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The Use of NMR Based Metabolomics to Discriminate Patients with Viral Diseases

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COVID-19 Metabolomics and Diagnosis

Abstract

Infectious diseases are one of the most common conditions impacting global health, being a matter of concern for health agencies due to their contagious capacity and periodic outbreaks of new diseases, such as the global pandemic COVID-19. Viruses are among the main causes of this illness and it is defined as obligate intracellular parasites for their need to have a host cell to live and reproduce, since they won’t produce proteins and compete for nutrients and metabolites leading to the alteration of the host metabolome. The diagnosis of these viral infections can be done by detecting viral particles or components, isolating the virus in cell culture, or even by evaluating immune responses. In this context, metabolomics comes as a very useful tool that reflects all “omics” techniques and best represents the phenotype. Since water-soluble metabolites and lipids are the major molecular constituents of human plasma, their abnormalities are commonly observed during disease, which contributes to the understanding of physiology and pathology. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the most widely used techniques in metabolomics. NMR spectroscopy has emerged as a valuable application due to its ability to identify compounds with simple sample preparation, in addition, to being a non-destructive, highly reproducible, and quantitative technique (primary ratio method). These features make NMR a valuable tool that is frequently used in metabolomics analysis, and nowadays used in the diagnosis of viral diseases. Therefore, in this chapter, we will address a short integrative description of viral diseases and diagnostics, metabolomics, and NMR concepts. Furthermore, we will explore the advances in NMR-based metabolomics applied in medicine, and finally, the viral diseases discriminated by NMR-based metabolomics.

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References

  1. World Health Organization, WHO Director-General’s opening remarks at the media briefing on COVID-19. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020

  2. S. Crimi, L. Fiorillo, A. Bianchi et al., Herpes virus, oral clinical signs, and qol: systematic review of recent data. Viruses 11, 1–18 (2019). https://doi.org/10.3390/v11050463

    Article  CAS  Google Scholar 

  3. M.H. Ebell, Epstein-Barr virus infectious mononucleosis. Am Fam Physician 70 (2004)

    Google Scholar 

  4. T. Wilkins, J.K. Malcolm, D. Raina, R.R. Schade, Hepatitis C: diagnosis and treatment. Am Fam Physician 81, 1351–1357 (2010)

    PubMed  Google Scholar 

  5. World Health Organization, Yellow fever (2019). https://www.who.int/news-room/fact-sheets/detail/yellow-fever#:~:text=Symptoms of yellow fever include, and Central and South America

  6. W.J. Wiersinga, A. Rhodes, A.C. Cheng et al., Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA 324, 782–793 (2020). https://doi.org/10.1001/jama.2020.12839

    Article  CAS  PubMed  Google Scholar 

  7. T. Hemachudha, G. Ugolini, S. Wacharapluesadee et al., Human rabies: neuropathogenesis, diagnosis, and management. Lancet Neurol 12, 498–513 (2013). https://doi.org/10.1016/S1474-4422(13)70038-3

    Article  PubMed  Google Scholar 

  8. P. Davison, J. Morris, Mumps, in StatPearls (StatPearls Publishing, Treasure Island, 2022)

    Google Scholar 

  9. A. Misin, R.M. Antonello, S. Di Bella et al., Measles: an overview of a re-emerging disease in children and immunocompromised patients. Microorganisms 8, 1–16 (2020). https://doi.org/10.3390/microorganisms8020276

    Article  CAS  Google Scholar 

  10. World Health Organization, Influenza (2018). https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal))

  11. World Health Organization, Dengue and severe dengue (2022). https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue

  12. A.R. Xavier, S. Kanaan, R.P. Bozzi, L.V. Amaral, Clinical and laboratory diagnosis of Zika fever: an update. J Bras Patol e Med Lab 53, 252–257 (2017). https://doi.org/10.5935/1676-2444.20170039

    Article  Google Scholar 

  13. World Health Organization, Ebola virus disease (2021). https://www.who.int/news-room/fact-sheets/detail/ebola-virus-disease

  14. F. Ayoade, S. Kumar, Varicella zoster, in StatPearls (StatPearls Publishing, Treasure Island, 2022)

    Google Scholar 

  15. K.A. Simonsen, SJ Smallpox, in StatPearls (StatPearls Publishing, Treasure Island)

    Google Scholar 

  16. M. Truong Lam, B. O’Sullivan, P. Gullane, S.H. Huang, Challenges in establishing the diagnosis of human papillomavirus-related oropharyngeal carcinoma. Laryngoscope 126, 2270–2275 (2016). https://doi.org/10.1002/lary.25985

