1
|
Mao J, Zhang H, Chen Y, Wei L, Liu J, Nielsen J, Chen Y, Xu N. Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms. Biotechnol Adv 2024; 74:108401. [PMID: 38944217 DOI: 10.1016/j.biotechadv.2024.108401] [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: 02/01/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 07/01/2024]
Abstract
Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.
Collapse
Affiliation(s)
- Jiwei Mao
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Hongyu Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, PR China
| | - Liang Wei
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jun Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen, Denmark.
| | - Yun Chen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kongens Lyngby, Denmark.
| | - Ning Xu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China.
| |
Collapse
|
2
|
Chi J, Shu J, Li M, Mudappathi R, Jin Y, Lewis F, Boon A, Qin X, Liu L, Gu H. Artificial Intelligence in Metabolomics: A Current Review. Trends Analyt Chem 2024; 178:117852. [PMID: 39071116 PMCID: PMC11271759 DOI: 10.1016/j.trac.2024.117852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.
Collapse
Affiliation(s)
- Jinhua Chi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Jingmin Shu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Ming Li
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA
- University of Arizona College of Medicine, Phoenix, AZ 85004, USA
| | - Rekha Mudappathi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Yan Jin
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Freeman Lewis
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Alexandria Boon
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Xiaoyan Qin
- College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| |
Collapse
|
3
|
Ahmad Z, Shareen, Ganie IB, Firdaus F, Ramakrishnan M, Shahzad A, Ding Y. Enhancing Withanolide Production in the Withania Species: Advances in In Vitro Culture and Synthetic Biology Approaches. PLANTS (BASEL, SWITZERLAND) 2024; 13:2171. [PMID: 39124289 PMCID: PMC11313931 DOI: 10.3390/plants13152171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
Withanolides are naturally occurring steroidal lactones found in certain species of the Withania genus, especially Withania somnifera (commonly known as Ashwagandha). These compounds have gained considerable attention due to their wide range of therapeutic properties and potential applications in modern medicine. To meet the rapidly growing demand for withanolides, innovative approaches such as in vitro culture techniques and synthetic biology offer promising solutions. In recent years, synthetic biology has enabled the production of engineered withanolides using heterologous systems, such as yeast and bacteria. Additionally, in vitro methods like cell suspension culture and hairy root culture have been employed to enhance withanolide production. Nevertheless, one of the primary obstacles to increasing the production of withanolides using these techniques has been the intricacy of the biosynthetic pathways for withanolides. The present article examines new developments in withanolide production through in vitro culture. A comprehensive summary of viable traditional methods for producing withanolide is also provided. The development of withanolide production in heterologous systems is examined and emphasized. The use of machine learning as a potent tool to model and improve the bioprocesses involved in the generation of withanolide is then discussed. In addition, the control and modification of the withanolide biosynthesis pathway by metabolic engineering mediated by CRISPR are discussed.
Collapse
Affiliation(s)
- Zishan Ahmad
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| | - Shareen
- Department of Environmental Engineering, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China;
| | - Irfan Bashir Ganie
- Department of Botany, Aligarh Muslim University, Aligarh 202002, India; (I.B.G.); (A.S.)
| | - Fatima Firdaus
- Chemistry Department, Lucknow University, Lucknow 226007, India;
| | - Muthusamy Ramakrishnan
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| | - Anwar Shahzad
- Department of Botany, Aligarh Muslim University, Aligarh 202002, India; (I.B.G.); (A.S.)
| | - Yulong Ding
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| |
Collapse
|
4
|
Perrot T, Marc J, Lezin E, Papon N, Besseau S, Courdavault V. Emerging trends in production of plant natural products and new-to-nature biopharmaceuticals in yeast. Curr Opin Biotechnol 2024; 87:103098. [PMID: 38452572 DOI: 10.1016/j.copbio.2024.103098] [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: 11/15/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 03/09/2024]
Abstract
Natural products represent an inestimable source of valuable compounds for human health. Notably, those produced by plants remain challenging to access due to their low production. Potential shortages of plant-derived biopharmaceuticals caused by climate change or pandemics also regularly tense the market trends. Thus, biotechnological alternatives of supply based on synthetic biology have emerged. These innovative strategies mostly rely on the use of engineered microbial systems for compound synthesis. In this regard, yeasts remain the easiest-tractable eukaryotic models and a convenient chassis for reconstructing whole biosynthetic routes for the heterologous production of plant-derived metabolites. Here, we highlight the recent discoveries dedicated to the bioproduction of new-to-nature compounds in yeasts and provide an overview of emerging strategies for optimising bioproduction.
Collapse
Affiliation(s)
- Thomas Perrot
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, Tours, France
| | - Jillian Marc
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, Tours, France
| | - Enzo Lezin
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, Tours, France
| | - Nicolas Papon
- Univ Angers, Univ Brest, IRF, SFR ICAT, F-49000 Angers, France
| | - Sébastien Besseau
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, Tours, France
| | - Vincent Courdavault
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, Tours, France.
| |
Collapse
|
5
|
Helmy M, Elhalis H, Rashid MM, Selvarajoo K. Can digital twin efforts shape microorganism-based alternative food? Curr Opin Biotechnol 2024; 87:103115. [PMID: 38547588 DOI: 10.1016/j.copbio.2024.103115] [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: 11/29/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 06/09/2024]
Abstract
With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.
Collapse
Affiliation(s)
- Mohamed Helmy
- Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, SK, Canada; Department of Computer Science, Lakehead University, ON, Canada; Department of Computer Science, College of Science and Engineering, Idaho State University, ID, USA; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Hosam Elhalis
- Research School of Biology, Australian National University, Canberra, Australia
| | - Md Mamunur Rashid
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Kumar Selvarajoo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Synthetic Biology Translational Research Program and SynCTI, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore 117456, Singapore; School of Biological Sciences, Nanyang Technological University (NTU), Singapore 637551, Singapore.
