Abstract
Introduction
Idiopathic acquired aplastic anemia (AA) is a bone marrow failure disorder where aberrant T-cell functions lead to depletion of hematopoietic stem and progenitor cells in the bone marrow (BM) microenvironment. T-cells undergo metabolic rewiring, which regulates their proliferation and differentiation. Therefore, studying metabolic variation in AA patients may aid us with a better understanding of the T-cell regulatory pathways governed by metabolites and their pathological engagement in the disease.
Objective
To identify the differential metabolites in BM plasma of AA patients, AA follow-up (AAF) in comparison to normal controls (NC) and to identify potential disease biomarker(s).
Methods
The study used 1D 1H NMR Carr–Purcell–Meiboom–Gill (CPMG) spectra to identify the metabolites present in the BM plasma samples of AA (n = 40), AAF (n = 16), and NC (n = 20). Metabolic differences between the groups and predictive biomarkers were identified by using multivariate analysis and receiver operating characteristic (ROC) module of Metaboanalyst V5.0 tool, respectively.
Results
The AA and AAF samples were well discriminated from NC group as per Principal Component analysis (PCA). Further, we found significant alteration in the levels of 17 metabolites in AA involved in amino-acid (Leucine, serine, threonine, phenylalanine, lysine, histidine, valine, tyrosine, and proline), carbohydrate (Glucose, lactate and mannose), fatty acid (Acetate, glycerol myo-inositol and citrate), and purine metabolism (hypoxanthine) in comparison to NC. Additionally, biomarker analysis predicted Hypoxanthine and Acetate can be used as a potential biomarker.
Conclusion
The study highlights the significant metabolic alterations in the BM plasma of AA patients which may have implication in the disease pathobiology.
Graphical Abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Most of the data relevant to this study has been included in the article. The additional data will be made available from the corresponding authors upon reasonable request. The 1 H CPMG NMR raw and CHENOMX processed data have been deposited to the Zenodo (https://zenodo.org/). Zenodo (https://doi.org/10.5281/zenodo.7390046). The CPMG NMR raw and CHENOMX processed data will be available for future validation studies on request to corresponding authors.
References
Al-Otaish, H., Al-Ayadhi, L., Bjørklund, G., Chirumbolo, S., Urbina, M. A., & El-Ansary, A. (2018). Relationship between absolute and relative ratios of glutamate, glutamine and GABA and severity of autism spectrum disorder. Metabolic Brain Disease, 33(3), 843–854. https://doi.org/10.1007/s11011-018-0186-6.
Arya, P., Kumar, U., Sharma, S., Durgappa, M., Guleria, A., Raj, R., Pande, G., & Kumar, D. (2021). Targeted NMR based serum metabolic profiling of serine, glycine and methionine in acute-on-chronic liver failure patients: possible insights into mitochondrial dysfunction. Analytical Science Advances. https://doi.org/10.1002/ansa.202000167
Basu, S., Duren, W., Evans, C. R., Burant, C. F., Michailidis, G., & Karnovsky, A. (2017). Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics, 33(10), 1545–1553. https://doi.org/10.1093/bioinformatics/btx012
Boddu, P. C., & Kadia, T. M. (2019). Molecular pathogenesis of acquired aplastic anemia. European Journal of Haematology, 102(2), 103–110. https://doi.org/10.1111/ejh.13182.
Chaturvedi, C. P., Tripathy, N. K., Minocha, E., Sharma, A., Rahman, K., & Nityanand, S. (2018). Altered expression of hematopoiesis regulatory molecules in lipopolysaccharide-induced bone marrow mesenchymal stem cells of patients with aplastic anemia. Stem Cells International. https://doi.org/10.1155/2018/6901761
Dubey, S., Shukla, P., & Nityanand, S. (2005). Expression of interferon-γ and tumor necrosis factor-α in bone marrow T cells and their levels in bone marrow plasma in patients with aplastic anemia. Annals of Hematology, 84, 572–577. https://doi.org/10.1007/s00277-005-1022-8.
