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Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning

Bruce K Patterson, Jose Guevara-Coto, Ram Yogendra, Edgar Francisco, Emily Long, Amruta Pise, Hallison Rodrigues, Purvi Parikh, Javier Mora, Rodrigo A Mora-Rodríguez
doi: https://doi.org/10.1101/2020.12.16.423122
Bruce K Patterson
1IncellDx Inc, San Carlos, CA
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  • For correspondence: brucep@incelldx.com
Jose Guevara-Coto
3Department of Computer Science and Informatics (ECCI), Universidad de Costa Rica
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Ram Yogendra
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Edgar Francisco
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Emily Long
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Amruta Pise
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Hallison Rodrigues
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Purvi Parikh
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Javier Mora
2Lab of Tumor Chemosensitivity, CIET / DC Lab, Faculty of Microbiology, Universidad de Costa Rica
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Rodrigo A Mora-Rodríguez
2Lab of Tumor Chemosensitivity, CIET / DC Lab, Faculty of Microbiology, Universidad de Costa Rica
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ABSTRACT

Individuals with systemic symptoms long after COVID-19 has cleared represent approximately ~10% of all COVID-19 infected individuals. Here we present a bioinformatics approach to predict and model the phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 144 individuals including normal individuals and patients spanning the COVID-19 disease continuum. We collected plasma and isolated PBMCs from 29 normal individuals, 26 individuals with mild-moderate COVID-19, 25 individuals with severe COVID-19, and 64 individuals with Chronic COVID-19 symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients. Data was analyzed using machine learning methods to predict and distinguish the groups from each other.Using a multi-class deep neural network classifier to better fit our prediction model, we recapitulated a 100% precision, 100% recall and F1 score of 1 on the test set. Moreover, a first score specific for the chronic COVID-19 patients was defined as S1 = (IFN-γ + IL-2)/ CCL4-MIP-1β. Second, a score specific for the severe COVID-19 patients was defined as S2 = (10*IL-10 + IL-6) - (IL-2 + IL-8). Severe cases are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While chronic patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.

Summary Immunologic Modeling of Severity and Chronicity of COVID-19

Competing Interest Statement

B.K.P, A.P., H.R., E.L. are employees of IncellDx

  • Abbreviations
    IL
    interleukin
    RANTES
    regulation on activation
    normal T
    expressed and secreted
    CCR
    chemokine receptor
    IFN
    interferon
    TNF
    tumor necrosis factor
    MIP
    macrophage inflammatory protein
    GM-CSF
    granulocyte-macrophage colonystimulating factor
    VEGF
    vascular endothelial growth factor
    HIV
    human immunodeficiency virus
    HCV
    hepatitis C virus
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted December 22, 2020.
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    Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
    Bruce K Patterson, Jose Guevara-Coto, Ram Yogendra, Edgar Francisco, Emily Long, Amruta Pise, Hallison Rodrigues, Purvi Parikh, Javier Mora, Rodrigo A Mora-Rodríguez
    bioRxiv 2020.12.16.423122; doi: https://doi.org/10.1101/2020.12.16.423122
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    Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
    Bruce K Patterson, Jose Guevara-Coto, Ram Yogendra, Edgar Francisco, Emily Long, Amruta Pise, Hallison Rodrigues, Purvi Parikh, Javier Mora, Rodrigo A Mora-Rodríguez
    bioRxiv 2020.12.16.423122; doi: https://doi.org/10.1101/2020.12.16.423122

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