| |
Collapse
|
6
|
Liu Z, Ying J, Liu C. Changes in Rhizosphere Soil Microorganisms and Metabolites during the Cultivation of Fritillaria cirrhosa. BIOLOGY 2024; 13:334. [PMID: 38785816 PMCID: PMC11117757 DOI: 10.3390/biology13050334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
Fritillaria cirrhosa is an important cash crop, and its industrial development is being hampered by continuous cropping obstacles, but the composition and changes of rhizosphere soil microorganisms and metabolites in the cultivation process of Fritillaria cirrhosa have not been revealed. We used metagenomics sequencing to analyze the changes of the microbiome in rhizosphere soil during a three-year cultivation process, and combined it with LC-MS/MS to detect the changes of metabolites. Results indicate that during the cultivation of Fritillaria cirrhosa, the composition and structure of the rhizosphere soil microbial community changed significantly, especially regarding the relative abundance of some beneficial bacteria. The abundance of Bradyrhizobium decreased from 7.04% in the first year to about 5% in the second and third years; the relative abundance of Pseudomonas also decreased from 6.20% in the first year to 2.22% in the third year; and the relative abundance of Lysobacter decreased significantly from more than 4% in the first two years of cultivation to 1.01% in the third year of cultivation. However, the relative abundance of some harmful fungi has significantly increased, such as Botrytis, which increased significantly from less than 3% in the first two years to 7.93% in the third year, and Talaromyces fungi, which were almost non-existent in the first two years of cultivation, significantly increased to 3.43% in the third year of cultivation. The composition and structure of Fritillaria cirrhosa rhizosphere metabolites also changed significantly, the most important of which were carbohydrates represented by sucrose (48.00-9.36-10.07%) and some amino acid compounds related to continuous cropping obstacles. Co-occurrence analysis showed that there was a significant correlation between differential microorganisms and differential metabolites, but Procrustes analysis showed that the relationship between bacteria and metabolites was closer than that between fungi and metabolites. In general, in the process of Fritillaria cirrhosa cultivation, the beneficial bacteria in the rhizosphere decreased, the harmful bacteria increased, and the relative abundance of carbohydrate and amino acid compounds related to continuous cropping obstacles changed significantly. There is a significant correlation between microorganisms and metabolites, and the shaping of the Fritillaria cirrhosa rhizosphere's microecology by bacteria is more relevant.
Collapse
Affiliation(s)
- Zhixiang Liu
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Jizhe Ying
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China;
| | - Chengcheng Liu
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| |
Collapse
|
7
|
Ko YJ, Lee ME, Cho BH, Kim M, Hyeon JE, Han JH, Han SO. Bioproduction of porphyrins, phycobilins, and their proteins using microbial cell factories: engineering, metabolic regulations, challenges, and perspectives. Crit Rev Biotechnol 2024; 44:373-387. [PMID: 36775664 DOI: 10.1080/07388551.2023.2168512] [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: 09/07/2022] [Revised: 11/21/2022] [Accepted: 01/03/2023] [Indexed: 02/14/2023]
Abstract
Porphyrins, phycobilins, and their proteins have abundant π-electrons and strongly absorb visible light, some of which bind a metal ion in the center. Because of the structural and optical properties, they not only play critical roles as an essential component in natural systems but also have attracted much attention as a high value specialty chemical in various fields, including renewable energy, cosmetics, medicines, and foods. However, their commercial application seems to be still limited because the market price of porphyrins and phycobilins is generally expensive to apply them easily. Furthermore, their petroleum-based chemical synthesis is energy-intensive and emits a pollutant. Recently, to replace petroleum-based production, many studies on the bioproduction of metalloporphyrins, including Zn-porphyrin, Co-porphyrin, and heme, porphyrin derivatives including chlorophyll, biliverdin, and phycobilins, and their proteins including hemoproteins, phycobiliproteins, and phytochromes from renewable carbon sources using microbial cell factories have been reported. This review outlines recent advances in the bioproduction of porphyrins, phycobilins, and their proteins using microbial cell factories developed by various microbial biotechnology techniques, provides well-organized information on metabolic regulations of the porphyrin metabolism, and then critically discusses challenges and future perspectives. Through these, it is expected to be able to achieve possible solutions and insights and to develop an outstanding platform to be applied to the industry in future research.
Collapse
Affiliation(s)
- Young Jin Ko
- Department of Biotechnology, Korea University, Seoul, Republic of Korea
- Institute of Life Science and Natural Resources, Korea University, Seoul, Korea
| | - Myeong-Eun Lee
- Department of Biotechnology, Korea University, Seoul, Republic of Korea
| | - Byeong-Hyeon Cho
- Department of Biotechnology, Korea University, Seoul, Republic of Korea
| | - Minhye Kim
- Department of Biotechnology, Korea University, Seoul, Republic of Korea
| | - Jeong Eun Hyeon
- Department of Next Generation Applied Sciences, The Graduate School of Sungshin University, Seoul, Korea
- Department of Food Science and Biotechnology, College of Knowledge-Based Services Engineering, Sungshin Women's University, Seoul, Korea
| | - Joo Hee Han
- Department of Next Generation Applied Sciences, The Graduate School of Sungshin University, Seoul, Korea
- Department of Food Science and Biotechnology, College of Knowledge-Based Services Engineering, Sungshin Women's University, Seoul, Korea
| | - Sung Ok Han
- Department of Biotechnology, Korea University, Seoul, Republic of Korea
| |
Collapse
|
8
|
Orsi E, Schada von Borzyskowski L, Noack S, Nikel PI, Lindner SN. Automated in vivo enzyme engineering accelerates biocatalyst optimization. Nat Commun 2024; 15:3447. [PMID: 38658554 PMCID: PMC11043082 DOI: 10.1038/s41467-024-46574-4] [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: 12/21/2023] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
Collapse
Affiliation(s)
- Enrico Orsi
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | | | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam-Golm, Germany.
- Department of Biochemistry, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, 10117, Berlin, Germany.
| |
Collapse
|
9
|
Siddharth T, Lewis NE. Predicting pathways for old and new metabolites through clustering. J Theor Biol 2024; 578:111684. [PMID: 38048983 PMCID: PMC11139542 DOI: 10.1016/j.jtbi.2023.111684] [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: 06/08/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 12/06/2023]
Abstract
The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While the discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB (Human Metabolome Database), only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.
Collapse
Affiliation(s)
- Thiru Siddharth
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Bhopal, MP 462003, India
| | - Nathan E Lewis
- Department of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
10
|
Freischem LJ, Oyarzún DA. A Machine Learning Approach for Predicting Essentiality of Metabolic Genes. Methods Mol Biol 2024; 2760:345-369. [PMID: 38468098 DOI: 10.1007/978-1-0716-3658-9_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.
Collapse
Affiliation(s)
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, UK.
