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

被引:3
作者
Henderickx, Jannie G. E. [1 ,2 ,3 ]
Crobach, Monique J. T. [2 ,3 ]
Terveer, Elisabeth M. [2 ,3 ,4 ]
Smits, Wiep Klaas [1 ,2 ,3 ]
Kuijper, Ed J. [1 ,2 ,3 ,4 ]
Zwittink, Romy D. [1 ,2 ,3 ]
机构
[1] Leiden Univ, Ctr Microbiome Anal & Therapeut, Dept Med Microbiol, Med Ctr, NL-2333 ZA Leiden, Netherlands
[2] Leiden Univ, Dept Med Microbiol, Med Ctr, Albinusdreef 2, NL-2333 ZA Leiden, Netherlands
[3] Leiden Univ, Leiden Univ Ctr Infect Dis LU CID, Med Ctr, Albinusdreef 2, NL-2333 ZA Leiden, Netherlands
[4] Leiden Univ, Dept Med Microbiol, Netherlands Donor Feces Bank, Med Ctr, NL-2333 ZA Leiden, Netherlands
来源
MICROBIOME RESEARCH REPORTS | 2024年 / 3卷 / 01期
关键词
Mycobiota; fungi; gut microbiota; Clostridioides difficile; CDI; OVERGROWTH; ADMISSION;
D O I
10.20517/mrr.2023.52
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
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.
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页数:15
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