Host metabolite-cytokine correlation landscape in SARS-CoV-2 infection

doi:10.21203/rs.3.rs-91866/v1

Download PDF

Research Article

Host metabolite-cytokine correlation landscape in SARS-CoV-2 infection

https://doi.org/10.21203/rs.3.rs-91866/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The systemic cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. It has been well-known that metabolism plays a role in modulating the immune responses in infectious diseases. Yet, how the host metabolism correlates with CRS in COVID-19 patients and how the perturbed metabolites affect the cytokine release remains unclear. Here, we performed both metabolomics and cytokine/chemokine profiling on serum samples from the same cohort of healthy controls, mild and severe COVID-19 patients and delineated the global metabolic and immune response landscape along disease progression. Intriguingly, the correlation analysis revealed the tight link between metabolites and proinflammatory cytokines and chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, implying the potential regulatory role of arginine metabolism, tryptophan metabolism, and purine metabolism in hyperinflammation. Importantly, we demonstrated that targeting metabolism markedly modulated the proinflammatory cytokines release by PBMCs isolated from SARS-CoV-2-infected rhesus macaques ex vivo. Beyond providing a comprehensive resource of metabolism and immunology data of SARS-CoV-2 infection, our study showed that metabolic alterations can be potentially exploited to develop novel strategy for the treatment of fatal CRS in COVID-19.

Virology

Cellular Metabolism

Immunology

COVID-19

SARS-COV-2

Metabolomics

Cytokine

Correlation analysis

FigS1.png
Extended Data Fig. 1 | Clinical information of mild and severe COVID-19 patients Clinical information of healthy controls, mild and severe COVID-19 patients. Data are presented as mean ± SEM. with individual data points shown. Statistical significance was assessed using either one-way ANOVA followed by Benjamini-Hochberg multiple comparison test or unpaired, two-sided t-test.
FigS2.png
Extended Data Fig. 2 | Overview of metabolomics data in COVID-19 patients a, General workflow of metabolomic profiling experiments and data analysis. b-c, Partial Least Squares Discriminant Analysis (PLS-DA) of targeted metabolomics data (b) and untargeted metabolomics data (c) of healthy controls, mild and severe COVID-19 patients. d, Venn diagram depicting the number of significantly altered serum metabolites in each group after integrating targeted and untargeted metabolomics data. e, Metabolic pathways enriched based on metabolites progressively increased or decreased in mild and severe patients compared with healthy controls. Statistical significance was assessed by Fisher’s exact test and followed by Benjamini-Hochberg multiple comparison test. f-i, Line charts of metabolites which significantly altered in severe patients involved in arginine metabolism (f), TCA cycle (g), tryptophan metabolism (h), NAD+ metabolism (i).
FigS3.jpg
Extended Data Fig. 3 | Overview of cytokine data in COVID-19 patients a, Alterations in serum cytokine abundance in mild and severe patients compared with healthy controls. Log2 fold change is represented by color intensity. Shape represents alteration significance, and FDR is size-coded. b, Violin plots comparing serum cytokines abundance in healthy controls, mild patients and severe patients. Data are presented as mean and quantiles with individual data points shown. Statistical significance was assessed using two-sided Mann-Whitney U test followed by Benjamini-Hochberg multiple comparison test.
FigS4.jpg
Extended Data Fig. 4 | Longitudinal cytokine trajectories in follow-up mild COVID- 19 patients a, Schematic diagram of sample collection for follow-up patients. Blue squares indicate the time of serum collection; red circles represent the time of symptoms onset; dark blue lines represent the time of hospitalization. b-c, Longitudinal antibodies (b), log10 transformed cytokine and chemokine concentration (c) trajectories in an interval of 3 days. Blue solid lines pass through the mean of each measurement at the specific time interval, and dotted lines represent the mean of measurements in healthy controls. GAM regression lines are indicated by the black solid lines, with 95% confidence intervals for the regression lines donated by gray filled areas. Data are presented as mean ± SEM. with individual data points shown.
FigS5.jpg
Extended Data Fig. 5 | Longitudinal metabolite trajectories in follow-up mild COVID-19 patients a, Heatmap comparison of metabolites at distinct time point in follow-up patients in each cluster. Log2 fold change of metabolite abundance in each interval relative to healthy controls is represented by color intensity. b, Serum metabolite trajectories based on normalized data in follow-up patients. Blue solid lines pass through the mean of each measurement at the specific time interval, and dotted lines represent the mean of measurements in healthy controls. GAM regression lines are represented by the black solid ines, with 95% confidence intervals for the regression donated by gray filled areas. Data are presented as mean ± SEM. with individual data points shown.
FigS6.png
Extended Data Fig. 6 |Cytokine abundance after supplementation with arginine Levels of indicated cytokines and chemokines measured 24 h after supplementation of 1.25 mM arginine in isolated PBMCs derived from mock-infected or SARS-CoV-2-infected hesus macaques. Data are presented as mean ± SEM. with individual data points shown. Statistical significance was assessed using one-way ANOVA followed by Benjamini- Hochberg multiple comparison test.
FigS7.png
Extended Data Fig. 7 | Cytokine abundance after treatment with IDO1 inhibitor a, Ratio of kynurenine (Kyn) to tryptophan (Try) in healthy controls and COVID-19 patients. b, Levels of indicated cytokines and chemokines measured 24 h after 0.1 mM IDO1 inhibitor Epacadostat treatment in isolated PBMCs derived from mock-infected or SARS-CoV-2-infected rhesus macaques. Data are presented as mean ± SEM with individual data points shown. Statistical significance was assessed using one-way ANOVA followed by Benjamini-Hochberg multiple comparison test.
FigS8.png
Extended Data Fig. 8 |Cytokine abundance after treatment with IMPDH inhibitor Levels of indicated cytokines and chemokines measured 24 h after treatment of 0.1 mM IMPDH inhibitor mycophenolic acid (MPA) in isolated PBMCs derived from mock infected or SARS-CoV-2-infected rhesus macaques. Data are presented as mean ± SEM. with individual data points shown. Statistical significance was assessed using one-way ANOVA followed by Benjamini-Hochberg multiple comparison test.
FigS9.png
Extended Data Fig. 9 | Viral RNA quantification in isolated PBMCs ex vivo a, PBMCs were isolated from uninfected rhesus macaque and were either mock or SARS CoV-2 infected and cultured for 24 h under different treatment conditions. Viral RNA in PBMCs were then assessed by quantitative PCR analysis. b, Levels of SARS-CoV-2 RNA in PBMCs quantified 24 h after infected and supplementation of 1.25 mM arginine, 0.1 mM IDO1 inhibitor Epacadostat, and 0.1mM IMPDH inhibitor Mycophenolic acid (MPA), respectively. Statistical significance was assessed using one-way ANOVA followed by Benjamini-Hochberg multiple comparison test. Data are presented as mean ± SEM. with individual data points shown.

Download PDF

Version 1

posted

You are reading this latest preprint version

Host metabolite-cytokine correlation landscape in SARS-CoV-2 infection

Status:

Version 1

Abstract

Figures

Full Text

Supplementary Files

Status:

Version 1