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
| |
Collapse
|
11
|
Li G, Jia L, Wang K, Sun T, Huang J. Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning. Molecules 2023; 28:8097. [PMID: 38138586 PMCID: PMC10746113 DOI: 10.3390/molecules28248097] [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: 09/07/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.
Collapse
Affiliation(s)
- Gaolin Li
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China;
| | - Lili Jia
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou 311400, China;
| | - Kang Wang
- Department of Physics, Zhejiang University of Science and Technology, Hangzhou 310023, China;
| | - Tingting Sun
- Department of Physics, Zhejiang University of Science and Technology, Hangzhou 310023, China;
| | - Jun Huang
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China;
| |
Collapse
|
12
|
Pepe M, Hesami M, de la Cerda KA, Perreault ML, Hsiang T, Jones AMP. A journey with psychedelic mushrooms: From historical relevance to biology, cultivation, medicinal uses, biotechnology, and beyond. Biotechnol Adv 2023; 69:108247. [PMID: 37659744 DOI: 10.1016/j.biotechadv.2023.108247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
Psychedelic mushrooms containing psilocybin and related tryptamines have long been used for ethnomycological purposes, but emerging evidence points to the potential therapeutic value of these mushrooms to address modern neurological, psychiatric health, and related disorders. As a result, psilocybin containing mushrooms represent a re-emerging frontier for mycological, biochemical, neuroscience, and pharmacology research. This work presents crucial information related to traditional use of psychedelic mushrooms, as well as research trends and knowledge gaps related to their diversity and distribution, technologies for quantification of tryptamines and other tryptophan-derived metabolites, as well as biosynthetic mechanisms for their production within mushrooms. In addition, we explore the current state of knowledge for how psilocybin and related tryptamines are metabolized in humans and their pharmacological effects, including beneficial and hazardous human health implications. Finally, we describe opportunities and challenges for investigating the production of psychedelic mushrooms and metabolic engineering approaches to alter secondary metabolite profiles using biotechnology integrated with machine learning. Ultimately, this critical review of all aspects related to psychedelic mushrooms represents a roadmap for future research efforts that will pave the way to new applications and refined protocols.
Collapse
Affiliation(s)
- Marco Pepe
- Department of Plant Agriculture, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | - Karla A de la Cerda
- School of Environmental Sciences, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | - Melissa L Perreault
- Departments of Biomedical Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Tom Hsiang
- School of Environmental Sciences, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | | |
Collapse
|
13
|
Han T, Nazarbekov A, Zou X, Lee SY. Recent advances in systems metabolic engineering. Curr Opin Biotechnol 2023; 84:103004. [PMID: 37778304 DOI: 10.1016/j.copbio.2023.103004] [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: 07/20/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 10/03/2023]
Abstract
Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.
Collapse
Affiliation(s)
- Taehee Han
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 34141 Daejeon, the Republic of Korea
| | - Alisher Nazarbekov
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea
| | - Xuan Zou
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 34141 Daejeon, the Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 34141 Daejeon, the Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon 34141, the Republic of Korea.
| |
Collapse
|
14
|
Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
Collapse
Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| |
Collapse
|
15
|
Qin J, Kurt E, LBassi T, Sa L, Xie D. Biotechnological production of omega-3 fatty acids: current status and future perspectives. Front Microbiol 2023; 14:1280296. [PMID: 38029217 PMCID: PMC10662050 DOI: 10.3389/fmicb.2023.1280296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Omega-3 fatty acids, including alpha-linolenic acids (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), have shown major health benefits, but the human body's inability to synthesize them has led to the necessity of dietary intake of the products. The omega-3 fatty acid market has grown significantly, with a global market from an estimated USD 2.10 billion in 2020 to a predicted nearly USD 3.61 billion in 2028. However, obtaining a sufficient supply of high-quality and stable omega-3 fatty acids can be challenging. Currently, fish oil serves as the primary source of omega-3 fatty acids in the market, but it has several drawbacks, including high cost, inconsistent product quality, and major uncertainties in its sustainability and ecological impact. Other significant sources of omega-3 fatty acids include plants and microalgae fermentation, but they face similar challenges in reducing manufacturing costs and improving product quality and sustainability. With the advances in synthetic biology, biotechnological production of omega-3 fatty acids via engineered microbial cell factories still offers the best solution to provide a more stable, sustainable, and affordable source of omega-3 fatty acids by overcoming the major issues associated with conventional sources. This review summarizes the current status, key challenges, and future perspectives for the biotechnological production of major omega-3 fatty acids.
Collapse
Affiliation(s)
| | | | | | | | - Dongming Xie
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, United States
| |
Collapse
|
16
|
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.
Collapse
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.
| |
Collapse
|
17
|
Qu G, Liu Y, Ma Q, Li J, Du G, Liu L, Lv X. Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:15926-15941. [PMID: 37856872 DOI: 10.1021/acs.jafc.3c05074] [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: 10/21/2023]
Abstract
To achieve an adequate sense of sweetness with a healthy low-sugar diet, it is necessary to explore and produce sugar alternatives. Recently, glycoside sweeteners and their biosynthetic approaches have attracted the attention of researchers. In this review, we first outlined the synthetic pathways of glycoside sweeteners, including the key enzymes and rate-limiting steps. Next, we reviewed the progress in engineered microorganisms producing glycoside sweeteners, including de novo synthesis, whole-cell catalysis synthesis, and in vitro synthesis. The applications of metabolic engineering strategies, such as cofactor engineering and enzyme modification, in the optimization of glycoside sweetener biosynthesis were summarized. Finally, the prospects of combining enzyme engineering and machine learning strategies to enhance the production of glycoside sweeteners were discussed. This review provides a perspective on synthesizing glycoside sweeteners in microbial cells, theoretically guiding the bioproduction of glycoside sweeteners.
