代谢组
生物
微生物群
代谢物
代谢组学
失调
移植
转录组
生物信息学
遗传学
医学
生物化学
内科学
基因
基因表达
作者
Emma Lauder,E. Anders Kiledal,Laure Maneix,Teal Furnholm,Ana Santibanez,Dongchang Zhao,Yaping Sun,Gregory J. Dick,Pavan Reddy
出处
期刊:Blood
[American Society of Hematology]
日期:2025-02-26
卷期号:145 (23): 2774-2787
被引量:3
标识
DOI:10.1182/blood.2024025924
摘要
Abstract Microbial dysbiosis and metabolite changes in the gastrointestinal (GI) tract have been linked to pathogenesis and severity of many diseases, including graft-versus-host disease (GVHD), the major complication of allogeneic hematopoietic stem cell transplantation. However, published studies have only considered the microbiome and metabolome of excreted stool and do not provide insight into the variability of the microbial community and metabolite composition throughout the GI tract or the unique temporal dynamics associated with different gut locations. Because such geographical variations are known to influence disease processes, we used a multi-omics approach to characterize the microbiome and metabolite profiles of gut contents from different intestinal regions in well-characterized mouse models of GVHD. Our analysis validated analyses from excreted stool, but importantly, uncovered new biological insights from the microbial and metabolite changes between syngeneic and allogeneic hosts that varied by GI location and time after transplantation. Our integrated analysis confirmed the involvement of known metabolic pathways, including short-chain fatty acid synthesis and bile acid metabolism, and identified additional functional genes, pathways, and metabolites, such as amino acids, fatty acids, and sphingolipids, linked to GI GVHD. Finally, we validated a biological relevance for one such newly identified microbial metabolite, phenyl lactate, that heretofore had not been linked to GI GVHD. Thus, our analysis of the geographic variability in the intestinal microbiome and metabolome offers new insights into GI GVHD pathogenesis and potential for novel therapeutics.
科研通智能强力驱动
Strongly Powered by AbleSci AI