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Multiplex Immunoassay for Prediction of Disease Severity Associated with the Cytokine Storm in COVID-19 Cases

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Multiplex Biomarker Techniques

Abstract

Severe cases of SARS-CoV-2 and other pathogenic virus infections are often associated with the uncontrolled release of proinflammatory cytokines, known as a “cytokine storm.” We present a protocol for multiplex analysis of three cytokines, tumor necrosis factor-alpha (TNF-a), interleukin 6 (IL-6), and IL-10, which are typically elevated in cytokine storm events and may be used as a predictive biomarker profile of disease severity or disease course.

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References

  1. Alijotas-Reig J, Esteve-Valverde E, Belizna C et al (2020) Immunomodulatory therapy for the management of severe COVID-19. Beyond the anti-viral therapy: a comprehensive review. Autoimmun Rev 19(7):102569. https://doi.org/10.1016/j.autrev.2020.102569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tufan A, Avanoğlu Güler A, Matucci-Cerinic M (2020) COVID-19, immune system response, hyper inflammation and repurposing antirheumatic drugs. Turk J Med Sci 50(SI-1):620–632

    Article  CAS  Google Scholar 

  3. Soy M, Keser G, Atagündüz P et al (2020) Cytokine storm in COVID-19: pathogenesis and overview of anti-inflammatory agents used in treatment. Clin Rheumatol 39(7):2085–2094

    Article  Google Scholar 

  4. Bindoli S, Felicetti M, Sfriso P et al (2020) The amount of cytokine-release defines different shades of Sars-Cov2 infection. Exp Biol Med (Maywood) 245(11):970–976

    Article  CAS  Google Scholar 

  5. Rabaan AA, Al-Ahmed SH, Muhammad J et al (2021) Role of inflammatory cytokines in COVID-19 patients: a review on molecular mechanisms, immune functions, immunopathology and immunomodulatory drugs to counter cytokine storm. Vaccines (Basel) 9(5):436. https://doi.org/10.3390/vaccines9050436

    Article  CAS  Google Scholar 

  6. Ramasamy S, Subbian S (2021) Critical determinants of cytokine storm and type I interferon response in COVID-19 pathogenesis. Clin Microbiol Rev 34(3):e00299–e00220. https://doi.org/10.1128/CMR.00299-20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Peiris JS, Hui KP, Yen HL (2010) Host response to influenza virus: protection versus immunopathology. Curr Opin Immunol 22(4):475–481

    Article  CAS  Google Scholar 

  8. Teijaro JR (2015) The role of cytokine responses during influenza virus pathogenesis and potential therapeutic options. Curr Top Microbiol Immunol 386:3–22

    CAS  PubMed  Google Scholar 

  9. Us D (2008) Cytokine storm in avian influenza. Mikrobiyol Bul 42(2):365–380

    CAS  PubMed  Google Scholar 

  10. Mares CA, Ojeda SS, Morris EG et al (2008) Initial delay in the immune response to Francisella tularensis is followed by hypercytokinemia characteristic of severe sepsis and correlating with upregulation and release of damage-associated molecular patterns. Infect Immun 76(7):3001–3010

    Article  CAS  Google Scholar 

  11. de Castro IF, Guzman-Fulgencio M, Garcia-Alvarez M et al (2010) First evidence of a pro-inflammatory response to severe infection with influenza virus H1N1. Crit Care 14(1):115. https://doi.org/10.1186/cc8846

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tisoncik JR, Korth MJ, Simmons CP et al (2012) Into the eye of the cytokine storm. Microbiol Mol Biol Rev 76(1):16–32

    Article  CAS  Google Scholar 

  13. Perrone LA, Plowden JK, Garcia-Sastre A et al (2008) H5N1 and 1918 pandemic influenza virus infection results in early and excessive infiltration of macrophages and neutrophils in the lungs of mice. PLoS Pathog 4(8):e1000115. https://doi.org/10.1371/journal.ppat.1000115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang SY, Le TQ, Kurihara N et al (2010) Influenza virus-cytokine-protease cycle in the pathogenesis of vascular hyperpermeability in severe influenza. J Infect Dis 202(7):991–1001