Collapse
Affiliation(s)
- Guanyi Qu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
- Shandong Jincheng Biological Pharmaceutical Company, Limited, Zibo 255000, P. R. China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
| | - Qinyuan Ma
- Shandong Jincheng Biological Pharmaceutical Company, Limited, Zibo 255000, P. R. China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
- Yixing Institute of Food Biotechnology Company, Limited, Yixing 214200, P. R. China
- Food Laboratory of Zhongyuan, Jiangnan University, Wuxi 214122, P. R. China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
- Yixing Institute of Food Biotechnology Company, Limited, Yixing 214200, P. R. China
| |
Collapse
|
18
|
Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
Collapse
Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
| |
Collapse
|
19
|
Khamwachirapithak P, Sae-Tang K, Mhuantong W, Tanapongpipat S, Zhao XQ, Liu CG, Wei DQ, Champreda V, Runguphan W. Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications. ACS Synth Biol 2023; 12:2897-2908. [PMID: 37681736 PMCID: PMC10594650 DOI: 10.1021/acssynbio.3c00199] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Indexed: 09/09/2023]
Abstract
Bioethanol has gained popularity in recent decades as an ecofriendly alternative to fossil fuels due to increasing concerns about global climate change. However, economically viable ethanol fermentation remains a challenge. High-temperature fermentation can reduce production costs, but Saccharomyces cerevisiae yeast strains normally ferment poorly under high temperatures. In this study, we present a machine learning (ML) approach to optimize bioethanol production in S. cerevisiae by fine-tuning the promoter activities of three endogenous genes. We created 216 combinatorial strains of S. cerevisiae by replacing native promoters with five promoters of varying strengths to regulate ethanol production. Promoter replacement resulted in a 63% improvement in ethanol production at 30 °C. We created an ML-guided workflow by utilizing XGBoost to train high-performance models based on promoter strengths and cellular metabolite concentrations obtained from ethanol production of 216 combinatorial strains at 30 °C. This strategy was then applied to optimize ethanol production at 40 °C, where we selected 31 strains for experimental fermentation. This reduced experimental load led to a 7.4% increase in ethanol production in the second round of the ML-guided workflow. Our study offers a comprehensive library of promoter strength modifications for key ethanol production enzymes, showcasing how machine learning can guide yeast strain optimization and make bioethanol production more cost-effective and efficient. Furthermore, we demonstrate that metabolic engineering processes can be accelerated and optimized through this approach.
Collapse
Affiliation(s)
- Peerapat Khamwachirapithak
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Kittapong Sae-Tang
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Wuttichai Mhuantong
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Sutipa Tanapongpipat
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Xin-Qing Zhao
- State
Key Laboratory of Microbial Metabolism, Joint International Research
Laboratory of Metabolic & Developmental Sciences, School of Life
Sciences and Biotechnology, Shanghai Jiao
Tong University, Shanghai 200240, People’s
Republic of China
| | - Chen-Guang Liu
- State
Key Laboratory of Microbial Metabolism, Joint International Research
Laboratory of Metabolic & Developmental Sciences, School of Life
Sciences and Biotechnology, Shanghai Jiao
Tong University, Shanghai 200240, People’s
Republic of China
| | - Dong-Qing Wei
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Verawat Champreda
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Weerawat Runguphan
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| |
Collapse
|
20
|
van Lent P, Schmitz J, Abeel T. Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering. ACS Synth Biol 2023; 12:2588-2599. [PMID: 37616156 PMCID: PMC10510747 DOI: 10.1021/acssynbio.3c00186] [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: 03/30/2023] [Indexed: 08/25/2023]
Abstract
Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.
Collapse
Affiliation(s)
- Paul van Lent
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
| | - Joep Schmitz
- Department
of Science and Research, Joep Schmitz -
dsm-firmenich, Science & Research, P.O. Box 1, 2600
MA Delft, The Netherlands
| | - Thomas Abeel
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
- Infectious
Disease and Microbiome Program, Broad Institute
of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| |
Collapse
|
21
|
Aida H, Ying BW. Efforts to Minimise the Bacterial Genome as a Free-Living Growing System. BIOLOGY 2023; 12:1170. [PMID: 37759570 PMCID: PMC10525146 DOI: 10.3390/biology12091170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023]
Abstract
Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.
Collapse
Affiliation(s)
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Ibaraki, Japan
| |
Collapse
|
22
|
Shah HA, Liu J, Yang Z, Yang F, Zhang Q, Feng J. DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural networks based on molecular properties. J Bioinform Comput Biol 2023; 21:2350017. [PMID: 37632195 DOI: 10.1142/s0219720023500178] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Metabolic pathways play a crucial role in understanding the biochemistry of organisms. In metabolic pathways, modules refer to clusters of interconnected reactions or sub-networks representing specific functional units or biological processes within the overall pathway. In pathway modules, compounds are major elements and refer to the various molecules that participate in the biochemical reactions within the pathway modules. These molecules can include substrates, intermediates and final products. Determining the presence relation of compounds and pathway modules is essential for synthesizing new molecules and predicting hidden reactions. To date, several computational methods have been proposed to address this problem. However, all methods only predict the metabolic pathways and their types, not the pathway modules. To address this issue, we proposed a novel deep learning model, DeepRT that integrates message passing neural networks (MPNNs) and transformer encoder. This combination allows DeepRT to effectively extract global and local structure information from the molecular graph. The model is designed to perform two tasks: first, determining the present relation of the compound with the pathway module, and second, predicting the relation of query compound and module classes. The proposed DeepRT model evaluated on a dataset comprising compounds and pathway modules, and it outperforms existing approaches.
Collapse
Affiliation(s)
- Hayat Ali Shah
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China
| | - Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China
| | - Feng Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China
| | - Qiang Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China
| | - Jing Feng
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China
| |
Collapse
|
23
|
Meng X, Xu P, Tao F. RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle. iScience 2023; 26:107069. [PMID: 37426353 PMCID: PMC10329182 DOI: 10.1016/j.isci.2023.107069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/18/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.
Collapse
Affiliation(s)
- Xuanlin Meng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| |
Collapse
|
24
|
Huang XY, Ao TJ, Zhang X, Li K, Zhao XQ, Champreda V, Runguphan W, Sakdaronnarong C, Liu CG, Bai FW. Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation. BIORESOURCE TECHNOLOGY 2023:129375. [PMID: 37352987 DOI: 10.1016/j.biortech.2023.129375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 06/25/2023]
Abstract
Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model's generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R2 at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R2 at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R2 at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.
