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Zhang C, Sánchez BJ, Li F, Eiden CWQ, Scott WT, Liebal UW, Blank LM, Mengers HG, Anton M, Rangel AT, Mendoza SN, Zhang L, Nielsen J, Lu H, Kerkhoven EJ. Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community. Mol Syst Biol 2024; 20:1134-1150. [PMID: 39134886 PMCID: PMC11450192 DOI: 10.1038/s44320-024-00060-7] [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: 01/05/2024] [Revised: 07/17/2024] [Accepted: 07/31/2024] [Indexed: 10/05/2024] Open
Abstract
Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.
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Affiliation(s)
- Chengyu Zhang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China
- State Key Laboratory of Bioreactor Engineering, and School of Biotechnology, East China University of Science and Technology (ECUST), 200237, Shanghai, China
| | - Benjamín J Sánchez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Feiran Li
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Cheng Wei Quan Eiden
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459, Singapore
| | - William T Scott
- UNLOCK, Wageningen University & Research, Wageningen, The Netherlands
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands
| | - Ulf W Liebal
- Institute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, 52074, Aachen, Germany
| | - Lars M Blank
- Institute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, 52074, Aachen, Germany
| | - Hendrik G Mengers
- Institute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, 52074, Aachen, Germany
| | - Mihail Anton
- Department of Life Sciences, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE412 58, Sweden
| | - Albert Tafur Rangel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, SE412 96, Sweden
| | - Sebastián N Mendoza
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Systems Biology Lab, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lixin Zhang
- State Key Laboratory of Bioreactor Engineering, and School of Biotechnology, East China University of Science and Technology (ECUST), 200237, Shanghai, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, SE412 96, Sweden
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark
| | - Hongzhong Lu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.
| | - Eduard J Kerkhoven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.
- Department of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, SE412 96, Sweden.
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Xie X, Gui L, Qiao B, Wang G, Huang S, Zhao Y, Sun S. Deep learning in template-free de novo biosynthetic pathway design of natural products. Brief Bioinform 2024; 25:bbae495. [PMID: 39373052 PMCID: PMC11456888 DOI: 10.1093/bib/bbae495] [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: 06/10/2024] [Revised: 09/12/2024] [Accepted: 09/20/2024] [Indexed: 10/08/2024] Open
Abstract
Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.
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Affiliation(s)
- Xueying Xie
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), No. 26 Hexing Road, Xiangfang District, Harbin 150001, China
- College of Life Science, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China
| | - Lin Gui
- College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China
| | - Baixue Qiao
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), No. 26 Hexing Road, Xiangfang District, Harbin 150001, China
- College of Life Science, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, No. 246 Xuefu Road, Nangang District,Harbin 150081, China
| | - Yuming Zhao
- College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China
| | - Shanwen Sun
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), No. 26 Hexing Road, Xiangfang District, Harbin 150001, China
- College of Life Science, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China
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3
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Kwon MS, Lee J, Kim HU. A machine learning framework for extracting information from biological pathway images in the literature. Metab Eng 2024; 86:1-11. [PMID: 39233197 DOI: 10.1016/j.ymben.2024.09.001] [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: 05/17/2024] [Revised: 08/03/2024] [Accepted: 09/01/2024] [Indexed: 09/06/2024]
Abstract
There have been significant advances in literature mining, allowing for the extraction of target information from the literature. However, biological literature often includes biological pathway images that are difficult to extract in an easily editable format. To address this challenge, this study aims to develop a machine learning framework called the "Extraction of Biological Pathway Information" (EBPI). The framework automates the search for relevant publications, extracts biological pathway information from images within the literature, including genes, enzymes, and metabolites, and generates the output in a tabular format. For this, this framework determines the direction of biochemical reactions, and detects and classifies texts within biological pathway images. Performance of EBPI was evaluated by comparing the extracted pathway information with manually curated pathway maps. EBPI will be useful for extracting biological pathway information from the literature in a high-throughput manner, and can be used for pathway studies, including metabolic engineering.
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Affiliation(s)
- Mun Su Kwon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Junkyu Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology and BioProcess Engineering Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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4
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Kim T, Lee S, Kwak Y, Choi MS, Park J, Hwang SJ, Kim SG. READRetro: natural product biosynthesis predicting with retrieval-augmented dual-view retrosynthesis. THE NEW PHYTOLOGIST 2024; 243:2512-2527. [PMID: 39081009 DOI: 10.1111/nph.20012] [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: 03/26/2024] [Accepted: 07/08/2024] [Indexed: 08/23/2024]
Abstract
Plants, as a sessile organism, produce various secondary metabolites to interact with the environment. These chemicals have fascinated the plant science community because of their ecological significance and notable biological activity. However, predicting the complete biosynthetic pathways from target molecules to metabolic building blocks remains a challenge. Here, we propose retrieval-augmented dual-view retrosynthesis (READRetro) as a practical bio-retrosynthesis tool to predict the biosynthetic pathways of plant natural products. Conventional bio-retrosynthesis models have been limited in their ability to predict biosynthetic pathways for natural products. READRetro was optimized for the prediction of complex metabolic pathways by incorporating cutting-edge deep learning architectures, an ensemble approach, and two retrievers. Evaluation of single- and multi-step retrosynthesis showed that each component of READRetro significantly improved its ability to predict biosynthetic pathways. READRetro was also able to propose the known pathways of secondary metabolites such as monoterpene indole alkaloids and the unknown pathway of menisdaurilide, demonstrating its applicability to real-world bio-retrosynthesis of plant natural products. For researchers interested in the biosynthesis and production of secondary metabolites, a user-friendly website (https://readretro.net) and the open-source code of READRetro have been made available.
