Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

High-resolution mycobiota analysis reveals dynamic intestinal translocation preceding invasive candidiasis

Nature Medicine volume 26, pages 59–64 (2020)Cite this article

Subjects

Abstract

The intestinal microbiota is a complex community of bacteria, archaea, viruses, protists and fungi1,2. Although the composition of bacterial constituents has been linked to immune homeostasis and infectious susceptibility3,4,5,6,7, the role of non-bacterial constituents and cross-kingdom microbial interactions in these processes is poorly understood2,8. Fungi represent a major cause of infectious morbidity and mortality in immunocompromised individuals, although the relationship of intestinal fungi (that is, the mycobiota) with fungal bloodstream infections remains undefined9. We integrated an optimized bioinformatics pipeline with high-resolution mycobiota sequencing and comparative genomic analyses of fecal and blood specimens from recipients of allogeneic hematopoietic cell transplant. Patients with Candida bloodstream infection experienced a prior marked intestinal expansion of pathogenic Candida species; this expansion consisted of a complex dynamic between multiple species and subspecies with a stochastic translocation pattern into the bloodstream. The intestinal expansion of pathogenic Candida spp. was associated with a substantial loss in bacterial burden and diversity, particularly in the anaerobes. Thus, simultaneous analysis of intestinal fungi and bacteria identifies dysbiosis states across kingdoms that may promote fungal translocation and facilitate invasive disease. These findings support microbiota-driven approaches to identify patients at risk of fungal bloodstream infections for pre-emptive therapeutic intervention.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Intestinal fungal burden in allo-HCT patients.
Fig. 2: Mycobiota dynamics in allo-HCT patients.
Fig. 3: High-resolution mycobiota analysis in allo-HCT patients with fungal BSI.
Fig. 4: Characterization of bacteria in fecal samples with high and low levels of pathogenic Candida spp.

Similar content being viewed by others

Data availability

The data that support the findings of the present study are available from the corresponding author upon request. All sequencing data generated in this study have been deposited in the Sequence Read Archive under BioProject PRJNA579121. Accession numbers of entries under this BioProject and previously submitted bacterial 16S rDNA-sequencing data7,20 are listed in Supplementary Table 4.

References

  1. Paterson, M. J., Oh, S. & Underhill, D. M. Host–microbe interactions: commensal fungi in the gut. Curr. Opin. Microbiol. 40, 131–137 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Richard, M. L. & Sokol, H. The gut mycobiota: insights into analysis, environmental interactions and role in gastrointestinal diseases. Nat. Rev. Gastroenterol. Hepatol. 16, 331–345 (2019).

    PubMed  Google Scholar 

  3. Blander, J. M., Longman, R. S., Iliev, I. D., Sonnenberg, G. F. & Artis, D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 18, 851–860 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Koh, A. Y. The microbiome in hematopoietic stem cell transplant recipients and cancer patients: opportunities for clinical advances that reduce infection. PLoS Pathog. 13, e1006342 (2017).

    PubMed  PubMed Central  Google Scholar 

  5. Lewis, B. B. & Pamer, E. G. Microbiota-based therapies for Clostridium difficile and antibiotic-resistant enteric infections. Annu. Rev. Microbiol. 71, 157–178 (2017).

    CAS  PubMed  Google Scholar 

  6. Keith, J. W. & Pamer, E. G. Enlisting commensal microbes to resist antibiotic-resistant pathogens. J. Exp. Med. 216, 10–19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Kim, S. G. et al. Microbiota-derived lantibiotic restores resistance against vancomycin-resistant Enterococcus. Nature 572, 665–669 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Underhill, D. M. & Iliev, I. D. The mycobiota: interactions between commensal fungi and the host immune system. Nat. Rev. Immunol. 14, 405–416 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Nucci, M. & Anaissie, E. Revisiting the source of candidemia: skin or gut? Clin. Infect. Dis. 33, 1959–1967 (2001).

    CAS  PubMed  Google Scholar 

  10. Cesaro, S. et al. Incidence, risk factors, and long-term outcome of acute leukemia patients with early candidemia after allogeneic stem cell transplantation: a study by the acute leukemia and infectious diseases working parties of European Society for Blood and Marrow Transplantation. Clin. Infect. Dis. 67, 564–572 (2018).