Collapse
Affiliation(s)
- Xiao-Yan Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tian-Jie Ao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xue Zhang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kai Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin-Qing Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Verawat Champreda
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA) 113 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Weerawat Runguphan
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA) 113 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Chularat Sakdaronnarong
- Department of Chemical Engineering, Faculty of Engineering, Mahidol University, 25/25, Putthamonthon 4 Road, Salaya, Putthamonthon, Nakhon Pathom 73170 Thailand
| | - Chen-Guang Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Feng-Wu Bai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
25
|
Wang Q, Xu T, Xu K, Lu Z, Ying J. Prediction of transport proteins from sequence information with the deep learning approach. Comput Biol Med 2023; 160:106974. [PMID: 37167658 DOI: 10.1016/j.compbiomed.2023.106974] [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: 12/13/2022] [Revised: 04/17/2023] [Accepted: 04/22/2023] [Indexed: 05/13/2023]
Abstract
Transport proteins (TPs) are vital to the growth and life of all living things, especially in fields of microbial pathogenesis and drug resistance of tumor cells. Accurately identifying potential TPs remains an important challenge for the advancement of functional genomics. This study aimed to develop a tool for predicting TPs using the deep learning approach. Here, we proposed DeepTP, a convolutional neural network model that uses parallel subnetworks to extract features from protein sequences and uses fully connected layers for TP classification. To train and evaluate the performance of the developed model, datasets were collected from the UniProtKB/Swiss-Prot database. The test results revealed that the proposed model could successfully identify TPs with the AUCROC, accuracy, F-value, and Matthews correlation coefficient of 0.9719, 0.9513, 0.8982, and 0.8679, respectively. By further comparison, DeepTP achieved better performance than other commonly used methods. Analysis of the gradients of prediction score concerning input suggested that DeepTP makes predictions by recognizing the functional domains of TPs. We anticipate that DeepTP will serve as a useful tool for predicting TPs in large-scale genome projects, which will facilitate the discovery of novel TPs.
Collapse
Affiliation(s)
- Qian Wang
- Department of Clinical Laboratory, Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, China
| | - Teng Xu
- Institute of Translational Medicine, Baotou Central Hospital, Baotou, China
| | - Kai Xu
- Department of Clinical Laboratory, Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, China
| | - Zhongqiu Lu
- Wenzhou Key Laboratory of Emergency, Critical Care, and Disaster Medicine, Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Jianchao Ying
- Central Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Wenzhou Key Laboratory of Emergency, Critical Care, and Disaster Medicine, Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| |
Collapse
|
26
|
Kwon MS, Adidjaja JJ, Kim HU. Predicting the effects of cultivation condition on gene regulation in Escherichia coli by using deep learning. Comput Struct Biotechnol J 2023; 21:2613-2620. [PMID: 38213890 PMCID: PMC10781998 DOI: 10.1016/j.csbj.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/02/2023] [Accepted: 04/12/2023] [Indexed: 01/13/2024] Open
Abstract
Cell's physiology is affected by cultivation conditions at varying degrees, including carbon sources and inorganic nutrients in growth medium, and the presence or absence of aeration. When examining the effects of cultivation conditions on the cell, the cell's transcriptional response is often examined first among other phenotypes (e.g., proteome and metabolome). In this regard, we developed DeepMGR, a deep learning model that predicts the effects of culture media on gene regulation in Escherichia coli. DeepMGR specifically classifies the direction of gene regulation (i.e., upregulation, no regulation, or downregulation) for an input gene in comparison with M9 minimal medium with glucose as a control condition. For this classification task, DeepMGR uses a feedforward neural network to process: i) DNA sequence of a target gene, ii) presence or absence of aeration and trace elements, and iii) concentration and structural information (SMILES) of up to ten nutrients. The complete DeepMGR showed accuracy of 0.867 and F1 score of 0.703 for a test set from the gold standard dataset. DeepMGR was further subjected to simulation studies for validation where regulation directions for groups of homologous genes were predicted, and the DeepMGR results were compared with the literature with focus on carbon sources that upregulate specific genes. DeepMGR will be useful for designing experiments to understand gene regulations, especially in the context of metabolic engineering.
Collapse
Affiliation(s)
- Mun Su Kwon
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Joshua Julio Adidjaja
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hyun Uk Kim
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
| |
Collapse
|
27
|
Cheng Y, Bi X, Xu Y, Liu Y, Li J, Du G, Lv X, Liu L. Machine learning for metabolic pathway optimization: A review. Comput Struct Biotechnol J 2023; 21:2381-2393. [PMID: 38213889 PMCID: PMC10781721 DOI: 10.1016/j.csbj.2023.03.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 03/29/2023] Open
Abstract
Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.
Collapse
Affiliation(s)
- Yang Cheng
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yameng Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| |
Collapse
|
28
|
Rappoport D, Jinich A. Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves. J Chem Inf Model 2023; 63:1637-1648. [PMID: 36802628 DOI: 10.1021/acs.jcim.3c00005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Compact and interpretable structural feature representations are required for accurately predicting properties and function of proteins. In this work, we construct and evaluate three-dimensional feature representations of protein structures based on space-filling curves (SFCs). We focus on the problem of enzyme substrate prediction, using two ubiquitous enzyme families as case studies: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine-dependent methyltransferases (SAM-MTases). Space-filling curves such as the Hilbert curve and the Morton curve generate a reversible mapping from discretized three-dimensional to one-dimensional representations and thus help to encode three-dimensional molecular structures in a system-independent way and with only a few adjustable parameters. Using three-dimensional structures of SDRs and SAM-MTases generated using AlphaFold2, we assess the performance of the SFC-based feature representations in predictions on a new benchmark database of enzyme classification tasks including their cofactor and substrate selectivity. Gradient-boosted tree classifiers yield binary prediction accuracy of 0.77-0.91 and area under curve (AUC) characteristics of 0.83-0.92 for the classification tasks. We investigate the effects of amino acid encoding, spatial orientation, and (the few) parameters of SFC-based encodings on the accuracy of the predictions. Our results suggest that geometry-based approaches such as SFCs are promising for generating protein structural representations and are complementary to the existing protein feature representations such as evolutionary scale modeling (ESM) sequence embeddings.
