Circulating Interleukin-8 Dynamics Parallels Disease Course and Is Linked to Clinical Outcomes in Severe COVID-19.
Ranit D'RozarioDeblina RaychaudhuriPurbita BandopadhyayJafar SarifPriyanka MehtaChinky Shiu Chen LiuBishnu Prasad SinhaJayasree RoyRitwik BhaduriMonidipa DasSanghamitra BandyopadhyayShekhar Ranjan PaulShilpak ChatterjeeRajesh PandeyYogiraj RayDipyaman GangulyPublished in: Viruses (2023)
Severe COVID-19 frequently features a systemic deluge of cytokines. Circulating cytokines that can stratify risks are useful for more effective triage and management. Here, we ran a machine-learning algorithm on a dataset of 36 plasma cytokines in a cohort of severe COVID-19 to identify cytokine/s useful for describing the dynamic clinical state in multiple regression analysis. We performed RNA-sequencing of circulating blood cells collected at different time-points. From a Bayesian Information Criterion analysis, a combination of interleukin-8 (IL-8), Eotaxin, and Interferon-γ (IFNγ) was found to be significantly linked to blood oxygenation over seven days. Individually testing the cytokines in receiver operator characteristics analyses identified IL-8 as a strong stratifier for clinical outcomes. Circulating IL-8 dynamics paralleled disease course. We also revealed key transitions in immune transcriptome in patients stratified for circulating IL-8 at three time-points. The study identifies plasma IL-8 as a key pathogenic cytokine linking systemic hyper-inflammation to the clinical outcomes in COVID-19.
Keyphrases
- coronavirus disease
- sars cov
- machine learning
- single cell
- emergency department
- end stage renal disease
- early onset
- oxidative stress
- dendritic cells
- gene expression
- newly diagnosed
- ejection fraction
- immune response
- deep learning
- chronic kidney disease
- drug induced
- artificial intelligence
- healthcare
- peritoneal dialysis
- cell proliferation
- prognostic factors
- cell death
- big data
- cell cycle arrest
- health information
- signaling pathway
- patient reported