Development and evaluation of blood-based prognostic biomarkers for COVID disease outcomes using EpiSwitch 3-dimensional genomic regulatory immuno-genetic profiling
Main Article Content
Abstract
Infection of humans by the SARS-CoV-2 virus leads to highly variable host responses and diverse clinical outcomes, ranging from asymptomatic to hospitalization, intensive care unit (ICU) admission and death. 10% of those with acute infections continue to display post-acute sequelae of coronavirus disease (PASC), now colloquially termed Post-COVID Syndrome (PCS). There is an acute unmet need for unbiased diagnostic biomarkers to predict outcomes before or during the early stages of acute infection, to discover more about PCS and to enable targeting of therapeutics to individual patients. Here, starting with whole blood taken at the time of diagnosis, a predictive classifier model containing six 3-dimensional (3D)-genomic biomarkers able to identify individuals at the highest risk of acute severe COVID disease with a positive predictive value of 93% and balanced accuracy of 88% was developed. The discovery process started with a whole 3D-genome microarray generating 964,631 data points per patient. Mapping the position of the most informative 3D markers to nearby genes revealed associations with ACE2, olfactory, Gby, Ca2+ and nitric oxide signalling; innate and adaptive immunity; programme death ligand 1 (PD-L1); prostaglandin E2 (PGE2); and the inflammatory cytokine CCL5, confirming variability in host immune responses, rather than viral genetics or load, as the primary determinant of disease outcomes, and supporting the use of mammalian target of rapamycin (mTOR) inhibitors and immunosuppressants to treat acute severe disease. Using the 3D genomics knowledgebase, with >1 billion 3D genomic datapoints derived from clinical studies, a subset of 77 of the acute COVID-associated prognostic 3D biomarkers were found close to 10 loci genetically linked to fatigue-dominant PCS, and to be informative biomarkers in 6 diseases with fatigue as a symptom. Network analysis linked individual 3D genomic markers to pathways, diseases and therapies. 3D-genomic profiling, as an integrator of multi-omic molecular regulation, offers a new approach for better understanding the complex heterogeneous clinical outcomes triggered by infectious agents.
Keywords:
SARS-CoV-2, acute COVID-19 disease, Post-COVID Syndrome (PSC), blood-based biomarkers, prediction of COVID disease severity
Article Details
How to Cite
HUNTER, Ewan et al.
Development and evaluation of blood-based prognostic biomarkers for COVID disease outcomes using EpiSwitch 3-dimensional genomic regulatory immuno-genetic profiling.
Medical Research Archives, [S.l.], v. 12, n. 9, sep. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5737>. Date accessed: 04 oct. 2024.
doi: https://doi.org/10.18103/mra.v12i9.5737.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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6. Wang Z, Tang K. Combating COVID-19: health equity matters. Nat Med. Apr 2020;26(4):458. doi:10.1038/s41591-020-0823-6
7. Chippa V, Aleem A, Anjum F. Postacute Coronavirus (COVID-19) Syndrome. StatPearls. 2024.
8. Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. Mar 2023;21(3):133-146. doi:10.1038/s41579-022-00846-2
9. Liew F, Efstathiou C, Fontanella S, et al. Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease. Nat Immunol. Apr 2024;25(4):607-621. doi:10.1038/s41590-024-01778-0
10. Taylor K, Pearson M, Das S, Sardell J, Chocian K, Gardner S. Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis. J Transl Med. Nov 1 2023;21(1):775. doi:10.1186/s12967-023-04588-4
11. Driggs D, Selby I, Roberts M, et al. Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise. Radiol Artif Intell. Jul 2021;3(4):e210011. doi:10.1148/ryai.2021210011
12. Mellor J. The impact of epigenetics on the future of personalised medicine. In: Carini C, Fidock M, Van Gool A, eds. Handbook of biomarkers and precision medicine. CRC Press; 2019.
13. Tordini F, Aldinucci M, Milanesi L, Lio P, Merelli I. The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer. Frontiers in genetics. 2016;7:194. doi:10.3389/fgene.2016.00194
14. Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. Feb 15 2002;295(5558):1306-11.
15. Kempfer R, Pombo A. Methods for mapping 3D chromosome architecture. Nat Rev Genet. Apr 2020;21(4):207-226. doi:10.1038/s41576-019-0195-2
16. Alshaker H, Hunter E, Salter M, et al. Monocytes acquire prostate cancer specific chromatin conformations upon indirect co-culture with prostate cancer cells. Frontiers in oncology. 2022;12:990842. doi:10.3389/fonc.2022.990842
17. Alshaker H, Mills R, Hunter E, et al. Chromatin conformation changes in peripheral blood can detect prostate cancer and stratify disease risk groups. J Transl Med. Jan 28 2021;19(1):46. doi:10.1186/s12967-021-02710-y
18. Carini C, Hunter E, Scottish Early Rheumatoid Arthritis Inception cohort I, et al. Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis. J Transl Med. Jan 29 2018;16(1):18. doi:10.1186/s12967-018-1387-9
19. Grand FH, Slater M, Hunter E, Akoulitchev A. Ectopic gene deregulations and chromosome conformations: integrating novel molecular testing into clinical applications from leukemias to gliomas. In: Carinia C, Fidock M, Van Gool A, eds. Handbook of biomarkers and precision medicine. CRC Press; 2019.
20. Hall ECR, Murgatroyd C, Stebbings GK, et al. The Prospective Study of Epigenetic Regulatory Profiles in Sport and Exercise Monitored Through Chromosome Conformation Signatures. Genes (Basel). Aug 7 2020;11(8)doi:10.3390/genes11080905
21. Hunter E, McCord R, Ramadass AS, et al. Comparative molecular cell-of-origin classification of diffuse large B-cell lymphoma based on liquid and tissue biopsies. Translational Medicine Communications. 2020/03/24 2020;5(1):5. doi:10.1186/s41231-020-00054-1
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