Fungal and bacterial gut microbiota differ between Clostridioides difficile colonization and infection

Microbiome Research Reports, vol. 3, pp. 8

Abstract

Aim: The bacterial microbiota is well-recognized for its role in Clostridioides difficile colonization and infection, while fungi and yeasts remain understudied. The aim of this study was to analyze the predictive value of the mycobiota and its interactions with the bacterial microbiota in light of C. difficile colonization and infection. Methods: The mycobiota was profiled by ITS2 sequencing of fecal DNA from C. difficile infection (CDI) patients (n = 29), asymptomatically C. difficile colonization (CDC) patients (n = 38), and hospitalized controls with C. difficile negative stool culture (controls; n = 38). Previously published 16S rRNA gene sequencing data of the same cohort were used additionally for machine learning and fungal-bacterial network analysis. Results: CDI patients were characterized by a significantly higher abundance of Candida spp. (MD 0.270 ± 0.089, P = 0.002) and Candida albicans (MD 0.165 ± 0.082, P = 0.023) compared to controls. Additionally, they were deprived of Aspergillus spp. (MD -0.067 ± 0.026, P = 0.000) and Penicillium spp. (MD -0.118 ± 0.043, P = 0.000) compared to CDC patients. Network analysis revealed a positive association between several fungi and bacteria in CDI and CDC, although the analysis did not reveal a direct association between Clostridioides spp. and fungi. Furthermore, the microbiota machine learning model outperformed the models based on the mycobiota and the joint microbiota-mycobiota model. The microbiota classifier successfully distinguished CDI from CDC [Area Under the Receiver Operating Characteristic (AUROC) = 0.884] and CDI from controls (AUROC = 0.905). Blautia and Bifidobacterium were marker genera associated with CDC patients and controls. Conclusion: The gut mycobiota differs between CDI, CDC, and controls and may affect Clostridioides spp. through indirect interactions. The mycobiota data alone could not successfully discriminate CDC from controls or CDI patients and did not have additional predictive value to the bacterial microbiota data. The identification of bacterial marker genera associated with CDC and controls warrants further investigation.

Authors 6

1. Jannie G. E. Henderickx Aachen Center for Microbiome Analyses and Therapeutics Department of Medical Microbiology Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID)

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

   Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

2. Monique J. T. Crobach Aachen Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID)

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

3. Elisabeth M. Terveer Aachen Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID) Netherlands Donor Feces Bank Department of Medical Microbiology

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

   Netherlands Donor Feces Bank, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

4. Wiep Klaas Smits corresponding Aachen Center for Microbiome Analyses and Therapeutics Department of Medical Microbiology Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID)

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

   Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

5. Ed J. Kuijper Aachen Center for Microbiome Analyses and Therapeutics Department of Medical Microbiology Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID) Netherlands Donor Feces Bank Department of Medical Microbiology

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

   Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

   Netherlands Donor Feces Bank, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

6. Romy Daniëlle Zwittink Aachen Center for Microbiome Analyses and Therapeutics Department of Medical Microbiology Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID)

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands

   Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands