MK, et al. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol Syst Biol. 2020; 16:e8982.

21. Ben Guebila M, Thiele I. Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes. Nat Comput Sci. 2021; 1:348–61.

22. Paul A, Azhar S, Das PN, Bairagi N, Chatterjee S. Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling. Comput Biol Med. 2022; 144:105365.

23. Chen M, Zheng H, Xu M, Zhao L, Zhang Q, Song J, et al. Changes in hepatic metabolic profile during the evolution of STZ-induced diabetic rats via an 1H NMR-based metabonomic investigation. Biosci Rep. 2019; 39:BSR20181379.

24. Rider OJ, Apps A, Miller JJ, Lau JY, Lewis AJ, Peterzan MA, et al. Noninvasive in vivo assessment of cardiac metabolism in the healthy and diabetic human heart using hyperpolarized 13C MRI. Circ Res. 2020; 126:725–36.

25. Haythorne E, Rohm M, van de Bunt M, Brereton MF, Tarasov AI, Blacker TS, et al. Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic β-cells. Nat Commun. 2019; 10:2474.

26. Rahim M, Nakhe AY, Banerjee DR, Overway EM, Bosma KJ, Rosch JC, et al. Glucose-6-phosphatase catalytic subunit 2 negatively regulates glucose oxidation and insulin secretion in pancreatic β-cells. J Biol Chem. 2022; 298:101729.

27. Miller RA, Shi Y, Lu W, Pirman DA, Jatkar A, Blatnik M, et al. Targeting hepatic glutaminase activity to ameliorate hyperglycemia. Nat Med. 2018; 24:518–24.

28. Wu Y, Wong CW, Chiles EN, Mellinger AL, Bae H, Jung S, et al. Glycerate from intestinal fructose metabolism induces islet cell damage and glucose intolerance. Cell Metab. 2022; 34:1042–53.

29. Perez-Ramirez CA, Nakano H, Law RC, Matulionis N, Thompson J, Pfeiffer A, et al. Atlas of fetal metabolism during mid-to-late gestation and diabetic pregnancy. bioRxiv. 2023; Mar. 19. [Preprint]. https://doi.org/10.1101/2023.03.16.532852.

30. Nieman LK. Recent updates on the diagnosis and management of Cushing’s syndrome. Endocrinol Metab (Seoul). 2018; 33:139–46.

31. Giordano R, Guaraldi F, Berardelli R, Karamouzis I, D’Angelo V, Marinazzo E, et al. Glucose metabolism in patients with subclinical Cushing’s syndrome. Endocrine. 2012; 41:415–23.

32. Kuo T, McQueen A, Chen TC, Wang JC. Regulation of glucose homeostasis by glucocorticoids. Adv Exp Med Biol. 2015; 872:99–126.

33. Kotłowska A, Puzyn T, Sworczak K, Stepnowski P, Szefer P. Metabolomic biomarkers in urine of Cushing’s syndrome patients. Int J Mol Sci. 2017; 18:294.

34. Eisenhofer G, Masjkur J, Peitzsch M, Di Dalmazi G, Bidlingmaier M, Gruber M, et al. Plasma steroid metabolome profiling for diagnosis and subtyping patients with Cushing syndrome. Clin Chem. 2018; 64:586–96.

35. Murakami M, Sun N, Li F, Feuchtinger A, Gomez-Sanchez C, Fassnacht M, et al. In situ metabolomics of cortisol-producing adenomas. Clin Chem. 2023; 69:149–59.

36. Ahn CH, Lee C, Shim J, Kong SH, Kim SJ, Kim YH, et al. Metabolic changes in serum steroids for diagnosing and subtyping Cushing’s syndrome. J Steroid Biochem Mol Biol. 2021; 210:105856.

37. Ijare OB, Baskin DS, Pichumani K. Ex vivo 1H NMR study of pituitary adenomas to differentiate various immunohistochemical subtypes. Sci Rep. 2019; 9:3007.

