Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.
Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.
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DOI：
10.1371/journal.pcbi.1000210
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发表时间：
2008-10
影响因子：
4.3
通讯作者：
Martins dos Santos VA
中科院分区：
文献类型：
--
作者：
Puchałka J;Oberhardt MA;Godinho M;Bielecka A;Regenhardt D;Timmis KN;Papin JA;Martins dos Santos VA
生物技术的一个基石是利用微生物有效生产化学品和消除有害废物。恶臭假单胞菌是此类微生物的原型，这是由于其代谢多功能性、抗胁迫性、对遗传修饰的顺从性以及环境和工业应用的巨大潜力。为了阐明恶臭假单胞菌中的代谢布线及其在生物催化中的用途，特别是用于生产非生长相关的生化物质，我们开发并在此提出了恶臭假单胞菌KT 2440代谢的基因组规模约束模型。网络重建和通量平衡分析（FBA）能够定义代谢网络的结构，识别知识缺口，并精确定位基本代谢功能，从而促进基因注释的细化。使用FBA和通量变异性分析来分析模型的性质、潜力和局限性。这些分析允许在各种条件下识别代谢的关键特征，如生长产量、资源分布、网络鲁棒性和基因重要性。该模型进行了验证，从连续的细胞培养，高通量表型数据，13 C-测量的内部通量分布，并专门产生敲除突变体的数据。在75%的病例中正确预测了肥大。这些系统的分析表明，代谢网络结构是决定预测精度的主要因素，而生物量组成的影响可以忽略不计。最后，我们利用该模型来设计代谢工程策略，以提高聚羟基链烷酸酯的产量，聚羟基链烷酸酯是一类生物技术上有用的化合物，其合成与细胞存活无关。经过可靠验证的模型对基因型-表型关系产生了有价值的见解，并为探索这种多功能细菌和利用其巨大的生物技术潜力提供了一个良好的框架。假单胞菌包括一组不同的细菌，其代谢多样性和遗传可塑性使其能够在广泛的环境中生存。该家族的许多成员能够降解有毒化合物或有效地生产高价值化合物，因此对生物修复和批量化学生产都有意义。为了更好地了解这些细菌的生长和代谢，我们开发了一个大规模的恶臭假单胞菌代谢的数学模型，恶臭假单胞菌是工业相关假单胞菌的代表。该模型最初通过基质利用数据和碳追踪数据进行了扩展和验证。接下来，该模型用于识别代谢的关键特征，如生长产量、资源的内部分布和网络鲁棒性。然后，我们使用该模型来预测生产与医疗和工业相关的生物塑料前体的新策略。这种集成的计算和实验方法可用于研究其代谢，并探索其他工业和环境重要微生物的潜力。
A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential. The pseudomonads include a diverse set of bacteria whose metabolic versatility and genetic plasticity have enabled their survival in a broad range of environments. Many members of this family are able to either degrade toxic compounds or to efficiently produce high value compounds and are therefore of interest for both bioremediation and bulk chemical production. To better understand the growth and metabolism of these bacteria, we developed a large-scale mathematical model of the metabolism of Pseudomonas putida, a representative of the industrially relevant pseudomonads. The model was initially expanded and validated with substrate utilization data and carbon-tracking data. Next, the model was used to identify key features of metabolism such as growth yield, internal distribution of resources, and network robustness. We then used the model to predict novel strategies for the production of precursors for bioplastics of medical and industrial relevance. Such an integrated computational and experimental approach can be used to study its metabolism and to explore the potential of other industrially and environmentally important microorganisms.
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影响因子：
3.2
作者：
del Castillo, Teresa;Ramos, Juan L.;Duque, Estrella
通讯作者：
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影响因子：
4.8
作者：
Goryshin, IY;Reznikoff, WS
通讯作者：
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影响因子：
2.8
作者：
Höschle, B;Gnau, V;Jendrossek, D
通讯作者：
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影响因子：
30.8
作者：
Fischer, E;Sauer, U
通讯作者：
Sauer, U
在基因组后时代制定基因组规模的动力学模型。
影响因子：
9.9
作者：
Jamshidi, Neema;Palsson, Bernhard O.
通讯作者：
Palsson, Bernhard O.