Collapse
Affiliation(s)
- Dmitrij Rappoport
- Department of Chemistry, University of California, Irvine, 1102 Natural Sciences 2, Irvine, California 92697, United States
| | - Adrian Jinich
- Weill Cornell Medicine, 1300 York Avenue, Box 65, New York, New York 10065, United States
| |
Collapse
|
29
|
Men P, Zhou Y, Xie L, Zhang X, Zhang W, Huang X, Lu X. Improving the production of the micafungin precursor FR901379 in an industrial production strain. Microb Cell Fact 2023; 22:44. [PMID: 36879280 PMCID: PMC9987125 DOI: 10.1186/s12934-023-02050-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/25/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND Micafungin is an echinocandin-type antifungal agent used for the clinical treatment of invasive fungal infections. It is semisynthesized from the sulfonated lipohexapeptide FR901379, a nonribosomal peptide produced by the filamentous fungus Coleophoma empetri. However, the low fermentation efficiency of FR901379 increases the cost of micafungin production and hinders its widespread clinical application. RESULTS Here, a highly efficient FR901379-producing strain was constructed via systems metabolic engineering in C. empetri MEFC09. First, the biosynthesis pathway of FR901379 was optimized by overexpressing the rate-limiting enzymes cytochrome P450 McfF and McfH, which successfully eliminated the accumulation of unwanted byproducts and increased the production of FR901379. Then, the functions of putative self-resistance genes encoding β-1,3-glucan synthase were evaluated in vivo. The deletion of CEfks1 affected growth and resulted in more spherical cells. Additionally, the transcriptional activator McfJ for the regulation of FR901379 biosynthesis was identified and applied in metabolic engineering. Overexpressing mcfJ markedly increased the production of FR901379 from 0.3 g/L to 1.3 g/L. Finally, the engineered strain coexpressing mcfJ, mcfF, and mcfH was constructed for additive effects, and the FR901379 titer reached 4.0 g/L under fed-batch conditions in a 5 L bioreactor. CONCLUSIONS This study represents a significant improvement for the production of FR901379 and provides guidance for the establishment of efficient fungal cell factories for other echinocandins.
Collapse
Affiliation(s)
- Ping Men
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China.,Shandong Energy Institute, Qingdao, 266101, China.,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yu Zhou
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China.,Shandong Energy Institute, Qingdao, 266101, China.,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China.,Institute for Smart Materials & Engineering, University of Jinan, Jinan, 250022, China
| | - Li Xie
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China.,Shandong Energy Institute, Qingdao, 266101, China.,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China.,State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, 330096, China
| | - Xuan Zhang
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China.,Shandong Energy Institute, Qingdao, 266101, China.,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Wei Zhang
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China.,Shandong Energy Institute, Qingdao, 266101, China.,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuenian Huang
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China. .,Shandong Energy Institute, Qingdao, 266101, China. .,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China.
| | - Xuefeng Lu
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China. .,Shandong Energy Institute, Qingdao, 266101, China. .,Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Marine Biology and Biotechnology Laboratory, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China.
| |
Collapse
|
30
|
Kim GB, Choi SY, Cho IJ, Ahn DH, Lee SY. Metabolic engineering for sustainability and health. Trends Biotechnol 2023; 41:425-451. [PMID: 36635195 DOI: 10.1016/j.tibtech.2022.12.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/17/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023]
Abstract
Bio-based production of chemicals and materials has attracted much attention due to the urgent need to establish sustainability and enhance human health. Metabolic engineering (ME) allows purposeful modification of cellular metabolic, regulatory, and signaling networks to achieve enhanced production of desired chemicals and degradation of environmentally harmful chemicals. ME has significantly progressed over the past 30 years through further integration of the strategies of synthetic biology, systems biology, evolutionary engineering, and data science aided by artificial intelligence. Here we review the field of ME from its emergence to the current state-of-the-art, highlighting its contribution to sustainable production of chemicals, health, and the environment through representative examples. Future challenges of ME and perspectives are also discussed.
Collapse
Affiliation(s)
- Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - So Young Choi
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - In Jin Cho
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Da-Hee Ahn
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
| |
Collapse
|
31
|
Zhang Y, Ma L, Su P, Huang L, Gao W. Cytochrome P450s in plant terpenoid biosynthesis: discovery, characterization and metabolic engineering. Crit Rev Biotechnol 2023; 43:1-21. [PMID: 34865579 DOI: 10.1080/07388551.2021.2003292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
As the largest family of natural products, terpenoids play valuable roles in medicine, agriculture, cosmetics and food. However, the traditional methods that rely on direct extraction from the original plants not only produce low yields, but also result in waste of resources, and are not applicable at all to endangered species. Modern heterologous biosynthesis is considered a promising, efficient, and sustainable production method, but it relies on the premise of a complete analysis of the biosynthetic pathway of terpenoids, especially the functionalization processes involving downstream cytochrome P450s. In this review, we systematically introduce the biotech approaches used to discover and characterize plant terpenoid-related P450s in recent years. In addition, we propose corresponding metabolic engineering approaches to increase the effective expression of P450 and improve the yield of terpenoids, and also elaborate on metabolic engineering strategies and examples of heterologous biosynthesis of terpenoids in Saccharomyces cerevisiae and plant hosts. Finally, we provide perspectives for the biotech approaches to be developed for future research on terpenoid-related P450.
Collapse
Affiliation(s)
- Yifeng Zhang
- Beijing Shijitan Hospital, Capital Medical University, Beijing, China.,School of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Lin Ma
- School of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Ping Su
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida, USA
| | - Luqi Huang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, Chinese Academy of Chinese Medical Sciences, Beijing, China
| | - Wei Gao
- Beijing Shijitan Hospital, Capital Medical University, Beijing, China.,School of Traditional Chinese Medicine, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| |
Collapse
|
32
|
Kang CK, Shin J, Cha Y, Kim MS, Choi MS, Kim T, Park YK, Choi YJ. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene. BIORESOURCE TECHNOLOGY 2023; 369:128455. [PMID: 36503092 DOI: 10.1016/j.biortech.2022.128455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.