    CAS  PubMed  Google Scholar 

  11. Kohler, J. R., Casadevall, A. & Perfect, J. The spectrum of fungi that infects humans. Cold Spring Harb. Perspect. Med. 5, a019273 (2014).

    PubMed  Google Scholar 

  12. Weiner, L. M. et al. Antimicrobial-resistant pathogens associated with healthcare-associated infections: summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2011–2014. Infect. Control Hosp. Epidemiol. 37, 1288–1301 (2016).

    PubMed  PubMed Central  Google Scholar 

  13. Snyder, G. M. & Wright, S. B. The epidemiology and prevention of Candida auris. Curr. Infect. Dis. Rep. 21, 19 (2019).

    PubMed  Google Scholar 

  14. Slavin, M. A. et al. Efficacy and safety of fluconazole prophylaxis for fungal infections after marrow transplantation: a prospective, randomized, double-blind study. J. Infect. Dis. 171, 1545–1552 (1995).

    CAS  PubMed  Google Scholar 

  15. Goodman, J. L. et al. A controlled trial of fluconazole to prevent fungal infections in patients undergoing bone marrow transplantation. N. Engl. J. Med. 326, 845–851 (1992).

    CAS  PubMed  Google Scholar 

  16. Ubeda, C. et al. Vancomycin-resistant Enterococcus domination of intestinal microbiota is enabled by antibiotic treatment in mice and precedes bloodstream invasion in humans. J. Clin. Invest. 120, 4332–4341 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Tamburini, F. B. et al. Precision identification of diverse bloodstream pathogens in the gut microbiome. Nat. Med. 24, 1809–1814 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Taur, Y. et al. Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. Clin. Infect. Dis. 55, 905–914 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Lee, Y. J. et al. Protective factors in the intestinal microbiome against Clostridium difficile infection in recipients of allogeneic hematopoietic stem cell transplantation. J. Infect. Dis. 215, 1117–1123 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Taur, Y. et al. Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Sci. Transl. Med. 10, eaap9489 (2018).

    PubMed  PubMed Central  Google Scholar 

  21. Sipsas, N. V. et al. Candidemia in patients with hematologic malignancies in the era of new antifungal agents (2001–2007): stable incidence but changing epidemiology of a still frequently lethal infection. Cancer 115, 4745–4752 (2009).

    CAS  PubMed  Google Scholar 

  22. Wang, E. et al. The ever-evolving landscape of candidaemia in patients with acute leukaemia: non-susceptibility to caspofungin and multidrug resistance are associated with increased mortality. J. Antimicrob. Chemother. 70, 2362–2368 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Forrest, G. N., Weekes, E. & Johnson, J. K. Increasing incidence of Candida parapsilosis candidemia with caspofungin usage. J. Infect. 56, 126–129 (2008).

    PubMed  Google Scholar 

  24. Nash, A. K. et al. The gut mycobiome of the human microbiome project healthy cohort. Microbiome 5, 153 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    CAS  PubMed  Google Scholar 

  28. Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Dubin, K. A. et al. Diversification and evolution of vancomycin-resistant Enterococcus faecium during intestinal domination. Infect. Immun. 87, e00102-19 (2019).

  30. Butler, G. et al. Evolution of pathogenicity and sexual reproduction in eight Candida genomes. Nature 459, 657–662 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Pryszcz, L. P., Nemeth, T., Gacser, A. & Gabaldon, T. Unexpected genomic variability in clinical and environmental strains of the pathogenic yeast Candida parapsilosis. Genome Biol. Evol. 5, 2382–2392 (2013).

    PubMed  PubMed Central  Google Scholar 

  32. Pryszcz, L. P. et al. The genomic aftermath of hybridization in the opportunistic pathogen Candida metapsilosis. PLoS Genet. 11, e1005626 (2015).

    PubMed  PubMed Central  Google Scholar 

  33. Schroder, M. S. et al. Multiple origins of the pathogenic yeast Candida orthopsilosis by separate hybridizations between two parental species. PLoS Genet. 12, e1006404 (2016).

    PubMed  PubMed Central  Google Scholar 

  34. Branco, J. et al. Impact of ERG3 mutations and expression of ergosterol genes controlled by UPC2 and NDT80 in Candida parapsilosis azole resistance. Clin. Microbiol. Infect. 23, 575 e571–575 e578 (2017).

