1
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Wu S, Qu Z, Chen D, Wu H, Caiyin Q, Qiao J. Deciphering and designing microbial communities by genome-scale metabolic modelling. Comput Struct Biotechnol J 2024; 23:1990-2000. [PMID: 38765607 PMCID: PMC11098673 DOI: 10.1016/j.csbj.2024.04.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
Abstract
Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Zheping Qu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Hao Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Qinggele Caiyin
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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2
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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3
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Zhang X, Liu J, Yang F, Zhang Q, Yang Z, Shah HA. Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search. Comput Biol Chem 2024; 111:108106. [PMID: 38833912 DOI: 10.1016/j.compbiolchem.2024.108106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 06/06/2024]
Abstract
Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search. The HybridMLP predicts precursors that will produce the target NPs, while the AND-OR tree generates the iterative multi-step biosynthetic pathways. The one-step bioretrosynthesis prediction experiments are conducted on MetaNetX dataset by using HybridMLP, which achieves 46.5%, 74.6%, 81.6% in terms of the top-1, top-5, top-10 accuracies. The great performance demonstrates the effectiveness of HybridMLP in one-step bioretrosynthesis. Besides, the evaluation of two benchmark datasets reveals that BioRetro can significantly improve the speed and success rate in predicting biosynthesis pathways. In addition, the BioRetro is further shown to find the synthetic pathway of compounds, such as ginsenoside F1 with the same substrates as reported but different enzymes, which may be the novel potential enzyme to have better catalytic performance.
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Affiliation(s)
- Xiaolei Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Qiang Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Hayat Ali Shah
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
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4
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Song Y, Prather KLJ. Strategies in engineering sustainable biochemical synthesis through microbial systems. Curr Opin Chem Biol 2024; 81:102493. [PMID: 38971129 DOI: 10.1016/j.cbpa.2024.102493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/24/2024] [Accepted: 06/05/2024] [Indexed: 07/08/2024]
Abstract
Growing environmental concerns and the urgency to address climate change have increased demand for the development of sustainable alternatives to fossil-derived fuels and chemicals. Microbial systems, possessing inherent biosynthetic capabilities, present a promising approach for achieving this goal. This review discusses the coupling of systems and synthetic biology to enable the elucidation and manipulation of microbial phenotypes for the production of chemicals that can substitute for petroleum-derived counterparts and contribute to advancing green biotechnology. The integration of artificial intelligence with metabolic engineering to facilitate precise and data-driven design of biosynthetic pathways is also discussed, along with the identification of current limitations and proposition of strategies for optimizing biosystems, thereby propelling the field of chemical biology towards sustainable chemical production.
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Affiliation(s)
- Yoseb Song
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kristala L J Prather
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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5
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Zeng T, Jin Z, Zheng S, Yu T, Wu R. Developing BioNavi for Hybrid Retrosynthesis Planning. JACS AU 2024; 4:2492-2502. [PMID: 39055138 PMCID: PMC11267531 DOI: 10.1021/jacsau.4c00228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024]
Abstract
Illuminating synthetic pathways is essential for producing valuable chemicals, such as bioactive molecules. Chemical and biological syntheses are crucial, and their integration often leads to more efficient and sustainable pathways. Despite the rapid development of retrosynthesis models, few of them consider both chemical and biological syntheses, hindering the pathway design for high-value chemicals. Here, we propose BioNavi by innovating multitask learning and reaction templates into the deep learning-driven model to design hybrid synthesis pathways in a more interpretable manner. BioNavi outperforms existing approaches on different data sets, achieving a 75% hit rate in replicating reported biosynthetic pathways and displaying superior ability in designing hybrid synthesis pathways. Additional case studies further illustrate the potential application of BioNavi in a de novo pathway design. The enhanced web server (http://biopathnavi.qmclab.com/bionavi/) simplifies input operations and implements step-by-step exploration according to user experience. We show that BioNavi is a handy navigator for designing synthetic pathways for various chemicals.
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Affiliation(s)
- Tao Zeng
- School
of Pharmaceutical Sciences, Sun Yat-sen
University, Guangzhou 510006, P. R. China
| | - Zhehao Jin
- Center
for Synthetic Biochemistry, CAS Key Laboratory of Quantitative Engineering
Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
(CAS), Shenzhen 518055, P. R. China
| | - Shuangjia Zheng
- Global
Institute of Future Technology, Shanghai
Jiao Tong University, Shanghai 200240, P. R. China
| | - Tao Yu
- Center
for Synthetic Biochemistry, CAS Key Laboratory of Quantitative Engineering
Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
(CAS), Shenzhen 518055, P. R. China
| | - Ruibo Wu
- School
of Pharmaceutical Sciences, Sun Yat-sen
University, Guangzhou 510006, P. R. China
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6
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Kumar M, Tibocha-Bonilla JD, Füssy Z, Lieng C, Schwenck SM, Levesque AV, Al-Bassam MM, Passi A, Neal M, Zuniga C, Kaiyom F, Espinoza JL, Lim H, Polson SW, Allen LZ, Zengler K. Mixotrophic growth of a ubiquitous marine diatom. SCIENCE ADVANCES 2024; 10:eado2623. [PMID: 39018398 PMCID: PMC466952 DOI: 10.1126/sciadv.ado2623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/12/2024] [Indexed: 07/19/2024]
Abstract
Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium's metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium's metabolic flexibility and its potential role in global carbon cycling.