    Article  CAS  Google Scholar 

  15. Cheng XW, Lu JA, Wu CL et al (2011) Three fatal cases of pandemic 2009 influenza A virus infection in Shenzhen are associated with cytokine storm. Respir Physiol Neurobiol 175(1):185–187

    Article  Google Scholar 

  16. Sharma J, Mares CA, Li Q et al (2011) Features of sepsis caused by pulmonary infection with Francisella tularensis type A strain. Microb Pathog 51(1–2):39–47

    Article  CAS  Google Scholar 

  17. Sharma J, Mares CA, Li Q, Morris EG, Teale JM (2011) Features of sepsis caused by pulmonary infection with Francisella tularensis type A strain. Microb Pathog 51(1–2):39–47

    Article  CAS  Google Scholar 

  18. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html

  19. https://www.worldometers.info/coronavirus/#countries

  20. https://coronavirus.jhu.edu/map.html

  21. Bhaskar S, Sinha A, Banach M et al (2020) Cytokine storm in COVID-19-immunopathological mechanisms, clinical considerations, and therapeutic approaches: the REPROGRAM consortium position paper. Front Immunol 11:1648. https://doi.org/10.3389/fimmu.2020.01648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mulchandani R, Lyngdoh T, Kakkar AK (2021) Deciphering the COVID-19 cytokine storm: systematic review and meta-analysis. Eur J Clin Investig 51(1):e13429. https://doi.org/10.1111/eci.13429

    Article  CAS  Google Scholar 

  23. Udomsinprasert W, Jittikoon J, Sangroongruangsri S et al (2021) Circulating levels of interleukin-6 and Interleukin-10, but not tumor necrosis factor-alpha, as potential biomarkers of severity and mortality for COVID-19: systematic review with meta-analysis. J Clin Immunol 41(1):11–22

    Article  CAS  Google Scholar 

  24. Morris G, Bortolasci CC, Puri BK et al (2020) The pathophysiology of SARS-CoV-2: a suggested model and therapeutic approach. Life Sci 258:118166. https://doi.org/10.1016/j.lfs.2020.118166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Stephen L (2017) Multiplex immunoassay profiling. Methods Mol Biol 1546:169–176

    Article  CAS  Google Scholar 

  26. https://www.euro.who.int/__data/assets/pdf_file/0005/268790/WHO-guidelines-on-drawing-blood-best-practices-in-phlebotomy-Eng.pdf

  27. https://www.fresnostate.edu/csm/rimi/documents/equipment/Magpix.pdf

  28. https://www.luminexcorp.com/download/manual-luminex-200-user-ivd-english/

  29. Han H, Ma Q, Li C et al (2020) Profiling serum cytokines in COVID-19 patients reveals IL-6 and IL-10 are disease severity predictors. Emerg Microbes Infect 9(1):1123–1130

    Article  CAS  Google Scholar 

  30. Kwon YJF, Toussie D, Finkelstein M et al (2020) Combining initial radiographs and clinical variables improves deep learning prognostication in patients with COVID-19 from the emergency department. Radiol Artif Intell 3(2):e200098. https://doi.org/10.1148/ryai.2020200098

    Article  PubMed  PubMed Central  Google Scholar 

  31. Akhtar S, Ahamad MM, Rashed-Al-Mahfuz M et al (2021) Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med Inform 9(4):e25884. https://doi.org/10.2196/25884

    Article  Google Scholar 

  32. Jiao Z, Choi JW, Halsey K et al (2021) Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. Lancet Digit Health 3(5):e286–e294

    Article  Google Scholar 

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Correspondence to Amirhossein Sahebkar .

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Guest, P.C., Abbasifard, M., Jamialahmadi, T., Majeed, M., Kesharwani, P., Sahebkar, A. (2022). Multiplex Immunoassay for Prediction of Disease Severity Associated with the Cytokine Storm in COVID-19 Cases. In: Guest, P.C. (eds) Multiplex Biomarker Techniques. Methods in Molecular Biology, vol 2511. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2395-4_18

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  • DOI: https://doi.org/10.1007/978-1-0716-2395-4_18

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2394-7

  • Online ISBN: 978-1-0716-2395-4

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