    Google Scholar 

  35. Rybak, J. M. et al. Loss of C-5 sterol desaturase activity results in increased resistance to azole and echinocandin antifungals in a clinical isolate of Candida parapsilosis. Antimicrob. Agents Chemother. 61, e00651-17 (2017).

  36. Whaley, S. G. et al. The RTA3 gene, encoding a putative lipid translocase, influences the susceptibility of Candida albicans to fluconazole. Antimicrob. Agents Chemother. 60, 6060–6066 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Srivastava, A. et al. Distinct roles of the 7-transmembrane receptor protein Rta3 in regulating the asymmetric distribution of phosphatidylcholine across the plasma membrane and biofilm formation in Candida albicans. Cell Microbiol. 19, e12767 (2017).

    Google Scholar 

  38. Fan, D. et al. Activation of HIF-1alpha and LL-37 by commensal bacteria inhibits Candida albicans colonization. Nat. Med. 21, 808–814 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Taur, Y. Intestinal microbiome changes and stem cell transplantation: lessons learned. Virulence 7, 930–938 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Zhao, J., Murray, S. & Lipuma, J. J. Modeling the impact of antibiotic exposure on human microbiota. Sci. Rep. 4, 4345 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. del Pilar Vercher, M. et al. Differentiation of Candida parapsilosis, C. orthopsilosis, and C. metapsilosis by specific PCR amplification of the RPS0 intron. Int. J. Med. Microbiol. 301, 531–535 (2011).

    PubMed  Google Scholar 

  42. Liu, C. M. et al. FungiQuant: a broad-coverage fungal quantitative real-time PCR assay. BMC Microbiol. 12, 255 (2012).

    PubMed  PubMed Central  Google Scholar 

  43. White, P. L., Shetty, A. & Barnes, R. A. Detection of seven Candida species using the Light-Cycler system. J. Med. Microbiol. 52, 229–238 (2003).

    PubMed  Google Scholar 

  44. Haak, B. W. et al. Impact of gut colonization with butyrate-producing microbiota on respiratory viral infection following allo-HCT. Blood 131, 2978–2986 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  PubMed  Google Scholar 

  46. Tatusova, T. et al. Update on RefSeq microbial genomes resources. Nucleic Acids Res. 43, D599–D605 (2015).

    CAS  PubMed  Google Scholar 

  47. McMurdie, P. J. & Holmes, S. Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data. Pac. Symp. Biocomput. 2012, 235–246 (2011).

    Google Scholar 

  48. Turner, S. A. & Butler, G. The Candida pathogenic species complex. Cold Spring Harb. Perspect. Med. 4, a019778 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. Riccombeni, A., Vidanes, G., Proux-Wera, E., Wolfe, K. H. & Butler, G. Sequence and analysis of the genome of the pathogenic yeast Candida orthopsilosis. PLoS ONE 7, e35750 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Lischer, H. E., Excoffier, L. & Heckel, G. Ignoring heterozygous sites biases phylogenomic estimates of divergence times: implications for the evolutionary history of microtus voles. Mol. Biol. Evol. 31, 817–831 (2014).

    CAS  PubMed  Google Scholar 

  52. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Smit, A., Hubley, R. & Green, P. RepeatMasker Open-4.0. 2013–2015, http://www.repeatmasker.org (2015).

  55. Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank E. Pamer, I. Iliev, J. Xavier, J. Heitman and R. Rao for discussions, I. Leiner, M. Gjonbalaj, R. Seok and MSKCC Integrated Genomics Operation Facility for technical assistance. The studies were supported by a Geoffrey Beene Foundation Award (to T.M.H.), a Burroughs Wellcome Fund Investigator in the Pathogenesis of Infectious Diseases Award (to T.M.H.), National Institutes of Health grants (nos. P30 CA008748 to the MSKCC, R01 AI093808 to T.M.H., R01 AI 139632 to T.M.H., R01 AI137269 to Y.T. and K08HL143189 to J.U.P.), a Deutsche Forschungsgemeinschaft (German Research Foundation) fellowship grant (no. RO5328/2-1 to T.R.) and the Science Foundation Ireland (grant no. 12/IA/1343 to G.B.).

