Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning

Maitray A Patel; Michael J Knauer; Michael Nicholson; Mark Daley; Logan R Van Nynatten; Gediminas Cepinskas; Douglas D Fraser

doi:10.1186/s10020-023-00610-z

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning

Mol Med. 2023 Feb 21;29(1):26. doi: 10.1186/s10020-023-00610-z.

Authors

Maitray A Patel¹, Michael J Knauer², Michael Nicholson³, Mark Daley^{1

4}, Logan R Van Nynatten³, Gediminas Cepinskas^{5

6}, Douglas D Fraser^{7

8

9

10

11

12}

Affiliations

¹ Epidemiology and Biostatistics, Western University, London, ON, N6A 3K7, Canada.
² Pathology and Laboratory Medicine, Western University, London, ON, N6A 3K7, Canada.
³ Medicine, Western University, London, ON, N6A 3K7, Canada.
⁴ Computer Science, Western University, London, ON, N6A 3K7, Canada.
⁵ Lawson Health Research Institute, London, ON, N6C 2R5, Canada.
⁶ Medical Biophysics, Western University, London, ON, N6A 3K7, Canada.
⁷ Lawson Health Research Institute, London, ON, N6C 2R5, Canada. douglas.fraser@lhsc.on.ca.
⁸ Children's Health Research Institute, London, ON, N6C 4V3, Canada. douglas.fraser@lhsc.on.ca.
⁹ Pediatrics, Western University, London, ON, N6A 3K7, Canada. douglas.fraser@lhsc.on.ca.
¹⁰ Clinical Neurological Sciences, Western University, London, ON, N6A 3K7, Canada. douglas.fraser@lhsc.on.ca.
¹¹ Physiology and Pharmacology, Western University, London, ON, N6A 3K7, Canada. douglas.fraser@lhsc.on.ca.
¹² Room C2-C82, London Health Sciences Centre, 800 Commissioners Road East, London, ON, N6A 5W9, Canada. douglas.fraser@lhsc.on.ca.

Abstract

Background: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID.

Methods: A case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase.

Results: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID.

Conclusions: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.

Keywords: Biomarker; COVID-19; Cell types; Long-COVID; Machine learning; Organ system; Targeted proteomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers
COVID-19*
Case-Control Studies
Humans
Machine Learning
Post-Acute COVID-19 Syndrome
Proteomics

Substances

Biomarkers