  17. S.U. Kalu, M. Loeffelholz, E. Beck, J.A. Patel, K. Revai, J. Fan, K.J. Henrickson, T. Chonmaitree, Persistence of adenovirus nucleic acids in nasopharyngeal secretions: a diagnostic conundrum. Bone 29, 746 (2010). https://doi.org/10.1097/INF.0b013e3181d743c8

    Article  Google Scholar 

  18. World Health Organization, Hepatitis B (2021). https://www.who.int/news-room/fact-sheets/detail/hepatitis-b

  19. C. Chu, P.A. Selwyn, Diagnosis and initial management of acute HIV infection. Am Fam Physician 81, 1239–1244 (2010)

    PubMed  Google Scholar 

  20. Centers of Disease Control and Prevention, Poliovirus diagnostic methods (2021). https://www.cdc.gov/polio/what-is-polio/lab-testing/diagnostic.html

  21. G.L. Kirkpatrick, The common cold. Prim Care Clin Off Pract 23, 657–675 (1996). https://doi.org/10.1016/S0095-4543(05)70355-9

    Article  CAS  Google Scholar 

  22. B. Alberts, D. Bray, A. Johnson et al., Fundamentos da Biologia Celular. Uma Introdução à Biologia Molecular da Célula. Artes Médicas Sul, Porto Alegre (2006)

    Google Scholar 

  23. J.B. Reece et al., Biologia de Campbell, 10th edn. (Artmed, Porto Alegre, 2015)

    Google Scholar 

  24. C.B. Clish, Metabolomics: an emerging but powerful tool for precision medicine. Mol Case Stud 1, a000588 (2015). https://doi.org/10.1101/mcs.a000588

    Article  Google Scholar 

  25. S.Z. Tan, P. Begley, G. Mullard et al., Introduction to metabolomics and its applications in ophthalmology. Eye 30, 773–783 (2016). https://doi.org/10.1038/eye.2016.37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. R.G. Duft, A. Castro, M.P.T. Chacon-Mikahil, C.R. Cavaglieri, Metabolomics and exercise: possibilities and perspectives. Mot Rev Educ Física 23 (2017). https://doi.org/10.1590/s1980-6574201700020010

  27. K.S. Smirnov, T.V. Maier, A. Walker et al., Challenges of metabolomics in human gut microbiota research. Int J Med Microbiol 306, 266–279 (2016). https://doi.org/10.1016/j.ijmm.2016.03.006

    Article  CAS  PubMed  Google Scholar 

  28. A. Smolinska, L. Blanchet, L.M.C. Buydens, S.S. Wijmenga, NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review. Anal. Chim. Acta. 750, 82–97 (2012). https://doi.org/10.1016/j.aca.2012.05.049

    Article  CAS  PubMed  Google Scholar 

  29. A. Marco-Ramell, M. Palau-Rodriguez, A. Alay et al., Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinformatics 19, 1–11 (2018). https://doi.org/10.1186/s12859-017-2006-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. C. Lema, M. Andrés, S. Aguadé-Bruix et al., 1H NMR serum metabolomic profiling of patients at risk of cardiovascular diseases performing stress test. Sci. Rep. 10, 1–10 (2020). https://doi.org/10.1038/s41598-020-74880-6

    Article  CAS  Google Scholar 

  31. V. Pareek, H. Tian, N. Winograd, S.J. Benkovic, Metabolomics and mass spectrometry imaging reveal channeled de novo purine synthesis in cells. Science 368, 283–290 (2020). https://doi.org/10.1126/science.aaz6465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. D.D. Fraser, G. Cepinskas, E.K. Patterson et al., Novel outcome biomarkers identified with targeted proteomic analyses of plasma from critically ill coronavirus disease 2019 patients. Crit. Care Explor. 2, e0189 (2020). https://doi.org/10.1097/cce.0000000000000189

    Article  PubMed  PubMed Central  Google Scholar 

  33. B.L. Marquez, R.T. Williamson, Quantitative applications of NMR spectroscopy. Chem. Eng. Pharm. Ind. 133–149 (2019). https://doi.org/10.1002/9781119600800.ch7

  34. J.L. Ward, J.M. Baker, M.H. Beale, Recent applications of NMR spectroscopy in plant metabolomics. FEBS J 274, 1126–1131 (2007). https://doi.org/10.1111/j.1742-4658.2007.05675.x

    Article  CAS  PubMed  Google Scholar 

  35. A.A. Crook, R. Powers, Quantitative NMR-based biomedical metabolomics: current status and applications. Molecules 25 (2020). https://doi.org/10.3390/molecules25215128

  36. G.A. Nagana Gowda, D. Raftery, Overview of NMR Spectroscopy-Based Metabolomics: Opportunities and Challenges (2019), pp. 3–14

