The evolution of metabolic networks of E. coli.
The evolution of metabolic networks of E. coli.
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DOI：
10.1186/1752-0509-5-182
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发表时间：
2011-11-01
影响因子：
--
通讯作者：
Perna NT
中科院分区：
文献类型：
--
作者：
Baumler DJ;Peplinski RG;Reed JL;Glasner JD;Perna NT
尽管有大量来自大肠杆菌菌株的完整基因组序列，但已发表的基因组规模的代谢模型只存在于两个共生的大肠杆菌菌株。这些模型已被证明在许多应用中是有用的，例如用于所需产品形成的工程菌株，我们试图探索如何为大肠杆菌菌株构建和评估额外的代谢模型可以加强这些努力。我们使用来自16个大肠杆菌菌株的基因组信息，通过评估它们的76,990个ORF来生成一个大肠杆菌泛基因组代谢网络。这些ORF中的每一个都被分配到17,647个同源基因组中的一个，其中包括与大肠杆菌K-12最新代谢模型中的反应相关的ORF。对于包含已在MG1655模型中表示的ORF的同源基团，该模型中表示的基因-蛋白质-反应关联随后可以很容易地传播到其他大肠杆菌菌株模型。评估所有剩余的同源基因组，看是否可以添加新的代谢反应来生成泛基因组规模的代谢模型(IEco1712_PAN)。Pangenome模型包括来自E.ColiK-12 MG1655(IEco1339_MG1655)代谢模型更新的反应，并使另外五个特定菌株基因组规模的代谢模型得以开发。这些额外的模型包括第二个K-12菌株(IEco1335_W3110)和四个致病菌株(两个肠出血性大肠杆菌O157：H7和两个尿路病原体)。与大肠杆菌K-12模型相比，肠出血性(iEco1344_EDL933和iEco1345_Sakai)和泌尿系致病株(iEco1288_CFT073和iEco1301_UTI89)的代谢模型包含了许多谱系特异性的基因和反应差异。通过将模型预测与好氧和厌氧条件下的碳源利用测量进行比较，以及在含0.2%(w/v)葡萄糖的最低限度培养基中批处理生长曲线，对所有六个大肠杆菌模型进行了评估。构建了一个基于所有16个大肠杆菌基因组中保守的同源基团的祖先基因组规模的代谢模型，反映了大肠杆菌代谢的保守祖先核心(IEco1053_Core)。对所有六个菌株特定的大肠杆菌模型的比较分析表明，一些致病性大肠杆菌菌株在其代谢网络中进行了反应，使葡萄糖获得了更高的生物量。最后，将谱系特定的代谢特征与祖先核心模型的预测进行比较，以获得对该物种新陈代谢进化的新见解。我们的发现表明，一个庞大的代谢模型可以用来快速构建额外的大肠杆菌菌株特异性模型，并且不同菌株的定量模型可以准确地预测菌株特异性表型。这样的pangenome和菌株特异性模型可以进一步用于设计感兴趣的代谢表型，例如设计新的工业大肠杆菌菌株。
Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts. We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale metabolic model based on conserved ortholog groups in all 16 E. coli genomes was also constructed, reflecting the conserved ancestral core of E. coli metabolism (iEco1053_core). Comparative analysis of all six strain-specific E. coli models revealed that some of the pathogenic E. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species. Our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional E. coli strain-specific models, and that quantitative models of different strains of E. coli can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial E. coli strains.
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影响因子：
5.4
作者：
Feist, Adam M.;Palsson, Bernhard O.
通讯作者：
Palsson, Bernhard O.
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：
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影响因子：
3.2
作者：
Fuhrer, Tobias;Chen, Lifeng;Vitkup, Dennis
通讯作者：
Vitkup, Dennis
影响因子：
14.8
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
通讯作者：
Herrgard, Markus J.
影响因子：
9.9
作者：
通讯作者：
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