Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Jingxian Zhang; Qinsheng Chen; Lianglong Zhang; Biru Shi; Men Yu; Qingxia Huang; Huiru Tang

doi:10.1016/j.jpha.2023.10.004

Volume 14 Issue 1

Jan. 2024

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Article Navigation > Journal of Pharmaceutical Analysis > 2024 > 14(1): 140-148

Jingxian Zhang, Qinsheng Chen, Lianglong Zhang, Biru Shi, Men Yu, Qingxia Huang, Huiru Tang. Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry[J]. Journal of Pharmaceutical Analysis, 2024, 14(1): 140-148. doi: 10.1016/j.jpha.2023.10.004

Citation:

Jingxian Zhang, Qinsheng Chen, Lianglong Zhang, Biru Shi, Men Yu, Qingxia Huang, Huiru Tang. Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry[J]. Journal of Pharmaceutical Analysis, 2024, 14(1): 140-148. doi: 10.1016/j.jpha.2023.10.004

Citation:

Jingxian Zhang, Qinsheng Chen, Lianglong Zhang, Biru Shi, Men Yu, Qingxia Huang, Huiru Tang. Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry[J]. Journal of Pharmaceutical Analysis, 2024, 14(1): 140-148. doi: 10.1016/j.jpha.2023.10.004

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Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

doi: 10.1016/j.jpha.2023.10.004

a. State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China;
b. Wuhan Laboratory for Shanghai Metabolome Institute (SMI) Ltd., Wuhan, 430000, China

Funds:

We acknowledge financial supports from the National Key R&D Program of China (Grant Nos.: 2022YFC3400700, 2022YFA0806400, and 2020YFE0201600), Shanghai Municipal Science and Technology Major Project (Grant No.: 2017SHZDZX01), and the National Natural Science Foundation of China (Grant No.: 31821002).

Received Date: May 06, 2023
Accepted Date: Oct. 14, 2023
Rev Recd Date: Sep. 28, 2023
Publish Date: Oct. 18, 2023

Abstract

Abstract

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (t_R) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (t_R, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.
- Acylcarnitine,
- UPLC-MS/MS,
- Quantitative structure-retention relationship,
- Molecular phenotype

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References(50)

