(174bj) Understanding Mutant Strategies of Clostridium Tyrobutyricum Using a Systems Identification-Based Framework for Genome-Scale Metabolic Model Analysis.

For sustainable biosynthesis of C4 and C4-derived chemicals, Clostridium tyrobutyricum has drawn increasing research attention for its native production of butyrate. However, the prevalence of C2 byproducts, namely acetate (with some lactate and ethanol), hinders C. tyrobutyricum’s economic viability for industrial applications. To improve the yield of desired C4 product, various mutant strategies have been tested, as some are shared in Table 1. Here, existing mutant strategies are mostly intuition-driven, such as enhance main product synthesis (e.g., addition of aldehyde-alcohol dehydrogenase), reducing byproduct synthesis (e.g., delete or downgrade acetate kinase), improve redox balance (e.g., addition of reversible hydrogenase). There is a lack of systems understanding on how these mutant strategies affect the global cellular metabolism and how the internal redox shifts lead to different product yields.

Genome-scale models (GEM’s) are linear reaction network models built from an organism’s genome. They have been used to predict cellular phenotypes, facilitate mutant design through in-silico experiments, and provide a platform for multi-omics data integration. In this work, we use GEM’s of C. tyrobutyricum to understand different mutant strategies and identify the key bottlenecks that limit the yield of desired C4 products.

Currently there are two GEM available for C. tyrobutyricum. The first is iKB917 developed by our group [1] based on the published model for C. beijerinckii [2]. The other GEM is iCT583 [3] which was constructed directly from C. tyrobutyricum’s genome. In our previous work, we presented a comprehensive evaluation of both models using published experimental data and knowledge [4]. In this work, we first performed additional curation of both models. An in-house MATLAB script unveiled that iKB917 and iCT583 contained 440 and 367 dead-end reactions, respectively, that were unresolved by gap-filling methods. When performing flux balance analysis (FBA), these reactions will always be inactivated due to the steady-state assumption utilized in FBA. Therefore, we removed these reactions and named the resulting model iKB_red and iCT_red, respectively. iKB917 contains 449 metabolites and 601 reactions, while iCT583 contains 431 metabolites and 571 reactions.

In this work, we utilized the reduced GEM’s to analyze the different mutant strategies listed in Table 1, following the system identification-based (SID-based) framework we developed in the past [5,6]. To understand how the mutant strategies affect the carbon flow through the central metabolic network, we first designed a series of in-silica experiments to gradually force increased flux through the modified reaction pathways with each condition corresponding to a given flux value. The flux profile of the whole reaction network is determined via FBA for each condition. These simulations resulted in a flux matrix, with columns corresponding to each condition and rows corresponding to different reactions. Next, the resulted flux matrix is subject to Principal Component Analysis (PCA), which extracts the information on how each reaction pathways in the whole metabolic network is affected by the mutant strategy. This information is reflected in the PCA loadings. Finally, these PCA loadings were visualized on a network map, clearly displaying how the mutant strategy will affect the carbon flow, redox and energy balance in the cellular metabolic network.

By analyzing each mutant strategy using the SID-based framework, we can obtain a systems-level understanding on what pathways are affected by the introduced genetic perturbation. In the past, it has been hypothesized that cofactor balancing, particularly NADH/NAD⁺ and ferredoxin redox, play a paramount role desired product yields. This work provides a clear picture on how these redox-related pathways and hydrogen production are coupled together to achieve the overall redox balance. This redox balance is critical for understanding the limitations for C4 chemical production. In addition, the effects of pH were tested using both GEM’s since proton motive forces and hydrogen gas production are closely tied to ferredoxin regulation. This analysis further suggests that optimal pH conditions are critical for high yields of C4 products.

