2025-03-03 ペンシルベニア州立大学 (Penn State)
<関連情報>
- https://www.psu.edu/news/research/story/synthetic-microbiome-therapy-suppresses-bacterial-infection-without-antibiotics
- https://www.sciencedirect.com/science/article/pii/S1931312825000551?via%3Dihub
クロストリジウム・ディフィシル(Clostridioides difficile)耐性菌の群集機能に関する知見を提供するデザインされた合成微生物叢 A designed synthetic microbiota provides insight to community function in Clostridioides difficile resistance
Shuchang Tian, Min Soo Kim, Jingcheng Zhao, Kerim Heber, Fuhua Hao, David Koslicki, Sangshan Tian, Vishal Singh, Andrew D. Patterson, Jordan E. Bisanz
Cell Host & Microbe Available online: 3 March 2025
DOI:https://doi.org/10.1016/j.chom.2025.02.007
Graphical abstract
Highlights
- Machine learning designs microbial communities through robust cross-cohort signals
- Synthetic consortia form stable communities in vivo suppressing C. difficile
- Proline-fermenting strains are necessary and sufficient for C. difficile repression
- P. anaerobius is as efficacious as a human fecal transplant in a gnotobiotic model
Summary
Clostridioides difficile, a major cause of antibiotic-associated diarrhea, is suppressed by the gut microbiome, but the precise mechanisms are not fully described. Through a meta-analysis of 12 human studies, we designed a synthetic fecal microbiota transplant (sFMT1) by reconstructing microbial networks negatively associated with C. difficile colonization. This lab-built 37-strain consortium formed a functional community suppressing C. difficile in vitro and in animal models. Using sFMT1 as a tractable model system, we find that bile acid 7α-dehydroxylation is not a determinant of sFMT1 efficacy while one strain performing Stickland fermentation—a pathway of competitive nutrient utilization—is both necessary and sufficient for the suppression of C. difficile, replicating the efficacy of a human fecal transplant in a gnotobiotic mouse model. Our data illustrate the significance of nutrient competition in suppression of C. difficile and a generalizable approach to interrogating complex community function through robust methods to leverage publicly available sequencing data.
