Department of Biology, University of Padova, Padua, Italy.
School of Medicine, University of Galway, Galway, Ireland.
Gut Microbes. 2023 Jan-Dec;15(1):2226921. doi: 10.1080/19490976.2023.2226921.
We report the first use of constraint-based microbial community modeling on a single individual with episodic inflammation of the gastrointestinal tract, who has a well documented set of colonic inflammatory biomarkers, as well as metagenomically-sequenced fecal time series covering seven dates over 16 months. Between the first two time steps the individual was treated with both steroids and antibiotics. Our methodology enabled us to identify numerous time-correlated microbial species and metabolites. We found that the individual's dynamical microbial ecology in the disease state led to time-varying overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. The microbe-metabolite contribution analysis revealed that some species changed metabolic pathways according to the inflammation phases. At the first time point, characterized by the highest levels of serum (complex reactive protein) and fecal (calprotectin) inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic strains and . We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of in the gut microbiome influenced the sulfur metabolism with a domino effect affecting the liver. These results revealed large longitudinal variations in an individual's gut microbiome ecology and metabolite production, potentially impacting other organs in the body. Future simulations with more time points from an individual could permit us to assess how external drivers, such as diet change or medical interventions, drive microbial community dynamics.
我们报告了首次在一位患有胃肠道炎症发作的个体上使用基于约束的微生物群落建模,该个体具有一套记录完善的结肠炎症生物标志物,以及涵盖 16 个月内七个日期的粪便宏基因组测序时间序列。在最初的两个时间点之间,该个体接受了类固醇和抗生素治疗。我们的方法使我们能够识别出许多时间相关的微生物物种和代谢物。我们发现,与健康对照相比,个体在疾病状态下的动态微生物生态学导致了超过 24 种重要生物代谢物的时间变化过度产生,包括甲烷、硫胺素、甲醛、三甲胺 N-氧化物、叶酸、血清素、组胺和色胺。微生物-代谢物贡献分析表明,一些 物种根据炎症阶段改变了代谢途径。在第一个时间点,以血清(复杂反应蛋白)和粪便(钙卫蛋白)炎症生物标志物的最高水平为特征,它们产生 L-丝氨酸或甲酸盐。通过级联效应产生的化合物通过与致病性 菌株和 的相互作用介导。我们将每个时间点的微生物群落代谢模型与男性全身、器官分辨率的人体代谢模型集成,以追踪不同身体部位的菌群失调的代谢后果。肠道微生物组中 的存在通过影响肝脏的多米诺骨牌效应影响硫代谢。这些结果揭示了个体肠道微生物组生态学和代谢产物产生的巨大纵向变化,可能对体内其他器官产生影响。来自个体的更多时间点的未来模拟可以使我们评估外部驱动因素(如饮食变化或医疗干预)如何驱动微生物群落动力学。