Metabolomics in the Diagnosis and Prognosis of COVID-19

Mohammad Rubayet Hasan, Mohammed Suleiman, Andrés Pérez-López, Mohammad Rubayet Hasan, Mohammed Suleiman, Andrés Pérez-López

Abstract

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

Keywords: COVID-19; SARS-CoV-2; diagnosis; mass-spectrometry; metabolomics; nuclear magnetic resonance; prognosis; volatile organic compounds.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Hasan, Suleiman and Pérez-López.

References

    1. Aksenov A. A., Sandrock C. E., Zhao W., Sankaran S., Schivo M., Harper R., et al. (2014). Cellular scent of influenza virus infection. Chembiochem 15 1040–1048. 10.1002/cbic.201300695
    1. Alseekh S., Fernie A. R. (2018). Metabolomics 20 years on: what have we learned and what hurdles remain? Plant J. 94 933–942. 10.1111/tpj.13950
    1. ASM (2020). Laboratory Supply Shortages Are Impacting COVID-19 and Non-COVID Diagnostic Testing. Washington, DC: American Society for Microbiology. Available Online at: .
    1. Banerjee S. (2020). Empowering Clinical Diagnostics with Mass Spectrometry. ACS Omega 5 2041–2048. 10.1021/acsomega.9b03764
    1. Barberis E., Timo S., Amede E., Vanella V. V., Puricelli C., Cappellano G., et al. (2020). Large-Scale Plasma Analysis Revealed New Mechanisms and Molecules Associated with the Host Response to SARS-CoV-2. Int. J. Mol. Sci. 21:8623. 10.3390/ijms21228623
    1. Bennuru S., Lustigman S., Abraham D., Nutman T. B. (2017). Metabolite profiling of infection-associated metabolic markers of onchocerciasis. Mol. Biochem. Parasitol. 215 58–69. 10.1016/j.molbiopara.2017.01.008
    1. Berna A. Z., Akaho E. H., Harris R. M., Congdon M., Korn E., Neher S., et al. (2020). Breath biomarkers of pediatric SARS-CoV-2 infection: a pilot study. medRxiv. [Preprint].
    1. Bille E., Dauphin B., Leto J., Bougnoux M. E., Beretti J. L., Lotz A., et al. (2012). MALDI-TOF MS Andromas strategy for the routine identification of bacteria, mycobacteria, yeasts, Aspergillus spp. and positive blood cultures. Clin. Microbiol. Infect. 18 1117–1125. 10.1111/j.1469-0691.2011.03688.x
    1. Blasco H., Bessy C., Plantier L., Lefevre A., Piver E., Bernard L., et al. (2020). The specific metabolome profiling of patients infected by SARS-COV-2 supports the key role of tryptophan-nicotinamide pathway and cytosine metabolism. Sci. Rep. 10:16824.
    1. Bonetti G., Manelli F., Patroni A., Bettinardi A., Borrelli G., Fiordalisi G., et al. (2020). Laboratory predictors of death from coronavirus disease 2019 (COVID-19) in the area of Valcamonica, Italy. Clin. Chem. Lab. Med. 58 1100–1105. 10.1515/cclm-2020-0459
    1. Broughton J. P., Deng X., Yu G., Fasching C. L., Servellita V., Singh J., et al. (2020). CRISPR-Cas12-based detection of SARS-CoV-2. Nat. Biotechnol. 38 870–874.
    1. Bruce E. A., Huang M. L., Perchetti G. A., Tighe S., Laaguiby P., Hoffman J. J., et al. (2020). Direct RT-qPCR detection of SARS-CoV-2 RNA from patient nasopharyngeal swabs without an RNA extraction step. PLoS Biol. 18:e3000896. 10.1371/journal.pbio.3000896
    1. Cambau E., Poljak M. (2020). Sniffing animals as a diagnostic tool in infectious diseases. Clin. Microbiol. Infect. 26 431–435. 10.1016/j.cmi.2019.10.036
    1. Capati A., Ijare O. B., Bezabeh T. (2017). Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics. Magn. Reson. Insights 10:1178623X17694346.
