Milne Caroline B, Eddy James A, Raju Ravali, Ardekani Soroush, Kim Pan-Jun, Senger Ryan S, Jin Yong-Su, Blaschek Hans P, Price Nathan D
Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, USA.
BMC Syst Biol. 2011 Aug 16;5:130. doi: 10.1186/1752-0509-5-130.
Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.
We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.
Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.
产溶剂梭菌为基于石油生产丁醇提供了一种可持续的替代方案，丁醇是一种重要的化学原料以及潜在的燃料添加剂或替代品。拜氏梭菌是用于菌株设计以提高丁醇产量的一种有吸引力的微生物，因为它：（i）作为发酵副产物自然产生有记录以来最高的丁醇浓度；以及（ii）能够共发酵戊糖和己糖（木质纤维素水解的主要产物）。使用基于约束的建模从系统角度研究拜氏梭菌的代谢，能够模拟基因改造的全局效应。
我们提出了首个针对拜氏梭菌的基因组规模代谢模型（iCM925），该模型包含925个基因、938个反应和881个代谢物。为构建该模型，我们采用了一种半自动程序，整合了来自KEGG、BioCyc和The SEED的基因组注释信息，并利用计算算法结合人工编目来提高模型的完整性。有趣的是，我们发现从这三个数据库收集的反应中只有34%的重叠——这凸显了评估所得基因组规模模型预测准确性的重要性。为验证iCM925，我们使用NCIMB 8052菌株进行了发酵实验，并评估了该模型模拟实测底物摄取和产物生成速率的能力。实验观察到的发酵曲线位于模型的解空间内；然而，在最优生长目标下，需要额外的约束条件来重现观察到的曲线——这表明存在除最优生长之外的选择压力。值得注意的是，在模拟中显著富集的一组积极利用的反应（受约束以反映实验速率）源自所有三个数据库之间重叠的反应集（P = 3.52 × 10 - 9，Fisher精确检验）。如实验观察到的那样，发现氢化酶反应的抑制对丁醇形成有强烈影响。
拜氏梭菌微生物生产丁醇为生成这种重要的化学物质和潜在生物燃料提供了一种有前景的、可持续的方法。iCM925是一个预测模型，能够准确重现生理行为并深入了解微生物丁醇生产的潜在机制。因此，该模型将有助于更好地理解和对这种微生物进行代谢工程改造以提高丁醇产量。
