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Gong G, Wu B, Liu L, Li J, He M. Engineering oleaginous red yeasts as versatile chassis for the production of olechemicals and valuable compounds: Current advances and perspectives. Biotechnol Adv 2024:108432. [PMID: 39163921 DOI: 10.1016/j.biotechadv.2024.108432] [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: 03/11/2024] [Revised: 07/04/2024] [Accepted: 08/16/2024] [Indexed: 08/22/2024]
Abstract
Enabling the transition towards a future circular bioeconomy based on industrial biomanufacturing necessitates the development of efficient and versatile microbial platforms for sustainable chemical and fuel production. Recently, there has been growing interest in engineering non-model microbes as superior biomanufacturing platforms due to their broad substrate range and high resistance to stress conditions. Among these non-conventional microbes, red yeasts belonging to the genus Rhodotorula have emerged as promising industrial chassis for the production of specialty chemicals such as oleochemicals, organic acids, fatty acid derivatives, terpenoids, and other valuable compounds. Advancements in genetic and metabolic engineering techniques, coupled with systems biology analysis, have significantly enhanced the production capacity of red yeasts. These developments have also expanded the range of substrates and products that can be utilized or synthesized by these yeast species. This review comprehensively examines the current efforts and recent progress made in red yeast research. It encompasses the exploration of available substrates, systems analysis using multi-omics data, establishment of genome-scale models, development of efficient molecular tools, identification of genetic elements, and engineering approaches for the production of various industrially relevant bioproducts. Furthermore, strategies to improve substrate conversion and product formation both with systematic and synthetic biology approaches are discussed, along with future directions and perspectives in improving red yeasts as more versatile biotechnological chassis in contributing to a circular bioeconomy. The review aims to provide insights and directions for further research in this rapidly evolving field. Ultimately, harnessing the capabilities of red yeasts will play a crucial role in paving the way towards next-generation sustainable bioeconomy.
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Affiliation(s)
- Guiping Gong
- Biomass Energy Technology Research Centre, Rural Energy and Ecology Research Center of CAAS, Key Laboratory of Development and Application of Rural Renewable Energy (Ministry of Agriculture and Rural Affairs), Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu 610041, PR China.
| | - Bo Wu
- Biomass Energy Technology Research Centre, Rural Energy and Ecology Research Center of CAAS, Key Laboratory of Development and Application of Rural Renewable Energy (Ministry of Agriculture and Rural Affairs), Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu 610041, PR China
| | - Linpei Liu
- Biomass Energy Technology Research Centre, Rural Energy and Ecology Research Center of CAAS, Key Laboratory of Development and Application of Rural Renewable Energy (Ministry of Agriculture and Rural Affairs), Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu 610041, PR China
| | - Jianting Li
- Biomass Energy Technology Research Centre, Rural Energy and Ecology Research Center of CAAS, Key Laboratory of Development and Application of Rural Renewable Energy (Ministry of Agriculture and Rural Affairs), Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu 610041, PR China
| | - Mingxiong He
- Biomass Energy Technology Research Centre, Rural Energy and Ecology Research Center of CAAS, Key Laboratory of Development and Application of Rural Renewable Energy (Ministry of Agriculture and Rural Affairs), Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu 610041, PR China
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2
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Sajid S, Mashkoor M, Jørgensen MG, Christensen LP, Hansen PR, Franzyk H, Mirza O, Prabhala BK. The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation. Mol Cell Biochem 2024; 479:1957-1968. [PMID: 37610616 DOI: 10.1007/s11010-023-04827-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023]
Abstract
The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.
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Affiliation(s)
- Salvia Sajid
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
- Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
| | - Maliha Mashkoor
- Department of Surgery, Center for Surgical Sciences, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark
| | - Mikkel Girke Jørgensen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
| | - Lars Porskjær Christensen
- Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
| | - Paul Robert Hansen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
| | - Henrik Franzyk
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
| | - Osman Mirza
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
| | - Bala Krishna Prabhala
- Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark.
