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Ntelkis N, Goossens A, Šola K. Cell type-specific control and post-translational regulation of specialized metabolism: opening new avenues for plant metabolic engineering. CURRENT OPINION IN PLANT BIOLOGY 2024; 81:102575. [PMID: 38901289 DOI: 10.1016/j.pbi.2024.102575] [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: 03/19/2024] [Revised: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 06/22/2024]
Abstract
Although plant metabolic engineering enables the sustainable production of valuable metabolites with many applications, we still lack a good understanding of many multi-layered regulatory networks that govern metabolic pathways at the metabolite, protein, transcriptional and cellular level. As transcriptional regulation is better understood and often reviewed, here we highlight recent advances in the cell type-specific and post-translational regulation of plant specialized metabolism. With the advent of single-cell technologies, we are now able to characterize metabolites and their transcriptional regulators at the cellular level, which can refine our searches for missing biosynthetic enzymes and cell type-specific regulators. Post-translational regulation through enzyme inhibition, protein phosphorylation and ubiquitination are clearly evident in specialized metabolism regulation, but not frequently studied or considered in metabolic engineering efforts. Finally, we contemplate how advances in cell type-specific and post-translational regulation can be applied in metabolic engineering efforts in planta, leading to optimization of plants as metabolite production vehicles.
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Affiliation(s)
- Nikolaos Ntelkis
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Ghent, Belgium; VIB Center for Plant Systems Biology, B-9052, Ghent, Belgium
| | - Alain Goossens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Ghent, Belgium; VIB Center for Plant Systems Biology, B-9052, Ghent, Belgium; Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland, 7600, South Africa.
| | - Krešimir Šola
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Ghent, Belgium; VIB Center for Plant Systems Biology, B-9052, Ghent, Belgium
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2
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Balzerani F, Blasco T, Pérez-Burillo S, Valcarcel LV, Hassoun S, Planes FJ. Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota. NPJ Syst Biol Appl 2024; 10:56. [PMID: 38802371 PMCID: PMC11130242 DOI: 10.1038/s41540-024-00381-1] [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: 05/15/2023] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.
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Affiliation(s)
- Francesco Balzerani
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Telmo Blasco
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Sergio Pérez-Burillo
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcarcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA.
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, 02155, USA.
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
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3
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Tkalec Ž, Antignac JP, Bandow N, Béen FM, Belova L, Bessems J, Le Bizec B, Brack W, Cano-Sancho G, Chaker J, Covaci A, Creusot N, David A, Debrauwer L, Dervilly G, Duca RC, Fessard V, Grimalt JO, Guerin T, Habchi B, Hecht H, Hollender J, Jamin EL, Klánová J, Kosjek T, Krauss M, Lamoree M, Lavison-Bompard G, Meijer J, Moeller R, Mol H, Mompelat S, Van Nieuwenhuyse A, Oberacher H, Parinet J, Van Poucke C, Roškar R, Togola A, Trontelj J, Price EJ. Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment. ENVIRONMENT INTERNATIONAL 2024; 186:108585. [PMID: 38521044 DOI: 10.1016/j.envint.2024.108585] [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: 08/18/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/25/2024]
Abstract
The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.
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Affiliation(s)
- Žiga Tkalec
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Jožef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia.
| | | | - Nicole Bandow
- German Environment Agency, Laboratory for Water Analysis, Colditzstraße 34, 12099 Berlin, Germany.
| | - Frederic M Béen
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; KWR Water Research Institute, Nieuwegein, The Netherlands.
| | - Lidia Belova
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium.
| | - Jos Bessems
- Flemish Institute for Technological Research (VITO), Mol, Belgium.
| | | | - Werner Brack
- Helmholtz Centre for Environmental Research GmbH - UFZ, Department of Effect-Directed Analysis, Permoserstraße 15, 04318 Leipzig, Germany; Goethe University Frankfurt, Department of Evolutionary Ecology and Environmental Toxicology, Max-von-Laue-Strasse 13, 60438 Frankfurt, Germany.
| | | | - Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
| | - Adrian Covaci
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium.
| | - Nicolas Creusot
- INRAE, French National Research Institute For Agriculture, Food & Environment, UR1454 EABX, Bordeaux Metabolome, MetaboHub, Gazinet Cestas, France.