38. Ju SH, Lee SE, Kang YE, Shong M. Development of metabolic synthetic lethality and its implications for thyroid cancer. Endocrinol Metab (Seoul). 2022; 37:53–61.

39. Kim JT, Lim MA, Lee SE, Kim HJ, Koh HY, Lee JH, et al. Adrenomedullin2 stimulates progression of thyroid cancer in mice and humans under nutrient excess conditions. J Pathol. 2022; 258:264–77.

40. Johnson JM, Lai SY, Cotzia P, Cognetti D, Luginbuhl A, Pribitkin EA, et al. Mitochondrial metabolism as a treatment target in anaplastic thyroid cancer. Semin Oncol. 2015; 42:915–22.

41. Abooshahab R, Gholami M, Sanoie M, Azizi F, Hedayati M. Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation. Endocrine. 2019; 65:1–14.

42. Tseng CH. Metformin reduces thyroid cancer risk in Taiwanese patients with type 2 diabetes. PLoS One. 2014; 9:e109852.

43. Evans JM, Donnelly LA, Emslie-Smith AM, Alessi DR, Morris AD. Metformin and reduced risk of cancer in diabetic patients. BMJ. 2005; 330:1304–5.

44. Yu Y, Feng C, Kuang J, Guo L, Guan H. Metformin exerts an antitumoral effect on papillary thyroid cancer cells through altered cell energy metabolism and sensitized by BACH1 depletion. Endocrine. 2022; 76:116–31.

45. Bolf EL, Beadnell TC, Rose MM, D’Alessandro A, Nemkov T, Hansen KC, et al. Dasatinib and trametinib promote anti-tumor metabolic activity. Cells. 2023; 12:1374.

46. Davidson CD, Tomczak JA, Amiel E, Carr FE. Inhibition of glycogen metabolism induces reactive oxygen species-dependent cytotoxicity in anaplastic thyroid cancer in female mice. Endocrinology. 2022; 163:bqac169.

47. Tanner LB, Goglia AG, Wei MH, Sehgal T, Parsons LR, Park JO, et al. Four key steps control glycolytic flux in mammalian cells. Cell Syst. 2018; 7:49–62.

48. Abu-Amero KK, Alzahrani AS, Zou M, Shi Y. High frequency of somatic mitochondrial DNA mutations in human thyroid carcinomas and complex I respiratory defect in thyroid cancer cell lines. Oncogene. 2005; 24:1455–60.

49. Su X, Wang W, Ruan G, Liang M, Zheng J, Chen Y, et al. A comprehensive characterization of mitochondrial genome in papillary thyroid cancer. Int J Mol Sci. 2016; 17:1594.

50. Kurashige T, Shimamura M, Hamada K, Matsuse M, Mitsutake N, Nagayama Y. Characterization of metabolic reprogramming by metabolomics in the oncocytic thyroid cancer cell line XTC.UC1. Sci Rep. 2023; 13:149.

51. Metere A, Graves CE, Chirico M, Caramujo MJ, Pisanu ME, Iorio E. Metabolomic reprogramming detected by 1HNMR spectroscopy in human thyroid cancer tissues. Biology (Basel). 2020; 9:112.

52. Lee SE, Park S, Yi S, Lim MA, Chang JW, Won HR, et al. Mitochondrial SHMT2 is a crucial therapeutic target in dedifferentiated thyroid cancer. Res Sq. 2022; Aug. 2. [Preprint]. https://doi.org/10.21203/rs.3.rs-1881482/v1.

53. Sugarman AJ, Huynh LD, Shabro A, Di Cristofano A. Anaplastic thyroid cancer cells upregulate mitochondrial onecarbon metabolism to meet purine demand, eliciting a critical targetable vulnerability. Cancer Lett. 2023; 568:216304.

