Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.
Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.
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DOI：
10.1002/bit.26380
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发表时间：
2017-11
影响因子：
3.8
通讯作者：
Oliver SG
中科院分区：
文献类型：
--
作者：
Cankorur-Cetinkaya A;Dikicioglu D;Oliver SG
基因组规模的代谢模型是设计新型工业微生物菌株的有价值的工具，例如Komagataella phaffii（syn.巴斯德毕赤酵母）。然而，与许多工业微生物的情况一样，K. phaffiii通过提供代谢物和反应ID来确认当前标准，以促进模型扩展和重复使用，以及基因反应关联，从而能够识别遗传操作的靶标。为了弥补这一不足，我们决定重建K. phaffii通过协调现存的模型和执行广泛的手动管理，以构建符合当前标准的可执行模型（Kp.1.0）。然后，我们使用该模型来研究生物质组成对模型预测成功的影响。十二种不同的生物质组合物，从公布的经验数据，在一系列的生长条件下获得的调查。我们发现，成功的Kp1.0在预测基因的必要性和生长特性是相对不受生物量组成。然而，我们发现生物质组成对脂质，DNA和类固醇生物合成过程，细胞醇代谢过程和氧化还原过程中所涉及的通量的分布有深远的影响。此外，我们研究了生物量组成对菌株开发的合适靶基因的鉴定的影响。分析显示，约40%的基因过表达或缺失的影响预测根据模型中生物量组成的表示而变化。考虑到计算机模拟通量分布对变化的生物量表示的鲁棒性，能够更好地解释实验结果，降低错误目标识别的风险，从而加快并改善定向菌株开发的过程。
Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.
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影响因子：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium
Miriam Resources：生成和解决系统生物学中强大的交叉引用的工具。
影响因子：
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作者：
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通讯作者：
Le Novere, Nicolas
影响因子：
14.9
作者：
Hastings J;de Matos P;Dekker A;Ennis M;Harsha B;Kale N;Muthukrishnan V;Owen G;Turner S;Williams M;Steinbeck C
通讯作者：
Steinbeck C
影响因子：
3.2
作者：
Foerster, Jan;Halbfeld, Christoph;Blank, Lars M.
通讯作者：
Blank, Lars M.
影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
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