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Affiliation(s)
- Taein Kim
- Department of Biological Sciences, KAIST, Daejeon, 34141, Korea
| | - Seul Lee
- Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Korea
| | - Yejin Kwak
- Department of BioMedical Convergence Engineering, Pusan National University, Yangsan, 50612, Korea
| | - Min-Soo Choi
- Department of Biological Sciences, KAIST, Daejeon, 34141, Korea
| | - Jeongbin Park
- Department of BioMedical Convergence Engineering, Pusan National University, Yangsan, 50612, Korea
| | - Sung Ju Hwang
- Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Korea
- School of Computing, KAIST, Daejeon, 34141, Korea
| | - Sang-Gyu Kim
- Department of Biological Sciences, KAIST, Daejeon, 34141, Korea
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5
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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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6
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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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7
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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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Liu CH, Shen PC, Lin WJ, Liu HC, Tsai MH, Huang TY, Chen IC, Lai YL, Wang YD, Hung MC, Cheng WC. LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis. Nucleic Acids Res 2024; 52:W390-W397. [PMID: 38709887 PMCID: PMC11223864 DOI: 10.1093/nar/gkae335] [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: 02/08/2024] [Revised: 03/29/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.
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Affiliation(s)
- Chia-Hsin Liu
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Pei-Chun Shen
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Wen-Jen Lin
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- School of Medicine, China Medical University, Taichung 404328, Taiwan
| | - Hsiu-Cheng Liu
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Meng-Hsin Tsai
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Tzu-Ya Huang
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - I-Chieh Chen
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Yo-Liang Lai
- Department of Radiation Oncology, China Medical University, Taichung 404328, Taiwan
| | - Yu-De Wang
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- Department of Urology, China Medical University, Taichung 404328, Taiwan
| | - Mien-Chie Hung
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- Institute of Biochemistry and Molecular Biology, China Medical University, Taichung 404328, Taiwan
- Molecular Medicine Center, China Medical University Hospital, China Medical University, Taichung 404328, Taiwan
- Department of Biotechnology, Asia University, Taichung 413305, Taiwan
| | - Wei-Chung Cheng
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- The Ph.D. program for Cancer Biology and Drug Discovery, China Medical University and Academia Sinica, Taichung 404328, Taiwan
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Hsieh YE, Tandon K, Verbruggen H, Nikoloski Z. Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches. NPJ Syst Biol Appl 2024; 10:54. [PMID: 38783065 PMCID: PMC11116368 DOI: 10.1038/s41540-024-00384-y] [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: 01/06/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.
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Affiliation(s)
- Yunli Eric Hsieh
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Kshitij Tandon
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Heroen Verbruggen
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Zoran Nikoloski
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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10
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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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11
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Santangelo BE, Apgar M, Colorado ASB, Martin CG, Sterrett J, Wall E, Joachimiak MP, Hunter LE, Lozupone CA. Integrating biological knowledge for mechanistic inference in the host-associated microbiome. Front Microbiol 2024; 15:1351678. [PMID: 38638909 PMCID: PMC11024261 DOI: 10.3389/fmicb.2024.1351678] [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: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.
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Affiliation(s)
- Brook E. Santangelo
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Madison Apgar
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Casey G. Martin
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - John Sterrett
- Department of Integrative Physiology, University of Colorado, Boulder, CO, United States
| | - Elena Wall
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcin P. Joachimiak
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Biosystems Data Science Department, Berkeley, CA, United States
| | - Lawrence E. Hunter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Catherine A. Lozupone
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
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12
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Kishk A, Pires Pacheco M, Heurtaux T, Sauter T. Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. Brief Bioinform 2024; 25:bbae199. [PMID: 38701414 PMCID: PMC11066901 DOI: 10.1093/bib/bbae199] [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: 01/15/2024] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Centre of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
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13
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Pranavathiyani G, Pan A. Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:138-147. [PMID: 38478777 DOI: 10.1089/omi.2024.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.
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Affiliation(s)
- Gnanasekar Pranavathiyani
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, India
| | - Archana Pan
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, India
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14
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Hari A, Zarrabi A, Lobo D. mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers. NAR Genom Bioinform 2024; 6:lqae010. [PMID: 38312936 PMCID: PMC10836943 DOI: 10.1093/nargab/lqae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/15/2023] [Accepted: 01/16/2024] [Indexed: 02/06/2024] Open
Abstract
Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.