54. Lenders JW, Eisenhofer G, Mannelli M, Pacak K. Phaeochromocytoma. Lancet. 2005; 366:665–75.

55. Jochmanova I, Yang C, Zhuang Z, Pacak K. Hypoxia-inducible factor signaling in pheochromocytoma: turning the rudder in the right direction. J Natl Cancer Inst. 2013; 105:1270–83.

56. Griffin JL. Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr Opin Chem Biol. 2003; 7:648–54.

57. Lendvai N, Pawlosky R, Bullova P, Eisenhofer G, Patocs A, Veech RL, et al. Succinate-to-fumarate ratio as a new metabolic marker to detect the presence of SDHB/D-related paraganglioma: initial experimental and ex vivo findings. Endocrinology. 2014; 155:27–32.

58. Richter S, Gieldon L, Pang Y, Peitzsch M, Huynh T, Leton R, et al. Metabolome-guided genomics to identify pathogenic variants in isocitrate dehydrogenase, fumarate hydratase, and succinate dehydrogenase genes in pheochromocytoma and paraganglioma. Genet Med. 2019; 21:705–17.

59. Richter S, Peitzsch M, Rapizzi E, Lenders JW, Qin N, de Cubas AA, et al. Krebs cycle metabolite profiling for identification and stratification of pheochromocytomas/paragangliomas due to succinate dehydrogenase deficiency. J Clin Endocrinol Metab. 2014; 99:3903–11.

60. Imperiale A, Moussallieh FM, Roche P, Battini S, Cicek AE, Sebag F, et al. Metabolome profiling by HRMAS NMR spectroscopy of pheochromocytomas and paragangliomas detects SDH deficiency: clinical and pathophysiological implications. Neoplasia. 2015; 17:55–65.

61. Erlic Z, Kurlbaum M, Deutschbein T, Nolting S, Prejbisz A, Timmers H, et al. Metabolic impact of pheochromocytoma/paraganglioma: targeted metabolomics in patients before and after tumor removal. Eur J Endocrinol. 2019; 181:647–57.

62. Ku EJ, Lee C, Shim J, Lee S, Kim KA, Kim SW, et al. Metabolic subtyping of adrenal tumors: prospective multicenter cohort study in Korea. Endocrinol Metab (Seoul). 2021; 36:1131–41.

63. Aprile M, Cataldi S, Perfetto C, Federico A, Ciccodicola A, Costa V. Targeting metabolism by B-raf inhibitors and diclofenac restrains the viability of BRAF-mutated thyroid carcinomas with Hif-1α-mediated glycolytic phenotype. Br J Cancer. 2023; 129:249–65.

64. Nagarajan A, Malvi P, Wajapeyee N. Oncogene-directed alterations in cancer cell metabolism. Trends Cancer. 2016; 2:365–77.

65. Levine AJ, Puzio-Kuter AM. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science. 2010; 330:1340–4.

66. Gaglio D, Metallo CM, Gameiro PA, Hiller K, Danna LS, Balestrieri C, et al. Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth. Mol Syst Biol. 2011; 7:523.

67. Ying H, Kimmelman AC, Lyssiotis CA, Hua S, Chu GC, Fletcher-Sananikone E, et al. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell. 2012; 149:656–70.

68. Amendola CR, Mahaffey JP, Parker SJ, Ahearn IM, Chen WC, Zhou M, et al. KRAS4A directly regulates hexokinase 1. Nature. 2019; 576:482–6.

69. Mukhopadhyay S, Vander Heiden MG, McCormick F. The metabolic landscape of RAS-driven cancers from biology to therapy. Nat Cancer. 2021; 2:271–83.

70. Mukhopadhyay S, Goswami D, Adiseshaiah PP, Burgan W, Yi M, Guerin TM, et al. Undermining glutaminolysis bolsters chemotherapy while NRF2 promotes chemoresistance in KRAS-driven pancreatic cancers. Cancer Res. 2020; 80:1630–43.

