Simultaneous quantitation of multiple myeloma related dietary metabolites in serum using HILIC-LC-MS/MS

Mo Wang; Rui Zhang; Shunli Zhang; Xiaojie Zhou; Yichuan Song; Qingtao Wang

doi:10.29219/fnr.v67.9135

Mo Wang Department of Clinical Laboratory, Beijing Chaoyang Hospital, Beijing Center for Clinical Laboratories, The Third Clinical Medical College of Capital Medical University, Beijing, P.R. China
Rui Zhang Department of Clinical Laboratory, Beijing Chaoyang Hospital, Beijing Center for Clinical Laboratories, The Third Clinical Medical College of Capital Medical University, Beijing, P.R. China
Shunli Zhang Department of Clinical Laboratory, Beijing Chaoyang Hospital, Beijing Center for Clinical Laboratories, The Third Clinical Medical College of Capital Medical University, Beijing, P.R. China
Xiaojie Zhou Department of Clinical Laboratory, Beijing Chaoyang Hospital, The Third Clinical Medical College of Capital Medical University, Beijing, P.R. China
Yichuan Song Department of Clinical Laboratory, Beijing Chaoyang Hospital, Capital Medical University, Beijing, P.R. China
Qingtao Wang Department of Clinical Laboratory, Beijing Chaoyang Hospital, Capital Medical University, Beijing, P.R. China

DOI: https://doi.org/10.29219/fnr.v67.9135

Keywords: metabolites, liquid chromatography tandem mass spectrometry, multiple myeloma, diet

Abstract

Background: Recent studies from targeted and untargeted metabolomics have consistently revealed that diet-related metabolites, including carnitine (C0), several species of acylcarnitines (AcyCNs), amino acids, ceramides, and lysophosphatidylcholines (LPCs) may serve as potential multiple myeloma (MM) biomarkers. However, most of these approaches had some intrinsic limitations, namely low reproducibility and compromising the accuracy of the results.

Objective: This study developed and validated a precise, efficient, and reliable liquid chromatography tandem mass spectrometric (LC-MS/MS) method for measuring these 28 metabolic risk factors in human serum.

Design: This method employed isopropanol to extract the metabolites from serum, gradient elution on a hydrophilic interaction liquid chromatographic column (HILIC) for chromatographic separation, and multiple reaction monitor (MRM) mode with positive electrospray ionization (ESI) for mass spectrometric detection.

Results: The correlation coefficients of linear response for this method were more than 0.9984. Analytical recoveries ranged from 91.3 to 106.3%, averaging 99.5%. The intra-run and total coefficients of variation were 1.1–5.9% and 2.0–9.6%, respectively. We have simultaneously determined the serological levels of C0, several subclasses of AcyCNs, amino acids, ceramides, and LPCs within 15 min for the first time.

Conclusion: The established LC-MS/MS method was accurate, sensitive, efficient, and could be valuable in providing insights into the association between diet patterns and MM disease and added value in further clinical research.

Downloads

Download data is not yet available.

References

1.
van de Donk NWCJ, Pawlyn C, Yong KL. Multiple myeloma. Lancet 2021; 397(10272): 410–27. doi: 10.1016/S0140-6736(21)00135-5

2.
Tabata M, Tsubaki M, Takeda T, Tateishi K, Maekawa S, Tsurushima K, et al. Inhibition of HSP90 overcomes melphalan resistance through downregulation of Src in multiple myeloma cells. Clin Exp Med 2020; 20(1): 63–71. doi: 10.1007/s10238-019-00587-2

3.
Wang S, Xu L, Feng Ji, Liu Y, Liu L, Wang J, et al. Prevalence and incidence of multiple myeloma in urban area in China: a national population-based analysis. Front Oncol 2020; 9: 1513. doi: 10.3389/fonc.2019.01513

4.
Harding T, Baughn L, Kumar S, Van Ness B. The future of myeloma precision medicine: integrating the compendium of known drug resistance mechanisms with emerging tumor profiling technologies. Leukemia 2019; 33(4): 863–83. doi 10.1038/s41375-018-0362-z

5.
Lee DH, Fung TT, Tabung FK, Colditz GA, Ghobrial IM, Rosner BA, et al. Dietary pattern and risk of multiple myeloma in two large prospective US cohort studies. JNCI Cancer Spectrum 2019; 3(2): pkz025. doi: 10.1093/jncics/pkz025