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Affiliation(s)
- Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
| | - Arveen Zarrabi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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15
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Jacopin E, Sakamoto Y, Nishida K, Kaizu K, Takahashi K. An architecture for collaboration in systems biology at the age of the Metaverse. NPJ Syst Biol Appl 2024; 10:12. [PMID: 38280851 PMCID: PMC10821884 DOI: 10.1038/s41540-024-00334-8] [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: 08/02/2023] [Accepted: 01/10/2024] [Indexed: 01/29/2024] Open
Abstract
As the current state of the Metaverse is largely driven by corporate interests, which may not align with scientific goals and values, academia should play a more active role in its development. Here, we present the challenges and solutions for building a Metaverse that supports systems biology research and collaboration. Our solution consists of two components: Kosmogora, a server ensuring biological data access, traceability, and integrity in the context of a highly collaborative environment such as a metaverse; and ECellDive, a virtual reality application to explore, interact, and build upon the data managed by Kosmogora. We illustrate the synergy between the two components by visualizing a metabolic network and its flux balance analysis. We also argue that the Metaverse of systems biology will foster closer communication and cooperation between experimentalists and modelers in the field.
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Affiliation(s)
- Eliott Jacopin
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.
| | - Yuki Sakamoto
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Kozo Nishida
- RIKEN, Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Tokyo University of Agriculture and Technology, Department of Biotechnology and Life Science, 2-24-16 Nakamachi, Koganei, Tokyo, 184-8588, Japan
| | - Kazunari Kaizu
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Koichi Takahashi
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
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16
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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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17
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Altenhoff A, Bairoch A, Bansal P, Baratin D, Bastian F, Bolleman* J, Bridge A, Burdet F, Crameri K, Dauvillier J, Dessimoz C, Gehant S, Glover N, Gnodtke K, Hayes C, Ibberson M, Kriventseva E, Kuznetsov D, Frédérique L, Mehl F, Mendes de Farias* T, Michel PA, Moretti S, Morgat A, Österle S, Pagni M, Redaschi N, Robinson-Rechavi M, Samarasinghe K, Sima AC, Szklarczyk D, Topalov O, Touré V, Unni D, von Mering C, Wollbrett J, Zahn-Zabal* M, Zdobnov E. The SIB Swiss Institute of Bioinformatics Semantic Web of data. Nucleic Acids Res 2024; 52:D44-D51. [PMID: 37878411 PMCID: PMC10767860 DOI: 10.1093/nar/gkad902] [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: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/05/2023] [Indexed: 10/27/2023] Open
Abstract
The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss/) is a federation of bioinformatics research and service groups. The international life science community in academia and industry has been accessing the freely available databases provided by SIB since its inception in 1998. In this paper we present the 11 databases which currently offer semantically enriched data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable), as well as the Swiss Personalized Health Network initiative (SPHN) which also employs this enrichment. The semantic enrichment facilitates the manipulation of large data sets from public databases and private data sets. Examples are provided to illustrate that the data from the SIB databases can not only be queried using precise criteria individually, but also across multiple databases, including a variety of non-SIB databases. Data manipulation, be it exploration, extraction, annotation, combination, and publication, is possible using the SPARQL query language. Providing documentation, tutorials and sample queries makes it easier to navigate this web of semantic data. Through this paper, the reader will discover how the existing SIB knowledge graphs can be leveraged to tackle the complex biological or clinical questions that are being addressed today.
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18
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Probst D. An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification. J Cheminform 2023; 15:113. [PMID: 37996942 PMCID: PMC10668483 DOI: 10.1186/s13321-023-00784-y] [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: 05/22/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.
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Affiliation(s)
- Daniel Probst
- Signal Processing Laboratory 2, Institute of Electrical and Micro Engineering, School of Engineering, EPFL, Rte Cantonale, 1015, Lausanne, Vaud, Switzerland.
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19
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Villalba H, Llambrich M, Gumà J, Brezmes J, Cumeras R. A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison. Metabolites 2023; 13:1167. [PMID: 38132849 PMCID: PMC10744506 DOI: 10.3390/metabo13121167] [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: 11/06/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.
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Affiliation(s)
- Héctor Villalba
- Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
| | - Maria Llambrich
- Department of Electrical Electronic Engineering and Automation, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
| | - Josep Gumà
- Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
- Department of Medicine and Surgery, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
| | - Jesús Brezmes
- Department of Electrical Electronic Engineering and Automation, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
| | - Raquel Cumeras
- Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
- Department of Electrical Electronic Engineering and Automation, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
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20
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Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metab Eng 2023; 80:130-141. [PMID: 37734652 DOI: 10.1016/j.ymben.2023.09.012] [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: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023]
Abstract
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.
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Affiliation(s)
- Gahyeon Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Taeho Yu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon, 34141, Republic of Korea.