    1. Cardozo K. H. M., Lebkuchen A., Okai G. G., Schuch R. A., Viana L. G., Olive A. N., et al. (2020). Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts. Nat. Commun. 11:6201.
    1. Chen X., Gu M., Li T., Sun Y. (2021). Metabolite reanalysis revealed potential biomarkers for COVID-19: a potential link with immune response. Future Microbiol. 16 577–588. 10.2217/fmb-2021-0047
    1. Chen Y. M., Zheng Y., Yu Y., Wang Y., Huang Q., Qian F., et al. (2020). Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage. EMBO J. 39:e105896.
    1. Collino S., Martin F. P., Rezzi S. (2013). Clinical metabolomics paves the way towards future healthcare strategies. Br. J. Clin. Pharmacol. 75 619–629. 10.1111/j.1365-2125.2012.04216.x
    1. UpToDate (2020). Coronavirus disease 2019 (COVID-19): Clinical features. Available online at:
    1. Danlos F. X., Grajeda-Iglesias C., Durand S., Sauvat A., Roumier M., Cantin D., et al. (2021). Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers. Cell Death Dis. 12:258.
    1. Das S., Dunbar S., Tang Y. W. (2018). Laboratory Diagnosis of Respiratory Tract Infections in Children - the State of the Art. Front. Microbiol. 9:2478. 10.3389/fmicb.2018.02478
    1. Davis C. E., Schivo M., Kenyon N. J. (2021). A breath of fresh air - the potential for COVID-19 breath diagnostics. EBioMedicine 63:103183. 10.1016/j.ebiom.2020.103183
    1. Davis I., Liu A. (2015). What is the tryptophan kynurenine pathway and why is it important to neurotherapeutics? Expert Rev. Neurother. 15 719–721. 10.1586/14737175.2015.1049999
    1. Deka S., Kalita D. (2020). Effectiveness of Sample Pooling Strategies for SARS-CoV-2 Mass Screening by RT-PCR: A Scoping Review. J. Lab. Physicians 12 212–218. 10.1055/s-0040-1721159
    1. Delafiori J., Navarro L. C., Siciliano R. F., de Melo G. C., Busanello E. N. B., Nicolau J. C., et al. (2021). Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning. Anal. Chem. 93 2471–2479.
    1. Deulofeu M., Garcia-Cuesta E., Pena-Mendez E. M., Conde J. E., Jimenez-Romero O., Verdu E., et al. (2021). Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence. Front. Med. 8:661358. 10.3389/fmed.2021.661358
    1. Dufort E. M., Koumans E. H., Chow E. J., Rosenthal E. M., Muse A., Rowlands J., et al. (2020). Multisystem Inflammatory Syndrome in Children in New York State. N. Engl. J. Med. 383 347–358.
    1. Ellen F. (2020). Dogs have an intense sense of smell — and love for their owners. Available online at: accessed November 1, 2020)
    1. Else H. (2020). Can dogs smell COVID? Here’s what the science says. Nature 587 530–531. 10.1038/d41586-020-03149-9
    1. Emwas A. H., Roy R., McKay R. T., Tenori L., Saccenti E., Gowda G. A. N., et al. (2019). NMR Spectroscopy for Metabolomics Research. Metabolites 9:123.
    1. Eskandari E., Ahmadi Marzaleh M., Roudgari H., Hamidi Farahani R., Nezami-Asl A., Laripour R., et al. (2021). Sniffer dogs as a screening/diagnostic tool for COVID-19: a proof of concept study. BMC Infect. Dis. 21:243. 10.1186/s12879-021-05939-6
    1. Evans E. D., Duvallet C., Chu N. D., Oberst M. K., Murphy M. A., Rockafellow I., et al. (2020). Predicting human health from biofluid-based metabolomics using machine learning. Sci. Rep. 10:17635.
