增强剂
微生物群
合成生物学
破译
计算生物学
钥匙(锁)
计算机科学
生化工程
生物
数据科学
风险分析(工程)
生态学
系统生物学
合理设计
适应(眼睛)
冗余(工程)
生物信息学
桥(图论)
托换
作者
Zhepu Ruan,Jialin Tan,Qingyu Feng,Kai Yang,Danning Li,Yuanqing Chao,P. N. Wang,Zhuobiao Ni,Jingjing Chen,Rongliang Qiu
标识
DOI:10.1038/s41467-025-67953-5
摘要
Abstract Environmental co-contamination presents significant challenges. To tackle these, while microbial consortia offer advantages over single-strain, such as functional redundancy and synergistic degradation, rationally designing effective synthetic microbiomes for complex co-contamination scenarios remains a major challenge. Here, we utilize our advanced genome-scale metabolic modeling (GSMM) tool, SuperCC, to simulate the metabolic behavior of communities consisting of six isolated key strains under single- and multi-carbon source conditions, mimicking single-pollutant or co-contamination scenarios respectively. By integrating multi-omics data with metabolic modeling of cultures, we systematically elucidate key strain interaction networks and adaptive strategies under co-contamination. This reveal that the specific secretory products of broad-spectrum resource-utilizing bacteria serve as key metabolites driving cooperation and highlight the pivotal role of indigenous keystone strains in stabilizing and enhancing community function. Consequently, we propose an innovative and rational paradigm for consortium design: DHP-Com (Degrader-Helper-Potentiator Consortium). Potentiators are top species with stable habitat abundance. Synthetic microbiomes built on this framework exhibit enhanced ecological fitness and substantially improve remediation performance across diverse co-contamination scenarios. Our findings advanced the practical application of GSMM predictions to decipher intricate multi-pollutant/multi-strain interaction networks, offering a powerful rational framework and robust methodological tools for engineering multi-functional and effective synthetic microbiomes for complex environmental remediation.
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