代谢建模在人类干预试验中揭示了共生疗效的决定因素
Nick Quinn-Bohmann1, Sean M Gibbons1,2,3,4
1Institute for Systems Biology, Seattle, WA 98109, USA.
微生物社区规模的代谢模型 (MCMMs) 准确地预测了共生干预的成功,预测了益生菌植入和短链脂肪酸 (SCFA) 生产. 这种代谢建模方法为个性化微生物组治疗提供了潜力.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
背景情况:
- 协生干预,结合益生菌和益生菌,产生可变的个体反应.
- 由于宿主微生物群相互作用和饮食,预测这些干预措施的成功具有挑战性.
- 微生物社区规模的代谢模型 (MCMMs) 为了解这些复杂的相互作用提供了一个有希望的方法.
研究的目的:
- 为了利用MCMM来预测益生菌植入和微生物群介导的短链脂肪酸 (SCFA) 产生的转变,在同生菌干预后.
- 为了验证MCMM预测与实验数据从一个安慰剂控制的共生试验验验证.
- 探索个性化的共生策略,以提高治疗疗效.
主要方法:
- 利用微生物社区规模的代谢模型 (MCMMs) 模拟共生干预结果.
- 使用定量PCR (qPCR) 来验证益生菌植入的模型预测.
- 分析了SCFA产量和C-反应蛋白 (CRP) 水平与共生治疗相关的变化.
- 将模型预测扩展到更大的观测队列,以探索多样化的共生组合.
主要成果:
- 在治疗组中,MCMMs准确地预测了益生菌植入,准确度超过85%.
- 植入因益生菌物种而异,其中*Akkermansia muciniphila*和*Bifidobacterium infantis*的比例更高.
- 在干预后,MCMMs预测酸盐和酸盐生产的显著增加.
- 预计酸产量的增加与C反应性蛋白 (CRP) 的减少有负相关性.
结论:
- MCMMs是预测对协生干预的个体特异性反应的有效工具.
- 代谢建模可以指导个性化的益生菌选择,以优化益生菌的有效性.
- 这种方法在开发精密微生物组疗法方面具有重大潜力.
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