References

[1]	L.L. Jones, D.A. McDonald, P.R. Borum, Acylcarnitines: role in brain, Prog. Lipid Res. 49 (2010) 61-75.
[2]	P.P. Van Veldhoven, Biochemistry and genetics of inherited disorders of peroxisomal fatty acid metabolism, J. Lipid Res. 51 (2010) 2863-2895.
[3]	Q. Qu, F. Zeng, X. Liu, et al., Fatty acid oxidation and carnitine palmitoyltransferase I: emerging therapeutic targets in cancer, Cell Death Dis. 7 (2016), e2226.
[4]	C.B. Newgard, J. An, J.R. Bain, et al., A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance, Cell Metabol. 9 (2009) 311-326.
[5]	P. Negro, F. Gossetti, M. La Pinta, et al., The effect of L-carnitine, administered through intravenous infusion of glucose, on both glucose and insulin levels in healthy subjects, Drugs. Exp. Clin. Res. 20 (1994) 257-262.
[6]	C.B. Newgard, Metabolomics and metabolic diseases: where do we stand? Cell Metabol. 25 (2017) 43-56.
[7]	R.J.A. Wanders, G. Visser, S. Ferdinandusse, et al., Mitochondrial fatty acid oxidation disorders: laboratory diagnosis, pathogenesis, and the complicated route to treatment, J. Lipid. Atheroscler. 9 (2020) 313-333.
[8]	M.M. Adeva-Andany, I. Calvo-Castro, C. Fernandez-Fernandez, et al., Significance of L-carnitine for human health, IUBMB Life 69 (2017) 578-594.
[9]	M. Dambrova, M. Makrecka-Kuka, J. Kuka, et al., Acylcarnitines: nomenclature, biomarkers, therapeutic potential, drug targets, and clinical trials, Pharmacol. Rev. 74 (2022) 506-551.
[10]	J.K. Reddy, T. Hashimoto, Peroxisomal beta-oxidation and peroxisome proliferator-activated receptor alpha: an adaptive metabolic system, Annu. Rev. Nutr. 21 (2001) 193-230.
[11]	D. Yu, L. Zhou, Q. Xuan, et al., Strategy for comprehensive identification of acylcarnitines based on liquid chromatography-high-resolution mass spectrometry, Anal. Chem. 90 (2018) 5712-5718.
[12]	X. Yan, S.P. Markey, R. Marupaka, et al., Mass spectral library of acylcarnitines derived from human urine, Anal. Chem. 92 (2020) 6521-6528.
[13]	M. Guasch-Ferre, M. Ruiz-Canela, J. Li, et al., Plasma acylcarnitines and risk of type 2 diabetes in a Mediterranean population at high cardiovascular risk, J. Clin. Endocrinol. Metab. 104 (2019) 1508-1519.
[14]	W. Li, C. Shao, C. Li, et al., Metabolomics: a useful tool for ischemic stroke research, J. Pharm. Anal. 2023. https://doi.org/10.1016/j.jpha.2023.05.015.
[15]	A.S. Koh, F. Gao, J. Liu, et al., Metabolomic profile of arterial stiffness in aged adults, Diabetes Vasc. Dis. Res. 15 (2017) 74-80.
[16]	J. Mill, V. Patel, O. Okonkwo, et al., Erythrocyte sphingolipid species as biomarkers of Alzheimer's disease, J. Pharm. Anal. 12 (2022) 178-185.
[17]	Y. Zhou, Z. Dai, K. Deng, et al., Eight Zhes Decoction ameliorates the lipid dysfunction of nonalcoholic fatty liver disease using integrated lipidomics, network pharmacology and pharmacokinetics, J. Pharm. Anal. 2023. https://doi.org/10.1016/j.jpha.2023.05.012.
[18]	K. Enooku, H. Nakagawa, N. Fujiwara, et al., Altered serum acylcarnitine profile is associated with the status of nonalcoholic fatty liver disease (NAFLD) and NAFLD-related hepatocellular carcinoma, Sci. Rep. 9 (2019), 10663.
[19]	E.C. Randall, B.G.C. Lopez, S. Peng, et al., Localized metabolomic gradients in patient-derived xenograft models of glioblastoma, Cancer Res. 80 (2020) 1258-1267.
[20]	A. Maguolo, G. Rodella, A. Dianin, et al., Diagnosis, genetic characterization and clinical follow up of mitochondrial fatty acid oxidation disorders in the new era of expanded newborn screening: a single centre experience, Mol. Genet. Metab. Rep. 24 (2020), 100632.
[21]	A. Ribel Madsen, R. Ribel Madsen, C. Broens, et al., Plasma acylcarnitine profiling indicates increased fatty acid oxidation relative to tricarboxylic acid cycle capacity in young, healthy low birth weight men, Phys. Rep. 4 (2016), e12977.
[22]	T. Teav, H. Gallart-Ayala, V. van der Velpen, et al., Merged targeted quantification and untargeted profiling for comprehensive assessment of acylcarnitine and amino acid metabolism, Anal. Chem. 91 (2019) 11757-11769.
[23]	G. Theodoridis, H.G. Gika, I.D. Wilson, Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies, Mass Spectrom. Rev. 30 (2011) 884-906.
[24]	S. Chen, H. Kong, X. Lu, et al., Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry, Anal. Chem. 85 (2013) 8326-8333.
[25]	R.W. Jiang, K. Jaroch, J. Pawliszyn, Solid-phase microextraction of endogenous metabolites from intact tissue validated using a Biocrates standard reference method kit, J. Pharm. Anal. 13 (2023) 55-62.
[26]	P. Giesbertz, J. Ecker, A. Haag, et al., An LC-MS/MS method to quantify acylcarnitine species including isomeric and odd-numbered forms in plasma and tissues, J. Lipid Res. 56 (2015) 2029-2039.
