Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome.
Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome.
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DOI：
10.1371/journal.pcbi.1004338
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发表时间：
2015-05
影响因子：
4.3
通讯作者：
Albert R
中科院分区：
文献类型：
--
作者：
Steinway SN;Biggs MB;Loughran TP Jr;Papin JA;Albert R
我们提出了一种新的方法，从时间序列元基因组信息构建布尔动态模型，并将该模型与基因组规模的代谢网络重建相结合，以确定微生物相互作用的代谢基础。我们将其应用于一个关键的健康问题：克林霉素抗生素治疗和机会性艰难梭菌感染。我们的模型概括了克林霉素、抗生素治疗和艰难梭菌感染的已知动态，并预测了益生菌干预治疗以抑制艰难梭菌感染。基因组规模的代谢网络重建揭示了群落成员之间的代谢差异，并用于探索代谢在观察到的微生物相互作用中的作用。体外实验数据验证了我们计算模型的一个关键结果，即人肠杆菌实际上可以减缓艰难梭菌的生长。生活在我们肠道中的细菌群落(称为“肠道微生物群”)对正常的肠道功能很重要，这个群落的破坏会导致包括肥胖、糖尿病甚至神经疾病在内的疾病。艰难梭菌是一种机会性致病菌，可引起潜在威胁生命的肠炎和腹泻，经常在抗生素治疗后发生，从而消灭正常的肠道细菌群落。我们使用一个数学模型来确定正常的细菌群落是如何相互作用的，以及这个群落是如何随着抗生素治疗和艰难梭菌感染而改变的。我们使用这个模型来识别可能抑制艰难梭菌生长的细菌。我们的模型和随后的实验表明，人肠巴氏杆菌抑制艰难梭菌的生长。这一结果表明，人肠杆菌可能被用作益生菌来治疗或预防艰难梭菌感染。
We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth. The community of bacteria that live in our intestines (called the “gut microbiome”) is important to normal intestinal function, and destruction of this community has a causative role in diseases including obesity, diabetes, and even neurological disorders. Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment, which wipes out the normal intestinal bacterial community. We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C. difficile infection. We use this model to identify bacteria that may inhibit C. difficile growth. Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C. difficile growth. This result suggests that B. intestinihominis could potentially be used as a probiotic to treat or prevent C. difficile infection.
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影响因子：
64.8
作者：
Brandl, Katharina;Plitas, George;Mihu, Coralia N.;Ubeda, Carles;Jia, Ting;Fleisher, Martin;Schnabl, Bernd;DeMatteo, Ronald P.;Pamer, Eric G.
通讯作者：
Pamer, Eric G.
DOI：
10.1038/nri3535
发表时间：
2013-11
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
影响因子：
--
作者：
Albert, Istvan;Thakar, Juilee;Li, Song;Zhang, Ranran;Albert, Reka
通讯作者：
Albert, Reka
DOI：
10.1038/nrmicro1949
发表时间：
2009-02
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.7
作者：
Jump RL;Polinkovsky A;Hurless K;Sitzlar B;Eckart K;Tomas M;Deshpande A;Nerandzic MM;Donskey CJ
通讯作者：
Donskey CJ