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Affiliation(s)
- Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Zoltán Füssy
- Department of Parasitology, Faculty of Science, Charles University, BIOCEV, Vestec, Czech Republic
| | - Chloe Lieng
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Sarah M. Schwenck
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Alice V. Levesque
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Mahmoud M. Al-Bassam
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Anurag Passi
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Maxwell Neal
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Farrah Kaiyom
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Josh L. Espinoza
- Department of Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Way, La Jolla, CA 92037, USA
| | - Hyungyu Lim
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Shawn W. Polson
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave., Newark, DE 19716, USA
- Center for Bioinformatics and Computational Biology, University of Delaware, 590 Avenue 1743, Newark, DE 19713, USA
| | - Lisa Zeigler Allen
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Way, La Jolla, CA 92037, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Program in Materials Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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7
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Jallet D, Soldan V, Shayan R, Stella A, Ismail N, Zenati R, Cahoreau E, Burlet-Schiltz O, Balor S, Millard P, Heux S. Integrative in vivo analysis of the ethanolamine utilization bacterial microcompartment in Escherichia coli. mSystems 2024:e0075024. [PMID: 39023255 DOI: 10.1128/msystems.00750-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 06/12/2024] [Indexed: 07/20/2024] Open
Abstract
Bacterial microcompartments (BMCs) are self-assembling protein megacomplexes that encapsulate metabolic pathways. Although approximately 20% of sequenced bacterial genomes contain operons encoding putative BMCs, few have been thoroughly characterized, nor any in the most studied Escherichia coli strains. We used an interdisciplinary approach to gain deep molecular and functional insights into the ethanolamine utilization (Eut) BMC system encoded by the eut operon in E. coli K-12. The eut genotype was linked with the ethanolamine utilization phenotype using deletion and overexpression mutants. The subcellular dynamics and morphology of the E. coli Eut BMCs were characterized in cellula by fluorescence microscopy and electron (cryo)microscopy. The minimal proteome reorganization required for ethanolamine utilization and the in vivo stoichiometric composition of the Eut BMC were determined by quantitative proteomics. Finally, the first flux map connecting the Eut BMC with central metabolism in cellula was obtained by genome-scale modeling and 13C-fluxomics. Our results reveal that contrary to previous suggestions, ethanolamine serves both as a nitrogen and a carbon source in E. coli K-12, while also contributing to significant metabolic overflow. Overall, this study provides a quantitative molecular and functional understanding of the BMCs involved in ethanolamine assimilation by E. coli.IMPORTANCEThe properties of bacterial microcompartments make them an ideal tool for building orthogonal network structures with minimal interactions with native metabolic and regulatory networks. However, this requires an understanding of how BMCs work natively. In this study, we combined genetic manipulation, multi-omics, modeling, and microscopy to address this issue for Eut BMCs. We show that the Eut BMC in Escherichia coli turns ethanolamine into usable carbon and nitrogen substrates to sustain growth. These results improve our understanding of compartmentalization in a widely used bacterial chassis.
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Affiliation(s)
- Denis Jallet
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
| | - Vanessa Soldan
- Plateforme de Microscopie Electronique Intégrative, Centre de Biologie Intégrative, Université de Toulouse, CNRS, Toulouse, France
| | - Ramteen Shayan
- Plateforme de Microscopie Electronique Intégrative, Centre de Biologie Intégrative, Université de Toulouse, CNRS, Toulouse, France
| | - Alexandre Stella
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III-Paul Sabatier (UT3), Toulouse, France
- Infrastructure nationale de protéomique, ProFI, Toulouse, France
| | - Nour Ismail
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
| | - Rania Zenati
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
| | - Edern Cahoreau
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
- MetaToul-MetaboHUB, National infrastructure of metabolomics and fluxomics, Toulouse, France
| | - Odile Burlet-Schiltz
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III-Paul Sabatier (UT3), Toulouse, France
- Infrastructure nationale de protéomique, ProFI, Toulouse, France
| | - Stéphanie Balor
- Plateforme de Microscopie Electronique Intégrative, Centre de Biologie Intégrative, Université de Toulouse, CNRS, Toulouse, France
| | - Pierre Millard
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
- MetaToul-MetaboHUB, National infrastructure of metabolomics and fluxomics, Toulouse, France
| | - Stéphanie Heux
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
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8
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Dehghan Manshadi M, Setoodeh P, Zare H. Systematic analysis of microorganisms' metabolism for selective targeting. Sci Rep 2024; 14:16446. [PMID: 39014020 PMCID: PMC11252421 DOI: 10.1038/s41598-024-65936-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/25/2024] [Indexed: 07/18/2024] Open
Abstract
Selective drugs with a relatively narrow spectrum can reduce the side effects of treatments compared to broad-spectrum antibiotics by specifically targeting the pathogens responsible for infection. Furthermore, combating an infectious pathogen, especially a drug-resistant microorganism, is more efficient by attacking multiple targets. Here, we combined synthetic lethality with selective drug targeting to identify multi-target and organism-specific potential drug candidates by systematically analyzing the genome-scale metabolic models of six different microorganisms. By considering microorganisms as targeted or conserved in groups ranging from one to six members, we designed 665 individual case studies. For each case, we identified single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets that are lethal for targeted microorganisms and neutral for conserved ones. As expected, the number of obtained solutions for each case depends on the genomic similarity between the studied microorganisms. Mapping the identified potential drug targets to their corresponding pathways highlighted the importance of key subsystems such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and the nucleotide salvage pathway. To assist in the validation and further investigation of our proposed potential drug targets, we introduced two sets of targets that can theoretically address a substantial portion of the 665 cases. We expect that the obtained solutions provide valuable insights into designing narrow-spectrum drugs that selectively cause system-wide damage only to the target microorganisms.
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Affiliation(s)
- Mehdi Dehghan Manshadi
- Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran
| | - Payam Setoodeh
- Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran.
- W Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada.
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA.
- Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.
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9
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Li B, Srivastava S, Shaikh M, Mereddy G, Garcia MR, Shah A, Ofori-Anyinam N, Chu T, Cheney N, Yang JH. Bioenergetic stress potentiates antimicrobial resistance and persistence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603336. [PMID: 39026737 PMCID: PMC11257553 DOI: 10.1101/2024.07.12.603336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Antimicrobial resistance (AMR) is a global health crisis and there is an urgent need to better understand AMR mechanisms. Antibiotic treatment alters several aspects of bacterial physiology, including increased ATP utilization, carbon metabolism, and reactive oxygen species (ROS) formation. However, how the "bioenergetic stress" induced by increased ATP utilization affects treatment outcomes is unknown. Here we utilized a synthetic biology approach to study the direct effects of bioenergetic stress on antibiotic efficacy. We engineered a genetic system that constitutively hydrolyzes ATP or NADH in Escherichia coli. We found that bioenergetic stress potentiates AMR evolution via enhanced ROS production, mutagenic break repair, and transcription-coupled repair. We also find that bioenergetic stress potentiates antimicrobial persistence via potentiated stringent response activation. We propose a unifying model that antibiotic-induced antimicrobial resistance and persistence is caused by antibiotic-induced. This has important implications for preventing or curbing the spread of AMR infections.
