Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA.
Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Gut Microbes. 2022 Jan-Dec;14(1):2135963. doi: 10.1080/19490976.2022.2135963.
Clostridioides difficile infection (CDI) is a gastro-intestinal (GI) infection that illustrates how perturbations in symbiotic host-microbiome interactions render the GI tract vulnerable to the opportunistic pathogens. CDI also serves as an example of how such perturbations could be reversed via gut microbiota modulation mechanisms, especially fecal microbiota transplantation (FMT). However, microbiome-mediated diagnosis of CDI remains understudied. Here, we evaluated the diagnostic capabilities of the fecal microbiome on the prediction of CDI. We used the metagenomic sequencing data from ten previous studies, encompassing those acquired from CDI patients treated by FMT, CDI-negative patients presenting other intestinal health conditions, and healthy volunteers taking antibiotics. We designed a hybrid species/function profiling approach that determines the abundances of microbial species in the community contributing to its functional profile. These functionally informed taxonomic profiles were then used for classification of the microbial samples. We used logistic regression (LR) models using these features, which showed high prediction accuracy (with an average ), substantiating that the species/function composition of the gut microbiome has a robust diagnostic prediction of CDI. We further assessed the confounding impact of antibiotic therapy on CDI prediction and found that it is distinguishable from the CDI impact. Finally, we devised a log-odds score computed from the output of the LR models to quantify the likelihood of CDI in a gut microbiome sample and applied it to evaluating the effectiveness of FMT based on post-FMT microbiome samples. The results showed that the gut microbiome of patients exhibited a gradual but steady improvement after receiving successful FMT, indicating the restoration of the normal microbiome functions.
艰难梭菌感染（CDI）是一种胃肠道（GI）感染，它说明了宿主-微生物共生体的失调如何使胃肠道容易受到机会性病原体的侵袭。CDI 也说明了如何通过肠道微生物群调节机制，特别是粪便微生物群移植（FMT）来逆转这种失调。然而，微生物组介导的 CDI 诊断仍研究不足。在这里，我们评估了粪便微生物组在预测 CDI 方面的诊断能力。我们使用了来自之前的十项研究的宏基因组测序数据，这些研究包括接受 FMT 治疗的 CDI 患者、患有其他肠道健康问题的 CDI 阴性患者和服用抗生素的健康志愿者。我们设计了一种混合物种/功能分析方法，该方法确定了对其功能谱有贡献的群落中微生物物种的丰度。然后，这些具有功能信息的分类学特征用于对微生物样本进行分类。我们使用基于这些特征的逻辑回归（LR）模型，这些模型显示出较高的预测准确性（平均 ），证实了肠道微生物组的物种/功能组成对 CDI 具有强大的诊断预测能力。我们进一步评估了抗生素治疗对 CDI 预测的混杂影响，发现它可以与 CDI 的影响区分开来。最后，我们设计了一个由 LR 模型输出计算得出的对数比值得分，以量化肠道微生物组样本中 CDI 的可能性，并将其应用于评估基于 FMT 后微生物组样本的有效性。结果表明，接受成功 FMT 后，患者的肠道微生物组逐渐但稳定地改善，表明正常微生物组功能得到恢复。