Author information

Author notes

  1. These authors contributed equally: Ying Taur, Tobias M. Hohl.

Authors and Affiliations

  1. Infectious Disease Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Bing Zhai, Thierry Rolling, Nicholas L. Tosini, Sari Joshowitz, Luigi A. Amoretti, Emily Fontana, Roberta J. Wright, Edwin Miranda, Charlotte A. Veelken, Sejal M. Morjaria, N. Esther Babady, Ying Taur & Tobias M. Hohl

  2. School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Belfield, Dublin, Ireland

    Mihaela Ola & Geraldine Butler

  3. Division of Infectious Diseases, First Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Thierry Rolling

  4. Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Eric R. Littmann, Marcel R. M. van den Brink & Tobias M. Hohl

  5. Clinical Microbiology Service, Department of Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Edwin Miranda & N. Esther Babady

  6. Weill Cornell Medical College, New York, NY, USA

    Sejal M. Morjaria, Jonathan U. Peled, Marcel R. M. van den Brink, Ying Taur & Tobias M. Hohl

  7. Adult Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jonathan U. Peled & Marcel R. M. van den Brink

Authors
  1. Bing Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mihaela Ola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thierry Rolling
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicholas L. Tosini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sari Joshowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eric R. Littmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luigi A. Amoretti
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Emily Fontana
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Roberta J. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Edwin Miranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Charlotte A. Veelken
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sejal M. Morjaria
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jonathan U. Peled
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Marcel R. M. van den Brink
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. N. Esther Babady
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Geraldine Butler
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ying Taur
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Tobias M. Hohl
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.M.H. and B.Z. conceived of this project. B.Z., N.L.T. and S.J. generated the fungal ITS1 sequencing library and fungal burden quantification. B.Z. isolated the fecal strains and prepared samples for the whole-genome sequencing. M.O. and G.B. performed the comparative genomic analyses of fecal and blood strains. T.R., Y.T. and B.Z. performed the bacterial–fungal interaction analyses. T.R., N.L.T. and E.R.L. contributed to the data interpretation and statistical analyses. E.R.L., Y.T. and T.M.H. acquired the clinical data. L.A.A. performed the 16S rDNA quantification. L.A.A., E.F. and R.J.W. generated the 16S rDNA-sequencing library and supported the fecal specimen collection and storage. E.M., S.M.M. and N.E.B. provided the bloodstream strains. C.A.V. assisted B.Z. with the isolation of C. orthopsilosis strains from fecal specimens. J.U.P. and M.R.M.v.d.B. provided helpful discussions on the study design. Y.T. developed the DADA2 algorithm-based ITS1 and 16S rDNA-sequencing pipeline and analyzed the amplicon-based sequencing data. B.Z. and T.M.H. wrote the manuscript. T.R., J.U.P., M.R.M.v.d.B., G.B. and Y.T. edited the manuscript. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Ying Taur or Tobias M. Hohl.

Ethics declarations

Competing interests

T.M.H. has participated in scientific advisory boards for Merck & Co, Inc. and Partner Therapeutics. J.U.P. reports research funding, intellectual property fees and travel reimbursement from Seres Therapeutics. M.R.M.v.d.B. has received research support from Seres Therapeutics, consulted, received honoraria from or participated in advisory boards for Seres Therapeutics, Flagship Ventures, Novartis, Evelo, Jazz Pharmaceuticals, Therakos, Amgen, Magenta Therapeutics, Merck & Co, Inc., Acute Leukemia Forum and the DKMS Medical Council (Board), and IP Licensing with Seres Therapeutics and Juno Therapeutics. All other authors have no competing interests.

Additional information

Peer review information A. Farrell is the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Quantitative Candida genus-specific 18S rDNA levels in fecal samples of candidemic patients.

The grey shade indicates the time range of first positive fungal blood cultures. N.D.: not detected.

Extended Data Fig. 2 Quantitation of relative abundance of pathogenic Candida species in patients without candidemia.

The solid line represents the dynamic trend, with the shaded area indicating the 95% confidence intervals, n = 57.

Extended Data Fig. 3 C. parapsilosis ASV1 and ASV2 sequence alignment.

The box indicates the single nucleotide difference between ASV1 and ASV2. This level of variation (1/279) cannot be differentiated with methods based on OTU clustering.

