Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile.
Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile.
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DOI：
10.1016/j.chom.2021.09.008
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发表时间：
2021-11-10
影响因子：
30.3
通讯作者：
Baliga NS
中科院分区：
文献类型：
--
作者：
Arrieta-Ortiz ML;Immanuel SRC;Turkarslan S;Wu WJ;Girinathan BP;Worley JN;DiBenedetto N;Soutourina O;Peltier J;Dupuy B;Bry L;Baliga NS
我们提出的预测模型的综合系统分析艰难梭菌，伪膜性结肠炎的病因。通过利用151个已发表的转录组，我们生成了一个EGRIN模型，该模型组织了90%的C。艰难梭菌基因的转录调控网络的297个共调节模块，涉及基因在孢子形成，碳水化合物运输和代谢。通过添加和管理代谢反应（包括营养摄取）来推进代谢模型，我们发现了14种氨基酸、多种碳水化合物和10种代谢基因对C。在肠道环境中艰难生长。最后，我们开发了一个PRIME模型，以揭示EGRIN推断的转录因子（如CcpA和CodY）的组合基因调控如何调节必要的代谢过程，以使C。相对于植物定植的艰难生长。梭difficile交互式门户网站提供了访问这些模型资源，以支持协作系统水平的研究，上下文特定的毒力机制，在C。很难C.艰难梭菌是医院获得性感染的主要原因之一。Arrieta-Ortiz等报道了三种预测模型（具有广泛的验证），用于剖析C.很难梭difficile交互式门户网站提供了访问这些模型，汇编数据集和算法。
We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems level studies of context-specific virulence mechanisms in C. difficile. C. difficile is one of the leading causes of hospital-acquired infections. Arrieta-Ortiz et al. report three predictive models (with extensive validations) for dissecting interplay of regulation and metabolism that underlies host-pathogen interactions of C. difficile. The C. difficile interactive web portal provides access to these models, compiled datasets and algorithms.
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影响因子：
9.9
作者：
Arrieta-Ortiz ML;Hafemeister C;Bate AR;Chu T;Greenfield A;Shuster B;Barry SN;Gallitto M;Liu B;Kacmarczyk T;Santoriello F;Chen J;Rodrigues CD;Sato T;Rudner DZ;Driks A;Bonneau R;Eichenberger P
通讯作者：
Eichenberger P
影响因子：
64.5
作者：
Bonneau, Richard;Facciotti, Marc T.;Baliga, Nitin S.
通讯作者：
Baliga, Nitin S.
影响因子：
3.1
作者：
Dingle, Tanis C.;Mulvey, George L.;Armstrong, Glen D.
通讯作者：
Armstrong, Glen D.
影响因子：
14.9
作者：
Bailey TL;Johnson J;Grant CE;Noble WS
通讯作者：
Noble WS
影响因子：
6.4
作者：
Dembek M;Barquist L;Boinett CJ;Cain AK;Mayho M;Lawley TD;Fairweather NF;Fagan RP
通讯作者：
Fagan RP