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10
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Schann K, Bakker J, Boinot M, Kuschel P, He H, Nattermann M, Paczia N, Erb T, Bar‐Even A, Wenk S. Design, construction and optimization of formaldehyde growth biosensors with broad application in biotechnology. Microb Biotechnol 2024; 17:e14527. [PMID: 39031508 PMCID: PMC11259041 DOI: 10.1111/1751-7915.14527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 07/02/2024] [Indexed: 07/22/2024] Open
Abstract
Formaldehyde is a key metabolite in natural and synthetic one-carbon metabolism. To facilitate the engineering of formaldehyde-producing enzymes, the development of sensitive, user-friendly, and cost-effective detection methods is required. In this study, we engineered Escherichia coli to serve as a cellular biosensor capable of detecting a broad range of formaldehyde concentrations. Using both natural and promiscuous formaldehyde assimilation enzymes, we designed three distinct E. coli growth biosensor strains that depend on formaldehyde for cell growth. These strains were engineered to be auxotrophic for one or several essential metabolites that could be produced through formaldehyde assimilation. The respective assimilating enzyme was expressed from the genome to compensate the auxotrophy in the presence of formaldehyde. We first predicted the formaldehyde dependency of the biosensors by flux balance analysis and then analysed it experimentally. Subsequent to strain engineering, we enhanced the formaldehyde sensitivity of two biosensors either through adaptive laboratory evolution or modifications at metabolic branch points. The final set of biosensors demonstrated the ability to detect formaldehyde concentrations ranging approximately from 30 μM to 13 mM. We demonstrated the application of the biosensors by assaying the in vivo activity of different methanol dehydrogenases in the most sensitive strain. The fully genomic nature of the biosensors allows them to be deployed as "plug-and-play" devices for high-throughput screenings of extensive enzyme libraries. The formaldehyde growth biosensors developed in this study hold significant promise for advancing the field of enzyme engineering, thereby supporting the establishment of a sustainable one-carbon bioeconomy.
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Affiliation(s)
- Karin Schann
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
| | - Jenny Bakker
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
| | - Maximilian Boinot
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
| | - Pauline Kuschel
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
| | - Hai He
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
- Max Planck Institute for Terrestrial MicrobiologyMarburgGermany
| | | | - Nicole Paczia
- Max Planck Institute for Terrestrial MicrobiologyMarburgGermany
| | - Tobias Erb
- Max Planck Institute for Terrestrial MicrobiologyMarburgGermany
| | - Arren Bar‐Even
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
| | - Sebastian Wenk
- Max Planck Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
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11
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Zhang L, Ye JW, Li G, Park H, Luo H, Lin Y, Li S, Yang W, Guan Y, Wu F, Huang W, Wu Q, Scrutton NS, Nielsen J, Chen GQ. A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM. Metab Eng 2024; 84:95-108. [PMID: 38901556 DOI: 10.1016/j.ymben.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/02/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024]
Abstract
Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.
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Affiliation(s)
- Lizhan Zhang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Jian-Wen Ye
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Helen Park
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Hao Luo
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Yina Lin
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Shaowei Li
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Weinan Yang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yuying Guan
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Fuqing Wu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Wuzhe Huang
- PhaBuilder Biotechnol Co. Ltd., PhaBuilder Biotech Co. Ltd., Shunyi District, Zhaoquan Ying, Beijing, 101309, China
| | - Qiong Wu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Nigel S Scrutton
- Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester, M1 7DN, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
| | - Guo-Qiang Chen
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China; MOE Key Laboratory for Industrial Biocatalysts, Dept Chemical Engineering, Tsinghua University, Beijing, 100084, China; Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China.
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12
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Noecker C, Turnbaugh PJ. Emerging tools and best practices for studying gut microbial community metabolism. Nat Metab 2024; 6:1225-1236. [PMID: 38961185 DOI: 10.1038/s42255-024-01074-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/30/2024] [Indexed: 07/05/2024]
Abstract
The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.
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Affiliation(s)
- Cecilia Noecker
- Department of Biological Sciences, Minnesota State University, Mankato, Mankato, MN, USA
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA.
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13
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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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14
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Bidart GN, Hyeuk S, Alter TB, Yang L, Welner DH. A growth selection system for sucrose synthases (SuSy): design and test. AMB Express 2024; 14:70. [PMID: 38865019 PMCID: PMC11169191 DOI: 10.1186/s13568-024-01727-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024] Open
Abstract
High throughput screening (HTS) methods of enzyme variants are essential for the development of robust biocatalysts suited for low impact, industrial scale, biobased synthesis of a myriad of compounds. However, for the majority of enzyme classes, current screening methods have limited throughput, or need expensive substrates in combination with sophisticated setups. Here, we present a straightforward, high throughput selection system that couples sucrose synthase activity to growth. Enabling high throughput screening of this enzyme class holds the potential to facilitate the creation of robust variants, which in turn can significantly impact the future of cost effective industrial glycosylation.
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Affiliation(s)
- Gonzalo N Bidart
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kgs. Lyngby, DK-2800, Denmark
| | - Se Hyeuk
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kgs. Lyngby, DK-2800, Denmark
| | - Tobias Benedikt Alter
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kgs. Lyngby, DK-2800, Denmark
- RWTH Aachen University, Templergraben 55, 52062, Aachen, Germany
| | - Lei Yang
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kgs. Lyngby, DK-2800, Denmark
| | - Ditte Hededam Welner
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kgs. Lyngby, DK-2800, Denmark.
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15
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Leonidou N, Ostyn L, Coenye T, Crabbé A, Dräger A. Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities. Microbiol Spectr 2024; 12:e0400623. [PMID: 38652457 PMCID: PMC11237427 DOI: 10.1128/spectrum.04006-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/20/2024] [Indexed: 04/25/2024] Open
Abstract
Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.IMPORTANCECystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium's genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.
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Affiliation(s)
- Nantia Leonidou
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Lisa Ostyn
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Tom Coenye
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Aurélie Crabbé
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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16
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Rodriguez-Flores CJ, Barrena N, Olaverri-Mendizabal D, Ochoa I, Valcárcel LV, Planes FJ. gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python. Bioinformatics 2024; 40:btae318. [PMID: 38748994 PMCID: PMC11199197 DOI: 10.1093/bioinformatics/btae318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/08/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
MOTIVATION The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. RESULTS Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. AVAILABILITY AND IMPLEMENTATION The Python package gMCSpy and the data underlying this manuscript can be accessed at: https://github.com/PlanesLab/gMCSpy.
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Affiliation(s)
- Carlos J Rodriguez-Flores
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Naroa Barrena
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Danel Olaverri-Mendizabal
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Idoia Ochoa
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| | - Luis V Valcárcel
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| | - Francisco J Planes
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
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17
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Zielinski DC, Matos MR, de Bree JE, Glass K, Sonnenschein N, Palsson BO. Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data. Metab Eng Commun 2024; 18:e00234. [PMID: 38711578 PMCID: PMC11070925 DOI: 10.1016/j.mec.2024.e00234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of 'bottom up' parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard 'macroscopic' kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and 'microscopic' rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.
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Affiliation(s)
- Daniel C. Zielinski
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Marta R.A. Matos
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - James E. de Bree
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Kevin Glass
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA
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18
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Gong Z, Chen J, Jiao X, Gong H, Pan D, Liu L, Zhang Y, Tan T. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects. Biotechnol Adv 2024; 72:108319. [PMID: 38280495 DOI: 10.1016/j.biotechadv.2024.108319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.
