Zhang Chenhong, Zhao Liping
State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
SJTU-Perfect China Joint Center on Microbiota and Health, Shanghai, 200233, China.
Genome Med. 2016 Apr 20;8(1):41. doi: 10.1186/s13073-016-0304-1.
The gut microbiota has been linked with metabolic diseases in humans, but demonstration of causality remains a challenge. The gut microbiota, as a complex microbial ecosystem, consists of hundreds of individual bacterial species, each of which contains many strains with high genetic diversity. Recent advances in genomic and metabolomic technologies are facilitating strain-level dissection of the contribution of the gut microbiome to metabolic diseases. Interventional studies and correlation analysis between variations in the microbiome and metabolome, captured by longitudinal sampling, can lead to the identification of specific bacterial strains that may contribute to human metabolic diseases via the production of bioactive metabolites. For example, high-quality draft genomes of prevalent gut bacterial strains can be assembled directly from metagenomic datasets using a canopy-based algorithm. Specific metabolites associated with a disease phenotype can be identified by nuclear magnetic resonance-based metabolomics of urine and other samples. Such multi-omics approaches can be employed to identify specific gut bacterial genomes that are not only correlated with detected metabolites but also encode the genes required for producing the precursors of those metabolites in the gut. Here, we argue that if a causative role can be demonstrated in follow-up mechanistic studies--for example, using gnotobiotic models--such functional strains have the potential to become biomarkers for diagnostics and targets for therapeutics.
肠道微生物群已被证明与人类代谢性疾病有关,但因果关系的证明仍然是一项挑战。肠道微生物群作为一个复杂的微生物生态系统,由数百种不同的细菌物种组成,其中每种细菌都包含许多具有高度遗传多样性的菌株。基因组学和代谢组学技术的最新进展有助于从菌株水平剖析肠道微生物群对代谢性疾病的影响。通过纵向采样获得的微生物组和代谢组变化之间的干预性研究和相关性分析,可以识别出可能通过产生生物活性代谢物而导致人类代谢性疾病的特定细菌菌株。例如,可以使用基于冠层的算法直接从宏基因组数据集中组装常见肠道细菌菌株的高质量草图基因组。通过基于核磁共振的尿液和其他样本代谢组学可以识别与疾病表型相关的特定代谢物。这种多组学方法可用于识别特定的肠道细菌基因组,这些基因组不仅与检测到的代谢物相关,而且还编码在肠道中产生这些代谢物前体所需的基因。在此,我们认为,如果在后续的机制研究中(例如,使用无菌动物模型)能够证明其因果作用,那么这种功能性菌株有可能成为诊断生物标志物和治疗靶点。