    Google Scholar 

  37. P. Soininen, in Quantitative 1H NMR Spectroscopy—Chemical and Biological Applications (2008)

    Google Scholar 

  38. C. Deborde, A. Moing, L. Roch et al., Plant metabolism as studied by NMR spectroscopy. Prog. Nucl. Magn. Reson. Spectrosc. 102–103, 61–97 (2017). https://doi.org/10.1016/j.pnmrs.2017.05.001

    Article  CAS  PubMed  Google Scholar 

  39. A. Vignoli, V. Ghini, G. Meoni et al., High-throughput metabolomics by 1D NMR. Angew Chemie. Int. Ed. 58, 968–994 (2019). https://doi.org/10.1002/anie.201804736

    Article  CAS  Google Scholar 

  40. M. Schmedes, A.D. Brejnrod, E.K. Aadland et al., The effect of lean-seafood and non-seafood diets on fecal metabolites and gut microbiome: results from a randomized crossover intervention study. Mol. Nutr. Food Res. 63, 1–8 (2019). https://doi.org/10.1002/mnfr.201700976

    Article  CAS  Google Scholar 

  41. R. Thøgersen, J.L. Castro-Mejía, U. Kræmer Sundekilde et al., Inulin and milk mineral fortification of a pork sausage exhibits distinct effects on the microbiome and biochemical activity in the gut of healthy rats. Food Chem. 331 (2020). https://doi.org/10.1016/j.foodchem.2020.127291

  42. P.S.X. Yap, C.W. Chong, A.A. Kamar et al., Neonatal intensive care unit (NICU) exposures exert a sustained influence on the progression of gut microbiota and metabolome in the first year of life. Sci. Rep. 11(1353), 1 (2021). https://doi.org/10.1038/s41598-020-80278-1;Sci. Rep. 11(1–14), 10 (2021). https://doi.org/10.1038/s41598-021-88758-8

  43. X. Li, K. Hu, Quantitative NMR studies of multiple compound mixtures. Annu. Reports NMR Spectrosc. 90, 85–143 (2017). https://doi.org/10.1016/bs.arnmr.2016.08.001

    Article  CAS  Google Scholar 

  44. P. Giraudeau, Challenges and perspectives in quantitative NMR. Magn. Reson. Chem. 55, 61–69 (2017). https://doi.org/10.1002/mrc.4475

    Article  CAS  PubMed  Google Scholar 

  45. R.L. Loo, S. Lodge, T. Kimhofer et al., Quantitative in-vitro diagnostic NMR spectroscopy for lipoprotein and metabolite measurements in plasma and serum: recommendations for analytical artifact minimization with special reference to COVID-19/SARS-CoV-2 samples. J Proteome. Res. 19, 4428–4441 (2020). https://doi.org/10.1021/acs.jproteome.0c00537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. M.P.M. Letertre, P. Giraudeau, P. de Tullio, Nuclear magnetic resonance spectroscopy in clinical metabolomics and personalized medicine: current challenges and perspectives. Front. Mol. Biosci. 8, 1–25 (2021). https://doi.org/10.3389/fmolb.2021.698337

    Article  CAS  Google Scholar 

  47. D.L. Pavia et al., Introduction to spectroscopy. Cengage Learn (2009)

    Google Scholar 

  48. T.D.W. Claridge, in High-Resolution NMR Techniques in Organic Chemistry (Elsevier, 2009)

    Google Scholar 

  49. C. Stavarache, A. Nicolescu, C. Duduianu et al., A real-life reproducibility assessment for NMR metabolomics. Diagnostics 12 (2022). https://doi.org/10.3390/diagnostics12030559

  50. B. Jiménez, E. Holmes, C. Heude et al., Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial. Anal. Chem. 90, 11962–11971 (2018). https://doi.org/10.1021/acs.analchem.8b02412

    Article  CAS  PubMed  Google Scholar 

  51. S. Lodge, P. Nitschke, T. Kimhofer et al., NMR spectroscopic windows on the systemic effects of SARS-CoV-2 infection on plasma lipoproteins and metabolites in relation to circulating cytokines. J Proteome Res 20, 1382–1396 (2021). https://doi.org/10.1021/acs.jproteome.0c00876

    Article  CAS  PubMed  Google Scholar 

  52. R. Masuda, S. Lodge, P. Nitschke et al., Integrative modeling of plasma metabolic and lipoprotein biomarkers of SARS-CoV-2 infection in Spanish and Australian COVID-19 patient cohorts. J. Proteome. Res. 20, 4139–4152 (2021). https://doi.org/10.1021/acs.jproteome.1c00458