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21
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Ma F, Wang Y, Quan H, Wang Z, Zhao C, Li X, Liang B, Zhang H, Hao L, Zhu T. Exploring the humification process of municipal sludge in hyperthermophilic composting through metagenomic and untargeted metabolomic. BIORESOURCE TECHNOLOGY 2023; 387:129575. [PMID: 37517706 DOI: 10.1016/j.biortech.2023.129575] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/17/2023] [Accepted: 07/25/2023] [Indexed: 08/01/2023]
Abstract
Hyperthermophilic composting (HC) has been widely recognized for the advantage of high treatment efficiency for organic wastes. However, the humification process is still unclear. In this study, the humification process of HC was investigated, compared to conventional composting (CK). The results showed that the highest composting temperature, organic matter degradation rate, and humification index in HC were 92.62 °C, 23.98%, and 1.59, while those in CK were 70.23 °C, 14.49 %, and 1.04, indicating HC accelerated humification process. Moreover, the results of metagenomic and untargeted metabolomic showed that the genes and metabolisms related to carbohydrate, lipid, amino acid, fatty acid, and nucleotide were more abundant in HC. Consequently, the metabolic pathways regarding organic matter degradation and microbial reproduction were enhanced in the high temperature stage of HC, further accelerating the humification reaction in the low temperature stage. This work contributes to the comprehension of the humification mechanism in HC.
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Affiliation(s)
- Feng Ma
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
| | - Youzhao Wang
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
| | - Haoyu Quan
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
| | - Zhipeng Wang
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
| | - Chaoyue Zhao
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
| | - Xu Li
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
| | - Baorui Liang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Hao Zhang
- The First Hospital of China Medical University, Shenyang 110001, China
| | - Liying Hao
- Department of Pharmaceutical Toxicology, School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Tong Zhu
- Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
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22
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Vezina B, Watts SC, Hawkey J, Cooper HB, Judd LM, Jenney AWJ, Monk JM, Holt KE, Wyres KL. Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models. eLife 2023; 12:RP87406. [PMID: 37815531 PMCID: PMC10564454 DOI: 10.7554/elife.87406] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here, we describe Bactabolize, a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae, and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically defined quality control criteria resulted in models with a high degree of completeness (≥99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.
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Affiliation(s)
- Ben Vezina
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Stephen C Watts
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Jane Hawkey
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Helena B Cooper
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Louise M Judd
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | | | - Jonathan M Monk
- Department of Bioengineering, University of California, San DiegoSan DiegoUnited States
| | - Kathryn E Holt
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
- Department of Infection Biology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Kelly L Wyres
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
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23
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Holland BL, Matthews ML, Bota P, Sweetlove LJ, Long SP, diCenzo GC. A genome-scale metabolic reconstruction of soybean and Bradyrhizobium diazoefficiens reveals the cost-benefit of nitrogen fixation. THE NEW PHYTOLOGIST 2023; 240:744-756. [PMID: 37649265 DOI: 10.1111/nph.19203] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/05/2023] [Indexed: 09/01/2023]
Abstract
Nitrogen-fixing symbioses allow legumes to thrive in nitrogen-poor soils at the cost of diverting some photoassimilate to their microsymbionts. Effort is being made to bioengineer nitrogen fixation into nonleguminous crops. This requires a quantitative understanding of its energetic costs and the links between metabolic variations and symbiotic efficiency. A whole-plant metabolic model for soybean (Glycine max) with its associated microsymbiont Bradyrhizobium diazoefficiens was developed and applied to predict the cost-benefit of nitrogen fixation with varying soil nitrogen availability. The model predicted a nitrogen-fixation cost of c. 4.13 g C g-1 N, which when implemented into a crop scale model, translated to a grain yield reduction of 27% compared with a non-nodulating plant receiving its nitrogen from the soil. Considering the lower nitrogen content of cereals, the yield cost to a hypothetical N-fixing cereal is predicted to be less than half that of soybean. Soybean growth was predicted to be c. 5% greater when the nodule nitrogen export products were amides versus ureides. This is the first metabolic reconstruction in a tropical crop species that simulates the entire plant and nodule metabolism. Going forward, this model will serve as a tool to investigate carbon use efficiency and key mechanisms within N-fixing symbiosis in a tropical species forming determinate nodules.
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Affiliation(s)
- Bethany L Holland
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Megan L Matthews
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Pedro Bota
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Stephen P Long
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Departments of Plant Biology and of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - George C diCenzo
- Department of Biology, Queen's University, Kingston, ON, K7L 3N6, Canada
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24
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Cunha E, Silva M, Chaves I, Demirci H, Lagoa DR, Lima D, Rocha M, Rocha I, Dias O. The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber. PLoS Comput Biol 2023; 19:e1011499. [PMID: 37729340 PMCID: PMC10545120 DOI: 10.1371/journal.pcbi.1011499] [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: 05/09/2023] [Revised: 10/02/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.