    1. Fernández-García M., Rojo D., Rey-Stolle F., García A., Barbas C. (2018). “Metabolomic-Based Methods in Diagnosis and Monitoring Infection Progression,” in Metabolic Interaction in Infection, eds Silvestre R., Torrado E. (Berlin: Springer International Publishing AG; ), 283–315. 10.1007/978-3-319-74932-7_7
    1. Fleurbaaij F., Leeuwen H. C. V., Klychnikov O. I., Kuijper E. J., Hensbergen P. J. (2015). Mass Spectrometry in Clinical Microbiology and Infectious Diseases. Chromatographia 2015 379–389. 10.1007/s10337-014-2839-x
    1. Foster M. W., Gerhardt G., Robitaille L., Plante P. L., Boivin G., Corbeil J., et al. (2015). Targeted Proteomics of Human Metapneumovirus in Clinical Samples and Viral Cultures. Anal. Chem. 87 10247–10254. 10.1021/acs.analchem.5b01544
    1. Fozouni P., Son S., Diaz, de Leon Derby M., Knott G. J., Gray C. N., et al. (2021). Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy. Cell 184 323–333e329.
    1. Fraser D. D., Slessarev M., Martin C. M., Daley M., Patel M. A., Miller M. R., et al. (2020). Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers. Crit. Care Explor. 2:e0272. 10.1097/cce.0000000000000272
    1. Giovannini G., Haick H., Garoli D. (2021). Detecting COVID-19 from Breath: A Game Changer for a Big Challenge. ACS Sens. 6 1408–1417. 10.1021/acssensors.1c00312
    1. Gouveia D., Grenga L., Gaillard J. C., Gallais F., Bellanger L., Pible O., et al. (2020). Shortlisting SARS-CoV-2 Peptides for Targeted Studies from Experimental Data-Dependent Acquisition Tandem Mass Spectrometry Data. Proteomics 20:e2000107.
    1. Graham M., Williams E., Isles N., Buadromo E., Toatu T., Druce J., et al. (2021). Sample pooling on the Cepheid Xpert(R) Xpress SARS-CoV-2 assay. Diagn. Microbiol. Infect. Dis. 99:115238. 10.1016/j.diagmicrobio.2020.115238
    1. Grandjean D., Sarkis R., Lecoq-Julien C., Benard A., Roger V., Levesque E., et al. (2020). Can the detection dog alert on COVID-19 positive persons by sniffing axillary sweat samples? A proof-of-concept study. PLoS One 15:e0243122. 10.1371/journal.pone.0243122
    1. Grassin-Delyle S., Roquencourt C., Moine P., Saffroy G., Carn S., Heming N., et al. (2021). Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study. EBioMed. 63:103154. 10.1016/j.ebiom.2020.103154
    1. Hasan M. R., Mirza F., Al-Hail H., Sundararaju S., Xaba T., Iqbal M., et al. (2020). Detection of SARS-CoV-2 RNA by direct RT-qPCR on nasopharyngeal specimens without extraction of viral RNA. PLoS One 15:e0236564. 10.1371/journal.pone.0236564
    1. Hu R., Han C., Pei S., Yin M., Chen X. (2020). Procalcitonin levels in COVID-19 patients. Int. J. Antimicrob. Agents 56:106051. 10.1016/j.ijantimicag.2020.106051
    1. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 497–506.
    1. Ihling C., Tanzler D., Hagemann S., Kehlen A., Huttelmaier S., Arlt C., et al. (2020). Mass Spectrometric Identification of SARS-CoV-2 Proteins from Gargle Solution Samples of COVID-19 Patients. J. Proteome Res. 19 4389–4392. 10.1021/acs.jproteome.0c00280
    1. Jacob M., Lopata A. L., Dasouki M., Abdel Rahman A. M. (2019). Metabolomics toward personalized medicine. Mass Spectrom. Rev. 38 221–238.
