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

Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predi...

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Autores principales: Patterson, Bruce K., Guevara-Coto, Jose, Yogendra, Ram, Francisco, Edgar B., Long, Emily, Pise, Amruta, Rodrigues, Hallison, Parikh, Purvi, Mora, Javier, Mora-Rodríguez, Rodrigo A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273732/
https://www.ncbi.nlm.nih.gov/pubmed/34262570
http://dx.doi.org/10.3389/fimmu.2021.700782
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author Patterson, Bruce K.
Guevara-Coto, Jose
Yogendra, Ram
Francisco, Edgar B.
Long, Emily
Pise, Amruta
Rodrigues, Hallison
Parikh, Purvi
Mora, Javier
Mora-Rodríguez, Rodrigo A.
author_facet Patterson, Bruce K.
Guevara-Coto, Jose
Yogendra, Ram
Francisco, Edgar B.
Long, Emily
Pise, Amruta
Rodrigues, Hallison
Parikh, Purvi
Mora, Javier
Mora-Rodríguez, Rodrigo A.
author_sort Patterson, Bruce K.
collection PubMed
description Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-γ + IL-2)/CCL4-MIP-1β. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC 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.
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spelling pubmed-82737322021-07-13 Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning Patterson, Bruce K. Guevara-Coto, Jose Yogendra, Ram Francisco, Edgar B. Long, Emily Pise, Amruta Rodrigues, Hallison Parikh, Purvi Mora, Javier Mora-Rodríguez, Rodrigo A. Front Immunol Immunology Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-γ + IL-2)/CCL4-MIP-1β. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC 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. Frontiers Media S.A. 2021-06-28 /pmc/articles/PMC8273732/ /pubmed/34262570 http://dx.doi.org/10.3389/fimmu.2021.700782 Text en Copyright © 2021 Patterson, Guevara-Coto, Yogendra, Francisco, Long, Pise, Rodrigues, Parikh, Mora and Mora-Rodríguez https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Patterson, Bruce K.
Guevara-Coto, Jose
Yogendra, Ram
Francisco, Edgar B.
Long, Emily
Pise, Amruta
Rodrigues, Hallison
Parikh, Purvi
Mora, Javier
Mora-Rodríguez, Rodrigo A.
Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
title Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
title_full Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
title_fullStr Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
title_full_unstemmed Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
title_short Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
title_sort immune-based prediction of covid-19 severity and chronicity decoded using machine learning
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273732/
https://www.ncbi.nlm.nih.gov/pubmed/34262570
http://dx.doi.org/10.3389/fimmu.2021.700782
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