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Affiliation(s)
- Emanuel Cunha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Silva
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
| | - Huseyin Demirci
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- SnT/University of Luxembourg, Luxembourg
| | | | - Diogo Lima
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
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25
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Potter AD, Baiocco CM, Papin JA, Criss AK. Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture. mSystems 2023; 8:e0126522. [PMID: 37387581 PMCID: PMC10470122 DOI: 10.1128/msystems.01265-22] [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: 12/15/2022] [Accepted: 05/19/2023] [Indexed: 07/01/2023] Open
Abstract
The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.
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Affiliation(s)
- Aimee D. Potter
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M. Baiocco
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Alison K. Criss
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
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26
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Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [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: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
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Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
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27
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Tec-Campos D, Posadas C, Tibocha-Bonilla JD, Thiruppathy D, Glonek N, Zuñiga C, Zepeda A, Zengler K. The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions. PLoS Comput Biol 2023; 19:e1011371. [PMID: 37556472 PMCID: PMC10441798 DOI: 10.1371/journal.pcbi.1011371] [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: 12/02/2022] [Revised: 08/21/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.
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Affiliation(s)
- Diego Tec-Campos
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Camila Posadas
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Deepan Thiruppathy
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla California, United States of America
| | - Nathan Glonek
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Alejandro Zepeda
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla California, United States of America
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, United States of America
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28
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Cesur MF, Basile A, Patil KR, Çakır T. A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease. Life Sci Alliance 2023; 6:e202201695. [PMID: 37236669 PMCID: PMC10215973 DOI: 10.26508/lsa.202201695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.
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Affiliation(s)
- Müberra Fatma Cesur
- Systems Biology and Bioinformatics Program, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Tunahan Çakır
- Systems Biology and Bioinformatics Program, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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29
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Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Res 2023; 33:972-987. [PMID: 37468308 PMCID: PMC10629481 DOI: 10.1101/gr.277056.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
| | - Jeanne Got
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
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30
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Kroll A, Rousset Y, Hu XP, Liebrand NA, Lercher MJ. Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nat Commun 2023; 14:4139. [PMID: 37438349 DOI: 10.1038/s41467-023-39840-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/27/2023] [Indexed: 07/14/2023] Open
Abstract
The turnover number kcat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental kcat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through differential reaction fingerprints and by representing enzymes through a modified and re-trained Transformer Network model for protein sequences. TurNuP outperforms previous models and generalizes well even to enzymes that are not similar to proteins in the training set. Parameterizing metabolic models with TurNuP-predicted kcat values leads to improved proteome allocation predictions. To provide a powerful and convenient tool for the study of molecular biochemistry and physiology, we implemented a TurNuP web server.
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Affiliation(s)
- Alexander Kroll
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Yvan Rousset
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Xiao-Pan Hu
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Nina A Liebrand
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
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31
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Molversmyr H, Øyås O, Rotnes F, Vik JO. Extracting functionally accurate context-specific models of Atlantic salmon metabolism. NPJ Syst Biol Appl 2023; 9:19. [PMID: 37244928 DOI: 10.1038/s41540-023-00280-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.
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Affiliation(s)
- Håvard Molversmyr
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Ove Øyås
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Filip Rotnes
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Jon Olav Vik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
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Clavijo-Buriticá DC, Arévalo-Ferro C, González Barrios AF. A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis. Metabolites 2023; 13:metabo13050659. [PMID: 37233700 DOI: 10.3390/metabo13050659] [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: 03/21/2023] [Revised: 04/26/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023] Open
Abstract
Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.
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Affiliation(s)
- Diana Carolina Clavijo-Buriticá
- Grupo de Comunicación y Comunidades Bacterianas, Departamento de Biología, Universidad Nacional de Colombia, Carrera 45 No. 26-85, Bogotá 111321, Colombia
| | - Catalina Arévalo-Ferro
- Grupo de Comunicación y Comunidades Bacterianas, Departamento de Biología, Universidad Nacional de Colombia, Carrera 45 No. 26-85, Bogotá 111321, Colombia
| | - Andrés Fernando González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Departamento de Ingeniería Química y de Alimentos, Universidad de los Andes, Edificio Mario Laserna, Carrera 1 Este No. 19ª-40, Bogotá 111711, Colombia
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Bannerman BP, Oarga A, Júlvez J. Mycobacterial metabolic model development for drug target identification. GIGABYTE 2023; 2023:gigabyte80. [PMID: 37153490 PMCID: PMC10154535 DOI: 10.46471/gigabyte.80] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 04/24/2023] [Indexed: 05/09/2023] Open
Abstract
Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are sources of widespread infections in humans. According to the World Health Organization, Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second leading infectious killer worldwide after COVID-19; and Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other healthcare-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical that we develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseases. In this study, metabolic models have been developed for two bacterial pathogens, M. leprae and My. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets for each pathogen are significant for precision medicine initiatives. The models and associated datasets described in this paper are available in GigaDB, Biomodels, and PatMeDB repositories.
