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Hou C, Song X, Xiong Z, Wang G, Xia Y, Ai L. Investigating the Role of β-Disodium Glycerophosphate and Urea in Promoting Growth of Streptococcus thermophilus from Omics-Integrated Genome-Scale Models. Foods 2024; 13:1006. [PMID: 38611312 PMCID: PMC11011449 DOI: 10.3390/foods13071006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024] Open
Abstract
This study investigates the impact of urea and β-GP on the growth of Streptococcus thermophilus S-3, a bacterium commonly used in industrial fermentation processes. Through a series of growth experiments, transcriptome, metabolome, and omics-based analyses, the research demonstrates that both urea and β-GP can enhance the biomass of S. thermophilus, with urea showing a more significant effect. The optimal urea concentration for growth was determined to be 3 g/L in M17 medium. The study also highlights the metabolic pathways influenced by urea and β-GP, particularly the galactose metabolism pathway, which is crucial for cell growth when lactose is the substrate. The integration of omics data into the genome-scale metabolic model of S. thermophilus, iCH502, allowed for a more accurate prediction of metabolic fluxes and growth rates. The study concludes that urea can serve as a viable substitute for β-GP in the cultivation of S. thermophilus, offering potential cost and efficiency benefits in industrial fermentation processes. The findings are supported by validation experiments with 11 additional strains of S. thermophilus, which showed increased biomass in UM17 medium.
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Affiliation(s)
| | | | | | | | | | - Lianzhong Ai
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (C.H.); (X.S.); (Z.X.); (G.W.); (Y.X.)
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Hou C, Song X, Xiong Z, Wang G, Xia Y, Ai L. Genome-scale reconstruction of the metabolic network in Streptococcus thermophilus S-3 and assess urea metabolism. J Sci Food Agric 2024; 104:1458-1469. [PMID: 37814322 DOI: 10.1002/jsfa.13026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 08/16/2023] [Accepted: 10/01/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Streptococcus thermophilus is an important strain widely used in dairy fermentation, with distinct urea metabolism characteristics compared to other lactic acid bacteria. The conversion of urea by S. thermophilus has been shown to affect the flavor and acidification characteristics of milk. Additionally, urea metabolism has been found to significantly increase the number of cells and reduce cell damage under acidic pH conditions, resulting in higher activity. However, the physiological role of urea metabolism in S. thermophilus has not been fully evaluated. A deep understanding of this metabolic feature is of great significance for its production and application. Genome-scale metabolic network models (GEMs) are effective tools for investigating the metabolic network of organisms using computational biology methods. Constructing an organism-specific GEM can assist us in comprehending its characteristic metabolism at a systemic level. RESULTS In the present study, we reconstructed a high-quality GEM of S. thermophilus S-3 (iCH492), which contains 492 genes, 608 metabolites and 642 reactions. Growth phenotyping experiments were employed to validate the model both qualitatively and quantitatively, yielding satisfactory predictive accuracy (95.83%), sensitivity (93.33%) and specificity (100%). Subsequently, a systematic evaluation of urea metabolism in S. thermophilus was performed using iCH492. The results showed that urea metabolism reduces intracellular hydrogen ions and creates membrane potential by producing and transporting ammonium ions. This activation of glycolytic fluxes and ATP synthase produces more ATP for biomass synthesis. The regulation of fluxes of reactions involving NAD(P)H by urea metabolism improves redox balance. CONCLUSION Model iCH492 represents the most comprehensive knowledge-base of S. thermophilus to date, serving as a potent tool. The evaluation of urea metabolism led to novel insights regarding the role of urease. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Chengjie Hou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xin Song
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhiqiang Xiong
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Guangqiang Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yongjun Xia
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lianzhong Ai
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Wu P, Yuan Q, Cheng T, Han Y, Zhao W, Liao X, Wang L, Cai J, He Q, Guo Y, Zhang X, Lu F, Wang J, Ma H, Huang Z. Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan. Front Microbiol 2023; 14:1277847. [PMID: 38053556 PMCID: PMC10694236 DOI: 10.3389/fmicb.2023.1277847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/01/2023] [Indexed: 12/07/2023] Open
Abstract
Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the Acidithiobacillus genus, along with the process of energy production, remain areas that necessitate further research and elucidation. In this study, a novel strain of sulfur-oxidizing bacterium, Acidithiobacillus Ameehan, was isolated. Several physiological characteristics of the strain Ameehan were verified and its complete genome sequence was presented in the study. Besides, the first genome-scale metabolic network model (AMEE_WP1377) was reconstructed for Acidithiobacillus Ameehan to gain a comprehensive understanding of the metabolic capacity of the strain.The characteristics of Acidithiobacillus Ameehan included morphological size and an optimal growth temperature range of 37-45°C, as well as an optimal growth pH range of pH 2.0-8.0. The microbe was found to be capable of growth when sulfur and K2O6S4 were supplied as the energy source and electron donor for CO2 fixation. Conversely, it could not utilize Na2S2O3, FeS2, and FeSO4·7H2O as the energy source or