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
| | - Laurent Debrauwer
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France.
| | | | - Radu Corneliu Duca
- Unit Environmental Hygiene and Human Biological Monitoring, Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg; Environment and Health, Department of Public Health and Primary Care, Katholieke Universiteit of Leuven (KU Leuven), 3000 Leuven, Belgium.
| | - Valérie Fessard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory of Fougères, Toxicology of Contaminants Unit, 35306 Fougères, France.
| | - Joan O Grimalt
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Catalonia, Spain.
| | - Thierry Guerin
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Strategy and Programs Department, F-94701 Maisons-Alfort, France.
| | - Baninia Habchi
- INRS, Département Toxicologie et Biométrologie Laboratoire Biométrologie 1, rue du Morvan - CS 60027 - 54519, Vandoeuvre Cedex, France.
| | - Helge Hecht
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
| | - Juliane Hollender
- Swiss Federal Institute of Aquatic Science and Technology - Eawag, 8600 Dübendorf, Switzerland; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland.
| | - Emilien L Jamin
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France.
| | - Jana Klánová
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
| | - Tina Kosjek
- Jožef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia.
| | - Martin Krauss
- Helmholtz Centre for Environmental Research GmbH - UFZ, Department of Effect-Directed Analysis, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Marja Lamoree
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Gwenaelle Lavison-Bompard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory for Food Safety, Pesticides and Marine Biotoxins Unit, F-94701 Maisons-Alfort, France.
| | - Jeroen Meijer
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Ruth Moeller
- Unit Medical Expertise and Data Intelligence, Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg.
| | - Hans Mol
- Wageningen Food Safety Research - Part of Wageningen University and Research, Akkermaalsbos 2, 6708 WB, Wageningen, The Netherlands.
| | - Sophie Mompelat
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory of Fougères, Toxicology of Contaminants Unit, 35306 Fougères, France.
| | - An Van Nieuwenhuyse
- Environment and Health, Department of Public Health and Primary Care, Katholieke Universiteit of Leuven (KU Leuven), 3000 Leuven, Belgium; Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg.
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Insbruck, 6020 Innsbruck, Austria.
| | - Julien Parinet
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory for Food Safety, Pesticides and Marine Biotoxins Unit, F-94701 Maisons-Alfort, France.
| | - Christof Van Poucke
- Flanders Research Institute for Agriculture, Fisheries And Food (ILVO), Brusselsesteenweg 370, 9090 Melle, Belgium.
| | - Robert Roškar
- University of Ljubljana, Faculty of Pharmacy, Slovenia.
| | - Anne Togola
- BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France.
| | | | - Elliott J Price
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
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Burz SD, Causevic S, Dal Co A, Dmitrijeva M, Engel P, Garrido-Sanz D, Greub G, Hapfelmeier S, Hardt WD, Hatzimanikatis V, Heiman CM, Herzog MKM, Hockenberry A, Keel C, Keppler A, Lee SJ, Luneau J, Malfertheiner L, Mitri S, Ngyuen B, Oftadeh O, Pacheco AR, Peaudecerf F, Resch G, Ruscheweyh HJ, Sahin A, Sanders IR, Slack E, Sunagawa S, Tackmann J, Tecon R, Ugolini GS, Vacheron J, van der Meer JR, Vayena E, Vonaesch P, Vorholt JA. From microbiome composition to functional engineering, one step at a time. Microbiol Mol Biol Rev 2023; 87:e0006323. [PMID: 37947420 PMCID: PMC10732080 DOI: 10.1128/mmbr.00063-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023] Open
Abstract
SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.