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3
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Jouhet J, Alves E, Boutté Y, Darnet S, Domergue F, Durand T, Fischer P, Fouillen L, Grube M, Joubès J, Kalnenieks U, Kargul JM, Khozin-Goldberg I, Leblanc C, Letsiou S, Lupette J, Markov GV, Medina I, Melo T, Mojzeš P, Momchilova S, Mongrand S, Moreira ASP, Neves BB, Oger C, Rey F, Santaeufemia S, Schaller H, Schleyer G, Tietel Z, Zammit G, Ziv C, Domingues R. Plant and algal lipidomes: Analysis, composition, and their societal significance. Prog Lipid Res 2024; 96:101290. [PMID: 39094698 DOI: 10.1016/j.plipres.2024.101290] [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: 03/18/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024]
Abstract
Plants and algae play a crucial role in the earth's ecosystems. Through photosynthesis they convert light energy into chemical energy, capture CO2 and produce oxygen and energy-rich organic compounds. Photosynthetic organisms are primary producers and synthesize the essential omega 3 and omega 6 fatty acids. They have also unique and highly diverse complex lipids, such as glycolipids, phospholipids, triglycerides, sphingolipids and phytosterols, with nutritional and health benefits. Plant and algal lipids are useful in food, feed, nutraceutical, cosmeceutical and pharmaceutical industries but also for green chemistry and bioenergy. The analysis of plant and algal lipidomes represents a significant challenge due to the intricate and diverse nature of their composition, as well as their plasticity under changing environmental conditions. Optimization of analytical tools is crucial for an in-depth exploration of the lipidome of plants and algae. This review highlights how lipidomics analytical tools can be used to establish a complete mapping of plant and algal lipidomes. Acquiring this knowledge will pave the way for the use of plants and algae as sources of tailored lipids for both industrial and environmental applications. This aligns with the main challenges for society, upholding the natural resources of our planet and respecting their limits.
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Affiliation(s)
- Juliette Jouhet
- Laboratoire de Physiologie Cellulaire et Végétale, CNRS/INRAE/CEA/Grenoble Alpes Univ., 38000 Grenoble, France.
| | - Eliana Alves
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal
| | - Yohann Boutté
- Laboratoire de Biogenèse Membranaire, UMR5200 CNRS-Université de Bordeaux, CNRS, Villenave-d'Ornon, France
| | | | - Frédéric Domergue
- Laboratoire de Biogenèse Membranaire, UMR5200 CNRS-Université de Bordeaux, CNRS, Villenave-d'Ornon, France
| | - Thierry Durand
- Institut des Biomolécules Max Mousseron (IBMM), Pôle Chimie Balard Recherche, University of Montpellier, ENSCN, UMR 5247 CNRS, France
| | - Pauline Fischer
- Institut des Biomolécules Max Mousseron (IBMM), Pôle Chimie Balard Recherche, University of Montpellier, ENSCN, UMR 5247 CNRS, France
| | - Laetitia Fouillen
- Laboratoire de Biogenèse Membranaire, UMR5200 CNRS-Université de Bordeaux, CNRS, Villenave-d'Ornon, France
| | - Mara Grube
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Jérôme Joubès
- Laboratoire de Biogenèse Membranaire, UMR5200 CNRS-Université de Bordeaux, CNRS, Villenave-d'Ornon, France
| | - Uldis Kalnenieks
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Joanna M Kargul
- Solar Fuels Laboratory, Center of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Inna Khozin-Goldberg
- Microalgal Biotechnology Laboratory, The French Associates Institute for Dryland Agriculture and Biotechnology, The J. Blaustein Institutes for Desert Research, Ben Gurion University, Midreshet Ben Gurion 8499000, Israel
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Sophia Letsiou
- Department of Food Science and Technology, University of West Attica, Ag. Spiridonos str. Egaleo, 12243 Athens, Greece
| | - Josselin Lupette
- Laboratoire de Biogenèse Membranaire, UMR5200 CNRS-Université de Bordeaux, CNRS, Villenave-d'Ornon, France
| | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Isabel Medina
- Instituto de Investigaciones Marinas - Consejo Superior de Investigaciones Científicas (IIM-CSIC), Eduardo Cabello 6, E-36208 Vigo, Galicia, Spain
| | - Tânia Melo
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal; CESAM-Centre for Environmental and Marine Studies, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal
| | - Peter Mojzeš
- Institute of Physics, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 5, CZ-12116 Prague 2, Czech Republic
| | - Svetlana Momchilova
- Department of Lipid Chemistry, Institute of Organic Chemistry with Centre of Phytochemistry, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl. 9, BG-1113 Sofia, Bulgaria
| | - Sébastien Mongrand
- Laboratoire de Biogenèse Membranaire, UMR5200 CNRS-Université de Bordeaux, CNRS, Villenave-d'Ornon, France
| | - Ana S P Moreira
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal
| | - Bruna B Neves
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal; CESAM-Centre for Environmental and Marine Studies, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal
| | - Camille Oger
- Institut des Biomolécules Max Mousseron (IBMM), Pôle Chimie Balard Recherche, University of Montpellier, ENSCN, UMR 5247 CNRS, France
| | - Felisa Rey
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal; CESAM-Centre for Environmental and Marine Studies, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal
| | - Sergio Santaeufemia
- Solar Fuels Laboratory, Center of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Hubert Schaller
- Institut de Biologie Moléculaire des Plantes du CNRS, Université de Strasbourg, 12 rue du Général Zimmer, F-67083 Strasbourg, France
| | - Guy Schleyer
- Department of Biomolecular Chemistry, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745 Jena, Germany
| | - Zipora Tietel
- Department of Food Science, Gilat Research Center, Agricultural Research Organization, Volcani Institute, M.P. Negev 8531100, Israel
| | - Gabrielle Zammit
- Laboratory of Applied Phycology, Department of Biology, University of Malta, Msida MSD 2080, Malta
| | - Carmit Ziv
- Department of Postharvest Science, Agricultural Research Organization, Volcani Institute, Rishon LeZion 7505101, Israel
| | - Rosário Domingues
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal; CESAM-Centre for Environmental and Marine Studies, Department of Chemistry, University of Aveiro, Santiago University Campus, Aveiro 3810-193, Portugal.