Collapse
Affiliation(s)
- Chang Keun Kang
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Jihoon Shin
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - YoonKyung Cha
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - TaeHo Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Young-Kwon Park
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Yong Jun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
| |
Collapse
|
33
|
Aida H, Uchida K, Nagai M, Hashizume T, Masuo S, Takaya N, Ying BW. Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites. Comput Struct Biotechnol J 2023; 21:2654-2663. [PMID: 37138901 PMCID: PMC10149329 DOI: 10.1016/j.csbj.2023.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/05/2023] Open
Abstract
The composition of medium components is crucial for achieving the best performance of synthetic construction in genetically engineered cells. Which and how medium components determine the performance, e.g., productivity, remain poorly investigated. To address the questions, a comparative survey with two genetically engineered Escherichia coli strains was performed. As a case study, the strains carried the synthetic pathways for producing the aromatic compounds of 4-aminophenylalanine (4APhe) or tyrosine (Tyr), common in the upstream but differentiated in the downstream metabolism. Bacterial growth and compound production were examined in hundreds of medium combinations that comprised 48 pure chemicals. The resultant data sets linking the medium composition to bacterial growth and production were subjected to machine learning for improved production. Intriguingly, the primary medium components determining the production of 4PheA and Tyr were differentiated, which were the initial resource (glucose) of the synthetic pathway and the inducer (IPTG) of the synthetic construction, respectively. Fine-tuning of the primary component significantly increased the yields of 4APhe and Tyr, indicating that a single component could be crucial for the performance of synthetic construction. Transcriptome analysis observed the local and global changes in gene expression for improved production of 4APhe and Tyr, respectively, revealing divergent metabolic strategies for producing the foreign and native metabolites. The study demonstrated that ML-assisted medium optimization could provide a novel point of view on how to make the synthetic construction meet the designed working principle and achieve the expected biological function.
Collapse
Affiliation(s)
- Honoka Aida
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Keisuke Uchida
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Motoki Nagai
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Takamasa Hashizume
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Shunsuke Masuo
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
- Microbiology Research Center for Sustainability, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Naoki Takaya
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
- Microbiology Research Center for Sustainability, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
- Corresponding author at: School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan.
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
- Corresponding author.
| |
Collapse
|
34
|
Current status and future prospects in cannabinoid production through in vitro culture and synthetic biology. Biotechnol Adv 2023; 62:108074. [PMID: 36481387 DOI: 10.1016/j.biotechadv.2022.108074] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/27/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
For centuries, cannabis has been a rich source of fibrous, pharmaceutical, and recreational ingredients. Phytocannabinoids are the most important and well-known class of cannabis-derived secondary metabolites and display a broad range of health-promoting and psychoactive effects. The unique characteristics of phytocannabinoids (e.g., metabolite likeness, multi-target spectrum, and safety profile) have resulted in the development and approval of several cannabis-derived drugs. While most work has focused on the two main cannabinoids produced in the plant, over 150 unique cannabinoids have been identified. To meet the rapidly growing phytocannabinoid demand, particularly many of the minor cannabinoids found in low amounts in planta, biotechnology offers promising alternatives for biosynthesis through in vitro culture and heterologous systems. In recent years, the engineered production of phytocannabinoids has been obtained through synthetic biology both in vitro (cell suspension culture and hairy root culture) and heterologous systems. However, there are still several bottlenecks (e.g., the complexity of the cannabinoid biosynthetic pathway and optimizing the bioprocess), hampering biosynthesis and scaling up the biotechnological process. The current study reviews recent advances related to in vitro culture-mediated cannabinoid production. Additionally, an integrated overview of promising conventional approaches to cannabinoid production is presented. Progress toward cannabinoid production in heterologous systems and possible avenues for avoiding autotoxicity are also reviewed and highlighted. Machine learning is then introduced as a powerful tool to model, and optimize bioprocesses related to cannabinoid production. Finally, regulation and manipulation of the cannabinoid biosynthetic pathway using CRISPR- mediated metabolic engineering is discussed.
Collapse
|
35
|
Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
Collapse
Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, 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.
| |
Collapse
|
36
|
Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
Collapse
Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| |
Collapse
|
37
|
Quantitative Methods for Metabolite Analysis in Metabolic Engineering. BIOTECHNOL BIOPROC E 2022. [DOI: 10.1007/s12257-022-0200-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
38
|
Lee JA, Kim HU, Na JG, Ko YS, Cho JS, Lee SY. Factors affecting the competitiveness of bacterial fermentation. Trends Biotechnol 2022; 41:798-816. [PMID: 36357213 DOI: 10.1016/j.tibtech.2022.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/05/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022]
Abstract
Sustainable production of chemicals and materials from renewable non-food biomass using biorefineries has become increasingly important in an effort toward the vision of 'net zero carbon' that has recently been pledged by countries around the world. Systems metabolic engineering has allowed the efficient development of microbial strains overproducing an increasing number of chemicals and materials, some of which have been translated to industrial-scale production. Fermentation is one of the key processes determining the overall economics of bioprocesses, but has recently been attracting less research attention. In this Review, we revisit and discuss factors affecting the competitiveness of bacterial fermentation in connection to strain development by systems metabolic engineering. Future perspectives for developing efficient fermentation processes are also discussed.
Collapse
Affiliation(s)
- Jong An 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
| | - Hyun Uk Kim
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea; Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
| | - Jeong-Geol Na
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, Republic of Korea
| | - Yoo-Sung Ko
- 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
| | - Jae Sung Cho
- 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.
| |
Collapse
|
39
|
Lao Z, Matsui Y, Ijichi S, Ying BW. Global coordination of the mutation and growth rates across the genetic and nutritional variety in Escherichia coli. Front Microbiol 2022; 13:990969. [PMID: 36204613 PMCID: PMC9530902 DOI: 10.3389/fmicb.2022.990969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 08/30/2022] [Indexed: 11/24/2022] Open
Abstract
Fitness and mutability are the primary traits of living organisms for adaptation and evolution. However, their quantitative linkage remained largely deficient. Whether there is any general relationship between the two features and how genetic and environmental variables influence them remained unclear and were addressed here. The mutation and growth rates of an assortment of Escherichia coli strain collections, including the wild-type strains and the genetically disturbed strains of either reduced genomes or deletion of the genes involved in the DNA replication fidelity, were evaluated in various media. The contribution of media to the mutation and growth rates was differentiated depending on the types of genetic disturbance. Nevertheless, the negative correlation between the mutation and growth rates was observed across the genotypes and was common in all media. It indicated the comprehensive association of the correlated mutation and growth rates with the genetic and medium variation. Multiple linear regression and support vector machine successfully predicted the mutation and growth rates and the categories of genotypes and media, respectively. Taken together, the study provided a quantitative dataset linking the mutation and growth rates, genotype, and medium and presented a simple and successful example of predicting bacterial growth and mutability by data-driven approaches.