[27]	P.E. Minkler, M.S.K. Stoll, S.T. Ingalls, et al., Validated method for the quantification of free and total carnitine, butyrobetaine, and acylcarnitines in biological samples, Anal. Chem. 87 (2015) 8994-9001.
[28]	L. Sun, L. Liang, X. Gao, et al., Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study, Diabetes Care 39 (2016) 1563-1570.
[29]	L. Xiang, J. Wei, X.Y. Tian, et al., Comprehensive analysis of acylcarnitine species in db/db mouse using a novel method of high-resolution parallel reaction monitoring reveals widespread metabolic dysfunction induced by diabetes, Anal. Chem. 89 (2017) 10368-10375.
[30]	T. Tang, P. Zhang, S. Li, et al., Absolute quantification of acylcarnitines using integrated Tmt-PP derivatization-based LC-MS/MS and quantitative analysis of multi-components by a single marker strategy, Anal. Chem. 93 (2021) 12973-12980.
[31]	Y. Wen, R.I.J. Amos, M. Talebi, et al., Retention index prediction using quantitative structure-retention relationships for improving structure identification in nontargeted metabolomics, Anal. Chem. 90 (2018) 9434-9440.
[32]	Q. Hu, Y. Sun, P. Yuan, et al., Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry, Talanta. 238 (2022), 123059.
[33]	A.A. D Archivio, M.A. Maggi, F. Ruggieri, Modelling of UPLC behaviour of acylcarnitines by quantitative structure-retention relationships, J. Pharmaceut. Biomed. Anal. 96 (2014) 224-230.
[34]	C. Feng, L. Xue, D. Lu, et al., Novel strategy for mining and identification of acylcarnitines using data-independent-acquisition-based retention time prediction modeling and pseudo-characteristic fragmentation ion matching, J. Proteome Res. 20 (2021) 1602-1611.
[35]	U.S. Food and Drug Administration, Bioanalytical method validation guidance for industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/bioanalyticalmethod-validation-guidance-industry. (Accessed 24 May 2018).
[36]	M. Aschi, A.A. D'Archivio, M.A. Maggi, et al., Quantitative structure-retention relationships of pesticides in reversed-phase high-performance liquid chromatography, Anal. Chim. Acta 582 (2007) 235-242.
[37]	K.P. Singh, S. Gupta, A. Kumar, et al., Linear and nonlinear modeling approaches for urban air quality prediction, Sci. Total Environ. 426 (2012) 244-255.
[38]	M. Peng, X. Fang, Y. Huang, et al., Separation and identification of underivatized plasma acylcarnitine isomers using liquid chromatography-tandem mass spectrometry for the differential diagnosis of organic acidemias and fatty acid oxidation defects, J. Chromatogr., A 1319 (2013) 97-106.
[39]	L. Vernez, G. Hopfgartner, M. Wenk, et al., Determination of carnitine and acylcarnitines in urine by high-performance liquid chromatography-electrospray ionization ion trap tandem mass spectrometry, J. Chromatogr., A 984 (2003) 203-213.
[40]	A. Zuniga, L. Li, Ultra-high performance liquid chromatography tandem mass spectrometry for comprehensive analysis of urinary acylcarnitines, Anal. Chim. Acta 689 (2011) 77-84.
[41]	F. Wang, L. Sun, Q. Sun, et al., Associations of plasma amino acid and acylcarnitine profiles with incident reduced glomerular filtration rate, Clin. J. Am. Soc. Nephrol. 13 (2018) 560-568.
[42]	The human metabolome database. https://hmdb.ca/.
[43]	A.K. Bhaskar, S. Naushin, A. Ray, et al., A high throughput lipidomics method using scheduled multiple reaction monitoring, Biomolecules. 12 (2022), 709.
[44]	C. Hellmuth, M. Weber, B. Koletzko, et al., Nonesterified fatty acid determination for functional lipidomics: comprehensive ultrahigh performance liquid chromatography-tandem mass spectrometry quantitation, qualification, and parameter prediction, Anal. Chem. 84 (2012) 1483-1490.
[45]	L.M. Hall, D.W. Hill, K. Bugden, et al., Development of a reverse phase HPLC retention index model for nontargeted metabolomics using synthetic compounds, J. Chem. Inf. Model. 58 (2018) 591-604.
[46]	K. Weitkunat, C.A. Bishop, M. Wittmuss, et al., Effect of microbial status on hepatic odd-chain fatty acids is diet-dependent, Nutrients 13 (2021), 1546.
[47]	E.P. Brass, Pivalate-generating prodrugs and carnitine homeostasis in man, Pharmacol. Rev. 54 (2002) 589-598.
[48]	T. Rezanka, L. Siristova, O. Schreiberova, et al., Pivalic acid acts as a starter unit in a fatty acid and antibiotic biosynthetic pathway in Alicyclobacillus, Rhodococcus and Streptomyces, Environ. Microbiol. 13 (2011) 1577-1589.
[49]	F. Peng, L. Sheng, B. Liu, et al., Comparison of different extraction methods: steam distillation, simultaneous distillation and extraction and headspace co-distillation, used for the analysis of the volatile components in aged flue-cured tobacco leaves, J. Chromatogr., A 1040 (2004) 1-17.
[50]	M.R. Soeters, M.J. Serlie, H.P. Sauerwein, et al., Characterization of D-3-hydroxybutyrylcarnitine (ketocarnitine): an identified ketosis-induced metabolite, Metabolism. 61 (2012) 966-973.