Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2021 Feb 18;12(1):1112.
doi: 10.1038/s41467-021-21310-4.

Interleukin-3 is a predictive marker for severity and outcome during SARS-CoV-2 infections

Affiliations
Observational Study

Interleukin-3 is a predictive marker for severity and outcome during SARS-CoV-2 infections

Alan Bénard et al. Nat Commun. .

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a worldwide health threat. In a prospective multicentric study, we identify IL-3 as an independent prognostic marker for the outcome during SARS-CoV-2 infections. Specifically, low plasma IL-3 levels is associated with increased severity, viral load, and mortality during SARS-CoV-2 infections. Patients with severe COVID-19 exhibit also reduced circulating plasmacytoid dendritic cells (pDCs) and low plasma IFNα and IFNλ levels when compared to non-severe COVID-19 patients. In a mouse model of pulmonary HSV-1 infection, treatment with recombinant IL-3 reduces viral load and mortality. Mechanistically, IL-3 increases innate antiviral immunity by promoting the recruitment of circulating pDCs into the airways by stimulating CXCL12 secretion from pulmonary CD123+ epithelial cells, both, in mice and in COVID-19 negative patients exhibiting pulmonary diseases. This study identifies IL-3 as a predictive disease marker for SARS-CoV-2 infections and as a potential therapeutic target for pulmunory viral infections.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Low plasma interleukin-3 levels are associated with severity and outcome in COVID-19.
a Plasma IL-3 levels of SARS-CoV-2+ patients with or without severe disease and in patients that had recovered from infection. p = 0.0468 for non-severe vs severe; p = 0.0016 for severe vs recovered; and p = 0.8441 for non-severe vs recovered. n = 92. b Plasma IL-3 levels of SARS-CoV-2+ patients with or without high viral load. n = 21. c Kaplan–Meier analysis showing the survival of SARS-CoV-2+ patients with high (≥20 pg/ml) or low (<20 pg/ml) plasma IL-3 levels (measured within 24 h after admission). n = 64. d Plasma IL-3 levels of SARS-CoV-2+ patients older or younger than 65 years. n = 92. e Analysis of plasma IL-3 levels (20 pg/ml identified by minimal p-value approach) and age defining risk groups to die (low and intermediate vs high; OR: 14.091; 95% CI: 1.680–118.218). n = 110. Data are mean ± S.E.M., *P < 0.05, **P < 0.01, ***P < 0.001, unpaired, two-tailed Student’s t test using Welch’s correction for unequal variances or Mann–Whitney test were used. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Plasma type I interferon levels are associated with plasma interleukin-3 levels and circulating pDCs in COVID-19.
a Absolute numbers of circulating pDCs and neutrophils in SARS-CoV-2+ patients from their admission to ICU and 1 to 7 days later. p = 0.0018 (pDCs d0), p = 0,0061 (pDCs d2), and p = 0.0227 (pDCs d7). n = 9. bd Absolute numbers of circulating pDCs (n = 16; p = 0.0331) (b) and plasma IFNα (n = 48, p = 0.0459) (c), and IFNλ (n = 19, p = 0.0167) (d) levels of patients with severe or non-severe COVID-19 infections. n = 16–48. ef Correlation between plasma IL-3 levels and plasma IFNα (e) and IFNλ (f) levels in SARS-CoV-2+ patients. n = 20–47. gh Correlation between absolute numbers of circulating pDCs and plasma IFNα (g) and IFNλ (h) levels in SARS-CoV-2+ patients. n = 12–13. Data are mean ± S.E.M., *P < 0.05, **P < 0.01, ***P < 0.001, unpaired, two-tailed Student’s t test using Welch’s correction for unequal variances was used. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Interleukin-3 protects against viral infection by increasing pDC numbers in the lungs.