    1. Jendrny P., Schulz C., Twele F., Meller S., von Kockritz-Blickwede M., Osterhaus A., et al. (2020). Scent dog identification of samples from COVID-19 patients - a pilot study. BMC Infect. Dis. 20:536. 10.1186/s12879-020-05281-3
    1. Kell D. B., Oliver S. G. (2016). The metabolome 18 years on: a concept comes of age. Metabolomics 12:148.
    1. Klassen A., Faccio A. T., Canuto G. A., da Cruz P. L., Ribeiro H. C., Tavares M. F., et al. (2017). Metabolomics: Definitions and Significance in Systems Biology. Adv. Exp. Med. Biol. 965 3–17. 10.1007/978-3-319-47656-8_1
    1. Kriegova E., Fillerova R., Kvapil P. (2020). Direct-RT-qPCR Detection of SARS-CoV-2 without RNA Extraction as Part of a COVID-19 Testing Strategy: From Sample to Result in One Hour. Diagnostics 10:605. 10.3390/diagnostics10080605
    1. Kuo T. C., Tan C. E., Wang S. Y., Lin O. A., Su B. H., Hsu M. T., et al. (2020). Human Breathomics Database. Database 2020:baz139.
    1. Li Y., Hou G., Zhou H., Wang Y., Tun H. M., Zhu A., et al. (2021). Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery. Signal Transduct. Target Ther. 6:155.
    1. Liu Z. M., Li J. P., Wang S. P., Chen D. Y., Zeng W., Chen S. C., et al. (2020). Association of procalcitonin levels with the progression and prognosis of hospitalized patients with COVID-19. Int. J. Med. Sci. 17 2468–2476. 10.7150/ijms.48396
    1. Majchrzykiewicz-Koehorst J. A., Heikens E., Trip H., Hulst A. G., de Jong A. L., Viveen M. C., et al. (2015). Rapid and generic identification of influenza A and other respiratory viruses with mass spectrometry. J. Virol. Methods 213 75–83. 10.1016/j.jviromet.2014.11.014
    1. Marin-Corral J., Rodriguez-Morato J., Gomez-Gomez A., Pascual-Guardia S., Munoz-Bermudez R., Salazar-Degracia A., et al. (2021). Metabolic Signatures Associated with Severity in Hospitalized COVID-19 Patients. Int. J. Mol. Sci. 22:4794. 10.3390/ijms22094794
    1. Mayo Clinic Laboratories (2021). Mayo Clinic Laboratories Test Catalog. Rochester, MN: Mayo Clinic Laboratories.
    1. MOI (2020). MOI successfully uses K9 police dog to detect COVID-19. Abu Dhabi: Ministry of Interior United Arab Emirates.
    1. Nachtigall F. M., Pereira A., Trofymchuk O. S., Santos L. S. (2020). Detection of SARS-CoV-2 in nasal swabs using MALDI-MS. Nat. Biotechnol. 38 1168–1173. 10.1038/s41587-020-0644-7
    1. Nikolaev E. N., Indeykina M. I., Brzhozovskiy A. G., Bugrova A. E., Kononikhin A. S., Starodubtseva N. L., et al. (2020). Mass-Spectrometric Detection of SARS-CoV-2 Virus in Scrapings of the Epithelium of the Nasopharynx of Infected Patients via Nucleocapsid N Protein. J. Proteome Res. 19 4393–4397. 10.1021/acs.jproteome.0c00412
    1. Nomura F., Tsuchida S., Murata S., Satoh M., Matsushita K. (2020). Mass spectrometry-based microbiological testing for blood stream infection. Clin. Proteomics 17:14.
    1. Nouri R., Tang Z., Dong M., Liu T., Kshirsagar A., Guan W. (2021). CRISPR-based detection of SARS-CoV-2: A review from sample to result. Biosens. Bioelectron. 178:113012. 10.1016/j.bios.2021.113012
    1. Paez-Franco J. C., Torres-Ruiz J., Sosa-Hernandez V. A., Cervantes-Diaz R., Romero-Ramirez S., Perez-Fragoso A., et al. (2021). Metabolomics analysis reveals a modified amino acid metabolism that correlates with altered oxygen homeostasis in COVID-19 patients. Sci. Rep. 11:6350.
    1. Parasher A. (2021). COVID-19: Current understanding of its Pathophysiology, Clinical presentation and Treatment. Postgrad. Med. J. 97 312–320. 10.1136/postgradmedj-2020-138577
    1. Peeling R. W., Olliaro P. (2021). Rolling out COVID-19 antigen rapid diagnostic tests: the time is now. Lancet Infect. Dis. [Preprint].
