Pearcy Nicole, Garavaglia Marco, Millat Thomas, Gilbert James P, Song Yoseb, Hartman Hassan, Woods Craig, Tomi-Andrino Claudio, Reddy Bommareddy Rajesh, Cho Byung-Kwan, Fell David A, Poolman Mark, King John R, Winzer Klaus, Twycross Jamie, Minton Nigel P
School of Life Sciences, University of Nottingham, Nottingham, United Kingdom.
Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
PLoS Comput Biol. 2022 May 23;18(5):e1010106. doi: 10.1371/journal.pcbi.1010106. eCollection 2022 May.
Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.
利用生物过程将可再生碳循环转化为高价值的平台化学品，为当前对石化产品的依赖提供了一种可持续且更环保的替代方案。在这方面，食酸丛毛单胞菌H16（Cupriavidus necator H16）是一个特别有前景的微生物底盘，因为它能够利用多种低成本原料生长，包括废气中的二氧化碳，同时在营养受限条件下还能自然产生大量聚羟基丁酸酯（PHB）。了解这种细菌复杂的代谢行为是设计成功的工程策略以优化产品产量的先决条件。我们提出了食酸丛毛单胞菌H16的基因组规模代谢模型（GSM，命名为iCN1361），该模型直接从BioCyc数据库构建，以提高模型的可读性和可重用性。在初始自动构建之后，我们进行了广泛的整理以及理论和实验验证。通过使用转座子导向插入位点测序（TraDIS）方法进行全基因组必需性筛选，我们表明该模型能够以高度准确性预测基因敲除表型。重要的是，我们指出了实验和计算预测如何可用于改进模型结构，从而提高模型准确性，以及评估实验中识别出的潜在假阳性。最后，通过将转录组学数据与iCN1361整合，我们创建了一个条件特异性模型，该模型重要的是能更好地反映食酸丛毛单胞菌H16中PHB的产生。然后利用组学数据中观察到的变化和通量的计算机模拟估计变化来预测关键细胞过程的调控控制。所呈现的结果表明，iCN1361是揭示食酸丛毛单胞菌H16系统水平代谢行为的有价值工具，可为设计代谢工程策略提供有用的见解。