Guleria, A., Pratap, A., Dubey, D., Rawat, A., Chaurasia, S., Sukesh, E., Phatak, S., Ajmani, S., Kumar, U., Khetrapal, C. L., & Bacon, P. (2016). NMR based serummetabolomics reveals a distinctive signature in patients with lupus nephritis. Scientific Reports, 6(1), 35309. https://doi.org/10.1038/srep35309
Gupta, N., Yadav, D. K., Gautam, S., Kumar, A., Kumar, D., & Prasad, N. (2023). Nuclear magnetic resonance-based metabolomics approach revealed the intervention effect of using complementary and alternative medicine (CAM) by CKD patients. ACS Omega, 8(8), 7722–7737. https://doi.org/10.1021/acsomega.2c06469.
Kato, G. J., McGowan, V., Machado, R. F., Little, J. A., Taylor, J., Morris, C. R., Nichols, J. S., Wang, X., Poljakovic, M., Morris, S. M., Jr., & Gladwin, M. T. (2006). Lactate dehydrogenase as a biomarker of hemolysis-associated nitric oxide resistance, priapism, leg ulceration, pulmonary hypertension, and death in patients with sickle cell disease. Blood, 107(6), 2279–2285. https://doi.org/10.1182/blood-2005-06-2373
King, M. E., Honeysett, J. M., & Howell, S. B. (1983). Regulation of de novo purine synthesis in human bone marrow mononuclear cells by hypoxanthine. The Journal of Clinical Investigation, 72(3), 965–970.
Kumar, U., Jain, A., Guleria, A., Misra, R. V. K., Goel, D. P., Danda, R., Misra, D., R., & Kumar, D. (2020). Circulatory glutamine/glucose ratio for evaluating disease activity in takayasu arteritis: A NMR based serum metabolomics study. Journal of Pharmaceutical and Biomedical Analysis, 180, 113080. https://doi.org/10.1016/j.jpba.2019.113080.
Kumar, U., Mehta, P., Kumar, S., Jain, A., Guleria, A., Kumar, V. R., Misra, R., & Kumar, D. (2021). Circulatory histidine levels as predictive indicators of disease activity in takayasu arteritis. Analytical Science Advances, 2(11–12), 527–535. https://doi.org/10.1002/ansa.202000181
Kusum, K., Raj, R., Rai, S., Pranjali, P., Ashish, A., Vicente-Muñoz, S., Chaube, R., & Kumar, D. (2022). Elevated circulatory proline to glutamine ratio (PQR) in endometriosis and its potential as a diagnostic biomarker. ACS Omega, 7(17), 14856–14866. https://doi.org/10.1021/acsomega.2c00332.
Lamarre, S. G., Morrow, G., Macmillan, L., Brosnan, M. E., & Brosnan, J. T. (2013). Formate: An essential metabolite, a biomarker, or more? Clinical Chemistry and Laboratory Medicine, 51(3), 571–578. https://doi.org/10.1515/cclm-2012-0552.
Leverve, X. M. (1999). From tissue perfusion to metabolic marker: Assessing organ competition and co-operation in critically ill patients? Intensive care Medicine, 25(9), 890–892. https://doi.org/10.1007/s001340050976.
Levy, B., Desebbe, O., Montemont, C., & Gibot, S. (2008). Increased aerobic glycolysis through beta2 stimulation is a common mechanism involved in lactate formation during shock states. Shock (Augusta Ga), 30(4), 417–421. https://doi.org/10.1097/SHK.0b013e318167378f.
Liu, X., Cooper, D. E., Cluntun, A. A., Warmoes, M. O., Zhao, S., Reid, M. A., Liu, J., Lund, P. J., Lopes, M., Garcia, B. A., Wellen, K. E., & Locasale, J. W. (2018). Acetate production from glucose and coupling to mitochondrial metabolism in mammals. Cell, 175(2), 502–513. https://doi.org/10.1016/j.cell.2018.08.040
Madeira, C., Vargas-Lopes, C., Brandão, C. O., Reis, T., Laks, J., Panizzutti, R., & Ferreira, S. T. (2018). Elevated glutamate and glutamine levels in the cerebrospinal fluid of patients with probable Alzheimer’s disease and depression. Frontiers in Psychiatry, 9, 561. https://doi.org/10.3389/fpsyt.2018.00561.