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Affiliation(s)
- Zhijin Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiayao Chen
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinyu Jiao
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hao Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Danzi Pan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lingli Liu
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yang Zhang
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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19
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Catoiu EA, Mih N, Lu M, Palsson B. Establishing comprehensive quaternary structural proteomes from genome sequence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590993. [PMID: 38712217 PMCID: PMC11071507 DOI: 10.1101/2024.04.24.590993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A critical body of knowledge has developed through advances in protein microscopy, protein-fold modeling, structural biology software, availability of sequenced bacterial genomes, large-scale mutation databases, and genome-scale models. Based on these recent advances, we develop a computational framework that; i) identifies the oligomeric structural proteome encoded by an organism's genome from available structural resources; ii) maps multi-strain alleleomic variation, resulting in the structural proteome for a species; and iii) calculates the 3D orientation of proteins across subcellular compartments with residue-level precision. Using the platform, we; iv) compute the quaternary E. coli K-12 MG1655 structural proteome; v) use a dataset of 12,000 mutations to build Random Forest classifiers that can predict the severity of mutations; and, in combination with a genome-scale model that computes proteome allocation, vi) obtain the spatial allocation of the E. coli proteome. Thus, in conjunction with relevant datasets and increasingly accurate computational models, we can now annotate quaternary structural proteomes, at genome-scale, to obtain a molecular-level understanding of whole-cell functions. Significance Advancements in experimental and computational methods have revealed the shapes of multi-subunit proteins. The absence of a unified platform that maps actionable datatypes onto these increasingly accurate structures creates a barrier to structural analyses, especially at the genome-scale. Here, we describe QSPACE, a computational annotation platform that evaluates existing resources to identify the best-available structure for each protein in a user's query, maps the 3D location of actionable datatypes ( e.g. , active sites, published mutations) onto the selected structures, and uses third-party APIs to determine the subcellular compartment of all amino acids of a protein. As proof-of-concept, we deployed QSPACE to generate the quaternary structural proteome of E. coli MG1655 and demonstrate two use-cases involving large-scale mutant analysis and genome-scale modelling.
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20
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Hernandez DM, Marzouk M, Cole M, Fortoul MC, Kethireddy SR, Contractor R, Islam H, Moulder T, Kalifa AR, Meneses EM, Mendoza MB, Thomas R, Masud S, Pubien S, Milanes P, Diaz-Tang G, Lopatkin AJ, Smith RP. Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588696. [PMID: 38645041 PMCID: PMC11030397 DOI: 10.1101/2024.04.09.588696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. We previously found that growth productivity, which captures the relationship between [ATP] and growth, can account for the strength of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. We show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. Specifically, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for β-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect and paves the way for intervention strategies to reduce the inoculum effect in the clinic.
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Affiliation(s)
- Daniella M. Hernandez
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Melissa Marzouk
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Madeline Cole
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Marla C. Fortoul
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Saipranavi Reddy Kethireddy
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Rehan Contractor
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Habibul Islam
- Department of Chemical Engineering, University of Rochester; Rochester, NY 14627; USA
| | - Trent Moulder
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Ariane R. Kalifa
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Estefania Marin Meneses
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Maximiliano Barbosa Mendoza
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Ruth Thomas
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Saad Masud
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Sheena Pubien
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Patricia Milanes
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Gabriela Diaz-Tang
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Allison J. Lopatkin
- Department of Chemical Engineering, University of Rochester; Rochester, NY 14627; USA
- Department of Microbiology and Immunology, University of Rochester Medical Center; Rochester, NY 14627; USA
- Department of Biomedical Engineering, University of Rochester Medical Center; Rochester, NY 14627; USA
| | - Robert P. Smith
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
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21
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Reardon S. How synthetic biologists are building better biofactories. Nature 2024; 628:224-226. [PMID: 38561408 DOI: 10.1038/d41586-024-00907-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
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22
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Hasibi R, Michoel T, Oyarzún DA. Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality. NPJ Syst Biol Appl 2024; 10:24. [PMID: 38448436 PMCID: PMC10917767 DOI: 10.1038/s41540-024-00348-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.
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Affiliation(s)
- Ramin Hasibi
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
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23
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Ortega-Arzola E, Higgins PM, Cockell CS. The minimum energy required to build a cell. Sci Rep 2024; 14:5267. [PMID: 38438463 PMCID: PMC11306549 DOI: 10.1038/s41598-024-54303-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/11/2024] [Indexed: 03/06/2024] Open
Abstract
Understanding the energy requirements for cell synthesis accurately and comprehensively has been a longstanding challenge. We introduce a computational model that estimates the minimum energy necessary to build any cell from its constituent parts. This method combines omics and internal cell compositions from various sources to calculate the Gibbs Free Energy of biosynthesis independently of specific metabolic pathways. Our public tool, Synercell, can be used with other models for minumum species-specific energy estimations in any well-sequenced species. The energy for synthesising the genome, transcriptome, proteome, and lipid bilayer of four cell types: Escherichia coli, Saccharomyces cerevisiae, an average mammalian cell and JCVI-syn3A were estimated. Their modelled minimum synthesis energies at 298 K were 9.54 × 10 - 11 J/cell, 4.99 × 10 - 9 J/cell, 3.71 × 10 - 7 J/cell and 3.69 × 10 - 12 respectively. Gram-for-gram synthesis of lipid bilayers requires the most energy, followed by the proteome, genome, and transcriptome. The average per gram cost of biomass synthesis is in the 300s of J/g for all four cells. Implications for the generalisability of cell construction and applications to biogeosciences, cellular biology, biotechnology, and astrobiology are discussed.