    Article  CAS  PubMed  Google Scholar 

  53. S. Lodge, P. Nitschke, R.L. Loo et al., Low volume in vitro diagnostic proton NMR spectroscopy of human blood plasma for lipoprotein and metabolite analysis: application to SARS-CoV-2 biomarkers. J. Proteome. Res. 20, 1415–1423 (2021). https://doi.org/10.1021/acs.jproteome.0c00815

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. G. Costa Dos Santos Junior, C.M. Pereira, T. Kelly Da Silva Fidalgo, A.P. Valente, Saliva NMR-based metabolomics in the war against COVID-19. Anal. Chem. 92, 15688–15692 (2020). https://doi.org/10.1021/acs.analchem.0c04679

  55. B. Khakimov, H.C.J. Hoefsloot, N. Mobaraki et al., Human blood lipoprotein predictions from 1H NMR spectra: protocol, model performances, and cage of covariance. Anal. Chem. 94, 628–636 (2022). https://doi.org/10.1021/acs.analchem.1c01654

    Article  CAS  PubMed  Google Scholar 

  56. C. Wang, I. Timári, B. Zhang et al., COLMAR lipids web server and ultrahigh-resolution methods for two-dimensional nuclear magnetic resonance- and mass spectrometry-based lipidomics. J. Proteome. Res. 19, 1674–1683 (2020). https://doi.org/10.1021/acs.jproteome.9b00845

    Article  CAS  PubMed  Google Scholar 

  57. B.S. Barbosa et al., Qualitative and quantitative NMR approaches in blood serum lipidomics, in Investigations of Early Nutrition Effects on Long-Term Health (Humana Press, New York, 2018), pp. 365–379

    Google Scholar 

  58. Y. Navarro, R. Soengas, M.J. Iglesias, F.L. Ortiz, Use of NMR for the analysis and quantification of the sugar composition in fresh and store-bought fruit juices. J. Chem. Educ. 97, 831–837 (2020). https://doi.org/10.1021/acs.jchemed.9b00651

    Article  CAS  Google Scholar 

  59. G.A.N. Gowda, D. Raftery, NMR based metabolomics. 19–37 (2022). https://doi.org/10.1007/978-3-030-51652-9

  60. Q. Wan, Y. Wang, H. Tang, Quantitative 13C traces of glucose fate in hepatitis B virus-infected hepatocytes. Anal. Chem. 89, 3293–3299 (2017). https://doi.org/10.1021/acs.analchem.6b03200

    Article  CAS  PubMed  Google Scholar 

  61. A.L. Guennec, P. Giraudeau, S. Caldarelli, Evaluation of fast 2D NMR for metabolomics. Anal. Chem. 86, 5946–5954 (2014). https://doi.org/10.1021/ac500966e

    Article  CAS  PubMed  Google Scholar 

  62. Maulidiani, F. Abas, R. Rudiyanto et al., Application of quantitative spectral deconvolution 1H NMR (qsd-NMR) in the simultaneous quantitative determination of creatinine and metformin in human urine. Anal. Methods 11, 5487–5499 (2019). https://doi.org/10.1039/c9ay00594c

  63. J. Farjon, C. Milande, E. Martineau et al., The FAQUIRE approach: fast, quantitative, highly resolved and sensitivity enhanced 1H, 13C Data. Anal. Chem. 90, 1845–1851 (2018). https://doi.org/10.1021/acs.analchem.7b03874

    Article  CAS  PubMed  Google Scholar 

  64. E. Martineau, J.N. Dumez, P. Giraudeau, Fast quantitative 2D NMR for metabolomics and lipidomics: a tutorial. Magn. Reson. Chem. 58, 390–403 (2020). https://doi.org/10.1002/mrc.4899

    Article  CAS  PubMed  Google Scholar 

  65. J. Marchand, E. Martineau, Y. Guitton et al., Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics. Curr. Opin. Biotechnol. 43, 49–55 (2017). https://doi.org/10.1016/j.copbio.2016.08.004

    Article  CAS  PubMed  Google Scholar 

  66. P. Schanda, Fast-pulsing longitudinal relaxation optimized techniques: enriching the toolbox of fast biomolecular NMR spectroscopy. Prog. Nucl. Magn. Reson. Spectrosc. 55, 238–265 (2009). https://doi.org/10.1016/j.pnmrs.2009.05.002

    Article  CAS  Google Scholar 

  67. E.R.F. Kupce, Fast multidimensional NMR by polarization sharing. Magn. Reson. Chem. 45, 2–4 (2007). https://doi.org/10.1002/mrc.1931

