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
. 2020 Aug 7;369(6504):718-724.
doi: 10.1126/science.abc6027. Epub 2020 Jul 13.

Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients

Jérôme Hadjadj #  1   2 , Nader Yatim #  2   3 , Laura Barnabei  1 , Aurélien Corneau  4 , Jeremy Boussier  3 , Nikaïa Smith  3 , Hélène Péré  5   6 , Bruno Charbit  7 , Vincent Bondet  3 , Camille Chenevier-Gobeaux  8 , Paul Breillat  2 , Nicolas Carlier  9 , Rémy Gauzit  10 , Caroline Morbieu  2 , Frédéric Pène  11   12 , Nathalie Marin  12 , Nicolas Roche  9   11 , Tali-Anne Szwebel  2 , Sarah H Merkling  13 , Jean-Marc Treluyer  14   15 , David Veyer  6   16 , Luc Mouthon  2   11 , Catherine Blanc  4 , Pierre-Louis Tharaux  5 , Flore Rozenberg  11   17 , Alain Fischer  1   18   19 , Darragh Duffy #  3   7 , Frédéric Rieux-Laucat #  1 , Solen Kernéis #  10   20   21 , Benjamin Terrier #  22   5
Affiliations

Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients

Jérôme Hadjadj et al. Science. .

Abstract

Coronavirus disease 2019 (COVID-19) is characterized by distinct patterns of disease progression that suggest diverse host immune responses. We performed an integrated immune analysis on a cohort of 50 COVID-19 patients with various disease severity. A distinct phenotype was observed in severe and critical patients, consisting of a highly impaired interferon (IFN) type I response (characterized by no IFN-β and low IFN-α production and activity), which was associated with a persistent blood viral load and an exacerbated inflammatory response. Inflammation was partially driven by the transcriptional factor nuclear factor-κB and characterized by increased tumor necrosis factor-α and interleukin-6 production and signaling. These data suggest that type I IFN deficiency in the blood could be a hallmark of severe COVID-19 and provide a rationale for combined therapeutic approaches.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1. Phenotyping of peripheral blood leukocytes in patients with SARS-CoV-2 infection.
(A) Lymphocyte counts in whole blood from COVID-19 patients were analyzed between days 8 and 12 after onset of first symptoms, according to disease severity. (B) viSNE map of blood leukocytes after exclusion of granulocytes, stained with 30 markers and measured with mass cytometry. Cells are automatically separated into spatially distinct subsets according to the combination of markers that they express. LTgd, γδ T cell; MAIT, mucosal-associated invariant T cell; LB, B lymphocyte. (C) viSNE map colored according to cell density across disease severity (classified as healthy controls, mild to moderate, severe, and critical). Red indicates the highest density of cells. (D) Absolute number of CD3+ T cells, CD8+ T cells, and CD3–CD56+ NK cells in peripheral blood from COVID-19 patients, according to disease severity. (E and F) Proportions (frequencies) of lymphocyte subsets from COVID-19 patients. (E) Proportions of CD3+ T cells among lymphocytes, CD8+ T cells among CD3+ T cells, and NK cells among lymphocytes. (F) Proportions of CD19+ B cells among lymphocytes and CD38hi CD27hi plasmablasts among CD19+ B cells. (G) Analysis of the functional status of specific T cell subsets and NK cells based on the expression of activation (CD38, HLA-DR) and exhaustion (PD-1, Tim-3) markers. In (D) to (G), data indicate median. Each dot represents a single patient. P values were determined with the Kruskal-Wallis test, followed by Dunn’s post-test for multiple group comparisons with median reported; *P < 0.05; **P < 0.01; ***P < 0.001.
Fig. 2
Fig. 2. Immunological transcriptional signature of SARS-CoV-2 infection.
RNA extracted from patient whole blood and RNA counts of 574 genes were determined by means of direct probe hybridization, using the Nanostring nCounter Human Immunology_v2 kit. (A) Heatmap representation of all genes, ordered by hierarchical clustering. Healthy controls (n = 13 patients), mild to moderate (n = 11 patients), severe (n = 10 patients), and critical (n = 11 patients). Up-regulated genes are shown in red, and down-regulated genes are shown in blue. (B) Volcano plots depicting log10 (P value) and log2 (fold change), as well as z value for each group comparison (supplementary materials, materials and methods). Gene expression comparisons allowed the identification of significantly differentially expressed genes between severity grades (heathy controls versus mild to moderate, 216 genes; mild to moderate versus severe, 43 genes; severe versus critical, 0 genes). (C) (Left) PCA of the transcriptional data. (Middle and right) Kinetic plots showing mean normalized values for each gene and severity grade, where each gray line corresponds to one gene. Median values over genes for each severity grade are plotted in black. Gene set enrichment analysis of pathways enriched in PC1 and PC2 are depicted under corresponding kinetic plot.
Fig. 3
Fig. 3. Impaired type I IFN response in patients with severe SARS-CoV-2 infection.
(A) Heatmap showing expression of type I IFN-related genes by using the reverse transcription- and PCR-free Nanostring nCounter technology in patients with mild-to-moderate (n = 11), severe (n = 10), and critical (n = 11) SARS-CoV2 infection, and healthy controls (n = 13). Up-regulated genes are shown in red, and down-regulated genes are shown in blue. (B) ISG score based on expression of six genes (IFI44L, IFI27, RSAD2, SIGLEC1, IFIT1, and IS15) measured with quantitative RT-PCR in whole blood cells from mild to moderate (n = 14), severe (n = 15), and critical (n = 17) patients and healthy controls (n = 18). (C) IFN-α2 (fg/ml) concentration evaluated by use of Simoa and (D) IFN activity in plasma according to clinical severity. (E) Mild to moderate (n = 14) and severe patients (n = 16) were separated in two groups depending on the clinical outcome, namely critical worsening requiring mechanical ventilation (to denote severe status). (Left) ISG score and (right) IFN-α2 plasma concentration are shown. (F) Time-dependent IFN-α2 concentrations are shown according to severity group. (G) Quantification of plasmacytoid dendritic cells (pDCs) as a percentage of PBMCs and as cells/milliliter according to severity group. (H) ISG score before and after stimulation of whole blood cells by IFN-α (103UI/ml for 3 hours). (I) Viral loads in nasal swabs estimated by means of RT-PCR and expressed in cycle threshold (Ct) and blood viral load evaluated by means of digital PCR. In (B) and (E), ISG score results represent the fold-increased expression compared with the mean of unstimulated controls and are normalized to GAPDH (glyceraldehyde phosphate dehydrogenase). In (B) to (I), data indicate median. Each dot represents a single patient. P values were determined with the Kruskal-Wallis test, followed by Dunn’s post-test for multiple group comparisons and the Mann-Whitney test for two group comparisons with median reported; *P < 0.05; **P < 0.01; ***P < 0.001.
Fig. 4
Fig. 4. Immune profiling in patients with severe and critical SARS-CoV-2 infection.
(A) Heatmap showing the expression of cytokines and chemokines that are significantly different in severe and critical patients, ordered by hierarchical clustering. Included are healthy controls (n = 13) and mild to moderate (n = 11), severe (n = 10), and critical (n = 11) patients. Up-regulated genes are shown in red, and down-regulated genes are shown in blue. (B) IL-6, (C) TNF-α, (D) IL-1β, and (E) IL-10 proteins were quantified in the plasma of patients by using Simoa technology or a clinical-grade ELISA assay (supplementary materials, materials and methods). Each group includes n = 10 to 18 patients. The dashed line indicates the limit of detection (LOD). (F) Kinetic plots showing mean normalized value for each gene and severity grade. Each gray line corresponds to one gene belonging to the NF-κB pathway. Median values over genes for each severity grade are plotted in black. (G) Plasma quantification of RIPK-3. Each group included n = 10 patients. (H) Absolute RNA count for (left) CXCR2, (middle) CXCL2 protein plasma concentration measured with Luminex technology, and (right) blood neutrophil count depending on severity group. The dashed line indicates the upper normal limit. Each group includes n = 10 to 13 patients. (I) Absolute RNA count for (left) CCR2; (middle left) CCL2 protein plasma concentration measured by Luminex technology; and (middle right) blood monocyte count depending on severity group. The dashed lines depict the normal range. (Right) The percentage of nonclassical monocytes, depending on severity grade. Each group shows n = 10 to 18 patients. RNA data are extracted from the Nanostring nCounter analysis (supplementary materials, materials and methods). In (B) to (I), data indicate median. Each dot represents a single patient. P values were determined with the Kruskal-Wallis test, followed by Dunn’s post-test for multiple group comparisons with median reported; *P < 0.05; **P < 0.01; ***P < 0.001.
See this image and copyright information in PMC