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Affiliation(s)
- Edwin Ortega-Arzola
- UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK.
| | - Peter M Higgins
- UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
- Department of Earth Sciences, University of Toronto, Toronto, ON, Canada
| | - Charles S Cockell
- UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
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24
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Mofidifar S, Yadegar A, Karimi-Jafari MH. A reconstructed genome-scale metabolic model of Helicobacter pylori for predicting putative drug targets in clarithromycin and rifampicin resistance conditions. Helicobacter 2024; 29:e13074. [PMID: 38615332 DOI: 10.1111/hel.13074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/27/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Helicobacter pylori is considered a true human pathogen for which rising drug resistance constitutes a drastic concern globally. The present study aimed to reconstruct a genome-scale metabolic model (GSMM) to decipher the metabolic capability of H. pylori strains in response to clarithromycin and rifampicin along with identification of novel drug targets. MATERIALS AND METHODS The iIT341 model of H. pylori was updated based on genome annotation data, and biochemical knowledge from literature and databases. Context-specific models were generated by integrating the transcriptomic data of clarithromycin and rifampicin resistance into the model. Flux balance analysis was employed for identifying essential genes in each strain, which were further prioritized upon being nonhomologs to humans, virulence factor analysis, druggability, and broad-spectrum analysis. Additionally, metabolic differences between sensitive and resistant strains were also investigated based on flux variability analysis and pathway enrichment analysis of transcriptomic data. RESULTS The reconstructed GSMM was named as HpM485 model. Pathway enrichment and flux variability analyses demonstrated reduced activity in the ribosomal pathway in both clarithromycin- and rifampicin-resistant strains. Also, a significant decrease was detected in the activity of metabolic pathways of clarithromycin-resistant strain. Moreover, 23 and 16 essential genes were exclusively detected in clarithromycin- and rifampicin-resistant strains, respectively. Based on prioritization analysis, cyclopropane fatty acid synthase and phosphoenolpyruvate synthase were identified as putative drug targets in clarithromycin- and rifampicin-resistant strains, respectively. CONCLUSIONS We present a robust and reliable metabolic model of H. pylori. This model can predict novel drug targets to combat drug resistance and explore the metabolic capability of H. pylori in various conditions.
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Affiliation(s)
- Sepideh Mofidifar
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Abbas Yadegar
- Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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25
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Woo H, Kim Y, Kim D, Yoon SH. Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources. Mol Syst Biol 2024; 20:170-186. [PMID: 38291231 PMCID: PMC10912204 DOI: 10.1038/s44320-024-00017-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 01/03/2024] [Accepted: 01/11/2024] [Indexed: 02/01/2024] Open
Abstract
Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.
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Affiliation(s)
- Hyunjae Woo
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Youngshin Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Dohyeon Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Sung Ho Yoon
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea.
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26
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Habibpour M, Razaghi-Moghadam Z, Nikoloski Z. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metab Eng 2024; 82:216-224. [PMID: 38367764 DOI: 10.1016/j.ymben.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/13/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.
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Affiliation(s)
- Mahdis Habibpour
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
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27
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Xue B, Liu Y, Yang C, Liu H, Yuan Q, Wang S, Su H. Co-Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306662. [PMID: 38093511 PMCID: PMC10916542 DOI: 10.1002/advs.202306662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/23/2023] [Indexed: 03/07/2024]
Abstract
Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome-scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co-cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of "independent + global". This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co-culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.
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Affiliation(s)
- Boyuan Xue
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Yu Liu
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Chen Yang
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Hao Liu
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Qianqian Yuan
- Biodesign CenterKey Laboratory of Engineering Biology for Low‐carbon ManufacturingTianjin Institute of Industrial BiotechnologyChinese Academy of SciencesTianjin300308P. R. China
| | - Shaojie Wang
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Haijia Su
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
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28
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Yuan H, Bai Y, Li X, Fu X. Cross-regulation between proteome reallocation and metabolic flux redistribution governs bacterial growth transition kinetics. Metab Eng 2024; 82:60-68. [PMID: 38309620 DOI: 10.1016/j.ymben.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/28/2023] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. It's still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.
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Affiliation(s)
- Huili Yuan
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yang Bai
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Xuefei Li
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, China
| | - Xiongfei Fu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, China.
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29
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Cooper HB, Vezina B, Hawkey J, Passet V, López-Fernández S, Monk JM, Brisse S, Holt KE, Wyres KL. A validated pangenome-scale metabolic model for the Klebsiella pneumoniae species complex. Microb Genom 2024; 10:001206. [PMID: 38376382 PMCID: PMC10926698 DOI: 10.1099/mgen.0.001206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
The Klebsiella pneumoniae species complex (KpSC) is a major source of nosocomial infections globally with high rates of resistance to antimicrobials. Consequently, there is growing interest in understanding virulence factors and their association with cellular metabolic processes for developing novel anti-KpSC therapeutics. Phenotypic assays have revealed metabolic diversity within the KpSC, but metabolism research has been neglected due to experiments being difficult and cost-intensive. Genome-scale metabolic models (GSMMs) represent a rapid and scalable in silico approach for exploring metabolic diversity, which compile genomic and biochemical data to reconstruct the metabolic network of an organism. Here we use a diverse collection of 507 KpSC isolates, including representatives of globally distributed clinically relevant lineages, to construct the most comprehensive KpSC pan-metabolic model to date, KpSC pan v2. Candidate metabolic reactions were identified using gene orthology to known metabolic genes, prior to manual curation via extensive literature and database searches. The final model comprised a total of 3550 reactions, 2403 genes and can simulate growth on 360 unique substrates. We used KpSC pan v2 as a reference to derive strain-specific GSMMs for all 507 KpSC isolates, and compared these to GSMMs generated using a prior KpSC pan-reference (KpSC pan v1) and two single-strain references. We show that KpSC pan v2 includes a greater proportion of accessory reactions (8.8 %) than KpSC pan v1 (2.5 %). GSMMs derived from KpSC pan v2 also generate more accurate growth predictions, with high median accuracies of 95.4 % (aerobic, n=37 isolates) and 78.8 % (anaerobic, n=36 isolates) for 124 matched carbon substrates. KpSC pan v2 is freely available at https://github.com/kelwyres/KpSC-pan-metabolic-model, representing a valuable resource for the scientific community, both as a source of curated metabolic information and as a reference to derive accurate strain-specific GSMMs. The latter can be used to investigate the relationship between KpSC metabolism and traits of interest, such as reservoirs, epidemiology, drug resistance or virulence, and ultimately to inform novel KpSC control strategies.
