Integrated Microbiota–Bile Acid Analysis as Potential NonInvasive Biomarkers for Ulcerative Colitis Staging Diagnose

胆汁酸 脱氧胆酸 熊去氧胆酸 溃疡性结肠炎 胃肠病学 微生物群 失调 肠道菌群 内科学 鹅去氧胆酸 胆酸 医学 粪便 生物 疾病 微生物学 生物信息学 免疫学
作者
Tianyu He,Y. Wang,Liang Zhao,Yuan Che,Lu Zhang,Y. Zeng,Hong Shen,Haiping Hao,Lijuan Cao
出处
期刊:The FASEB Journal [Wiley]
卷期号:39 (15)
标识
DOI:10.1096/fj.202501078r
摘要

ABSTRACT Clinical staging diagnosis and progression tracking for ulcerative colitis (UC) is as challenging as poor patient compliance with endoscopic biopsy. We perform a study that integrates metabolomic profiling, 16S rRNA, and metagenomic sequencing on serum and fecal samples from 23 active state UC patients, 24 remission state UC patients, and 20 healthy volunteers from China, aiming to explore a non‐invasive integrative biochemical index to quantitatively track and monitor pathological activity of UC. Besides the known associations of microbes such as Fusobacterium nucleatum and Clostridium symbiosum with UC, we found several bile acid‐transforming species, including 7α‐dehydroxygenase and 7α/β‐dehydrogenase expressing microbiota, were significantly correlated with UC pathological activity. We identified 2 bacterial gene markers related to secondary bile acid synthesis besides Clostridium scindens that differentiated active and remission stage UC and healthy control microbiomes. Relevantly, reduced serum deoxycholic acid (DCA)/cholic acid (CA) species ratio and increased fecal ursodeoxycholic acid (UDCA)/chenodeoxycholic acid (CDCA) ratio were associated with the pathological activity of UC. Moreover, receiver operating characteristic analysis based on serum/fecal bile acid ratios was much more accurate in the prediction of active and remission stage outcomes. This species‐specific temporal change and bile acid dysregulation pattern linked to disease severity indicate that integrated microbiome–bile acid profiles may be implied for disease activity prediction and that targeting microbiome‐mediated gut flora and bile acid homeostasis may be implicative of therapy efficacy. These insights will help improve clinical diagnosis and optimize existing medical treatments.
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