electron donor for CO2 fixation, nor could it grow using glucose or yeast extract as a carbon source. Genome annotation revealed that the strain Ameehan possessed a series of sulfur oxidizing genes that enabled it to oxidize elemental sulfur or various reduced inorganic sulfur compounds (RISCs). In addition, the bacterium also possessed carbon fixing genes involved in the incomplete Calvin-Benson-Bassham (CBB) cycle. However, the bacterium lacked the ability to oxidize iron and fix nitrogen. By implementing a constraint-based flux analysis to predict cellular growth in the presence of 71 carbon sources, 88.7% agreement with experimental Biolog data was observed. Five sulfur oxidation pathways were discovered through model simulations. The optimal sulfur oxidation pathway had the highest ATP production rate of 14.81 mmol/gDW/h, NADH/NADPH production rate of 5.76 mmol/gDW/h, consumed 1.575 mmol/gDW/h of CO2, and 1.5 mmol/gDW/h of sulfur. Our findings provide a comprehensive outlook on the most effective cellular metabolic pathways implicated in sulfur oxidation within Acidithiobacillus Ameehan. It suggests that the OMP (outer-membrane proteins) and SQR enzymes (sulfide: quinone oxidoreductase) have a significant impact on the energy production efficiency of sulfur oxidation, which could have potential biotechnological applications.
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Affiliation(s)
- Peng Wu
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Qianqian Yuan
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Tingting Cheng
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Yifan Han
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Wei Zhao
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Xiaoping Liao
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Lu Wang
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Jingyi Cai
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Qianqian He
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Ying Guo
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Xiaoxia Zhang
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Fuping Lu
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
| | - Jingjing Wang
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Hongwu Ma
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Zhiyong Huang
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
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Zhou J, Liu P, Xia J, Zhuang Y. [Advances in the development of constraint-based genome-scale metabolic network models]. Sheng Wu Gong Cheng Xue Bao 2021; 37:1526-1540. [PMID: 34085441 DOI: 10.13345/j.cjb.200498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.
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Affiliation(s)
- Jingru Zhou
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Peng Liu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
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Zhang C, Wu Y, Xu X, Lv X, Li J, Du G, Liu L. [Current status and future perspectives of metabolic network models of industrial microorganisms]. Sheng Wu Gong Cheng Xue Bao 2021; 37:860-873. [PMID: 33783155 DOI: 10.13345/j.cjb.200640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Genome-scale metabolic network model (GSMM) is an extremely important guiding tool in the targeted modification of industrial microbial strains, which helps researchers to quickly obtain industrial microbes with specific traits and has attracted increasing attention. Here we reviewe the development history of GSMM and summarized the construction method of GSMM. Furthermore, the development and application of GSMM in industrial microorganisms are elaborated by using four typical industrial microorganisms (Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae) as examples. In addition, prospects in the development trend of GSMM are proposed.
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Affiliation(s)
- Chenyang Zhang
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Yaokang Wu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Xianhao Xu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Xueqin Lv
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Jianghua Li
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Long Liu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, Jiangsu, China.,Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
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Sui Y, Ouyang L, Lu H, Zhuang Y, Zhang S. [Progress in omics research of Aspergillus niger]. Sheng Wu Gong Cheng Xue Bao 2016; 32:1010-1025. [PMID: 29022303 DOI: 10.13345/j.cjb.150513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aspergillus niger, as an important industrial fermentation strain, is widely applied in the production of organic acids and industrial enzymes. With the development of diverse omics technologies, the data of genome, transcriptome, proteome and metabolome of A. niger are increasing continuously, which declared the coming era of big data for the research in fermentation process of A. niger. The data analysis from single omics and the comparison of multi-omics, to the integrations of multi-omics based on the genome-scale metabolic network model largely extends the intensive and systematic understanding of the efficient production mechanism of A. niger. It also provides possibilities for the reasonable global optimization of strain performance by genetic modification and process regulation. We reviewed and summarized progress in omics research of A. niger, and proposed the development direction of omics research on this cell factory.
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Affiliation(s)
- Yufei Sui
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Liming Ouyang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Hongzhong Lu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Siliang Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
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