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Affiliation(s)
- Helena B. Cooper
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
- Centre to Impact AMR, Monash University, Clayton, Victoria 3800, Australia
| | - Ben Vezina
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
- Centre to Impact AMR, Monash University, Clayton, Victoria 3800, Australia
| | - Jane Hawkey
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
| | - Virginie Passet
- Institut Pasteur, Université de Paris, Biodiversity and Epidemiology of Bacterial Pathogens, 75015 Paris, France
| | - Sebastián López-Fernández
- Institut Pasteur, Université de Paris, Biodiversity and Epidemiology of Bacterial Pathogens, 75015 Paris, France
| | - Jonathan M. Monk
- Department of Bioengineering, University of California, San Diego, California 92093, USA
| | - Sylvain Brisse
- Institut Pasteur, Université de Paris, Biodiversity and Epidemiology of Bacterial Pathogens, 75015 Paris, France
| | - Kathryn E. Holt
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Kelly L. Wyres
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
- Centre to Impact AMR, Monash University, Clayton, Victoria 3800, Australia
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30
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Batista RS, Chaves GL, Oliveira DB, Pantaleão VL, Neves JDDS, da Silva AJ. Glycerol as substrate and NADP +-dependent glyceraldehyde-3-phosphate dehydrogenase enable higher production of 3-hydroxypropionic acid through the β-alanine pathway in E. coli. BIORESOURCE TECHNOLOGY 2024; 393:130142. [PMID: 38049020 DOI: 10.1016/j.biortech.2023.130142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
Microbial engineering is a promising way to produce3-HP using biorenewable substrates such as glycerol. However, theglycerol pathway to obtain 3-HPrequires vitamin B-12, which hinders its economic viability. The present work showed that 3-HP can be efficiently produced from glycerol through the β-alanine pathway. To develop a cell factory for this purpose, glycerol was evaluated as a substrate and showed more than two-fold improved 3-HP production compared to glucose. Next, the reducing power was modulated by overexpression of an NADP+ -dependent glyceraldehyde-3-phosphate dehydrogenase coupled with CRISPR-based repression of the endogenous gapA gene, resulting in a 91 % increase in 3-HP titer. Finally, the toxicity of 3-HP accumulation was addressed by overexpressing a putative exporter (YohJK). Fed-batch cultivation of the final strain yielded 72.2 g/L of 3-HP and a productivity of 1.64 g/L/h, which are the best results for the β-alanine pathway and are similar to those found for other pathways.
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Affiliation(s)
- Raquel Salgado Batista
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Gabriel Luz Chaves
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Davi Benedito Oliveira
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Vitor Leonel Pantaleão
- Department of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - José Davi Dos Santos Neves
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Adilson José da Silva
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil; Department of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil.
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31
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Flamholz AI, Goyal A, Fischer WW, Newman DK, Phillips R. The proteome is a terminal electron acceptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578293. [PMID: 38352589 PMCID: PMC10862836 DOI: 10.1101/2024.01.31.578293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. E. coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of metabolic flexibility, we developed a mathematical model coupling redox chemistry with principles of cellular resource allocation. Our integrated model clarifies key phenomena, including demonstrating that autotrophs grow slower than heterotrophs because of constraints imposed by intracellular production of reduced carbon. Our model further indicates that growth is improved by adapting the redox state of biomass to nutrients, revealing an unexpected mode of evolution where proteins accumulate mutations benefiting organismal redox balance.
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Affiliation(s)
- Avi I. Flamholz
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA 91125
| | - Akshit Goyal
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology; Cambridge, MA 02139
- International Centre for Theoretical Sciences, Tata Institute of Fundamental Research; Bengaluru 560089
| | - Woodward W. Fischer
- Division of Geological & Planetary Sciences, California Institute of Technology; Pasadena, CA 91125
| | - Dianne K. Newman
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA 91125
- Division of Geological & Planetary Sciences, California Institute of Technology; Pasadena, CA 91125
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA 91125
- Department of Physics, California Institute of Technology; Pasadena, CA 91125, USA
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32
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Hassani L, Moosavi MR, Setoodeh P, Zare H. FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization. Microb Cell Fact 2024; 23:37. [PMID: 38287320 PMCID: PMC10823710 DOI: 10.1186/s12934-023-02277-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/15/2023] [Indexed: 01/31/2024] Open
Abstract
Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .
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Affiliation(s)
- Leila Hassani
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Mohammad R Moosavi
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Payam Setoodeh
- Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran
- Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada
| | - Habil Zare
- Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, USA.
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33
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Gricourt G, Duigou T, Dérozier S, Faulon JL. neo4jsbml: import systems biology markup language data into the graph database Neo4j. PeerJ 2024; 12:e16726. [PMID: 38250720 PMCID: PMC10798154 DOI: 10.7717/peerj.16726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model's components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j's query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j's graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml.
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Affiliation(s)
- Guillaume Gricourt
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Thomas Duigou
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Sandra Dérozier
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
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Han S, Kim D, Kim Y, Yoon SH. Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12. BMC Genomics 2024; 25:63. [PMID: 38229031 DOI: 10.1186/s12864-023-09940-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/25/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Pseudomonas putida S12 is a gram-negative bacterium renowned for its high tolerance to organic solvents and metabolic versatility, making it attractive for various applications, including bioremediation and the production of aromatic compounds, bioplastics, biofuels, and value-added compounds. However, a metabolic model of S12 has yet to be developed. RESULTS In this study, we present a comprehensive and highly curated genome-scale metabolic network model of S12 (iSH1474), containing 1,474 genes, 1,436 unique metabolites, and 2,938 metabolic reactions. The model was constructed by leveraging existing metabolic models and conducting comparative analyses of genomes and phenomes. Approximately 2,000 different phenotypes were measured for S12 and its closely related KT2440 strain under various nutritional and environmental conditions. These phenotypic data, combined with the reported experimental data, were used to refine and validate the reconstruction. Model predictions quantitatively agreed well with in vivo flux measurements and the batch cultivation of S12, which demonstrated that iSH1474 accurately represents the metabolic capabilities of S12. Furthermore, the model was simulated to investigate the maximum theoretical metabolic capacity of S12 growing on toxic organic solvents. CONCLUSIONS iSH1474 represents a significant advancement in our understanding of the cellular metabolism of P. putida S12. The combined results of metabolic simulation and comparative genome and phenome analyses identified the genetic and metabolic determinants of the characteristic phenotypes of S12. This study could accelerate the development of this versatile organism as an efficient cell factory for various biotechnological applications.
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Affiliation(s)
- Sol Han
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Dohyeon Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Youngshin Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Sung Ho Yoon
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea.
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35
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Baker M, Zhang X, Maciel-Guerra A, Babaarslan K, Dong Y, Wang W, Hu Y, Renney D, Liu L, Li H, Hossain M, Heeb S, Tong Z, Pearcy N, Zhang M, Geng Y, Zhao L, Hao Z, Senin N, Chen J, Peng Z, Li F, Dottorini T. Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China. Nat Commun 2024; 15:206. [PMID: 38182559 PMCID: PMC10770378 DOI: 10.1038/s41467-023-44272-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 12/06/2023] [Indexed: 01/07/2024] Open
Abstract
Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.