Moco, S. (2022). Studying metabolism by NMR-based metabolomics. Frontiers in Molecular Biosciences. https://doi.org/10.3389/fmolb.2022.882487. 372.
Mohamed, A. A., & Essam, A. (2016). Disturbed fluid responsiveness and lactate/pyruvate ratio as predictors for mortality of septic shock patients. Egyptian Journal of Anaesthesia, 32(4), 451–461. https://doi.org/10.1016/j.egja.2016.04.009.
Moyer, J. D., & Henderson, J. F. (1983). Salvage of circulating hypoxanthine by tissues of the mouse. Canadian Journal of Biochemistry and cell Biology, 61(11), 1153–1157. https://doi.org/10.1139/o83-148.
Muhammed, H., Kumar, D., Dubey, D., Kumar, S., Chaurasia, S., Guleria, A., Majumder, S., Singh, R., Agarwal, V., & Misra, R. (2020). Metabolomics analysis revealed significantly higher synovial Phe/Tyr ratio in reactive arthritis and undifferentiated spondyloarthropathy. Rheumatology (Oxford England), 59(7), 1587–1590. https://doi.org/10.1093/rheumatology/kez493.
Musharraf, S. G., Siddiqui, A. J., Shamsi, T., Choudhary, M. I., & Rahman, A. U. (2016). Serum metabonomics of acute leukemia using nuclear magnetic resonance spectroscopy. Scientific Reports, 6(1), 30693. https://doi.org/10.1038/srep30693.
Psychogios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., Sinelnikov, I., Krishnamurthy, R., Eisner, R., Gautam, B., & Young, N. (2011). The human serum metabolome. PloS one, 6(2), 16957. https://doi.org/10.1371/journal.pone.0016957
Rhee, S. Y., Jung, E. S., Park, H. M., Jeong, S. J., Kim, K., Chon, S., Yu, S. Y., Woo, J. T., & Lee, C. H. (2018). Plasma glutamine and glutamic acid are potential biomarkers for predicting diabetic retinopathy. Metabolomics: Official Journal of the Metabolomic Society, 14(7), 89. https://doi.org/10.1007/s11306-018-1383-3.
Rimachi, R., Bruzzi de Carvahlo, F., Orellano-Jimenez, C., Cotton, F., Vincent, J. L., & De Backer, D. (2012). Lactate/pyruvate ratio as a marker of tissue hypoxia in circulatory and septic shock. Anaesthesia and Intensive care, 40(3), 427–432. https://doi.org/10.1177/0310057X1204000307.
Saigusa, D., Matsukawa, N., Hishinuma, E., & Koshiba, S. (2021). Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metabolism and Pharmacokineticshttps://doi.org/10.1016/j.dmpk.2020.11.008.
Saveljeva, S., Sewell, G. W., Ramshorn, K., Cader, M. Z., West, J. A., Clare, S., Haag, L. M., de Almeida Rodrigues, R. P., Unger, L. W., Iglesias-Romero, A. B., Holland, L. M., & Kaser, A. (2022). A purine metabolic checkpoint that prevents autoimmunity and autoinflammation. Cell Metabolism, 34(1), 106–124. https://doi.org/10.1016/j.cmet.2021.12.009
Sena, L. A., Li, S., Jairaman, A., Prakriya, M., Ezponda, T., Hildeman, D. A., Schumacker, P. T., Licht, J. D., Perlman, H., Bryce, P. J., & Chandel, N. S. (2013). Mitochondria are required for antigen-specific T cell activation through reactive oxygen species signaling. Immunity, 38(2), 225–236. https://doi.org/10.1016/j.immuni.2012.10.020
Setälä, L. P., Korvenoja, E. M., Härmä, M. A., Alhava, E. M., Uusaro, A. V., & Tenhunen, J. J. (2004). Glucose, lactate, and pyruvate response in an experimental model of microvascular flap ischemia and reperfusion: A microdialysis study. Microsurgery, 24(3), 223–231. https://doi.org/10.1002/micr.20045.