    Article  CAS  PubMed  Google Scholar 

  68. B. Vitorge, G. Bodenhausen, P. Pelupessy, Speeding up nuclear magnetic resonance spectroscopy by the use of SMAll Recovery Times—SMART NMR. J Magn Reson 207, 149–152 (2010). https://doi.org/10.1016/j.jmr.2010.07.017

    Article  CAS  PubMed  Google Scholar 

  69. M. Quinternet, J.P. Starck, M.A. Delsuc, B. Kieffer, Heteronuclear NMR provides an accurate assessment of therapeutic insulin’s quality. J. Pharm. Biomed. Anal. 78–79, 252–254 (2013). https://doi.org/10.1016/j.jpba.2013.02.016

    Article  CAS  PubMed  Google Scholar 

  70. M. D’Onofrio, L. Ragona, D. Fessas et al., NMR unfolding studies on a liver bile acid binding protein reveal a global two-state unfolding and localized singular behaviors. Arch. Biochem. Biophys. 481, 21–29 (2009). https://doi.org/10.1016/j.abb.2008.10.017

    Article  CAS  PubMed  Google Scholar 

  71. M. Pathan, S. Akoka, I. Tea, B. Charrier, P. Giraudeau, “Multi-scan single shot” quantitative 2D NMR: a valuable alternative to fast conventional quantitative 2D NMR. Analyst 136, 3157–3163 (2011). https://doi.org/10.1039/c1an15278e

    Article  CAS  PubMed  Google Scholar 

  72. E. Martineau, P. Giraudeau, I. Tea, S. Akoka, Fast and precise quantitative analysis of metabolic mixtures by 2D 1H INADEQUATE NMR. J. Pharm. Biomed. Anal. 54, 252–257 (2011). https://doi.org/10.1016/j.jpba.2010.07.046

    Article  CAS  PubMed  Google Scholar 

  73. T. Jézéquel, C. Deborde, M. Maucourt et al., Absolute quantification of metabolites in tomato fruit extracts by fast 2D NMR. Metabolomics 11, 1231–1242 (2015). https://doi.org/10.1007/s11306-015-0780-0

    Article  CAS  Google Scholar 

  74. S. Akoka, P. Giraudeau, Fast hybrid multi-dimensional NMR methods based on ultrafast 2D NMR. Magn. Reson. Chem. 53, 986–994 (2015). https://doi.org/10.1002/mrc.4237

    Article  CAS  PubMed  Google Scholar 

  75. A.L. Guennec, I. Tea, I. Antheaume et al., Fast determination of absolute metabolite concentrations by spatially encoded 2D NMR: application to breast cancer cell extracts. Anal. Chem. 84, 10831–10837 (2012). https://doi.org/10.1021/ac3033504

    Article  CAS  PubMed  Google Scholar 

  76. I. Timári, C. Wang, A.L. Hansen et al., Real-time pure shift HSQC NMR for untargeted metabolomics. Anal. Chem. 91, 2304–2311 (2019). https://doi.org/10.1021/acs.analchem.8b04928

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. D. Uhrín, T. Liptaj, K.E. Kövér, Modified BIRD pulses and design of heteronuclear pulse sequences. J. Magn. Reson. Ser. A 101, 41–46 (1993)

    Article  Google Scholar 

  78. N.M. Byers, A.C. Fleshman, R. Perera, C.R. Molins, Metabolomic insights into human arboviral infections: dengue, chikungunya, and zika viruses. Viruses 11, 1–30 (2019). https://doi.org/10.3390/v11030225

    Article  CAS  Google Scholar 

  79. E.C. da Nunes, G.A.B. Canuto, Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: a review. Electrophoresis 41, 2102–2113 (2020). https://doi.org/10.1002/elps.202000133

    Article  CAS  Google Scholar 

  80. V. Tounta, Y. Liu, A. Cheyne, G. Larrouy-Maumus, Metabolomics in infectious diseases and drug discovery. Mol. Omi. 17, 376–393 (2021). https://doi.org/10.1039/d1mo00017a

    Article  CAS  Google Scholar 

  81. H. Zheng, M. Chen, S. Lu et al., Metabolic characterization of hepatitis B virus-related liver cirrhosis using NMR-based serum metabolomics. Metabolomics 13, 1–9 (2017). https://doi.org/10.1007/s11306-017-1260-5

    Article  CAS  Google Scholar 

  82. M.M.G. Godoy, E.P.A. Lopes, R.O. Silva et al., Hepatitis C virus infection diagnosis using metabonomics. J. Viral. Hepat. 17, 854–858 (2010). https://doi.org/10.1111/j.1365-2893.2009.01252.x

