用肠道代谢物预测艰难梭菌感染复发
创作:柚子表妹 审核:mildbreeze 06月15日
  • 纳入53名非免疫受损的原发性艰难梭菌感染患者,分析抗生素治疗前后的肠道菌群和代谢组变化,及其与复发的关系;
  • 复发患者的菌群多样性和组成的恢复减缓,一些重要的厌氧菌类群(如梭菌属XIVa和IV)耗竭;
  • 复发患者的肠道代谢组改变,特点为菌群代谢功能(如去结合作用)降低、宿主炎症/肠道损伤、免疫调节能力降低(如抗炎代谢物和内源性大麻素减少);
  • 机器学习模型可基于治疗后2周内的代谢物数据预测后续的复发情况(AUC 0.77)。
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mildbreeze
艰难梭菌感染(CDI)患者中,有一小部分在抗生素治疗后会复发。Microbiome近期发表研究,对53名CDI患者进行了前瞻性的多时间点采样,揭示了与CDI复发相关的肠道菌群和肠道代谢组特征,表明治疗后的特定肠道代谢物(而非肠道菌群)数据可用于预测后续的复发风险。
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Microbiome [IF:16.837]

Gut metabolites predict Clostridioides difficile recurrence

肠道代谢物预测艰难梭菌复发

10.1186/s40168-022-01284-1

06-09, Article

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Background: Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence.
Results: We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to 8 weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at 1 week (AUC 0.77 [0.71, 0.86; 95% interval]) and 2 weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI.
Conclusions: The prospective, longitudinal, and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.

First Authors:
Jennifer J Dawkins,Jessica R Allegretti

Correspondence Authors:
Georg K Gerber

All Authors:
Jennifer J Dawkins,Jessica R Allegretti,Travis E Gibson,Emma McClure,Mary Delaney,Lynn Bry,Georg K Gerber

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