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Affiliation(s)
- Bridget P. Bannerman
- Lucy Cavendish College, University of Cambridge, Lady Margaret Rd, Cambridge, CB3 0BU, UK
- Science Resources Foundation, 128 City Road, London, EC1V 2NX, UK
| | - Alexandru Oarga
- Department of Computer Science and Systems Engineering, University of Zaragoza, C/María de Luna n 1, 50018, Zaragoza, Spain
| | - Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, C/María de Luna n 1, 50018, Zaragoza, Spain
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Blázquez B, San León D, Rojas A, Tortajada M, Nogales J. New Insights on Metabolic Features of Bacillus subtilis Based on Multistrain Genome-Scale Metabolic Modeling. Int J Mol Sci 2023; 24:ijms24087091. [PMID: 37108252 PMCID: PMC10138676 DOI: 10.3390/ijms24087091] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/01/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
Bacillus subtilis is an effective workhorse for the production of many industrial products. The high interest aroused by B. subtilis has guided a large metabolic modeling effort of this species. Genome-scale metabolic models (GEMs) are powerful tools for predicting the metabolic capabilities of a given organism. However, high-quality GEMs are required in order to provide accurate predictions. In this work, we construct a high-quality, mostly manually curated genome-scale model for B. subtilis (iBB1018). The model was validated by means of growth performance and carbon flux distribution and provided significantly more accurate predictions than previous models. iBB1018 was able to predict carbon source utilization with great accuracy while identifying up to 28 metabolites as potential novel carbon sources. The constructed model was further used as a tool for the construction of the panphenome of B. subtilis as a species, by means of multistrain genome-scale reconstruction. The panphenome space was defined in the context of 183 GEMs representative of 183 B. subtilis strains and the array of carbon sources sustaining growth. Our analysis highlights the large metabolic versatility of the species and the important role of the accessory metabolism as a driver of the panphenome, at a species level.
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Affiliation(s)
- Blas Blázquez
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), 28040 Madrid, Spain
| | - David San León
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), 28040 Madrid, Spain
| | - Antonia Rojas
- Archer Daniels Midland, Nutrition, Biopolis S.L. Parc Científic Universitat de València, Carrer del Catedrático Agustín Escardino Benlloch, 9, 46980 Paterna, Spain
| | - Marta Tortajada
- Archer Daniels Midland, Nutrition, Biopolis S.L. Parc Científic Universitat de València, Carrer del Catedrático Agustín Escardino Benlloch, 9, 46980 Paterna, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), 28040 Madrid, Spain
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Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions. Metab Eng 2023; 76:120-132. [PMID: 36720400 DOI: 10.1016/j.ymben.2023.01.008] [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: 06/18/2022] [Revised: 12/13/2022] [Accepted: 01/21/2023] [Indexed: 01/29/2023]
Abstract
Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.
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36
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Yu T, Boob AG, Volk MJ, Liu X, Cui H, Zhao H. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal 2023. [DOI: 10.1038/s41929-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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37
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Lim PK, Julca I, Mutwil M. Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data. Comput Struct Biotechnol J 2023; 21:1639-1650. [PMID: 36874159 PMCID: PMC9976193 DOI: 10.1016/j.csbj.2023.01.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
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Affiliation(s)
- Peng Ken Lim
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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38
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Merging enzymatic and synthetic chemistry with computational synthesis planning. Nat Commun 2022; 13:7747. [PMID: 36517480 PMCID: PMC9750992 DOI: 10.1038/s41467-022-35422-y] [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: 09/13/2022] [Accepted: 11/30/2022] [Indexed: 12/15/2022] Open
Abstract
Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which are valuable due to their selectivity and sustainability but are few in number. We report a retrosynthetic search algorithm using two neural network models for retrosynthesis-one covering 7984 enzymatic transformations and one 163,723 synthetic transformations-that balances the exploration of enzymatic and synthetic reactions to identify hybrid synthesis plans. This approach extends the space of retrosynthetic moves by thousands of uniquely enzymatic one-step transformations, discovers routes to molecules for which synthetic or enzymatic searches find none, and designs shorter routes for others. Application to (-)-Δ9 tetrahydrocannabinol (THC) (dronabinol) and R,R-formoterol (arformoterol) illustrates how our strategy facilitates the replacement of metal catalysis, high step counts, or costly enantiomeric resolution with more elegant hybrid proposals.
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39
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Systems biology's role in leveraging microalgal biomass potential: Current status and future perspectives. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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40
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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41
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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42
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Enuh BM, Nural Yaman B, Tarzi C, Aytar Çelik P, Mutlu MB, Angione C. Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use. Microbiologyopen 2022; 11:e1328. [PMID: 36314754 PMCID: PMC9597258 DOI: 10.1002/mbo3.1328] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 10/01/2022] [Indexed: 11/06/2022] Open
Abstract
Salt tolerant organisms are increasingly being used for the industrial production of high-value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole-genome sequencing allow in-depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome-scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology.