71. Grabocka E, Bar-Sagi D. Mutant KRAS enhances tumor cell fitness by upregulating stress granules. Cell. 2016; 167:1803–13.

72. Song YS, Lim JA, Park YJ. Mutation profile of well-differentiated thyroid cancer in Asians. Endocrinol Metab (Seoul). 2015; 30:252–62.

73. Park SJ, Kang YE, Kim JH, Park JL, Kim SK, Baek SW, et al. Transcriptomic analysis of papillary thyroid cancer: a focus on immune-subtyping, oncogenic fusion, and recurrence. Clin Exp Otorhinolaryngol. 2022; 15:183–93.

74. Kang YE, Hwang B, Lee JH, Shong M, Yi HS, Koo BS, et al. The significance of transcriptomic signatures in the multifocal papillary thyroid carcinoma: two mRNA expression patterns with distinctive clinical behavior from the Cancer Genome Atlas (TCGA) database. Int J Thyroidol. 2020; 13:1–12.

75. Gao Y, Yang F, Yang XA, Zhang L, Yu H, Cheng X, et al. Mitochondrial metabolism is inhibited by the HIF1α-MYCPGC-1β axis in BRAF V600E thyroid cancer. FEBS J. 2019; 286:1420–36.

76. Falchook GS, Millward M, Hong D, Naing A, Piha-Paul S, Waguespack SG, et al. BRAF inhibitor dabrafenib in patients with metastatic BRAF-mutant thyroid cancer. Thyroid. 2015; 25:71–7.

77. Haq R, Shoag J, Andreu-Perez P, Yokoyama S, Edelman H, Rowe GC, et al. Oncogenic BRAF regulates oxidative metabolism via PGC1α and MITF. Cancer Cell. 2013; 23:302–15.

78. Roesch A, Vultur A, Bogeski I, Wang H, Zimmermann KM, Speicher D, et al. Overcoming intrinsic multidrug resistance in melanoma by blocking the mitochondrial respiratory chain of slow-cycling JARID1B(high) cells. Cancer Cell. 2013; 23:811–25.

79. Fischer GM, Jalali A, Kircher DA, Lee WC, McQuade JL, Haydu LE, et al. Molecular profiling reveals unique immune and metabolic features of melanoma brain metastases. Cancer Discov. 2019; 9:628–45.

80. Strohecker AM, Guo JY, Karsli-Uzunbas G, Price SM, Chen GJ, Mathew R, et al. Autophagy sustains mitochondrial glutamine metabolism and growth of BrafV600E-driven lung tumors. Cancer Discov. 2013; 3:1272–85.

81. Yukimoto R, Nishida N, Hata T, Fujino S, Ogino T, Miyoshi N, et al. Specific activation of glycolytic enzyme enolase 2 in BRAF V600E-mutated colorectal cancer. Cancer Sci. 2021; 112:2884–94.

82. Chang CH, Qiu J, O’Sullivan D, Buck MD, Noguchi T, Curtis JD, et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell. 2015; 162:1229–41.

83. Triozzi PL, Stirling ER, Song Q, Westwood B, Kooshki M, Forbes ME, et al. Circulating immune bioenergetic, metabolic, and genetic signatures predict melanoma patients’ response to anti-PD-1 immune checkpoint blockade. Clin Cancer Res. 2022; 28:1192–202.

84. Kurniawan H, Franchina DG, Guerra L, Bonetti L, SorianoBaguet L, Grusdat M, et al. Glutathione restricts serine metabolism to preserve regulatory T cell function. Cell Metab. 2020; 31:920–36.

85. Ma EH, Verway MJ, Johnson RM, Roy DG, Steadman M, Hayes S, et al. Metabolic profiling using stable isotope tracing reveals distinct patterns of glucose utilization by physiologically activated CD8+ T cells. Immunity. 2019; 51:856–70.