    1. Peeling R. W., Olliaro P. L., Boeras D. I., Fongwen N. (2021). Scaling up COVID-19 rapid antigen tests: promises and challenges. Lancet Infect. Dis. [Preprint].
    1. Peng Y., Zhang Q., Xu C., Shi W. (2019). MALDI-TOF MS for the rapid identification and drug susceptibility testing of filamentous fungi. Exp. Ther. Med. 18 4865–4873.
    1. Petersen E., Koopmans M., Go U., Hamer D. H., Petrosillo N., Castelli F., et al. (2020). Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. Lancet Infect. Dis. 20 e238–e244.
    1. Ponti G., Maccaferri M., Ruini C., Tomasi A., Ozben T. (2020). Biomarkers associated with COVID-19 disease progression. Crit. Rev. Clin. Lab. Sci. 57 389–399. 10.1080/10408363.2020.1770685
    1. Ravi N., Cortade D. L., Ng E., Wang S. X. (2020). Diagnostics for SARS-CoV-2 detection: A comprehensive review of the FDA-EUA COVID-19 testing landscape. Biosens. Bioelectron. 165:112454. 10.1016/j.bios.2020.112454
    1. Roberts L. D., Souza A. L., Gerszten R. E., Clish C. B. (2012). Targeted metabolomics. Curr. Protoc. Mol. Biol. 32 31–24.
    1. Rocca M. F., Zintgraff J. C., Dattero M. E., Santos L. S., Ledesma M., Vay C., et al. (2020). A combined approach of MALDI-TOF mass spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs. J. Virol. Methods 286:113991. 10.1016/j.jviromet.2020.113991
    1. Ruszkiewicz D. M., Sanders D., O’Brien R., Hempel F., Reed M. J., Riepe A. C., et al. (2020). Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study. EClinicalMedicine 29:100609. 10.1016/j.eclinm.2020.100609
    1. Saez-Cirion A., Sereti I. (2021). Immunometabolism and HIV-1 pathogenesis: food for thought. Nat. Rev. Immunol. 21 5–19. 10.1038/s41577-020-0381-7
    1. Sakr R., Ghsoub C., Rbeiz C., Lattouf V., Riachy R., Haddad C., et al. (2021). COVID-19 detection by dogs: from physiology to field application-a review article. Postgrad. Med. J. [Preprint].
    1. Samprathi M., Jayashree M. (2020). Biomarkers in COVID-19: An Up-To-Date Review. Front. Pediatr. 8:607647. 10.3389/fped.2020.607647
    1. Sauget M., Bertrand X., Hocquet D. (2018). Rapid antibiotic susceptibility testing on blood cultures using MALDI-TOF MS. PLoS One 13:e0205603. 10.1371/journal.pone.0205603
    1. Savitz J. (2020). The kynurenine pathway: a finger in every pie. Mol. Psychiatry. 25 131–147. 10.1038/s41380-019-0414-4
    1. Schivo M., Aksenov A. A., Linderholm A. L., McCartney M. M., Simmons J., Harper R. W., et al. (2014). Volatile emanations from in vitro airway cells infected with human rhinovirus. J. Breath Res. 8:037110. 10.1088/1752-7155/8/3/037110
    1. Schrimpe-Rutledge A. C., Codreanu S. G., Sherrod S. D., McLean J. A. (2016). Untargeted Metabolomics Strategies-Challenges and Emerging Directions. J. Am. Soc. Mass Spectrom. 27 1897–1905. 10.1007/s13361-016-1469-y
    1. Schuster O., Zvi A., Rosen O., Achdout H., Ben-Shmuel A., Shifman O., et al. (2021). Specific and Rapid SARS-CoV-2 Identification Based on LC-MS/MS Analysis. ACS Omega 6 3525–3534. 10.1021/acsomega.0c04691
    1. Scohy A., Anantharajah A., Bodeus M., Kabamba-Mukadi B., Verroken A., Rodriguez-Villalobos H. (2020). Low performance of rapid antigen detection test as frontline testing for COVID-19 diagnosis. J. Clin. Virol. 129:104455. 10.1016/j.jcv.2020.104455
    1. Shi D., Yan R., Lv L., Jiang H., Lu Y., Sheng J., et al. (2021). The serum metabolome of COVID-19 patients is distinctive and predictive. Metabolism 118:154739. 10.1016/j.metabol.2021.154739
    1. Shi S., Qin M., Shen B., Cai Y., Liu T., Yang F., et al. (2020). Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol. 5 802–810. 10.1001/jamacardio.2020.0950
    1. Sindelar M., Stancliffe E., Schwaiger-Haber M., Anbukumar D. S., Albrecht R. A., Liu W. C., et al. (2021). Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity. medRxiv. [Preprint].