Extended Data Fig. 4 Patient example with C. metapsilosis BSI.

a, The panel shows clinical data, ITS rDNA sequencing results, quantification of amplicons of 5 different ASVs, and genotyping results of fecal and blood strains from patient 5. b, Phylogenic trees of C. metapsilosis strains from patient 5 and from other institutions. Solid lines indicate the calculated distance between strains (bootstrap support of 100%, or otherwise labeled). Dashed lines indicate the bootstrap value of 0, suggesting that the fecal and blood strains are highly similar.

Extended Data Fig. 5 SNP trees of C. parapsilosis and C. orthopsilosis strains with complete information on previously sequenced strains.

The bootstrap values are 100% for all the solid lines and 0% for all the dashed lines in the C. parapsilosis tree. For the C. orthopsilosis tree, the bootstrap values are 100% for the lines except those with specific bootstrap value labeled.

Extended Data Fig. 6 The amplification of RTA3 and the adjacent region on the genomics sequences of C. parapsilosis cluster II strains.

a, The graph shows the enrichment of reads aligned with RTA3 and the adjacent region of chromosome 1 in the genome of cluster II strains, compared to cluster I strains and strain Pt3.fecal.day2. b, Quantitation of RTA3 copy number of all sequenced C. parapsilosis strains from this study (cluster I strains and the strain from patient 2: n = 8; cluster II strains: n = 23).

Extended Data Fig. 7 Bacterial 16S rDNA sequencing data of candidemic and non-candidemic patients.

The grey dashed line and arrow indicate the day of transplant. The grey box indicates day -10 to day +30 of transplant. The black dashed line and arrow indicate the day of the first positive bacterial blood culture. Five samples (one from patient 3, four from patient 5) failed 16S rDNA sequencing and were excluded from the bacterial diversity or LEfSe analysis. A subset of the 16S rDNA sequencing data has been previously reported7,20.

Extended Data Fig. 8 Bacterial 16S rDNA burden and α-diversity in patient fecal samples.

a, Quantitative bacterial 16S rDNA levels in candidemic (red, n = 38) and non-candidemic (green, n = 54) patient groups at indicated time points during allo-HCT. Ten samples were further excluded from Fig. 7 since they failed the 16S rDNA qPCR reaction. b, α-diversity of bacterial 16S rDNA in candidemic (red, n = 45) and non-candidemic (green, n = 57) patient groups, measured by Inverse Simpson index. The solid line represents the dynamic trend, with the shaded area indicating the 95% confidence intervals. The grey shaded area indicates the day of first positive fungal blood culture. The y-axis in panel (b) is rescaled with Logε. A subset of the 16S rDNA sequencing data has been previously reported7,20.

Extended Data Fig. 9 Full bacterial sequencing data aligned according to Candida relative abundance.

Alignment of bacterial (16S) rDNA sequencing data from all 15 study patients, according to the relative abundance of pathogenic Candida species. The bacterial 16S rDNA sequencing data of each sample are presented in the bottom row. A subset of the 16S rDNA sequencing data has been previously reported7,20.

Extended Data Fig. 10 Characterization of intestinal bacterial microbiota with high Saccharomyces cerevisiae relative abundance.

a, 16S rDNA Alignment of bacterial (16S) rDNA sequencing data from all 15 study patients, according to the relative abundance of S. cerevisiae. b, quantification of bacterial burden (two-sided Wilcoxon rank sum p = 0.36) in samples with high (n = 29) and low (n = 64) S. cerevisiae relative abundance. Box plots represent median, IQR and range. c, quantification of bacterial diversity (two-sided Wilcoxon rank sum p = 0.87) in samples with high (n = 32) and low (n = 71) S. cerevisiae relative abundance. Box plots represent median, IQR and range. A subset of the 16S rDNA sequencing data has been previously reported7,20.

Supplementary information

Supplementary Information

Supplementary Table 1.

Reporting Summary

Supplementary Tables 2–4

Supplementary Table 2 Information of whole-genome sequencing data. Supplementary Table 3a, List of high-quality SNPs of C. parapsilosis cluster II strains. b, The matrix for PCA. Supplementary Table 4 SRA accessions of original ITS/16S/genome sequencing data included in the present study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhai, B., Ola, M., Rolling, T. et al. High-resolution mycobiota analysis reveals dynamic intestinal translocation preceding invasive candidiasis. Nat Med 26, 59–64 (2020). https://doi.org/10.1038/s41591-019-0709-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-019-0709-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology