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Candida albicans extracellular vesicles trigger type I IFN signalling via cGAS and STING

Nature Microbiology volume 9, pages 95–107 (2024)Cite this article

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Abstract

The host type I interferon (IFN) pathway is a major signature of inflammation induced by the human fungal pathogen, Candida albicans. However, the molecular mechanism for activating this pathway in the host defence against C. albicans remains unknown. Here we reveal that mice lacking cyclic GMP–AMP synthase (cGAS)–stimulator of IFN genes (STING) pathway components had improved survival following an intravenous challenge by C. albicans. Biofilm-associated C. albicans DNA packaged in extracellular vesicles triggers the cGAS–STING pathway as determined by induction of interferon-stimulated genes, IFNβ production, and phosphorylation of IFN regulatory factor 3 and TANK-binding kinase 1. Extracellular vesicle-induced activation of type I IFNs was independent of the Dectin-1/Card9 pathway and did not require toll-like receptor 9. Single nucleotide polymorphisms in cGAS and STING potently altered inflammatory cytokine production in human monocytes challenged by C. albicans. These studies provide insights into the early innate immune response induced by a clinically significant fungal pathogen.

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Fig. 1: Deletion of cGAS or STING renders mice more resistant to intravenous C. albicans.
Fig. 2: C. albicans EVs and BF DNA activate the STING pathway.
Fig. 3: Early ISG upregulation in WT macrophages stimulated by C. albicans EVs and BF DNA.
Fig. 4: Functional consequence of activating the STING pathway.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank all the members of the Vyas and Mansour lab at MGH for useful discussions and reading of the paper. We thank K. Timmer for his expert assistance with all flow cytometry experiments. Financial support: H.B.H. is supported by the Fund for Medical Discovery Fellowship from MGH. J.M.V. is supported by R01AI150181, R01AI136529 and R21AI152499; J.L.R. is supported by NIH/NIAID grant 1K08AI14755; D.R.A. is supported by R01AI073289; R.P.B. is supported by R01AI153405; J.E.N. is supported by R21AI159583 and R01AI145939; J.C.K. is supported by R01AI167993 and R37AI116550; and J.D.N. is supported by R21AI156104.

Author information

Authors and Affiliations

  1. Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Hannah Brown Harding, Geneva N. Kwaku, Christopher M. Reardon, Nida S. Khan, Arianne J. Crossen, Jennifer L. Reedy, Rebecca A. Ward, Diego A. Vargas-Blanco, Kyle J. Basham, Roby P. Bhattacharyya, Michael K. Mansour & Jatin M. Vyas

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Hannah Brown Harding, Jennifer L. Reedy, Diego A. Vargas-Blanco, Roby P. Bhattacharyya, Michael K. Mansour & Jatin M. Vyas

  3. Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA

    Daniel Zamith-Miranda & Joshua D. Nosanchuk

  4. Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA

    Daniel Zamith-Miranda & Joshua D. Nosanchuk

  5. Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA

    Robert Zarnowski, Jeniel E. Nett & David R. Andes

  6. Department of Microbiology and Immunology, University of Wisconsin Madison, Madison, WI, USA

    Robert Zarnowski, Jeniel E. Nett & David R. Andes

  7. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Jenny M. Tam

  8. Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands

    Collins K. Bohaen, Frank L. van de Veerdonk & Vinod Kumar

  9. Tufts Comparative Medicine Services, Tufts University, Boston, MA, USA

    Lauren Richey

  10. Division of Gastroenterology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Kenta Mosallanejad & Jonathan C. Kagan

  11. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Roby P. Bhattacharyya & Jatin M. Vyas

  12. University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, the Netherlands

    Vinod Kumar

  13. Nitte University Centre for Science Education and Research, Medical Sciences Complex, Mangaluru, India

    Vinod Kumar

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  1. Hannah Brown Harding
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Contributions

Conceptualization: H.B.H. and J.M.V. Investigation: H.B.H., G.K., C.M.R., N.S.K., D.Z.M., R.Z., J.M.T., A.C., C.K.B., L.R., V.K., D.A.V.-B., K.J.B. and J.L.R. Writing: H.B.H., R.A.W. and J.V. Paper review and revision: all authors. Resources: K.M., R.B., J.E.N., F.L.v.d.V., L.R., J.C.K., D.R.A. and J.D.N.

Corresponding author

Correspondence to Jatin M. Vyas.