    Article  CAS  PubMed  Google Scholar 

  83. S. Wei, Y. Suryani, G.A.N. Gowda et al., Differentiating hepatocellular carcinoma from hepatitis C using metabolite profiling. Metabolites 2, 701–716 (2012). https://doi.org/10.3390/metabo2040701

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. N. Embade, O. Millet, Molecular determinants of chronic liver disease as studied by NMR-metabolomics. Curr. Top. Med. Chem. 17, 2752–2766 (2017). https://doi.org/10.2174/1568026617666170707124539

    Article  CAS  PubMed  Google Scholar 

  85. L.R. Gouveia, J.C. Santos, R.D. Silva et al., Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics. PLoS ONE 12, 1–11 (2017). https://doi.org/10.1371/journal.pone.0182196

    Article  Google Scholar 

  86. G. Meoni, S. Lorini, M. Monti et al., The metabolic fingerprints of HCV and HBV infections studied by nuclear magnetic resonance spectroscopy. Sci. Rep. 9, 1–13 (2019). https://doi.org/10.1038/s41598-019-40028-4

    Article  CAS  Google Scholar 

  87. M. Shanmuganathan, M.O. Sarfaraz, Z. Kroezen et al., A cross-platform metabolomics comparison identifies serum metabolite signatures of liver fibrosis progression in chronic hepatitis C patients. Front. Mol. Biosci. 8, 1–15 (2021). https://doi.org/10.3389/fmolb.2021.676349

    Article  CAS  Google Scholar 

  88. C.M. Slupsky, K.N. Rankin, H. Fu et al., Pneumococcal pneumonia: potential for diagnosis through a urinary metabolic profile. J. Proteome. Res. 8, 5550–5558 (2009). https://doi.org/10.1021/pr9006427

    Article  CAS  PubMed  Google Scholar 

  89. S.U. Munshi, S. Taneja, N.S. Bhavesh et al., Metabonomic analysis of hepatitis e patients shows deregulated metabolic cycles and abnormalities in amino acid metabolism. J. Viral. Hepat. 18 (2011). https://doi.org/10.1111/j.1365-2893.2011.01488.x

  90. R. Hewer, J. Vorster, F.E. Steffens, D. Meyer, Applying biofluid 1H NMR-based metabonomic techniques to distinguish between HIV-1 positive/AIDS patients on antiretroviral treatment and HIV-1 negative individuals. J. Pharm. Biomed. Anal. 41, 1442–1446 (2006). https://doi.org/10.1016/j.jpba.2006.03.006

    Article  CAS  PubMed  Google Scholar 

  91. S.U. Kaur, B.F. Oyeyemi, A. Shet et al., Plasma metabolomic study in perinatally HIV-infected children using 1H NMR spectroscopy reveals perturbed metabolites that sustain during therapy. PLoS ONE 15, 1–17 (2020). https://doi.org/10.1371/journal.pone.0238316

    Article  CAS  Google Scholar 

  92. T.R. McKnight, H.A.I. Yoshihara, L.J. Sitole et al., A combined chemometric and quantitative NMR analysis of HIV/AIDS serum discloses metabolic alterations associated with disease status. Mol. Biosyst. 10, 2889–2897 (2014). https://doi.org/10.1039/c4mb00347k

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. C. Philippeos, F.E. Steffens, D. Meyer, Comparative 1H NMR-based metabonomic analysis of HIV-1 sera. J. Biomol. NMR 44, 127–137 (2009). https://doi.org/10.1007/s10858-009-9329-8

    Article  CAS  PubMed  Google Scholar 

  94. E. Rodríguez-Gallego, J. Gómez, P. Domingo et al., Circulating metabolomic profile can predict dyslipidemia in HIV patients undergoing antiretroviral therapy. Atherosclerosis 273, 28–36 (2018). https://doi.org/10.1016/j.atherosclerosis.2018.04.008

    Article  CAS  PubMed  Google Scholar 

  95. L.J. Sitole, F. Tugizimana, D. Meyer, Multi-platform metabonomics unravel amino acids as markers of HIV/combination antiretroviral therapy-induced oxidative stress. J. Pharm. Biomed. Anal. 176, 112796 (2019). https://doi.org/10.1016/j.jpba.2019.112796

    Article  CAS  PubMed  Google Scholar 

  96. S.U. Munshi, B.B. Rewari, N.S. Bhavesh, S. Jameel, Nuclear magnetic resonance based profiling of biofluids reveals metabolic dysregulation in HIV-infected persons and those on anti-retroviral therapy. PLoS One 8 (2013). https://doi.org/10.1371/journal.pone.0064298