Shao, Y., Qi, W., Zhang, X., Ran, N., Liu, C., Fu, R., & Shao, Z. (2021). Plasma metabolomic and intestinal microbial analyses of patients with severe aplastic anemia. Frontiers in Cell and Developmental Biology, 9, 669887. https://doi.org/10.3389/fcell.2021.669887.
Singh, A., Prakash, V., Gupta, N., Kumar, A., Kant, R., & Kumar, D. (2022). Serum metabolic disturbances in lung cancer investigated through an elaborative NMR-Based serum metabolomics approach. ACS Omega, 7(6), 5510–5520. https://doi.org/10.1021/acsomega.1c06941.
Solomou, E. E. (2019). Idiopathic aplastic anemia: An update. Clinical Hematology International, 1(1), 52. https://doi.org/10.2991/chi.d.190321.002.
Sumi, K., Hatanaka, Y., Takahashi, R., Wada, N., Ono, C., Sakamoto, Y., Sone, H., & Iida, K. (2022). Citrate synthase insufficiency leads to specific metabolic adaptations in the heart and skeletal muscles upon low-carbohydrate diet feeding in mice. Frontiers in Nutrition, 9, 925908. https://doi.org/10.3389/fnut.2022.925908.
Tripathy, N. K., Singh, S. P., & Nityanand, S. (2014). Enhanced adipogenicity of bone marrow mesenchymal stem cells in aplastic anemia. Stem Cells International. https://doi.org/10.1155/2014/276862
Vilaplana-Lopera, N., Cuminetti, V., Almaghrabi, R., Papatzikas, G., Rout, A. K., Jeeves, M., González, E., Alyahyawi, Y., Cunningham, A., Erdem, A., Schnütgen, F., & Garcia, P. (2022). Crosstalk between AML and stromal cells triggers acetate secretion through the metabolic rewiring of stromal cells. Elife, 11, e75908. https://doi.org/10.7554/eLife.75908
Vogel, M., Moehrle, B., Brown, A., Eiwen, K., Sakk, V., & Geiger, H. (2019). HPRT and purine salvaging are critical for hematopoietic stem cell function. Stem Cells, 37(12), 1606–1614. https://doi.org/10.1002/stem.3087.
Wang, T., Gnanaprakasam, J. R., Chen, X., Kang, S., Xu, X., Sun, H., Liu, L., Rodgers, H., Miller, E., Cassel, T. A., Sun, Q., & Wang, R. (2020). Inosine is an alternative carbon source for CD8+-T-cell function under glucose restriction. Nature Metabolism, 2(7), 635–647.
Wang, X., Yang, R., Zhang, W., Wang, S., Mu, H., Li, H., Dong, J., Chen, W., Yu, X., & Ji, F. (2022). Serum glutamate and glutamine-to-glutamate ratio are associated with coronary angiography defined coronary artery disease. Nutrition Metabolism and Cardiovascular Diseases: NMCD, 32(1), 186–194. https://doi.org/10.1016/j.numecd.2021.09.021.
Wasserman, K., Beaver, W. L., Davis, J. A., Pu, J. Z., Heber, D., & Whipp, B. J. (1985). Lactate, pyruvate, and lactate-to-pyruvate ratio during exercise and recovery. Journal of Applied Physiology, 59(3), 935–940. https://doi.org/10.1152/jappl.1985.59.3.935
Wei, J., Raynor, J., Nguyen, T. L. M., & Chi, H. (2017). Nutrient and metabolic sensing in T cell responses. Frontiers in Immunology, 8, 247. https://doi.org/10.3389/fimmu.2017.00247.