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Affiliation(s)
- Blaise Manga Enuh
- Biotechnology and Biosafety Department, Graduate and Natural Applied ScienceEskişehir Osmangazi UniversityEskişehirTurkey
| | - Belma Nural Yaman
- Biotechnology and Biosafety Department, Graduate and Natural Applied ScienceEskişehir Osmangazi UniversityEskişehirTurkey
- Department of Biomedical Engineering, Faculty of Engineering and ArchitectureEskişehir Osmangazi UniversityEskişehirTurkey
| | - Chaimaa Tarzi
- School of Computing, Engineering & Digital TechnologiesTeesside UniversityMiddlesbroughUK
| | - Pınar Aytar Çelik
- Biotechnology and Biosafety Department, Graduate and Natural Applied ScienceEskişehir Osmangazi UniversityEskişehirTurkey
- Environmental Protection and Control ProgramEskişehir Osmangazi UniversityEskişehirTurkey
| | - Mehmet Burçin Mutlu
- Department of Biology, Faculty of ScienceEskisehir Technical UniversityEskisehirTurkey
| | - Claudio Angione
- School of Computing, Engineering & Digital TechnologiesTeesside UniversityMiddlesbroughUK
- Centre for Digital InnovationTeesside UniversityMiddlesbroughUK
- National Horizons CentreTeesside UniversityDarlingtonUK
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43
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The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering. Nat Commun 2022; 13:5082. [PMID: 36038542 PMCID: PMC9424320 DOI: 10.1038/s41467-022-32661-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/11/2022] [Indexed: 11/27/2022] Open
Abstract
Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.
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44
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Cheawchanlertfa P, Chitcharoen S, Raethong N, Liu Q, Chumnanpuen P, Soommat P, Song Y, Koffas M, Laoteng K, Vongsangnak W. Enhancing Genome-Scale Model by Integrative Exometabolome and Transcriptome: Unveiling Carbon Assimilation towards Sphingolipid Biosynthetic Capability of Cordyceps militaris. J Fungi (Basel) 2022; 8:887. [PMID: 36012875 PMCID: PMC9409897 DOI: 10.3390/jof8080887] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/15/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Cordyceps militaris is an industrially important fungus, which is often used in Asia as traditional medicine. There has been a published genome-scale metabolic model (GSMM) of C. militaris useful for predicting its growth behaviors; however, lipid metabolism, which plays a vital role in cellular functions, remains incomplete in the GSMM of C. militaris. A comprehensive study on C. militaris was thus performed by enhancing GSMM through integrative analysis of metabolic footprint and transcriptome data. Through the enhanced GSMM of C. militaris (called iPC1469), it contained 1469 genes, 1904 metabolic reactions and 1229 metabolites. After model evaluation, in silico growth simulation results agreed well with the experimental data of the fungal growths on different carbon sources. Beyond the model-driven integrative data analysis, interestingly, we found key metabolic responses in alteration of lipid metabolism in C. militaris upon different carbon sources. The sphingoid bases (e.g., sphinganine, sphingosine, and phytosphingosine) and ceramide were statistically significant accumulated in the xylose culture when compared with other cultures; this study suggests that the sphingolipid biosynthetic capability in C. militaris was dependent on the carbon source assimilated for cell growth; this finding provides a comprehensive basis for the sphingolipid biosynthesis in C. militaris that can help to further redesign its metabolic control for medicinal and functional food applications.
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Affiliation(s)
| | - Suwalak Chitcharoen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand
| | - Nachon Raethong
- Institute of Nutrition, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Qing Liu
- Colin Ratledge Center for Microbial Lipids, School of Agriculture Engineering and Food Sciences, Shandong University of Technology, Zibo 255000, China
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
| | - Panyawarin Soommat
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Genetic Engineering and Bioinformatics Program, Graduate School, Kasetsart University, Bangkok 10900, Thailand
| | - Yuanda Song
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Colin Ratledge Center for Microbial Lipids, School of Agriculture Engineering and Food Sciences, Shandong University of Technology, Zibo 255000, China
| | - Mattheos Koffas
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Kobkul Laoteng
- Industrial Bioprocess Technology Research Team, Functional Ingredients and Food Innovation Research Group, National Center for Genetic Engineering and Biotechnology BIOTEC, National Science and Technology Development Agency NSTDA, Pathum Thani 12120, Thailand
| | - Wanwipa Vongsangnak
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
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45
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Soudier P, Zúñiga A, Duigou T, Voyvodic PL, Bazi-Kabbaj K, Kushwaha M, Vendrell JA, Solassol J, Bonnet J, Faulon JL. PeroxiHUB: A Modular Cell-Free Biosensing Platform Using H 2O 2 as Signal Integrator. ACS Synth Biol 2022; 11:2578-2588. [PMID: 35913043 DOI: 10.1021/acssynbio.2c00138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cell-free systems have great potential for delivering robust, inexpensive, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of PeroxiHUB, an optimized H2O2-centered sensing platform supporting cell-free detection of different metabolites. H2O2 is a central metabolite and a byproduct of numerous enzymatic reactions. PeroxiHUB uses enzymatic transducers to convert metabolites of interest into H2O2, enabling rapid reprogramming of sensor specificity using alternative transducers. We first screen several transcription factors and optimize OxyR for the transcriptional response to H2O2 in a cell-free system, highlighting the need for preincubation steps to obtain suitable signal-to-noise ratios. We then demonstrate modular detection of metabolites of clinical interest─lactate, sarcosine, and choline─using different transducers mined via a custom retrosynthesis workflow publicly available on the SynBioCAD Galaxy portal. We find that expressing the transducer during the preincubation step is crucial for optimal sensor operation. We then show that different reporters can be connected to PeroxiHUB, providing high adaptability for various applications. Finally, we demonstrate that a peroxiHUB lactate biosensor can detect endogenous levels of this metabolite in clinical samples. Given the wide range of enzymatic reactions producing H2O2, the PeroxiHUB platform will support cell-free detection of a large number of metabolites in a modular and scalable fashion.