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Affiliation(s)
- Michelle Baker
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Xibin Zhang
- Shandong New Hope Liuhe Group Co. Ltd. and Qingdao Key Laboratory of Animal Feed Safety, Qingdao, Shandong, 266000, P.R. China
| | - Alexandre Maciel-Guerra
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Kubra Babaarslan
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Yinping Dong
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - Wei Wang
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - Yujie Hu
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - David Renney
- Nimrod Veterinary Products Limited, 2, Wychwood Court, Cotswold Business Village, Moreton-in-Marsh, GL56 0JQ, London, UK
| | - Longhai Liu
- Shandong Kaijia Food Co. Ltd, Weifang, P. R. China
| | - Hui Li
- Luoyang Center for Disease Control and Prevention, No. 9, Zhenghe Road, Luolong District, Luoyang City, Henan Province, Luolong, 471000, P. R. China
| | - Maqsud Hossain
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Stephan Heeb
- School of Life Sciences, University of Nottingham, East Drive, Nottingham, Nottinghamshire, NG7 2RD, UK
| | - Zhiqin Tong
- Luoyang Center for Disease Control and Prevention, No. 9, Zhenghe Road, Luolong District, Luoyang City, Henan Province, Luolong, 471000, P. R. China
| | - Nicole Pearcy
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
- School of Life Sciences, University of Nottingham, East Drive, Nottingham, Nottinghamshire, NG7 2RD, UK
| | - Meimei Zhang
- Liaoning Provincial Center for Disease Control and Prevention, No. 168, Jinfeng Street, Hunnan District, Shenyang City, Liaoning Province, 110072, P. R. China
| | - Yingzhi Geng
- Liaoning Provincial Center for Disease Control and Prevention, No. 168, Jinfeng Street, Hunnan District, Shenyang City, Liaoning Province, 110072, P. R. China
| | - Li Zhao
- Agricultural Biopharmaceutical Laboratory, College of Chemistry and Pharmaceutical Sciences, Qingdao Agricultural University, No. 700 Changcheng Road, Chengyang District, Qingdao City, Shandong Province, 266109, P. R. China
| | - Zhihui Hao
- Chinese Veterinary Medicine Innovation Center, College of Veterinary Medicine, China Agricultural University, Haidian District, Beijing City, 100193, P. R. China
| | - Nicola Senin
- Department of Engineering, University of Perugia, Perugia, I06125, Italy
| | - Junshi Chen
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - Zixin Peng
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China.
| | - Fengqin Li
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China.
| | - Tania Dottorini
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK.
- Centre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China.
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36
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Freischem LJ, Oyarzún DA. A Machine Learning Approach for Predicting Essentiality of Metabolic Genes. Methods Mol Biol 2024; 2760:345-369. [PMID: 38468098 DOI: 10.1007/978-1-0716-3658-9_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.
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Affiliation(s)
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, UK.
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
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37
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Chew YH, Marucci L. Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology. Methods Mol Biol 2024; 2774:71-84. [PMID: 38441759 DOI: 10.1007/978-1-0716-3718-0_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.
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Affiliation(s)
- Yin Hoon Chew
- School of Mathematics, University of Birmingham, Birmingham, UK
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, UK.
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
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38
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Paklao T, Suratanee A, Plaimas K. ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology. BMC Bioinformatics 2023; 24:492. [PMID: 38129786 PMCID: PMC10740312 DOI: 10.1186/s12859-023-05599-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights. RESULTS To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering. CONCLUSION ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .
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Affiliation(s)
- Thummarat Paklao
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand
| | - Kitiporn Plaimas
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
- Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
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39
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Gwynne PJ, Stocks KLK, Karozichian ES, Pandit A, Hu LT. Metabolic modeling predicts unique drug targets in Borrelia burgdorferi. mSystems 2023; 8:e0083523. [PMID: 37855615 PMCID: PMC10734484 DOI: 10.1128/msystems.00835-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 10/20/2023] Open
Abstract
IMPORTANCE Lyme disease is often treated using long courses of antibiotics, which can cause side effects for patients and risks the evolution of antimicrobial resistance. Narrow-spectrum antimicrobials would reduce these risks, but their development has been slow because the Lyme disease bacterium, Borrelia burgdorferi, is difficult to work with in the laboratory. To accelerate the drug discovery pipeline, we developed a computational model of B. burgdorferi's metabolism and used it to predict essential enzymatic reactions whose inhibition prevented growth in silico. These predictions were validated using small-molecule enzyme inhibitors, several of which were shown to have specific activity against B. burgdorferi. Although the specific compounds used are not suitable for clinical use, we aim to use them as lead compounds to develop optimized drugs targeting the pathways discovered here.
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Affiliation(s)
- Peter J. Gwynne
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Kee-Lee K. Stocks
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Elysse S. Karozichian
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Aarya Pandit
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Linden T. Hu
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
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40
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Wu T, Gómez-Coronado PA, Kubis A, Lindner SN, Marlière P, Erb TJ, Bar-Even A, He H. Engineering a synthetic energy-efficient formaldehyde assimilation cycle in Escherichia coli. Nat Commun 2023; 14:8490. [PMID: 38123535 PMCID: PMC10733421 DOI: 10.1038/s41467-023-44247-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
One-carbon (C1) substrates, such as methanol or formate, are attractive feedstocks for circular bioeconomy. These substrates are typically converted into formaldehyde, serving as the entry point into metabolism. Here, we design an erythrulose monophosphate (EuMP) cycle for formaldehyde assimilation, leveraging a promiscuous dihydroxyacetone phosphate dependent aldolase as key enzyme. In silico modeling reveals that the cycle is highly energy-efficient, holding the potential for high bioproduct yields. Dissecting the EuMP into four modules, we use a stepwise strategy to demonstrate in vivo feasibility of the modules in E. coli sensor strains with sarcosine as formaldehyde source. From adaptive laboratory evolution for module integration, we identify key mutations enabling the accommodation of the EuMP reactions with endogenous metabolism. Overall, our study demonstrates the proof-of-concept for a highly efficient, new-to-nature formaldehyde assimilation pathway, opening a way for the development of a methylotrophic platform for a C1-fueled bioeconomy in the future.
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Affiliation(s)
- Tong Wu
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Biochemistry, Charitéplatz 1, 10117, Berlin, Germany
| | - Paul A Gómez-Coronado
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany
| | - Armin Kubis
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Biochemistry, Charitéplatz 1, 10117, Berlin, Germany
| | - Philippe Marlière
- TESSSI, The European Syndicate of Synthetic Scientists and Industrialists, 81 rue Réaumur, 75002, Paris, France
| | - Tobias J Erb
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany
| | - Arren Bar-Even
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Hai He
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany.
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany.
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41
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Stalidzans E, Muiznieks R, Dubencovs K, Sile E, Berzins K, Suleiko A, Vanags J. A Fermentation State Marker Rule Design Task in Metabolic Engineering. Bioengineering (Basel) 2023; 10:1427. [PMID: 38136018 PMCID: PMC10740952 DOI: 10.3390/bioengineering10121427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate μ is above 0.060 h-1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2-10, where all metabolic fluxes units are mmol ∗ gDW-1 ∗ h-1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.