Wishart, D. S. (2019). Metabolomics for investigating physiological and pathophysiological processes. Physiological Reviews, 99(4), 1819–1875. https://doi.org/10.1152/physrev.00035.2018.
Xie, X., & Chen, X. (2022). Deciphering the core metabolites of Fanconi anemia by using a multi-omics composite network. Journal of Microbiology and Biotechnology, 32(3), 387. https://doi.org/10.4014/jmb.2106.06027.
Yin, J., Ren, W., Huang, X., Deng, J., Li, T., & Yin, Y. (2018). Potential mechanisms connecting purine metabolism and cancer therapy. Frontiers in Immunology, 9, 1697. https://doi.org/10.3389/fimmu.2018.01697.
You, X., Yang, Q., Yan, K., Wang, S. R., Huang, R. R., Wang, S. Q., Gao, C. Y., Li, L., & Lian, Z. X. (2022). Multi-omics profiling identifies pathways associated with CD8 + T-cell activation in severe aplastic anemia. Frontiers in Genetics. https://doi.org/10.3389/fgene.2021.790990
Young, N. S. (2018). Aplastic anemia. New England Journal of Medicine, 379(17), 1643–1656. https://doi.org/10.1056/NEJMra1413485.
Young, A., Gardiner, D., Brosnan, M. E., Brosnan, J. T., & Mailloux, R. J. (2017). Physiological levels of formate activate mitochondrial superoxide/hydrogen peroxide release from mouse liver mitochondria. FEBS Letters, 591(16), 2426–2438. https://doi.org/10.1002/1873-3468.12777.
Zhang, A., Sun, H., Xu, H., Qiu, S., & Wang, X. (2013). Cell metabolomics. Omics: A Journal of Integrative Biology, 17(10), 495–501. https://doi.org/10.1089/omi.2012.0090.
Zhang, S. Y., Li, J. Z., Hao, Y. S., Dai, Y. F., Zhang, N., Jiang, L. B., & Liu, N. (2019). Evaluation of related abnormal metabolite profiles in patients with bone marrow failure syndrome (BMFS). International Journal of Clinical and Experimental Medicine, 12(6), 7127–7136.
Zhong, P., Zhang, J., & Cui, X. (2015). Abnormal metabolites related to bone marrow failure in aplastic anemia patients. Genetics and Molecular Research, 14(4), 13709–13718. https://doi.org/10.4238/2015.October.28.33.
Acknowledgements
The authors would like to thank Gurvinder Singh, PhD Student, CBMR, SGPGIMS, Lucknow for his help with the initial processing of samples, to Neetika Mishra, Technical assistant at Department of Hematology, SGPGIMS, Lucknow for technical assistance. The authors would also like to express their deepest gratitude to the patients who provided their consent to participate in the study.
Funding
The work in the CPC laboratory is supported by funding from the Department of Biotechnology (DBT) Grant No. (BT/PR31421/MED/31/407/2019) and Wellcome Trust DBT India Alliance Grant No. (IA/I/16/1/502374). Jyotika Srivastava is a recipient of INSPIRE Ph.D. Fellowship (IF170881) from the Department of Science and Technology (DST) and Rimjhim Trivedi is a recipient of Academy of Scientific and Innovative Research (AcSIR) fellowship (10BB22j71001).
Author information
Authors and Affiliations
Contributions
JS: conceptualization, methodology, resources, investigation, formal analysis, visualization and writing; RT: methodology, resources, investigation, formal analysis, software, investigation, visualization; PS: methodology, resources, writing; SY: data curation, resources; RG: Visualization, writing; SN: resources, supervision; DK: conceptualization, resources, software, data curation, validation, writing, visualization, supervision; CPC: conceptualization, writing, investigation, supervision, project administration and funding acquisition.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Srivastava, J., Trivedi, R., Saxena, P. et al. Bone marrow plasma metabonomics of idiopathic acquired aplastic anemia patients using 1H nuclear magnetic resonance spectroscopy. Metabolomics 19, 94 (2023). https://doi.org/10.1007/s11306-023-02056-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11306-023-02056-0