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Fungal microbiota sustains lasting immune activation of neutrophils and their progenitors in severe COVID-19

Nature Immunology volume 24, pages 1879–1889 (2023)Cite this article

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Abstract

Gastrointestinal fungal dysbiosis is a hallmark of several diseases marked by systemic immune activation. Whether persistent pathobiont colonization during immune alterations and impaired gut barrier function has a durable impact on host immunity is unknown. We found that elevated levels of Candida albicans immunoglobulin G (IgG) antibodies marked patients with severe COVID-19 (sCOVID-19) who had intestinal Candida overgrowth, mycobiota dysbiosis and systemic neutrophilia. Analysis of hematopoietic stem cell progenitors in sCOVID-19 revealed transcriptional changes in antifungal immunity pathways and reprogramming of granulocyte myeloid progenitors (GMPs) for up to a year. Mice colonized with C. albicans patient isolates experienced increased lung neutrophilia and pulmonary NETosis during severe acute respiratory syndrome coronavirus-2 infection, which were partially resolved with antifungal treatment or by interleukin-6 receptor blockade. sCOVID-19 patients treated with tocilizumab experienced sustained reductions in C. albicans IgG antibodies titers and GMP transcriptional changes. These findings suggest that gut fungal pathobionts may contribute to immune activation during inflammatory diseases, offering potential mycobiota-immune therapeutic strategies for sCOVID-19 with prolonged symptoms.

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Fig. 1: Antibodies to common intestinal fungi are elevated in patients with COVID-19.
Fig. 2: Candida species of the gut mycobiota expand in sCOVID-19.
Fig. 3: Neutrophils and ACAL IgG differentiate mCOVID-19 from sCOVID-19 and link intestinal Candida overgrowth to proinflammatory immunity in the lung.
Fig. 4: Persistently altered function of neutrophil progenitors in patients recovering from sCOVID-19.
Fig. 5: IL-6 mediates immune activation in patients with severe COVID-19 and in a mouse model of COVID-19 during C. albicans-aggravated lung NETosis.

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Data availability

16S and ITS sequencing data are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive with the accession code PRJNA732432. The single-cell transcriptome and ATAC sequencing data have been deposited in NCBI GEO under the accession number GSE196990. Source data are provided with this paper.

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Acknowledgements

We thank members of the Iliev Laboratory for their critical reviews of the manuscript. We thank all contributing members of the Department of Pathology and Laboratory Medicine, of the JRI IBD Live Cell Bank Consortium and the Microbiome Core Laboratory of Weill Cornell Medicine, the NYU Langone Health Microscopy Laboratory (NCI P30CA016087), Khanna Laboratory (supported by R01AI143861 and R01AI143861-02S1) and R. Albrecht, R. Cadogan, D. Flores for support with the BSL3 facility and procedures. The authors were supported by WCM WCG COVID-19; WCGS Merit Fellowship (to W.-Y.L); Asan foundation, ROK (to J.-G.C.); K08MH130773 (to C.N.P.); R01AI148416, R01AI148416-S1, R01AI148416-S2, Burrough Welcome Trust PATH Award, and the Hirschl Weill-Caulier Award (to S.Z.J); R01AI160706 and R01DK130425 (to M. Schotsaert); CRIPT, CEIRR (contract # 75N93021C00014) U19AI135972, U19AI168631 and U19AI142733 (to A.G.-S.). Research in the Iliev Laboratory is supported by the US National Institutes of Health (R01DK113136, R01DK121977 and R01AI137157), the Leona M. and Harry B. Helmsley Charitable Trust, the Irma T. Hirschl Career Scientist Award, the Research Corporation for Science Advancement Award, the Cancer Research Institute Lloyd J. Old STAR Award, and the Burrough Welcome Trust PATH Award. I.D.I. is a fellow of the CIFAR program Fungal Kingdom—Threats and Opportunities.