86. Karakelides H, Irving BA, Short KR, O’Brien P, Nair KS. Age, obesity, and sex effects on insulin sensitivity and skeletal muscle mitochondrial function. Diabetes. 2010; 59:89–97.

87. Chia CW, Egan JM, Ferrucci L. Age-related changes in glucose metabolism, hyperglycemia, and cardiovascular risk. Circ Res. 2018; 123:886–904.

88. Hong Y, Kim HJ, Park S, Yi S, Lim MA, Lee SE, et al. Single cell analysis of human thyroid reveals the transcriptional signatures of aging. Endocrinology. 2023; 164:bqad029.

89. Lee J, Yi S, Kang YE, Kim HW, Joung KH, Sul HJ, et al. Morphological and functional changes in the thyroid follicles of the aged murine and humans. J Pathol Transl Med. 2016; 50:426–35.

90. Martocchia A, Stefanelli M, Falaschi GM, Toussan L, Ferri C, Falaschi P. Recent advances in the role of cortisol and metabolic syndrome in age-related degenerative diseases. Aging Clin Exp Res. 2016; 28:17–23.

91. Bednarski TK, Rahim M, Young JD. In vivo2H/13C flux analysis in metabolism research. Curr Opin Biotechnol. 2021; 71:1–8.

92. Fernandez-Garcia J, Altea-Manzano P, Pranzini E, Fendt SM. Stable isotopes for tracing mammalian-cell metabolism in vivo. Trends Biochem Sci. 2020; 45:185–201.

93. Hasenour CM, Rahim M, Young JD. In vivo estimates of liver metabolic flux assessed by 13C-propionate and 13Clactate are impacted by tracer recycling and equilibrium assumptions. Cell Rep. 2020; 32:107986.

94. Hui S, Cowan AJ, Zeng X, Yang L, TeSlaa T, Li X, et al. Quantitative fluxomics of circulating metabolites. Cell Metab. 2020; 32:676–88.

95. Law RC, Lakhani A, O’Keeffe S, Ersan S, Park JO. Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes. Curr Opin Biotechnol. 2022; 75:102701.

96. Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Drager A, Mih N, et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol. 2018; 36:272–81.

97. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007; 35(Database issue):D521–6.

98. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000; 28:27–30.

99. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005; 6:R2.

100. Schellenberger J, Park JO, Conrad TM, Palsson BO. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics. 2010; 11:213.

101. Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc. 2019; 14:639–702.

102. Karp PD, Paley S, Romero P. The pathway tools software. Bioinformatics. 2002; 18 Suppl 1:S225–32.

103. Rahim M, Ragavan M, Deja S, Merritt ME, Burgess SC, Young JD. INCA 2.0: a tool for integrated, dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis. Metab Eng. 2022; 69:275–85.

104. Yoo H, Antoniewicz MR, Stephanopoulos G, Kelleher JK. Quantifying reductive carboxylation flux of glutamine to lipid in a brown adipocyte cell line. J Biol Chem. 2008; 283:20621–7.

105. Weitzel M, Noh K, Dalman T, Niedenfuhr S, Stute B, Wiechert W. 13CFLUX2: high-performance software suite for (13)C-metabolic flux analysis. Bioinformatics. 2013; 29:143–5.

106. Matsuda F, Maeda K, Taniguchi T, Kondo Y, Yatabe F, Okahashi N, et al. mfapy: an open-source Python package for 13C-based metabolic flux analysis. Metab Eng Commun. 2021; 13:e00177.

107. Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009; 37(Web Server issue):W652–60.

108. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003; 4:P3.

109. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 14:128.

110. Xia J, Wishart DS. MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics. 2010; 26:2342–4.

111. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019; 47(D1):D607–13.

112. Zhou G, Xia J. OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res. 2018; 46(W1):W514–22.

113. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13:2498–504.

114. Cottret L, Wildridge D, Vinson F, Barrett MP, Charles H, Sagot MF, et al. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Res. 2010; 38(Web Server issue):W132–7.