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Guan W.-J., Ni Z.-Y., Hu Y., Liang W.-H., Ou C.-Q., He J.-X., Liu L., Shan H., Lei C.-L., Hui D. S. C., Du B., Li L.-J., Zeng G., Yuen K.-Y., Chen R.-C., Tang C.-L., Wang T., Chen P.-Y., Xiang J., Li S.-Y., Wang J.-L., Liang Z.-J., Peng Y.-X., Wei L., Liu Y., Hu Y.-H., Peng P., Wang J.-M., Liu J.-Y., Chen Z., Li G., Zheng Z.-J., Qiu S.-Q., Luo J., Ye C.-J., Zhu S.-Y., Zhong N.-S., China Medical Treatment Expert Group for Covid-19 , Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 382, 1708–1720 (2020). 10.1056/NEJMoa2002032 - DOI - PMC - PubMed
    1. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang R., Gao Z., Jin Q., Wang J., Cao B., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020). 10.1016/S0140-6736(20)30183-5 - DOI - PMC - PubMed
    1. Salje H., Tran Kiem C., Lefrancq N., Courtejoie N., Bosetti P., Paireau J., Andronico A., Hozé N., Richet J., Dubost C.-L., Le Strat Y., Lessler J., Levy-Bruhl D., Fontanet A., Opatowski L., Boelle P.-Y., Cauchemez S., Estimating the burden of SARS-CoV-2 in France. Science eabc3517 (2020). 10.1126/science.abc3517 - DOI - PubMed
    1. Chow E. J., Doyle J. D., Uyeki T. M., Influenza virus-related critical illness: Prevention, diagnosis, treatment. Crit. Care 23, 214 (2019). 10.1186/s13054-019-2491-9 - DOI - PMC - PubMed
    1. Wu C., Chen X., Cai Y., Xia J., Zhou X., Xu S., Huang H., Zhang L., Zhou X., Du C., Zhang Y., Song J., Wang S., Chao Y., Yang Z., Xu J., Zhou X., Chen D., Xiong W., Xu L., Zhou F., Jiang J., Bai C., Zheng J., Song Y., Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern. Med. 10.1001/jamainternmed.2020.0994 (2020). 10.1001/jamainternmed.2020.0994 - DOI - PMC - PubMed

Publication types

MeSH terms

LinkOut - more resources

-