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Authors and Affiliations

  1. Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York City, NY, USA

    Takato Kusakabe, Woan-Yu Lin, Stephen T. Yeung, Marissa Mesko, Meghan Bialt DeCelie, Guilhermina Carriche, Itai Doron, Christopher N. Parkhurst, Chun-Jun Guo, Mirella Salvatore & Iliyan D. Iliev

  2. The Jill Roberts Institute for Research in Inflammatory Bowel Disease (JRI), Weill Cornell Medicine, New York City, NY, USA

    Takato Kusakabe, Woan-Yu Lin, Marissa Mesko, Meghan Bialt DeCelie, Guilhermina Carriche, Itai Doron, Gregory G. Putzel, Chun-Jun Guo & Iliyan D. Iliev

  3. Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, Cornell University, New York City, NY, USA

    Woan-Yu Lin, Jin-Gyu Cheong, Stefan Worgall, Chun-Jun Guo, Steven Z. Josefowicz & Iliyan D. Iliev

  4. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York City, NY, USA

    Jin-Gyu Cheong, Arjun Ravishankar, Zhen Zhao, Sophie Rand, Melissa Cushing, Lars Westblade, Giorgio Inghirami & Steven Z. Josefowicz

  5. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Gagandeep Singh, Michael Schotsaert & Adolfo García-Sastre

  6. Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Gagandeep Singh, Michael Schotsaert & Adolfo García-Sastre

  7. Department of Microbiology, New York University, Langone Health, New York City, NY, USA

    Stephen T. Yeung

  8. Department of Pediatrics, Weill Cornell Medicine, New York City, NY, USA

    Stefan Worgall

  9. Department of Microbiology and Immunology, Weill Cornell Medicine, New York City, NY, USA

    Chun-Jun Guo & Iliyan D. Iliev

  10. Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Adolfo García-Sastre

  11. Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai New York, New York City, NY, USA

    Adolfo García-Sastre

  12. The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Adolfo García-Sastre

  13. Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA

    Mirella Salvatore

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  1. Takato Kusakabe
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Contributions

T.K. and I.D.I. conceived and conceptualized the study. T.K., G.S., J.C., A.R., S.T.Y. and M. Schotsaert designed experiments and analyzed data. M. Salvatore was involved in the investigation, clinical samples and clinical data acquisition. T.K., W.-Y.L., J.C., G.S., G.C, S.T.Y., C.J.G., M.M., M.B.D., I.D. and G.G.P. performed experiments and acquired and analyzed data. S.R., M.C., L.W., C.N.P., Z.Z. and G.I. provided clinical samples and participated in clinical data acquisition. S.W., L.N. contributed to experimental interpretation. I.D.I., M. Salvatore, A.G.-S. and S.Z.J. supervised the study. T.K., J.C., W.-Y.L. and I.D.I. generated figures and legends from analyzed data. I.D.I. administered the project and acquired funding. T.K. and I.D.I. wrote the manuscript.

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Correspondence to Iliyan D. Iliev.

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Competing interests

The AGS Laboratory has received research support from GSK, Pfizer, Senhwa Biosciences, Kenall Manufacturing, Blade Therapeutics, Avimex, Johnson & Johnson, Dynavax, 7 Hills Pharma, Pharmamar, ImmunityBio, Accurius, Nanocomposix, Hexamer, N-fold LLC, Model Medicines, Atea Pharma, Applied Biological Laboratories and Merck, outside of the reported work. A.G.-S. has consulting agreements for the following companies involving cash and/or stock: Castlevax, Amovir, Vivaldi Biosciences, Contrafect, 7 Hills Pharma, Avimex, Pagoda, Accurius, Esperovax, Farmak, Applied Biological Laboratories, Pharmamar, CureLab Oncology, CureLab Veterinary, Synairgen, Paratus, Pfizer and Prosetta, outside of the reported work. A.G.-S. has been an invited speaker in meeting events organized by Seqirus, Janssen, Abbott and AstraZeneca; and is inventor on patents and patent applications on the use of antivirals and vaccines for the treatment and prevention of virus infections and cancer, owned by the Icahn School of Medicine at Mount Sinai, New York, outside of the reported work. M. Schotsaert has received unrelated research funding in sponsored research agreements from ArgenX BV, Moderna, 7 Hills Pharma and Phio Pharmaceuticals, which has no competing interest with this work. The other authors declare no competing interests related to this study.

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Extended data

Extended Data Fig. 1 Serological testing in Cohorts 1 and 2.

a-b, SARS-CoV-2 RBD IgG titration curves in HD (n = 36, a) and sCOVID-19 (n = 66, b). c, Plasma IgG antibody titers to SARS-CoV-2 RBD in HD (n = 36), mCOVID-19 (n = 25) and sCOVID-19 (n = 66) (Extended Data Table 1). The data are shown as endpoint titers normalized to ELISA reciprocal dilution as in a and b. The dotted line indicates the limit of detection. In the boxplots, the center is drawn through the median of the measurement, and the lower and upper bounds of the box correspond to the first and third quartile. The whiskers go down to the smallest value and up to the largest. Statistical significance was determined by the one-way ANOVA followed by Tukey’s multiple-comparison. Related to Fig. 1.