  97. C.D. French, R.E. Willoughby, A. Pan et al., NMR metabolomics of cerebrospinal fluid differentiates inflammatory diseases of the central nervous system. PLoS Negl. Trop. Dis. 12, 1–17 (2018). https://doi.org/10.1371/journal.pntd.0007045

    Article  CAS  Google Scholar 

  98. M.M. Banoei, H.J. Vogel, A.M. Weljie et al., Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia. Crit. Care. 21, 1–15 (2017). https://doi.org/10.1186/s13054-017-1672-7

    Article  Google Scholar 

  99. J.L. Izquierdo-Garcia, N. Nin, J. Jimenez-Clemente et al., Metabolomic profile of ards by nuclear magnetic resonance spectroscopy in patients with h1n1 influenza virus pneumonia. Shock 50, 504–510 (2018). https://doi.org/10.1097/SHK.0000000000001099

    Article  CAS  PubMed  Google Scholar 

  100. N. Shahfiza, H. Osman, T.T. Hock et al., Metabolomics for characterization of gender differences in patients infected with dengue virus. Asian Pac. J. Trop. Med. 8, 451–456 (2015). https://doi.org/10.1016/j.apjtm.2015.05.012

    Article  CAS  PubMed  Google Scholar 

  101. S.P. Young, M. Nessim, F. Falciani et al., Metabolomic analysis of human vitreous humor differentiates ocular inflammatory disease. Mol. Vis. 15, 1210–1217 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  102. J. Shrinet, J.S. Shastri, R. Gaind et al., Serum metabolomics analysis of patients with chikungunya and dengue mono/co-infections reveals distinct metabolite signatures in the three disease conditions. Sci. Rep. 6, 1–12 (2016). https://doi.org/10.1038/srep36833

    Article  CAS  Google Scholar 

  103. D.J. Adamko, E. Saude, M. Bear et al., Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study. BMC Infect. Dis. 16 (2016). https://doi.org/10.1186/s12879-016-1709-6

  104. C. Bruzzone, S. Lu, T. Diercks et al., in iScience ll SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum the Metabolomic and Lipidomic Profiles of Serum (2020). https://doi.org/10.1016/j.isci.2020.101645

  105. D.D. Fraser, M. Slessarev, C.M. Martin et al., Metabolomics profiling of critically ill coronavirus disease 2019 patients: identification of diagnostic and prognostic biomarkers. Crit. Care Explor. 2, e0272 (2020). https://doi.org/10.1097/cce.0000000000000272

    Article  PubMed  PubMed Central  Google Scholar 

  106. A. Valdés, L.O. Moreno, S.R. Rello et al., Metabolomics study of COVID-19 patients in four different clinical stages. Sci. Rep. 12, 1–11 (2022). https://doi.org/10.1038/s41598-022-05667-0

    Article  CAS  Google Scholar 

  107. E. Holmes, J. Wist, R. Masuda et al., in Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome (2021). https://doi.org/10.1021/acs.jproteome.1c00224

  108. T. Kimhofer, S. Lodge, L. Whiley et al., in Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV—2 Infection (2020). https://doi.org/10.1021/acs.jproteome.0c00519

  109. F. Schmelter, B. Föh, A. Mallagaray et al., Metabolic and lipidomic markers differentiate COVID-19 from non-hospitalized and other intensive care patients. Front. Mol. Biosci. 8, 1–12 (2021). https://doi.org/10.3389/fmolb.2021.737039

    Article  CAS  Google Scholar 

  110. A.F. Rendeiro, C.K. Vorkas, J. Krumsiek et al., Metabolic and immune markers for precise monitoring of COVID-19 severity and treatment. Front. Immunol. 12, 1–13 (2022). https://doi.org/10.3389/fimmu.2021.809937

    Article  CAS  Google Scholar 

  111. J.A. Lorente, N. Nin, P. Villa et al., Metabolomic diferences between COVID-19 and H1N1 influenza induced ARDS. Crit. Care 25, 1–11 (2021). https://doi.org/10.1186/s13054-021-03810-3

    Article  Google Scholar 

  112. N. Embade, Z. Mariño, T. Diercks et al., Metabolic characterization of advanced liver fibrosis in HCV patients as studied by serum 1H-NMR spectroscopy. PLoS ONE 11, 1–19 (2016). https://doi.org/10.1371/journal.pone.0155094

    Article  CAS  Google Scholar 

  113. E. Baranovicova, A. Bobcakova, R. Vysehradsky et al., The ability to normalise energy metabolism in advanced covid-19 disease seems to be one of the key factors determining the disease progression—a metabolomic nmr study on blood plasma. Appl. Sci. 11, 4–6 (2021). https://doi.org/10.3390/app11094231