    1. Singh P., Chakraborty R., Marwal R., Radhakrishan V. S., Bhaskar A. K., Vashisht H., et al. (2020). A rapid and sensitive method to detect SARS-CoV-2 virus using targeted-mass spectrometry. J. Proteins Proteom. 2020 1–7.
    1. Thomas T., Stefanoni D., Reisz J. A., Nemkov T., Bertolone L., Francis R. O., et al. (2020). COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status. JCI Insight 5:e140327.
    1. Thomson R. B., Jr. (1999). Laboratory diagnosis of respiratory infections. Curr. Opin. Infect. Dis. 1999 115–119.
    1. Tran N. K., Howard T., Walsh R., Pepper J., Loegering J., Phinney B., et al. (2021). Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept. Sci. Rep. 11:8219.
    1. UpToDate (2021). COVID-19: Diagnosis. Available online at: (accessed June 23, 2021)
    1. Vogels C. B. F., Watkins A. E., Harden C. A., Brackney D. E., Shafer J., Wang J., et al. (2021). SalivaDirect: A simplified and flexible platform to enhance SARS-CoV-2 testing capacity. Med 2 263–280e266.
    1. Wang Q., Fang P., He R., Li M., Yu H., Zhou L., et al. (2020). O-GlcNAc transferase promotes influenza A virus-induced cytokine storm by targeting interferon regulatory factor-5. Sci. Adv. 6:eaaz7086. 10.1126/sciadv.aaz7086
    1. WHO (2020a). Coronavirus disease (COVID-19) Pandemic. Geneva: who. Available online at:
    1. WHO (2020b). PCR protocol - World Health Organization. Geneva: who. Available online at:
    1. Wiersinga W. J., Rhodes A., Cheng A. C., Peacock S. J., Prescott H. C. (2020). Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. JAMA 324 782–793. 10.1001/jama.2020.12839
    1. Wieser A., Schneider L., Jung J., Schubert S. (2012). MALDI-TOF MS in microbiological diagnostics-identification of microorganisms and beyond (mini review). Appl. Microbiol. Biotechnol. 93 965–974. 10.1007/s00253-011-3783-4
    1. Wintjens A., Hintzen K. F. H., Engelen S. M. E., Lubbers T., Savelkoul P. H. M., Wesseling G., et al. (2020). Applying the electronic nose for pre-operative SARS-CoV-2 screening. Surg. Endosc. [Preprint].
    1. Wishart D. S. (2019). Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol. Rev. 99 1819–1875. 10.1152/physrev.00035.2018
    1. Xiao N., Nie M., Pang H., Wang B., Hu J., Meng X., et al. (2021). Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications. Nat. Commun. 12:1618.
    1. Yan C., Cui J., Huang L., Du B., Chen L., Xue G., et al. (2020). Rapid and visual detection of 2019 novel coronavirus (SARS-CoV-2) by a reverse transcription loop-mediated isothermal amplification assay. Clin. Microbiol. Infect. 26 773–779.
    1. Yan L., Yi J., Huang C., Zhang J., Fu S., Li Z., et al. (2021). Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling. Anal. Chem. 93 4782–4787. 10.1021/acs.analchem.0c04590
    1. Zhou P., Yang X. L., Wang X. G., Hu B., Zhang L., Zhang W., et al. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579 270–273. 10.1038/s41586-020-2012-7

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