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4
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Palanikumar I, Sinha H, Raman K. Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models. iScience 2024; 27:110358. [PMID: 39092173 PMCID: PMC11292516 DOI: 10.1016/j.isci.2024.110358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/10/2024] [Accepted: 06/20/2024] [Indexed: 08/04/2024] Open
Abstract
Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.
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Affiliation(s)
- Indumathi Palanikumar
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai 600 036, India
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5
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Atasoy M, Scott WT, Regueira A, Mauricio-Iglesias M, Schaap PJ, Smidt H. Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches. Biotechnol Adv 2024; 73:108363. [PMID: 38657743 DOI: 10.1016/j.biotechadv.2024.108363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/03/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024]
Abstract
In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.
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Affiliation(s)
- Merve Atasoy
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Department of Environmental Technology, Wageningen University & Research, Wageningen, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
| | - William T Scott
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Alberte Regueira
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium; Center for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Frieda Saeysstraat 1, Ghent, Belgium.
| | - Miguel Mauricio-Iglesias
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
| | - Peter J Schaap
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Hauke Smidt
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
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6
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Noecker C, Turnbaugh PJ. Emerging tools and best practices for studying gut microbial community metabolism. Nat Metab 2024; 6:1225-1236. [PMID: 38961185 DOI: 10.1038/s42255-024-01074-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/30/2024] [Indexed: 07/05/2024]
Abstract
The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.
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Affiliation(s)
- Cecilia Noecker
- Department of Biological Sciences, Minnesota State University, Mankato, Mankato, MN, USA
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA.
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7
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Shene C, Leyton A, Flores L, Chavez D, Asenjo JA, Chisti Y. Genome-scale metabolic modeling of Thraustochytrium sp. RT2316-16: Effects of nutrients on metabolism. Biotechnol Bioeng 2024; 121:1986-2001. [PMID: 38500406 DOI: 10.1002/bit.28689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/17/2024] [Accepted: 02/20/2024] [Indexed: 03/20/2024]
Abstract
Marine thraustochytrids produce metabolically important lipids such as the long-chain omega-3 polyunsaturated fatty acids, carotenoids, and sterols. The growth and lipid production in thraustochytrids depends on the composition of the culture medium that often contains yeast extract as a source of amino acids. This work discusses the effects of individual amino acids provided in the culture medium as the only source of nitrogen, on the production of biomass and lipids by the thraustochytrid Thraustochytrium sp. RT2316-16. A reconstructed metabolic network based on the annotated genome of RT2316-16 in combination with flux balance analysis was used to explain the observed growth and consumption of the nutrients. The culture kinetic parameters estimated from the experimental data were used to constrain the flux via the nutrient consumption rates and the specific growth rate of the triacylglycerol-free biomass in the genome-scale metabolic model (GEM) to predict the specific rate of ATP production for cell maintenance. A relationship was identified between the specific rate of ATP production for maintenance and the specific rate of glucose consumption. The GEM and the derived relationship for the production of ATP for maintenance were used in linear optimization problems, to successfully predict the specific growth rate of RT2316-16 in different experimental conditions.
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Affiliation(s)
- Carolina Shene
- Department of Chemical Engineering, Center of Food Biotechnology and Bioseparations, BIOREN, and Centre of Biotechnology and Bioengineering (CeBiB), Universidad de La Frontera, Temuco, Chile
| | - Allison Leyton
- Department of Chemical Engineering, Center of Food Biotechnology and Bioseparations, BIOREN, and Centre of Biotechnology and Bioengineering (CeBiB), Universidad de La Frontera, Temuco, Chile
| | - Liset Flores
- Department of Chemical Engineering, Center of Food Biotechnology and Bioseparations, BIOREN, and Centre of Biotechnology and Bioengineering (CeBiB), Universidad de La Frontera, Temuco, Chile
| | - Daniela Chavez
- Department of Chemical Engineering, Center of Food Biotechnology and Bioseparations, BIOREN, and Centre of Biotechnology and Bioengineering (CeBiB), Universidad de La Frontera, Temuco, Chile
| | - Juan A Asenjo
- Department of Chemical Engineering, Biotechnology and Materials, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile
| | - Yusuf Chisti
- Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
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8
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Hsieh YE, Tandon K, Verbruggen H, Nikoloski Z. Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches. NPJ Syst Biol Appl 2024; 10:54. [PMID: 38783065 PMCID: PMC11116368 DOI: 10.1038/s41540-024-00384-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.