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Ethical approval and consent to participate

The 500FG cohort was approved by the Arnhem-Nijmegen Medical Ethical Committee (500FG:NL42561.091.12) and performed in accordance with the Declaration of Helsinki. All individuals gave written informed consent to donate venous peripheral blood for research.

Competing interests

The authors declare no competing interests.

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Peer review information

Nature Microbiology thanks Tobias Hohl, Guilhem Janbon, Mairi Noverr and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of organs and serum from C. albicans-infected WT and Sting−/− mice.

a. Quantification of fungal burden following a C. albicans infection in WT and Sting−/− mice in the kidneys, liver, spleen, and brains (n = 4 for WT and Sting−/− for each time point). Kidney BUN (b) and creatinine levels (c) were measured from serum of infected and uninfected WT and Sting−/− mice over 28 days. n = 5 for each time point. d. Representative gating strategy for flow cytometry analysis showing selection of total kidney cells (SSC v FSC) → live white blood cells (WBC) (7AAD- and CD45 + ) → WBCs that are not B cells or T cells (CD90.2-, CD19-), and of these, how many are neutrophils (Siglec F + , Ly6G + ). e. Quantification of flow-based immunophenotyping of CD45+ WBCs and (f) Siglec F + , Ly6G+ neutrophils from single cell suspensions of kidneys from WT and Sting−/− mice (n = 6 for each group). g. Immunoblot of viperin from kidney homogenates harvested from WT and Sting−/− mice infected with 150,000 C. albicans at day 3 and day 5, compared to the positive control (immortalized WT macrophages stimulated with cGAMP for 6 h prior to lysis). h. Semi-quantification of fungal burden severity in GMS-stained kidney sections of WT and Sting−/− mice (n = 12). Statistical analyses were performed on raw data by means of a non-parametric, two-tailed Mann Whitney U test = 38.5, p = 0.0364. Data are presented as mean values +/- SD.

Source data

Extended Data Fig. 2 Further characterization of the viperin and IFNß response to C. albicans EVs.

a. Immunoblot of viperin in lysates from WT macrophages stimulated with PBS, ergosterol alone, EVs from C. albicans or C. auris, or cGAMP. b. IFNß production in the supernatants from the same stimulated WT macrophages as in (a). Significance determined using a one-way ANOVA, ***adj. p = 0.0004, **adj. p = 0.004 c. Average sizes of EVs extracted from C. albicans (grown in different conditions) and C. auris. d. Immunoblot of viperin induction in WT, Card9−/−, Dectin-1−/−, and Tlr9−/− immortalized macrophages following stimulation with PBS or C. albicans EVs. e. IFNß production by WT, cGAS−/−, Sting−/−, Card9−/−, Dectin-1−/−, and Tlr9−/− macrophages following a stimulation with PBS, cGAMP, or C. albicans EVs. Significance determined using a two-way ANOVA and subsequent Dunnett’s multiple comparison test with the exception of Card9−/−, which was an ordinary one-way ANOVA and subsequent Dunnett’s multiple comparison test, *adj. p = 0.0031, **adj. p < 0.01, ***adj. p < 0.001, ****adj. p < 0.0001 vs PBS f. Immunoblot of viperin and g. IFNß production by WT macrophages stimulated with graded amounts of EVs extracted from C. albicans 48 h BF. Significance determined using a one-way ANOVA and subsequent Dunnett’s multiple comparison test *adj. p = 0.0364, ***adj. p = 0.0008, ****adj. p < 0.0001 vs PBS. n = 3 biologically independent samples for all ELISAs and data are presented as mean values +/- SD. All Westerns were done in biological triplicate, representative blot shown. Macrophages were stimulated for 6 h prior to processing.

Source data

Extended Data Fig. 3 C. albicans EVs induce translocation of cGAS from the nuclear membrane to the cytosol.

Representative microscopy images of macrophages expressing cGAS-GFP and the localization of cGAS following no stimulation or stimulation with DiI only, C. albicans EVs only, or DiI-labeled C. albicans EVs. Macrophages were stimulated for 3 h prior to imaging. Size bar = 5 µm. Microscopy experiments were repeated with appropriate controls in biological triplicate, representative images shown of similar results.