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Affiliation(s)
- Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (R.M.); (K.B.)
| | - Reinis Muiznieks
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (R.M.); (K.B.)
| | - Konstantins Dubencovs
- Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia (E.S.); (A.S.); (J.V.)
- Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, LV-1006 Riga, Latvia
| | - Elina Sile
- Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia (E.S.); (A.S.); (J.V.)
| | - Kristaps Berzins
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (R.M.); (K.B.)
| | - Arturs Suleiko
- Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia (E.S.); (A.S.); (J.V.)
- Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, LV-1006 Riga, Latvia
| | - Juris Vanags
- Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia (E.S.); (A.S.); (J.V.)
- Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, LV-1006 Riga, Latvia
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Karp PD, Paley S, Caspi R, Kothari A, Krummenacker M, Midford PE, Moore LR, Subhraveti P, Gama-Castro S, Tierrafria VH, Lara P, Muñiz-Rascado L, Bonavides-Martinez C, Santos-Zavaleta A, Mackie A, Sun G, Ahn-Horst TA, Choi H, Covert MW, Collado-Vides J, Paulsen I. The EcoCyc Database (2023). EcoSal Plus 2023; 11:eesp00022023. [PMID: 37220074 PMCID: PMC10729931 DOI: 10.1128/ecosalplus.esp-0002-2023] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/04/2023] [Indexed: 01/28/2024]
Abstract
EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.
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Affiliation(s)
- Peter D. Karp
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Suzanne Paley
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Ron Caspi
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Markus Krummenacker
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Peter E. Midford
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Lisa R. Moore
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Pallavi Subhraveti
- Bioinformatics Research Group, SRI International, Menlo Park, California, USA
| | - Socorro Gama-Castro
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Victor H. Tierrafria
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Paloma Lara
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Luis Muñiz-Rascado
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - César Bonavides-Martinez
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Alberto Santos-Zavaleta
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Amanda Mackie
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Travis A. Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Heejo Choi
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Markus W. Covert
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Ian Paulsen
- School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia
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43
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Carrasco Muriel J, Long C, Sonnenschein N. Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO. Microbiol Spectr 2023; 11:e0170523. [PMID: 37931133 PMCID: PMC10783817 DOI: 10.1128/spectrum.01705-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/11/2023] [Indexed: 11/08/2023] Open
Abstract
IMPORTANCE The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.
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Affiliation(s)
- Jorge Carrasco Muriel
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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44
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Bernstein DB, Akkas B, Price MN, Arkin AP. Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data. Mol Syst Biol 2023; 19:e11566. [PMID: 37888487 DOI: 10.15252/msb.202311566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 09/23/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.
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Affiliation(s)
- David B Bernstein
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Batu Akkas
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Morgan N Price
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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45
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Yang X, Mao Z, Huang J, Wang R, Dong H, Zhang Y, Ma H. Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments. Synth Syst Biotechnol 2023; 8:597-605. [PMID: 37743907 PMCID: PMC10514394 DOI: 10.1016/j.synbio.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/12/2023] [Accepted: 09/06/2023] [Indexed: 09/26/2023] Open
Abstract
Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.
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Affiliation(s)
- Xue Yang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Zhitao Mao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Jianfeng Huang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Ruoyu Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Huaming Dong
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
| | - Yanfei Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Hongwu Ma
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
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46
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Yuan Q, Wei F, Deng X, Li A, Shi Z, Mao Z, Li F, Ma H. Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501. Synth Syst Biotechnol 2023; 8:688-696. [PMID: 37927897 PMCID: PMC10624960 DOI: 10.1016/j.synbio.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/21/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023] Open
Abstract
Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.
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Affiliation(s)
- Qianqian Yuan
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Fan Wei
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Xiaogui Deng
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
- School of Biological Engineering, Tianjin University of Science and Technology, Tianjin, China
| | - Aonan Li
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
- School of Biological Engineering, Tianjin University of Science and Technology, Tianjin, China
| | - Zhenkun Shi
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Zhitao Mao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Feiran Li
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
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47
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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48
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Gotsmy M, Strobl F, Weiß F, Gruber P, Kraus B, Mairhofer J, Zanghellini J. Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes. Microb Cell Fact 2023; 22:242. [PMID: 38017439 PMCID: PMC10685491 DOI: 10.1186/s12934-023-02248-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/11/2023] [Indexed: 11/30/2023] Open
Abstract
Plasmid DNA (pDNA) is a key biotechnological product whose importance became apparent in the last years due to its role as a raw material in the messenger ribonucleic acid (mRNA) vaccine manufacturing process. In pharmaceutical production processes, cells need to grow in the defined medium in order to guarantee the highest standards of quality and repeatability. However, often these requirements result in low product titer, productivity, and yield. In this study, we used constraint-based metabolic modeling to optimize the average volumetric productivity of pDNA production in a fed-batch process. We identified a set of 13 nutrients in the growth medium that are essential for cell growth but not for pDNA replication. When these nutrients are depleted in the medium, cell growth is stalled and pDNA production is increased, raising the specific and volumetric yield and productivity. To exploit this effect we designed a three-stage process (1. batch, 2. fed-batch with cell growth, 3. fed-batch without cell growth). The transition between stage 2 and 3 is induced by sulfate starvation. Its onset can be easily controlled via the initial concentration of sulfate in the medium. We validated the decoupling behavior of sulfate and assessed pDNA quality attributes (supercoiled pDNA content) in E. coli with lab-scale bioreactor cultivations. The results showed an increase in supercoiled pDNA to biomass yield by 33% and an increase of supercoiled pDNA volumetric productivity by 13 % upon limitation of sulfate. In conclusion, even for routinely manufactured biotechnological products such as pDNA, simple changes in the growth medium can significantly improve the yield and quality.
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Affiliation(s)
- Mathias Gotsmy
- Department of Analytical Chemistry, University of Vienna, Vienna, 1090, Austria
- Doctorate School of Chemistry, University of Vienna, Vienna, 1090, Austria
| | | | | | - Petra Gruber
- Baxalta Innovations GmbH, A Part of Takeda Companies, Orth an der Donau, 2304, Austria
| | - Barbara Kraus
- Baxalta Innovations GmbH, A Part of Takeda Companies, Orth an der Donau, 2304, Austria
| | | | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, Vienna, 1090, Austria.
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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] [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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50
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Hu XP, Schroeder S, Lercher MJ. Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow. mSystems 2023; 8:e0076023. [PMID: 37795991 PMCID: PMC10654084 DOI: 10.1128/msystems.00760-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
IMPORTANCE Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.
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Affiliation(s)
- Xiao-Pan Hu
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Stefan Schroeder
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Martin J. Lercher
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
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