6.
Lee DH, Fung TT, Tabung FK, Marinac CR, Devore EE, Rosner BA, et al. Prediagnosis dietary pattern and survival in patients with multiple myeloma. Cancer Epidemiol 2020; 147(7): 1823–30. doi: 10.1002/ijc.32928

7.
Brown LM, Gridley G, Pottern LM, Baris D, Swanso CA, Silverman DT, et al. Diet and nutrition as risk factors for multiple myeloma among blacks and whites in the United States. Cancer Causes Control 2001; 12(2): 117–25. doi: 10.1023/a:1008937901586

8.
Beam A, Clinger E, Hao L. Effect of diet and dietary components on the composition of the gut microbiota. Nutrients 2021; 13(8): 2795. doi: 10.3390/nu13082795

9.
Thorburn AN, Macia L, Mackay CR. Diet, metabolites, and ‘western-lifestyle’ inflammatory diseases. Immunity 2014; 40(6): 833–42. doi: 10.1016/j.immuni.2014.05.014

10.
Zhao J, Zhang X, Liu H, Brown MA, Qiao S. Dietary protein and gut microbiota composition and function. Curr Protein Peptide Sci 2019; 20(2): 145–54. doi: 10.2174/1389203719666180514145437

11.
Schoeler M, Caesar R. Dietary lipids, gut microbiota and lipid metabolism. Rev Endocr Metab Disord 2019; 20(4): 461–72. doi: 10.1007/s11154-019-09512-0

12.
Schmedes M, Balderas C, Aadland EK, Jacques H, Lavigne C, Graff IE, et al. The effect of lean-seafood and non-seafood diets on fasting and postprandial serum metabolites and lipid species: results from a randomized crossover intervention study in healthy adults. Nutrients 2018; 10(5): 598. doi: 10.3390/nu10050598

13.
Puchades-Carrasco L, Lecumberri R, Martínez-López J, Lahuerta JJ, Mateos MV, Prósper F, et al. Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clin Cancer Res 2013; 19(17): 4770–9. doi: 10.1158/1078-0432.CCR-12-2917

14.
Yue L, Zeng P, Li Y, Chai Y, Wu C, Gao B. Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma. PeerJ 2022; 10: e12918. doi: 10.7717/peerj.12918

15.
Chanukuppa V, More TH, Taunk K, Taware R, Chatterjee T, Sharma S, et al. Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study. RSC Adv 2019; 9(51): 29522–32. doi: 10.1039/c9ra04458b

16.
Lodi A, Tiziani S, Khanim FL, Günther UL, Viant MR, Morgan G.J, et al. Proton NMR-based metabolite analyses of archived serial paired serum and urine samples from myeloma patients at different stages of disease activity identifies acetylcarnitine as a novel marker of active disease. PLoS One 2013; 8(2): e56422. doi: 10.1371/journal.pone.0056422

17.
Ludwig C, Williams DS, Bartlett DB, Essex SJ, McNee G, Allwood JW, et al. Alterations in bone marrow metabolism are an early and consistent feature during the development of MGUS and multiple myeloma. Blood Cancer J 2015; 5(10): e359. doi: 10.1038/bcj.2015.85

18.
Kuliszkiewicz-Janus M, Baczyński S, Jurczyk A. Bone marrow transplantation in the course of hematological malignancies – follow-up study in blood serum by 31P MRS. Med Sci Monit 2004; 10(8): CR485–92.

19.
Steiner N, Müller U, Hajek R, Sevcikova S, Borjan B, Jöhrer K, et al. The metabolomic plasma profile of myeloma patients is considerably different from healthy subjects and reveals potential new therapeutic targets. PLoS One 2018; 13(8): e0202045. doi: 10.1371/journal.pone.0202045

20.
Han M, Xie M, Han J, Yuan D, Yang T, Xie Y. Development and validation of a rapid, selective, and sensitive LC-MS/MS method for simultaneous determination of D- and L-amino acids in human serum: application to the study of hepatocellular carcinoma. Anal Bioanal Chem 2018; 410(10): 2517–31. doi: 10.1007/s00216-018-0883-3

21.
Magiera S, Baranowska I, Kusa J, Baranowski J. A liquid chromatography and tandem mass spectrometry method for the determination of potential biomarkers of cardiovascular disease. J Chromatogr B Analyt Technol Biomed Life Sci 2013; 919–920: 20–9. doi: 10.1016/j.jchromb.2012.12.015

22.
Schoeny H, Rampler E, Abiead Yasin E, Hildebrand F, Zach O, Hermann G, et al. A combined flow injection/reversed-phase chromatography-high-resolution mass spectrometry workflow for accurate absolute lipid quantification with 13 C internal standards. Analyst 2021; 146(8): 2591–9. doi: 10.1039/d0an02443k