    Article  CAS  Google Scholar 

  114. M. Manchester, A. Anand, in Metabolomics: Strategies to Define the Role of Metabolism in Virus Infection and Pathogenesis, 1st edn. (Elsevier Inc., 2017)

    Google Scholar 

  115. D.S. Wishart, C. Knox, A.C. Guo et al., HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, 603–610 (2009). https://doi.org/10.1093/nar/gkn810

    Article  CAS  Google Scholar 

  116. O. Beckonert, H.C. Keun, T.M.D. Ebbels et al., Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007). https://doi.org/10.1038/nprot.2007.376

    Article  CAS  PubMed  Google Scholar 

  117. G.F. Giskeødegård, T. Andreassen, H. Bertilsson et al., The effect of sampling procedures and day-to-day variations in metabolomics studies of biofluids. Anal. Chim. Acta. 1081, 93–102 (2019). https://doi.org/10.1016/j.aca.2019.07.026

    Article  CAS  PubMed  Google Scholar 

  118. A.C. Dona, B. Jiménez, H. Schafer et al., Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal. Chem. 86, 9887–9894 (2014). https://doi.org/10.1021/ac5025039

    Article  CAS  PubMed  Google Scholar 

  119. B. Khakimov, N. Mobaraki, A. Trimigno et al., Signature mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data. Anal. Chim. Acta. 1108, 142–151 (2020). https://doi.org/10.1016/j.aca.2020.02.025

    Article  CAS  PubMed  Google Scholar 

  120. R.W. Evans, Diagnostic testing for headache. Med. Clin. North. Am. 85, 865–885 (2001). https://doi.org/10.1016/S0025-7125(05)70348-5

    Article  CAS  PubMed  Google Scholar 

  121. B. Shen, X. Yi, Y. Sun et al., Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 59-72.e15 (2020). https://doi.org/10.1016/j.cell.2020.05.032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. M.R. Hasan, M. Suleiman, A. Pérez-López, Metabolomics in the diagnosis and prognosis of COVID-19. Front. Genet. 12 (2021). https://doi.org/10.3389/fgene.2021.721556

  123. M. Costanzo, M. Caterino, R. Fedele et al., COVIDomics: the proteomic and metabolomic signatures of COVID-19. Int. J. Mol. Sci. 23, 2414 (2022). https://doi.org/10.3390/ijms23052414

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. B.S.B. Correia et al., 1H qNMR based metabolomics discrimination of COVID-19 severity. J. Proteome. Res. (in press)

    Google Scholar 

  125. V. Khullar, R.J. Firpi, Hepatitis C cirrhosis: new perspectives for diagnosis and treatment. World J. Hepatol. 7, 1843–1855 (2015). https://doi.org/10.4254/wjh.v7.i14.1843

    Article  PubMed  PubMed Central  Google Scholar 

  126. R.H. Westbrook, G. Dusheiko, Natural history of hepatitis C. J. Hepatol. 61, S58–S68 (2014). https://doi.org/10.1016/j.jhep.2014.07.012

    Article  PubMed  Google Scholar 

  127. R.F. Schwabe, J.J. Maher, Lipids in liver disease: Looking beyond steatosis. Gastroenterology 142, 8–11 (2012). https://doi.org/10.1053/j.gastro.2011.11.004

    Article  PubMed  Google Scholar 

  128. F.S. Macaluso, M. Maida, M.G. Minissale et al., Metabolic factors and chronic hepatitis C: a complex interplay. Biomed. Res. Int. 2013 (2013). https://doi.org/10.1155/2013/564645

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Acknowledgements

Authors acknowledge the financial support from CAPES (Grant no. 88887.504531/2020-00, from notice no. 09/2020) and FAPESP (grant no. 17/01189-0). DRC acknowledge the continued support from CNPq Research Productivity Program (309212/2019-7).

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  1. University of Sao Paulo, Chemistry Institute of Sao Carlos, São Carlos, Sao Paulo, 13560-970, CP 780, Brazil

    Banny Silva Barbosa Correia, Priscila Marques Firmiano Dalle Piagge, Luísa Souza Almeida, Cristina de Souza Peixoto & Daniel Rodrigues Cardoso

  2. Embrapa Instrumentation, Brazilian Agricultural Research Corporation, Sao Carlos, Sao Paulo, 13570-970, Brazil

    Gabriel Henrique Ribeiro & Luiz Alberto Colnago

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Correia, B.S.B. et al. (2023). The Use of NMR Based Metabolomics to Discriminate Patients with Viral Diseases. In: Crespilho, F.N. (eds) COVID-19 Metabolomics and Diagnosis. Springer, Cham. https://doi.org/10.1007/978-3-031-15889-6_7

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