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Affiliation(s)
- Sebastian Dan Burz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Senka Causevic
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Alma Dal Co
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marija Dmitrijeva
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Philipp Engel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Daniel Garrido-Sanz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Gilbert Greub
- Institut de microbiologie, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | | | | | - Clara Margot Heiman
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | | | - Christoph Keel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Soon-Jae Lee
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Julien Luneau
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Lukas Malfertheiner
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Sara Mitri
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Bidong Ngyuen
- Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | | | | | - Grégory Resch
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | - Asli Sahin
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Ian R. Sanders
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Emma Slack
- Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | | | - Janko Tackmann
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Robin Tecon
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Jordan Vacheron
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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5
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Chainani Y, Bonnanzio G, Tyo KE, Broadbelt LJ. Coupling chemistry and biology for the synthesis of advanced bioproducts. Curr Opin Biotechnol 2023; 84:102992. [PMID: 37688985 DOI: 10.1016/j.copbio.2023.102992] [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: 06/15/2023] [Revised: 07/30/2023] [Accepted: 08/05/2023] [Indexed: 09/11/2023]
Abstract
Chemical and biological syntheses can both lead to a myriad of compounds. Biology enables us to harness the metabolism of microbial cell factories to produce key target molecules from renewable biomass-derived substrates. Although bio-based feedstocks are sustainably sourced and more benign than the rapidly depleting fossil fuels that chemical processes have historically relied on, limiting pathways solely to biological reactions may not equate to a greener process overall. In fact, bioreactors rely on substantial quantities of water and can be inefficient since organisms typically operate around ambient conditions and are sensitive to perturbations in their environment. Hybridizing biosynthetic pathways with green chemistry can instead be a more potent strategy to reduce our net manufacturing footprint. Emerging chemistries have demonstrated considerable success in performing complex transformations on biological feedstocks without significant solvent use. Many of these transformations would be too slow to perform enzymatically or infeasible altogether. Here, we put forth the concept that by carefully considering the merits and drawbacks of synthetic biology and chemistry as well as one's own use case, there exist many opportunities for coupling the two. Merging these syntheses can unlock a wider suite of functional group transformations, thereby enabling future manufacturing processes to sustainably access a larger space of valuable, platform chemicals.
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Affiliation(s)
- Yash Chainani
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
| | - Geoffrey Bonnanzio
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
| | - Keith Ej Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA.
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6
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Sokolova N, Peng B, Haslinger K. Design and engineering of artificial biosynthetic pathways-where do we stand and where do we go? FEBS Lett 2023; 597:2897-2907. [PMID: 37777818 DOI: 10.1002/1873-3468.14745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/29/2023] [Accepted: 09/12/2023] [Indexed: 10/02/2023]
Abstract
The production of commodity and specialty chemicals relies heavily on fossil fuels. The negative impact of this dependency on our environment and climate has spurred a rising demand for more sustainable methods to obtain such chemicals from renewable resources. Herein, biotransformations of these renewable resources facilitated by enzymes or (micro)organisms have gained significant attention, since they can occur under mild conditions and reduce waste. These biotransformations typically leverage natural metabolic processes, which limits the scope and production capacity of such processes. In this mini-review, we provide an overview of advancements made in the past 5 years to expand the repertoire of biotransformations in engineered microorganisms. This ranges from redesign of existing pathways driven by retrobiosynthesis and computational design to directed evolution of enzymes and de novo pathway design to unlock novel routes for the synthesis of desired chemicals. We highlight notable examples of pathway designs for the production of commodity and specialty chemicals, showcasing the potential of these approaches. Lastly, we provide an outlook on future pathway design approaches.
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Affiliation(s)
- Nika Sokolova
- Department of Chemical and Pharmaceutical Biology, University of Groningen, The Netherlands
| | - Bo Peng
- Department of Chemical and Pharmaceutical Biology, University of Groningen, The Netherlands
| | - Kristina Haslinger
- Department of Chemical and Pharmaceutical Biology, University of Groningen, The Netherlands
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7
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Probst D. An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification. J Cheminform 2023; 15:113. [PMID: 37996942 PMCID: PMC10668483 DOI: 10.1186/s13321-023-00784-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.
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Affiliation(s)
- Daniel Probst
- Signal Processing Laboratory 2, Institute of Electrical and Micro Engineering, School of Engineering, EPFL, Rte Cantonale, 1015, Lausanne, Vaud, Switzerland.
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8
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Cordell WT, Avolio G, Takors R, Pfleger BF. Milligrams to kilograms: making microbes work at scale. Trends Biotechnol 2023; 41:1442-1457. [PMID: 37271589 DOI: 10.1016/j.tibtech.2023.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023]
Abstract
If biomanufacturing can become a sustainable route for producing chemicals, it will provide a critical step in reducing greenhouse gas emissions to fight climate change. However, efforts to industrialize microbial synthesis of chemicals have met with varied success, due, in part, to challenges in translating laboratory successes to industrial scale. With a particular focus on Escherichia coli, this review examines the lessons learned when studying microbial physiology and metabolism under conditions that simulate large-scale bioreactors and methods to minimize cellular waste through reduction of maintenance energy, optimizing the stress response and minimizing culture heterogeneity. With general strategies to overcome these challenges, biomanufacturing process scale-up could be de-risked and the time and cost of bringing promising syntheses to market could be reduced.