23.
Huang Q, Hao S, Yao X, You J, Li X, Lai D, et al. High-throughput quantitation of serological ceramides/dihydroceramides by LC/MS/MS: pregnancy baseline biomarkers and potential metabolic messengers. J Pharm Biomed Anal 2021; 192: 113639. doi: 10.1016/j.jpba.2020.113639

24.
Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, et al. The human serum metabolome. PLoS One 2011; 6(2): e16957. doi: 10.1371/journal.pone.0016957

25.
Zordoky BN, Sung MM, Ezekowitz J, Mandal R, Han B, Bjorndahl TC, et al. Metabolomic fingerprint of heart failure with preserved ejection fraction. PLoS One 2015; 10(5): e0124844. doi: 10.1371/journal.pone.0124844

26.
Yang R, Dong J, Guo H, Li H, Wang S, Zhao H, et al. Rapid and precise measurement of serum branched-chain and aromatic amino acids by isotope dilution liquid chromatography tandem mass spectrometry. PLoS One 2013; 8(12): e81144. doi: 10.1371/journal.pone.0081144

27.
Filippatou AG, Moniruzzaman M, Sotirchos ES, Fitzgerald KC, Kalaitzidis G, Lambe J, et al. Serum ceramide levels are altered in multiple sclerosis. Multi Scler J 2021; 27(10): 1506–19. doi: 10.1177/1352458520971816

28.
Castaneda-Avila MA, Ulbricht CM, Epstein MM. Risk factors for monoclonal gammopathy of undetermined significance: a systematic review. Ann Hematol 2021; 100(4): 855–63. doi: 10.1007/s00277-021-04400-7

29.
Thordardottir M, Lindqvist EK, Lund SH, Costello R, Burton D, Steingrimsdottir L, et al. Dietary intake is associated with risk of multiple myeloma and its precursor disease. PLoS One 2018; 13(11): e0206047. doi: 10.1371/journal.pone.0206047

30.
Du H, Wang L, Liu B, Wang J, Su H, Zhang T, et al. Analysis of the metabolic characteristics of serum samples in patients with multiple myeloma. Front Pharmacol 2018; 9: 884. doi: 10.3389/fphar.2018.00884

31.
Piszcz J, Lemancewicz D, Dudzik D, Ciborowski M. Differences and similarities between LC-MS derived serum fingerprints of patients with B-cell malignancies. Electrophoresis 2013; 34(19): 2857–64. doi: 10.1002/elps.201200606

32.
Gonsalves WI, Broniowska K, Jessen E, Petterson X, Bush AG, Gransee J, et al. Metabolomic and lipidomic profiling of bone marrow plasma differentiates patients with monoclonal gammopathy of undetermined significance from multiple myeloma. Sci Rep 2020; 10(1): 10250. doi: 10.1038/s41598-020-67105-3

33.
Cui L, Lu H, Lee YH. Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. Mass Spectrom Rev 2018; 37(6): 772–92. doi: 10.1002/mas.21562

34.
Monteiro MS, Carvalho M, Bastos ML, Pinho PG. Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem 2013; 20(2): 257–71. doi: 10.2174/092986713804806621

35.
Wang M, Yang R, Dong J, Zhang T, Wang S, Zhou W, et al. Simultaneous quantification of cardiovascular disease related metabolic risk factors using liquid chromatography tandem mass spectrometry in human serum. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1009–1010: 144–51. doi: 10.1016/j.jchromb.2015.12.019

36.
Masood MA, Yuan C, Acharya JK, Veenstra TD, Blonder J. Quantitation of ceramide phosphorylethanolamines containing saturated and unsaturated sphingoid base cores. Anal Biochem 2010; 400(2): 259–69. doi: 10.1016/j.ab.2010.01.033

37.
Wang M, Yang R, Mu H, Zeng J, Zhang T, Zhou W, et al. A simple and precise method for measurement of serum free carnitine and acylcarnitines by isotope dilution HILIC-ESI-MS/MS. Int J Mass Spectrom 2019; 446: 116208. doi: 10.1016/j.ijms.2019.116208

38.
Cho WH, Yeo HJ, Yoon SH, Lee SE, Jeon DS, Kim YS, et al. Lysophosphatidylcholine as a prognostic marker in community-acquired pneumonia requiring hospitalization: a pilot study. Eur J Clin Microbiol Infect Dis 2015; 34(2): 309–15. doi: 10.1007/s10096-014-2234-4