从宏基因组数据到个性化计算微生物群：预测克罗恩病的膳食补充剂
From metagenomic data to personalized computational microbiotas: Predicting dietary supplements for Crohn's disease
摘要：
克罗恩病（CD）与肠道微生物群的生态失衡有关，肠道微生物群由数百种物种组成。潜在的复杂性以及患者之间的个体差异导致难以定义标准化治疗。计算建模可以系统地研究肠道微生物之间的代谢相互作用，以揭示新的机制见解。在这项研究中，我们将CD患者和健康对照的宏基因组数据与基因组规模的代谢模型整合到个性化的计算机微生物学中。我们预测了患者和对照组的短链脂肪酸（SFCA）水平，这与实验结果总体一致。作为一种紧急性质，CD患者的SCFA浓度预计较低，并且SCFA特征对每位患者都是独特的。因此，我们建议个性化的饮食治疗，可以提高每个患者的SCFA水平。潜在的建模方法可以帮助临床实践找到新的饮食治疗方法，并通过合理地建议食物营养来指导康复。
英文摘要：
Crohn's disease (CD) is associated with an ecological imbalance of the intestinal microbiota, consisting of hundreds of species. The underlying complexity as well as individual differences between patients contributes to the difficulty to define a standardized treatment. Computational modeling can systematically investigate metabolic interactions between gut microbes to unravel novel mechanistic insights. In this study, we integrated metagenomic data of CD patients and healthy controls with genome-scale metabolic models into personalized in silico microbiotas. We predicted short chain fatty acid (SFCA) levels for patients and controls, which were overall congruent with experimental findings. As an emergent property, low concentrations of SCFA were predicted for CD patients and the SCFA signatures were unique to each patient. Consequently, we suggest personalized dietary treatments that could improve each patient's SCFA levels. The underlying modeling approach could aid clinical practice to find novel dietary treatment and guide recovery by rationally proposing food aliments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