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Affiliation(s)
- William T Cordell
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Gennaro Avolio
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart 70569, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart 70569, Germany
| | - Brian F Pfleger
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; DOE Center Advanced Bioenergy and Bioproducts Innovation, University of Wisconsin-Madison, Madison, WI 53706, USA; DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53706, USA.
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9
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Zhang K, Fenner K. enviRule: an end-to-end system for automatic extraction of reaction patterns from environmental contaminant biotransformation pathways. Bioinformatics 2023; 39:btad407. [PMID: 37354527 PMCID: PMC10322654 DOI: 10.1093/bioinformatics/btad407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/02/2023] [Accepted: 06/23/2023] [Indexed: 06/26/2023] Open
Abstract
MOTIVATION Transformation products (TPs) of man-made chemicals, formed through microbially mediated transformation in the environment, can have serious adverse environmental effects, yet the analytical identification of TPs is challenging. Rule-based prediction tools are successful in predicting TPs, especially in environmental chemistry applications that typically have to rely on small datasets, by imparting the existing knowledge on enzyme-mediated biotransformation reactions. However, the rules extracted from biotransformation reaction databases usually face the issue of being over/under-generalized and are not flexible to be updated with new reactions. RESULTS We developed an automatic rule extraction tool called enviRule. It clusters biotransformation reactions into different groups based on the similarities of reaction fingerprints, and then automatically extracts and generalizes rules for each reaction group in SMARTS format. It optimizes the genericity of automatic rules against the downstream TP prediction task. Models trained with automatic rules outperformed the models trained with manually curated rules by 30% in the area under curve (AUC) scores. Moreover, automatic rules can be easily updated with new reactions, highlighting enviRule's strengths for both automatic extraction of optimized reactions rules and automated updating thereof. AVAILABILITY AND IMPLEMENTATION enviRule code is freely available at https://github.com/zhangky12/enviRule.
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Affiliation(s)
- Kunyang Zhang
- Department of Environmental Chemistry, Eawag, Dübendorf 8600, Switzerland
- Department of Chemistry, University of Zürich, Zürich 8057, Switzerland
| | - Kathrin Fenner
- Department of Environmental Chemistry, Eawag, Dübendorf 8600, Switzerland
- Department of Chemistry, University of Zürich, Zürich 8057, Switzerland
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10
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Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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11
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Nielsen JR, Weusthuis RA, Huang WE. Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration. Biotechnol Adv 2023; 63:108102. [PMID: 36681133 DOI: 10.1016/j.biotechadv.2023.108102] [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: 09/01/2022] [Revised: 01/11/2023] [Accepted: 01/15/2023] [Indexed: 01/20/2023]
Abstract
Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD+/NADH and NADP+/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD+/NADH and NADP+/NADPH pairs, and of non-canonical redox cofactor pairs NMN+/NMNH and NCD+/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.
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Affiliation(s)
- Jochem R Nielsen
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.
| | - Ruud A Weusthuis
- Department of Bioprocess Engineering, Wageningen University & Research, Wageningen 6700AA, the Netherlands.
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.
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12
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Lim PK, Julca I, Mutwil M. Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data. Comput Struct Biotechnol J 2023; 21:1639-1650. [PMID: 36874159 PMCID: PMC9976193 DOI: 10.1016/j.csbj.2023.01.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
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Affiliation(s)
- Peng Ken Lim
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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13
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Wei X, Huang Z, Jiang L, Li Y, Zhang X, Leng Y, Jiang C. Charting the landscape of the environmental exposome. IMETA 2022; 1:e50. [PMID: 38867899 PMCID: PMC10989948 DOI: 10.1002/imt2.50] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/13/2022] [Accepted: 07/30/2022] [Indexed: 06/14/2024]
Abstract
The exposome depicts the total exposures in the lifetime of an organism. Human exposome comprises exposures from environmental and humanistic sources. Biological, chemical, and physical environmental exposures pose potential health threats, especially to susceptible populations. Although still in its nascent stage, we are beginning to recognize the vast and dynamic nature of the exposome. In this review, we systematically summarize the biological and chemical environmental exposomes in three broad environmental matrices-air, soil, and water; each contains several distinct subcategories, along with a brief introduction to the physical exposome. Disease-related environmental exposures are highlighted, and humans are also a major source of disease-related biological exposures. We further discuss the interactions between biological, chemical, and physical exposomes. Finally, we propose a list of outstanding challenges under the exposome research framework that need to be addressed to move the field forward. Taken together, we present a detailed landscape of environmental exposome to prime researchers to join this exciting new field.