a Percentage of pDCs in BALF of patients with pulmonary diseases with high or low BALF IL-3 levels. p = 0.004. Mann–Whitney test. n = 12. b Absolute numbers of pDCs and neutrophils in the lungs of naive mice 24 h after the i.n. injection of PBS or rIL-3, followed by an i.n. injection of CpG 8 h later. p = 0.0091. n = 8. c Levels of IFNα and IFNλ in the BALF of naive mice that received an i.n. injection of PBS or rIL-3, followed by an i.n. injection of CpG 8 h later, and were sacrificed 16 h later. n = 14 for IFNα (p = 0.0205) and n = 22 for IFNλ (p = 0.018). d, e Survival (d) and viral load in the lungs (e) of naive WT mice after i.n. infection with 6 × 106 PFU of HSV-1. Mice received an i.n. injection of PBS or rIL-3 just before HSV-1 infection. n = 26 (d) and n = 19 for d1 (p = 0.0151) and n = 15 for d3 (p = 0.0439) (e). Data are mean ± S.E.M., *P < 0.05, **P < 0.01, ***P < 0.001, unpaired, two-tailed Student’s t test using Welch’s correction for unequal variances was used. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. Interleukin-3 is produced by T cells in COVID-19.
a Absolute numbers of circulating T cells in SARS-CoV-2+ patients from their admission to ICU and 1 to 7 days later. p = 0.0154 (d6). n = 9. b Correlation between the absolute numbers of circulating T cells and circulating pDCs in SARS-CoV-2+ patients. Pearson r test (p < 0.0001). n = 87. c Absolute numbers of circulating CD4+ T cells, CD8+ T cells, and B cells expressing IL-3 in SARS-CoV-2+ patients. p = 0.024 for CD4 T cells vs CD8 T cells; p = 0.0036 for CD4 T cells vs B cells. n = 7. d Correlation between the number of circulating T cells and the number of circulating pDCs of patients with pulmonary diseases. Pearson r test (p < 0.0001). n = 12. e Correlation between the percentage of BALF T cells and the percentage of BALF pDCs of patients with pulmonary diseases. Pearson r test (p = 0.0288). n = 20. f Percentage of T cells in the BALF of patients with pulmonary diseases with high or low IL-3 BALF levels. p = 0.0452. n = 13. Data are mean ± S.E.M., *P < 0.05, **P < 0.01, ***P < 0.001, unpaired, two-tailed Student’s t test using Welch’s correction for unequal variances and paired, two-tailed Student’s t test were used. Source data are provided as a Source Data file.
Fig. 5
Fig. 5. Interleukin-3 promotes the recruitment of pDCs into the lung in a CXCL12-dependent manner.
a Relative mRNA expression of Cxcl12 in the lungs of naive mice 24 h after the i.n. injection of PBS or rIL-3. p = 0.0009. n = 11–12. b Level of CXCL12 in the BALF of WT or Cd131−/− mice 24 h after the i.n. injection of PBS or rIL-3. n = 7 for WT (p = 0.0416) and n = 8 for Cd131−/− (p = 0.8046). c Absolute number of pDCs and neutrophils in the lungs of naive mice 24 h after the i.n.injection of PBS, IgG or anti-CXCL12 followed by i.n. injection of PBS (black) or IL-3 (grey). p = 0.0371 for IgG vs αCXCL12 and p = 0.437 for PBS vs IgG. n = 8–9. d Correlation between plasma IL-3 and CXCL12 levels of SARS-CoV-2+ patients. n = 181. e Level of CXCL12 in the BALF of patients with pulmonary diseases with high or low IL-3 BALF levels. p = 0.0078. n = 13. f Percentage of pDCs in the BALF of patients with pulmonary diseases with high or low CXCL12 BALF levels. p = 0.0051. n = 12. g Percentage of CXCL12+ epithelial cells in the lungs of patients with pulmonary inflammation. p < 0.0001. n = 16. h Immunohistochemistry of CD123, EpCAM, CXCL12, CD31, and IgG in the lungs of patients with pulmonary inflammation. n = 3 different lungs. Data are mean ± S.E.M., *P < 0.05, **P < 0.01, ***P < 0.001, two-tailed unpaired or Mann–Whitney test were used. Source data are provided as a Source Data file.

References

    1. Zhu N, et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 2020;382:727–733. doi: 10.1056/NEJMoa2001017. - DOI - PMC - PubMed
    1. Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
    1. Huang C, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5. - DOI - PMC - PubMed
    1. Hoffmann M, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181:271–280 e278. doi: 10.1016/j.cell.2020.02.052. - DOI - PMC - PubMed
    1. Yap, J. K. Y., Moriyama, M. & Iwasaki, A. Inflammasomes and pyroptosis as therapeutic targets for COVID-19. J. Immunol.10.4049/jimmunol.2000513 (2020). - PMC - PubMed

Publication types

MeSH terms