References

[1] Badr, K., He, Q. P. & Wang, J. (2022). Knowledge-matching based computational framework for genome-scale metabolic model refinement. Computer Aided Chemical Engineering, 919–924. https://doi.org/10.1016/b978-0-323-85159-6.50153-6

[2] Milne, C. B., Eddy, J. A., Raju, R., Ardekani, S., Kim, P.-J., Senger, R. S., Jin, Y.-S., Blaschek, H. P. & Price, N. D. (2011). Metabolic Network Reconstruction and genome-scale model of butanol-producing strain clostridium beijerinckii NCIMB 8052. BMC Systems Biology, 5(1). https://doi.org/10.1186/1752-0509-5-130

[3] Feng, J., Guo, X., Cai, F., Fu, H. & Wang, J. (2022). Model-based driving mechanism analysis for butyric acid production in Clostridium tyrobutyricum. Biotechnology for Biofuels and Bioproducts, 15(1). https://doi.org/10.1186/s13068-022-02169-z

[4] Murphy, L., Wang, Y. & Wang, J. (Nov 7, 2023). Systematic Evaluation of Two Genome-Scale Models for Clostridium tyrobutyricum through Knowledge Matching. 2023 AIChE Annual Meeting. Orlando, FL, USA.

[5] Damiani, A. L., He, Q. P., Jeffries, T. W., & Wang, J. (2015). Comprehensive evaluation of two genome-scale metabolic network models for Scheffersomyces stipitis. Biotechnology and Bioengineering, 112(6), 1250–1262. https://doi.org/10.1002/bit.25535

[6] Hilliard, M., Damiani, A., He, Q.P., Jeffries, T. & Wang, J. (2018). Elucidating redox balance shift in Scheffersomyces stipitis’ fermentative metabolism using a modified genome-scale metabolic model. Microbial Cell Factories, 17, 140. https://doi.org/10.1186/s12934-018-0983-y.

References in Table 1

[1] Lee, J., Jang, Y.S., Han, M.J., Kim, J.Y., & Lee, S.Y. (2016). Deciphering Clostridium tyrobutyricum Metabolism Based on the Whole-Genome Sequence and Proteome Analyses. mBio, 7(3). https://doi.org/10.1128/mbio.00743-16.

[2] Feng, J., Guo, X., Cai, F., Fu, H., & Wang, J. (2022). Model-based driving mechanism analysis for butyric acid production in Clostridium tyrobutyricum. Biotechnology for Biofuels and Bioproducts, 15(1). https://doi.org/10.1186/s13068-022-02169-z.

[3] Ma, C., Ou, J., Xu, N., Fierst, J.S., Yang, S.T., Liu, X. (2015). Rebalancing Redox to Improve Biobutanol Production by Clostridium tyrobutyricum. Bioengineering, 3(1), 2. https://doi.org/10.3390/bioengineering3010002.

[4] Liu, X., Zhu, Y., & Yang, S.T. (2006). Butyric acid and hydrogen production by Clostridium tyrobutyricum ATCC 25755 and mutants. Enzyme and Microbial Technology, 38(3), 521-528. https://doi.org/10.1016/j.enzmictec.2005.07.008.

[5] Li, W., Cheng, C., Cao, G., Yang, S.T., & Ren, N. (2020). Comparative transcriptome analysis of Clostridium tyrobutyricum expressing a heterologous uptake hydrogenase. Science of the Total Environment, 749, 142022. https://doi.org/10.1016/j.scitotenv.2020.142022.

[6] Yu, M., Zhang, Y., Tang, I.C. & Yang, S.T. (2011). Metabolic engineering of Clostridium tyrobutyricum for n-butanol production. Metabolic Engineering, 13(4), 373-382. https://doi.org/10.1016/j.ymben.2011.04.002.

[7] Zhang, J., Zong, W., Hong, W., Zhang, Z.T. & Wang, Y. (2018). Exploiting endogenous CRISPR-Cas system for multiplex genome editing in Clostridium tyrobutyricum and engineer the strain of high-level butanol production. Metabolic Engineering, 47, 49-59. https://doi.org/10.1016/j.ymben.2018.03.007.

[8] Ma, C., Kojima, K., Xu, N., Mobley, J., Zhou, L., Yang, S.T. & Liu, X. (2015). Comparative proteomics analysis of high n-butanol producing metabolically engineered Clostridium tyrobutyricum. Journal of Biotechnology, 193, 108-119. https://doi.org/10.1016/j.jbiotec.2014.10.036.

[9] Guo, X., Li, X., Feng, J., Yue, Z., Fu, H. & Wang, J. (2024). Engineering of Clostridium tyrobutyricum for butyric acid and butyl butyrate production from cassava starch. Bioresource Technology, 391(A), 129914. https://doi.org/10.1016/j.biortech.2023.129914.