Collapse
|
40
|
Yilmaz S, Nyerges A, van der Oost J, Church GM, Claassens NJ. Towards next-generation cell factories by rational genome-scale engineering. Nat Catal 2022. [DOI: 10.1038/s41929-022-00836-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
41
|
Aida H, Hashizume T, Ashino K, Ying BW. Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity. eLife 2022; 11:76846. [PMID: 36017903 PMCID: PMC9417415 DOI: 10.7554/elife.76846] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 08/15/2022] [Indexed: 12/30/2022] Open
Abstract
Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituents to population dynamics via a high-throughput assay and data-driven analysis of a wild-type Escherichia coli strain. A large dataset constituting a total of 12,828 bacterial growth curves with 966 medium combinations, which were composed of 44 pure chemical compounds, was acquired. Machine learning analysis of the big data relating the growth parameters to the medium combinations revealed that the decision-making components for bacterial growth were distinct among various growth phases, e.g., glucose, sulfate, and serine for maximum growth, growth rate, and growth delay, respectively. Further analyses and simulations indicated that branched-chain amino acids functioned as global coordinators for population dynamics, as well as a survival strategy of risk diversification to prevent the bacterial population from undergoing extinction.
Collapse
Affiliation(s)
- Honoka Aida
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Takamasa Hashizume
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Kazuha Ashino
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| |
Collapse
|
42
|
Beardall WA, Stan GB, Dunlop MJ. Deep Learning Concepts and Applications for Synthetic Biology. GEN BIOTECHNOLOGY 2022; 1:360-371. [PMID: 36061221 PMCID: PMC9428732 DOI: 10.1089/genbio.2022.0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/14/2022] [Indexed: 12/24/2022]
Abstract
Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
Collapse
Affiliation(s)
- William A.V. Beardall
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Mary J. Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
| |
Collapse
|
43
|
Yang D, Eun H, Prabowo CPS, Cho S, Lee SY. Metabolic and cellular engineering for the production of natural products. Curr Opin Biotechnol 2022; 77:102760. [PMID: 35908315 DOI: 10.1016/j.copbio.2022.102760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/14/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022]
Abstract
Increased awareness of the environmental and health concerns of consuming chemically synthesized products has led to a rising demand for natural products that are greener and more sustainable. Despite their importance, however, industrial-scale production of natural products has been challenging due to the low yield and high cost of the bioprocesses. To cope with this problem, systems metabolic engineering has been employed to efficiently produce natural products from renewable biomass. Here, we review the recent systems metabolic engineering strategies employed for enhanced production of value-added natural products, together with accompanying examples. Particular focus is set on systems-level engineering and cell physiology engineering strategies. Future perspectives are also discussed.
Collapse
Affiliation(s)
- Dongsoo Yang
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea.
| | - Hyunmin Eun
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea
| | - Cindy Pricilia Surya Prabowo
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea
| | - Sumin Cho
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea.
| |
Collapse
|
44
|
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.
Collapse
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
| |
Collapse
|
45
|
Lo-Thong-Viramoutou O, Charton P, Cadet XF, Grondin-Perez B, Saavedra E, Damour C, Cadet F. Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model. Front Artif Intell 2022; 5:744755. [PMID: 35757298 PMCID: PMC9226554 DOI: 10.3389/frai.2022.744755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.
Collapse
Affiliation(s)
- Ophélie Lo-Thong-Viramoutou
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Philippe Charton
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | | | - Brigitte Grondin-Perez
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Emma Saavedra
- Departamento de Bioquímica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico
| | - Cédric Damour
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Frédéric Cadet
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| |
Collapse
|
46
|
Liao X, Ma H, Tang YJ. Artificial intelligence: a solution to involution of design–build–test–learn cycle. Curr Opin Biotechnol 2022; 75:102712. [DOI: 10.1016/j.copbio.2022.102712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/05/2022] [Accepted: 03/01/2022] [Indexed: 01/08/2023]
|
47
|
Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. Int J Mol Sci 2022; 23:5538. [PMID: 35628351 PMCID: PMC9141736 DOI: 10.3390/ijms23105538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 12/14/2022] Open
Abstract
A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.
Collapse
Affiliation(s)
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC G1V 0A6, Canada;
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| |
Collapse
|
48
|
Agarwal A, Liu YA, Dooley L, McDowell C, Thaysen M. Large-Scale Industrial Fermenter Foaming Control: Automated Machine Learning for Antifoam Prediction and Defoaming Process Implementation. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c05006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Aman Agarwal
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Y. A. Liu
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Luke Dooley
- Novozymes Biologicals, Inc., 5400 Corporate Circle, Salem, Virginia 24153, United States
| | - Christopher McDowell
- Novozymes Biologicals, Inc., 5400 Corporate Circle, Salem, Virginia 24153, United States
| | - Mads Thaysen
- Novozymes A/S, Krogshøjvej 36, 2880 Bagsvaerd, Denmark
| |
Collapse
|
49
|
Jiang YQ, Lin JP. Recent progress in strategies for steroid production in yeasts. World J Microbiol Biotechnol 2022; 38:93. [PMID: 35441962 DOI: 10.1007/s11274-022-03276-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
Abstract
As essential structural molecules of fungal cell membrane, ergosterol is not only an important component of fungal growth and stress-resistance but also a key precursor for manufacturing steroid drugs of pharmaceutical or agricultural significance. So far, ergosterol biosynthesis in yeast has been elucidated elaborately, and efforts have been made to increase ergosterol production through regulation of ergosterol metabolism and storage. Furthermore, the same intermediates shared by yeasts and animals or plants make the construction of heterologous sterol pathways in yeast a promising approach to synthesize valuable steroids, such as phytosteroids and animal steroid hormones. During these challenging processes, several obstacles have arisen and been combated with great endeavors. This paper reviews recent research progress of yeast metabolic engineering for improving the production of ergosterol and heterologous steroids. The remaining tactics are also discussed.
Collapse
Affiliation(s)
- Yi-Qi Jiang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Jian-Ping Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.
| |
Collapse
|
50
|
Smith K, Shen F, Lee HJ, Chandrasekaran S. Metabolic signatures of regulation by phosphorylation and acetylation. iScience 2022; 25:103730. [PMID: 35072016 PMCID: PMC8762462 DOI: 10.1016/j.isci.2021.103730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 12/15/2021] [Accepted: 12/30/2021] [Indexed: 10/31/2022] Open
Abstract
Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.
Collapse
Affiliation(s)
- Kirk Smith
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Fangzhou Shen
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ho Joon Lee
- Department of Genetics, Yale University, New Haven, CT 06510, USA.,Yale Center for Genome Analysis, Yale University, New Haven, CT 06510, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|