Henderickx Jannie G E, Crobach Monique J T, Terveer Elisabeth M, Smits Wiep Klaas, Kuijper Ed J, Zwittink Romy D
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.
Microbiome Res Rep. 2023 Dec 6;3(1):8. doi: 10.20517/mrr.2023.52. eCollection 2024.
The bacterial microbiota is well-recognized for its role in 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 colonization and infection. The mycobiota was profiled by ITS2 sequencing of fecal DNA from infection (CDI) patients ( = 29), asymptomatically colonization (CDC) patients ( = 38), and hospitalized controls with negative stool culture (controls; = 38). Previously published 16S rRNA gene sequencing data of the same cohort were used additionally for machine learning and fungal-bacterial network analysis. CDI patients were characterized by a significantly higher abundance of spp. (MD 0.270 ± 0.089, = 0.002) and (MD 0.165 ± 0.082, = 0.023) compared to controls. Additionally, they were deprived of spp. (MD -0.067 ± 0.026, = 0.000) and spp. (MD -0.118 ± 0.043, = 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 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). and were marker genera associated with CDC patients and controls. The gut mycobiota differs between CDI, CDC, and controls and may affect 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.
细菌微生物群因其在定植和感染中的作用而广为人知，而真菌和酵母仍未得到充分研究。本研究的目的是根据定植和感染情况分析真菌微生物群的预测价值及其与细菌微生物群的相互作用。通过对感染（CDI）患者（n = 29）、无症状定植（CDC）患者（n = 38）和粪便培养阴性的住院对照（对照组；n = 38）的粪便DNA进行ITS2测序来分析真菌微生物群。另外，使用同一队列先前发表的16S rRNA基因测序数据进行机器学习和真菌 - 细菌网络分析。与对照组相比，CDI患者的白色念珠菌属（MD 0.270±0.089，P = 0.002）和光滑念珠菌（MD 0.165±0.082，P = 0.023）丰度显著更高。此外，与CDC患者相比，他们缺乏近平滑念珠菌属（MD -0.067±0.026，P = 0.000）和酿酒酵母属（MD -0.118±0.043，P = 0.000）。网络分析显示CDI和CDC中几种真菌与细菌之间存在正相关，尽管分析未揭示白色念珠菌属与真菌之间的直接关联。此外，微生物群机器学习模型优于基于真菌微生物群和联合微生物群 - 真菌微生物群模型。微生物群分类器成功区分了CDI与CDC [受试者操作特征曲线下面积（AUROC）= 0.884]以及CDI与对照组（AUROC = 0.905）。德巴利酵母属和毕赤酵母属是与CDC患者和对照组相关的标记属。肠道真菌微生物群在CDI、CDC和对照组之间存在差异，可能通过间接相互作用影响念珠菌属。仅真菌微生物群数据无法成功区分CDC与对照组或CDI患者，对细菌微生物群数据也没有额外的预测价值。鉴定与CDC和对照组相关的细菌标记属值得进一步研究。