Extended Data Fig. 4 Further characterization of the viperin and IFNß response to C. albicans BF DNA.

a. Immunoblot of viperin induction in WT macrophages stimulated with PBS, C. albicans EVs, Benzonase-treated EVs, and transfected EV DNA. b. Immunoblot of viperin induction and c. IFNβ production in WT macrophages stimulated with DNA extracted from C. albicans biofilms (BF) grown for 48 h, 72 h, 96 h and Lipofectamine reagents only. Significance was determined with an ordinary one-way ANOVA and subsequent Dunnett’s multiple comparison test. **adj. p = 0.0012, ***adj. p = 0.0002, ***adj. p < 0.0001 vs untreated control (Lipofectamine). d. Immunoblot of viperin induction in WT, Sting−/−, Card9−/−, Dectin-1−/−, and Tlr9−/− macrophages following a stimulation with PBS, cGAMP, and DNA extracted from C. albicans BF grown for 72 h. e. IFNß production by WT, cGas−/−, Sting−/−, Card9−/−, Dectin-1−/−, and Tlr9−/− macrophages following a stimulation with PBS and DNA extracted from C. albicans BF grown for 72 h. Significance determined using a two-way ANOVA and subsequent Dunnett’s multiple comparison test. ****p < 0.0001 vs untreated control (PBS). f. Immunoblot of STING pathway component in WT, cGas−/−, and Sting−/− immortalized macrophages treated with increasing concentrations of the STING inhibitor, H-151. Cells were treated with H-151 1 h prior to stimulation with PBS, cGAMP, C. albicans BF DNA, or LPS (STING independent positive control). g. Immunoblot of viperin and actin in WT, cGas−/−, and Sting−/− macrophages treated with the H-151 1 h prior to PBS, cGAMP, C. albicans BF DNA, or C. albicans EV stimulation. All macrophages were stimulated for 6 h prior to processing. n = 3 biologically independent samples for all ELISAs and data are presented as mean values +/- SD. All Westerns were done in biological triplicate, representative blot shown.

Source data

Extended Data Fig. 5 Regional association plots for SNPs identified in cGAS and STING.

Regional association plot of SNPs around the MB21D1gene locus for Candida-induced TNF–α concentrations (a) and for Candida-induced IL-6 concentrations (b). Regional association plot of SNPs around the TMEM173 gene locus for Candida- induced TNF–α concentrations (c) and for Candida-induced IL- 6 concentrations (d). The -log10 p-values of imputed SNPs are plotted on the y-axis against their physical position (NCBI build 36) on the x-axis. The most strongly associated SNPs in the regions are represented with purple diamond and surrounding markers are color coded according to their correlation coefficient (r2) with the top SNP using the hg19/1000 Genomes European samples. The light blue lines denote the estimated recombination rates. e. Evidence of the top SNPs regulation the expression levels of MB21D1 and TMEM173. We queried the most significant or top SNPs for statistical evidence of association with the target genes using the eQTLGen database and considered a p value of < 5 × 10−8 to be the threshold for significant cytokine QTLs. f. TNF production by WT, cGas−/−, and Sting−/− murine macrophages following a 2 h co-culture with live C. albicans. ****adj. p < 0.0001 vs. WT. n = 3 biologically independent samples for ELISA and data are presented as mean values +/- SD. Significance determined using an ordinary one-way ANOVA and subsequent Dunnett’s multiple comparison test.

Supplementary information

Reporting Summary

Supplementary Table 1

Extended Data Table 1. C. albicans genes in BF EVs.

Supplementary Table 2

Extended Data Table 2. Normalized counts of Ncounter host response panel analysis ratios. Genes have been normalized to the controls included in the plate for each genotype to account for variability. Tabs A-B, and E-H includes these normalized ratios counts ± s.d. for each gene. Each NanoString experiment was run in biological duplicate. Data analysis was performed using nSolver 4.0. To account for differences in total RNA per lane, hybridization efficiency and post-hybridization processing, the counts of 773 target RNAs were normalized on the basis of negative controls (background subtraction) and the geometric mean of 12 positive control RNA counts. Fold changes and P values (Benjamini–Yekutieli) were obtained using nSolver Advance Analysis 4.0.

Source data

Source Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

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Brown Harding, H., Kwaku, G.N., Reardon, C.M. et al. Candida albicans extracellular vesicles trigger type I IFN signalling via cGAS and STING. Nat Microbiol 9, 95–107 (2024). https://doi.org/10.1038/s41564-023-01546-0

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