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Affiliation(s)
- Paul Soudier
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France.,Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Ana Zúñiga
- Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Thomas Duigou
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Peter L Voyvodic
- Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Kenza Bazi-Kabbaj
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Manish Kushwaha
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Julie A Vendrell
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France
| | - Jerome Solassol
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France.,IRCM, INSERM, Univ Montpellier, ICM, 34298 Montpellier, France
| | - Jerome Bonnet
- Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Jean-Loup Faulon
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
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46
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Capela J, Lagoa D, Rodrigues R, Cunha E, Cruz F, Barbosa A, Bastos J, Lima D, Ferreira EC, Rocha M, Dias O. merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models. Nucleic Acids Res 2022; 50:6052-6066. [PMID: 35694833 PMCID: PMC9226533 DOI: 10.1093/nar/gkac459] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/10/2022] [Indexed: 01/18/2023] Open
Abstract
Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.
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Affiliation(s)
- João Capela
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Davide Lagoa
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Ruben Rodrigues
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Emanuel Cunha
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Fernando Cruz
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Ana Barbosa
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - José Bastos
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Diogo Lima
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Eugénio C Ferreira
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
- LABBELS – Associate Laboratory, Braga/Guimarães, Portugal
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47
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Li F, Yuan L, Lu H, Li G, Chen Y, Engqvist MKM, Kerkhoven EJ, Nielsen J. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat Catal 2022. [DOI: 10.1038/s41929-022-00798-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
AbstractEnzyme turnover numbers (kcat) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput kcat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture kcat changes for mutated enzymes and identify amino acid residues with a strong impact on kcat values. We applied this approach to predict genome-scale kcat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted kcat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.
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48
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Shi Z, Liu P, Liao X, Mao Z, Zhang J, Wang Q, Sun J, Ma H, Ma Y. Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing. BIODESIGN RESEARCH 2022; 2022:9898461. [PMID: 37850146 PMCID: PMC10521697 DOI: 10.34133/2022/9898461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/26/2022] [Indexed: 10/19/2023] Open
Abstract
Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.
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Affiliation(s)
- Zhenkun Shi
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Pi Liu
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Xiaoping Liao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Zhitao Mao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jianqi Zhang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Qinhong Wang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jibin Sun
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Hongwu Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Yanhe Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
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49
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Zheng S, Zeng T, Li C, Chen B, Coley CW, Yang Y, Wu R. Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP. Nat Commun 2022; 13:3342. [PMID: 35688826 PMCID: PMC9187661 DOI: 10.1038/s41467-022-30970-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 05/27/2022] [Indexed: 12/30/2022] Open
Abstract
The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs. The complete biosynthetic pathway from most natural products (NPs) are unknown. Here, the authors report BioNavi-NP, a computational toolkit for bio-retrosynthetic pathway elucidation or reconstruction for both NPs and NP-like compounds.
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Affiliation(s)
- Shuangjia Zheng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.,School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Galixir, Beijing, China.,School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Tao Zeng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
| | | | - Binghong Chen
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
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50
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Zakhartsev M, Rotnes F, Gulla M, Øyås O, van Dam JCJ, Suarez-Diez M, Grammes F, Hafþórsson RA, van Helvoirt W, Koehorst JJ, Schaap PJ, Jin Y, Mydland LT, Gjuvsland AB, Sandve SR, Martins dos Santos VAP, Vik JO. SALARECON connects the Atlantic salmon genome to growth and feed efficiency. PLoS Comput Biol 2022; 18:e1010194. [PMID: 35687595 PMCID: PMC9223387 DOI: 10.1371/journal.pcbi.1010194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 06/23/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022] Open
Abstract
Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds. Atlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.
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Affiliation(s)
- Maksim Zakhartsev
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Filip Rotnes
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Marie Gulla
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ove Øyås
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Jesse C. J. van Dam
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Fabian Grammes
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - Wout van Helvoirt
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Jasper J. Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Yang Jin
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Liv Torunn Mydland
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Arne B. Gjuvsland
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Simen R. Sandve
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - Jon Olav Vik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
- * E-mail:
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