Extended Data Fig. 2 Compositional analysis of gut mycobiota.

a-b, Ratio between Ascomycota and Basidiomycota (a) and relative abundance of fungal species (b) in ITS1 sequencing of fungal rDNA from stool samples of HD (n = 10) and COVID-19 patients (n = 10). Lower and upper hinges correspond to the first and third quartile; dots represent individual patients’ samples. P values were calculated using a two-tailed Mann-Whitney testing between all groups. Related to Fig. 2.

Extended Data Fig. 3 Immune responses to C.albicans isolates from COVID-19 patients.

a, Representative graphs depicting the flow cytometry gating strategy for defining murine neutrophil populations in tissues. b-d, Anti-C. albicans specific IgG titers (b), the amount of neutrophils in peripheral blood (c) and lung (d) in antibiotic-treated mice orally gavaged or not (PBS, n = 7) with either C. glabrata (CgCOV3, n = 7) or C. albicans (CaCOV1, CaCOV5, CaCOV2, n = 7) isolated from COVID-19 patient’s stool. Immune responses were assessed at 2 weeks after colonization. In boxplots in b, the center is drawn through the median of the measurement, and the lower and upper bounds of the box correspond to the first and third quartile. The whiskers go down to the smallest value and up to the largest. The bar graphs in c and d presented as mean ± SEM. The results were pooled from two experiments. P values were calculated using the one-way ANOVA followed by Tukey’s multiple comparison. ns = not significant. Related to Figs. 2 and 3.

Extended Data Fig. 4 Linear regression analysis of the Immune cell frequencies and levels of ACAL IgG in peripheral blood of mCOVID-19 and sCOVID-19.

a-e, Comparison of linear regression analysis of CD3+ T cell (a), CD19+CD20+ B cell (b), NK cell (c), CD4+ T cell (d) and CD8+ T cell (e) frequencies and levels of ACAL IgG in peripheral blood of sCOVID-19 (n = 13) and mCOVID-19 (n = 17), see Extended Data Table 1. Crosses indicate deceased patients. Red and black lines show linear fits within severe and low to moderate groups, respectively, with 95% confidence intervals shown in gray. Spearman correlation estimates (rho) and associated p values are shown in red and black for sCOVID-19 and mCOVID19, respectively. Related to Fig. 3.

Extended Data Fig. 5 Strong correlation between ACAL IgG and GMPs among multiple progenitor cell types.

Correlation and linear regression of frequency of hematopoietic stem cells/multipotent progenitor cells (HSC/MPP), lymphoid-primed multipotent progenitor cells (LMPP), granulocyte-macrophage progenitor cells (GMP), erythroid progenitor cells (Ery), megakaryocyte-erythroid progenitor cells (MEP) with ACAL IgG (log10 titer) in enrichment of CD34+ HSPC from PBMC of HD(n = 5), sCOVID-19 (n = 12) and nonCOV-19 (n = 5), see Extended Data Table 2. Blue line indicates the regression line for all patients. The associated linear regression equation, Pearson’s correlation. Coefficient and significance are shown. Related to Fig. 4.

Extended Data Fig. 6 HLA-related transcriptional signatures of GMPs form HD, sCOVID19 and sCOVID19 treated with tocilizumab.

a, Heatmap showing antigen presentation marker that are differentially expressed in GMP of HD (n = 7) and sCOVID-19 who received (n = 6) or not (n = 7) IL-6R blockade treatment. Data are average of normalized expression for each gene in each group. b, Antibiotic treated mice were colonized with C. albicans CaCOV5 by oral gavage twice for two weeks prior to SARS-COV-2 challenge and were harvested at Day 20. Water with or without antifungal fluconazole was provided to mice two days after second oral gavage. Related to Fig. 5.

Extended Data Table 1 Clinical characteristics of cohort 1
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Extended Data Table 2 Clinical characteristics of cohort 2
Full size table
Extended Data Table 3 Clinical characteristics of cohort 3
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Extended Data Table 4 Antimicrobial treatment and Candida spp. isolation from COVID-19 patients in cohort 3
Full size table

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Kusakabe, T., Lin, WY., Cheong, JG. et al. Fungal microbiota sustains lasting immune activation of neutrophils and their progenitors in severe COVID-19. Nat Immunol 24, 1879–1889 (2023). https://doi.org/10.1038/s41590-023-01637-4

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