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Affiliation(s)
- Xin Wei
- Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences InstituteZhejiang UniversityHangzhouZhejiangChina
| | - Zinuo Huang
- Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences InstituteZhejiang UniversityHangzhouZhejiangChina
| | - Liuyiqi Jiang
- Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences InstituteZhejiang UniversityHangzhouZhejiangChina
| | - Yueer Li
- Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences InstituteZhejiang UniversityHangzhouZhejiangChina
| | - Xinyue Zhang
- Department of GeneticsStanford UniversityStanfordCaliforniaUSA
| | - Yuxin Leng
- Department of Intensive Care UnitPeking University Third HospitalBeijingChina
| | - Chao Jiang
- Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences InstituteZhejiang UniversityHangzhouZhejiangChina
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, First Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiangChina
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14
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Li W, Zhou Z, Li X, Ma L, Guan Q, Zheng G, Liang H, Yan Y, Shen X, Wang J, Sun X, Yuan Q. Biosynthesis of plant hemostatic dencichine in Escherichia coli. Nat Commun 2022; 13:5492. [PMID: 36123371 PMCID: PMC9485241 DOI: 10.1038/s41467-022-33255-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 09/08/2022] [Indexed: 11/25/2022] Open
Abstract
Dencichine is a plant-derived nature product that has found various pharmacological applications. Currently, its natural biosynthetic pathway is still elusive, posing challenge to its heterologous biosynthesis. In this work, we design artificial pathways through retro-biosynthesis approaches and achieve de novo production of dencichine. First, biosynthesis of the two direct precursors L-2, 3-diaminopropionate and oxalyl-CoA is achieved by screening and integrating microbial enzymes. Second, the solubility of dencichine synthase, which is the last and only plant-derived pathway enzyme, is significantly improved by introducing 28 synonymous rare codons into the codon-optimized gene to slow down its translation rate. Last, the metabolic network is systematically engineered to direct the carbon flux to dencichine production, and the final titer reaches 1.29 g L-1 with a yield of 0.28 g g-1 glycerol. This work lays the foundation for sustainable production of dencichine and represents an example of how synthetic biology can be harnessed to generate unnatural pathways to produce a desired molecule.
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Affiliation(s)
- Wenna Li
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Zhao Zhou
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Xianglai Li
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Lin Ma
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Qingyuan Guan
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Guojun Zheng
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Hao Liang
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Yajun Yan
- School of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA
| | - Xiaolin Shen
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Jia Wang
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Xinxiao Sun
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China.
| | - Qipeng Yuan
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China.
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15
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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.
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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.
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16
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Wohlgemuth R, Littlechild J. Complexity reduction and opportunities in the design, integration and intensification of biocatalytic processes for metabolite synthesis. Front Bioeng Biotechnol 2022; 10:958606. [PMID: 35935499 PMCID: PMC9355135 DOI: 10.3389/fbioe.2022.958606] [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: 05/31/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
The biosynthesis of metabolites from available starting materials is becoming an ever important area due to the increasing demands within the life science research area. Access to metabolites is making essential contributions to analytical, diagnostic, therapeutic and different industrial applications. These molecules can be synthesized by the enzymes of biological systems under sustainable process conditions. The facile synthetic access to the metabolite and metabolite-like molecular space is of fundamental importance. The increasing knowledge within molecular biology, enzyme discovery and production together with their biochemical and structural properties offers excellent opportunities for using modular cell-free biocatalytic systems. This reduces the complexity of synthesizing metabolites using biological whole-cell approaches or by classical chemical synthesis. A systems biocatalysis approach can provide a wealth of optimized enzymes for the biosynthesis of already identified and new metabolite molecules.
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Affiliation(s)
- Roland Wohlgemuth
- Institute of Molecular and Industrial Biotechnology, Lodz University of Technology, Lodz, Poland
- Swiss Coordination Committee for Biotechnology, Zurich, Switzerland
| | - Jennifer Littlechild
- Henry Wellcome Building for Biocatalysis, Biosciences, University of Exeter, Exeter, United Kingdom
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