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Affiliation(s)
- Yunli Eric Hsieh
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Kshitij Tandon
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Heroen Verbruggen
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Zoran Nikoloski
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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9
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Sauter R, Sharma S, Heiland I. Accounting for NAD Concentrations in Genome-Scale Metabolic Models Captures Important Metabolic Alterations in NAD-Depleted Systems. Biomolecules 2024; 14:602. [PMID: 38786009 PMCID: PMC11117748 DOI: 10.3390/biom14050602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
Nicotinamide adenine dinucleotide (NAD) is a ubiquitous molecule found within all cells, acting as a crucial coenzyme in numerous metabolic reactions. It plays a vital role in energy metabolism, cellular signaling, and DNA repair. Notably, NAD levels decline naturally with age, and this decline is associated with the development of various age-related diseases. Despite this established link, current genome-scale metabolic models, which offer powerful tools for understanding cellular metabolism, do not account for the dynamic changes in NAD concentration. This impedes our understanding of a fluctuating NAD level's impact on cellular metabolism and its contribution to age-related pathologies. To bridge this gap in our knowledge, we have devised a novel method that integrates altered NAD concentration into genome-scale models of human metabolism. This approach allows us to accurately reflect the changes in fatty acid metabolism, glycolysis, and oxidative phosphorylation observed experimentally in an engineered human cell line with a compromised level of subcellular NAD.
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Affiliation(s)
- Roland Sauter
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, 9019 Tromsø, Norway;
| | - Suraj Sharma
- Department of Biomedicine, University of Bergen, 5020 Bergen, Norway;
| | - Ines Heiland
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, 9019 Tromsø, Norway;
- Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
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Herbst K, Wang T, Forchielli EJ, Thommes M, Paschalidis IC, Segrè D. Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations. Commun Biol 2024; 7:407. [PMID: 38570615 PMCID: PMC10991586 DOI: 10.1038/s42003-024-06093-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 03/22/2024] [Indexed: 04/05/2024] Open
Abstract
The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.
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Affiliation(s)
- Konrad Herbst
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Taiyao Wang
- Division of Systems Engineering, Boston University, Boston, MA, USA
| | - Elena J Forchielli
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biology, Boston University, Boston, MA, USA
| | - Meghan Thommes
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Ioannis Ch Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA.
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA.
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11
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Jiang F, Ruan Y, Chen XH, Yu HL, Cheng T, Duan XY, Liu YG, Zhang HY, Zhang QY. Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications. Microbiol Spectr 2024; 12:e0234223. [PMID: 38391229 PMCID: PMC10986615 DOI: 10.1128/spectrum.02342-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.
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Affiliation(s)
- Feng Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yao Ruan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xiao-Hui Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Hai-Long Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Ting Cheng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xin-Ya Duan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yan-Guang Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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12
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Román L, Melis-Arcos F, Pröschle T, Saa PA, Garrido D. Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis. mSystems 2024; 9:e0071523. [PMID: 38363147 PMCID: PMC10949479 DOI: 10.1128/msystems.00715-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024] Open
Abstract
Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.IMPORTANCEThis work presents a detailed reconstruction of the metabolism of Bifidobacterium longum subsp. infantis, a prominent member of the infant gut microbiome, providing a systems view of its metabolism of human milk oligosaccharides.
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Affiliation(s)
- Loreto Román
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Felipe Melis-Arcos
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás Pröschle
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pedro A. Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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13
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Hari A, Zarrabi A, Lobo D. mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers. NAR Genom Bioinform 2024; 6:lqae010. [PMID: 38312936 PMCID: PMC10836943 DOI: 10.1093/nargab/lqae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/15/2023] [Accepted: 01/16/2024] [Indexed: 02/06/2024] Open
Abstract
Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.
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Affiliation(s)
- Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
| | - Arveen Zarrabi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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14
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Castaneda EU, Baker EJ. KNeXT: a NetworkX-based topologically relevant KEGG parser. Front Genet 2024; 15:1292394. [PMID: 38415058 PMCID: PMC10896898 DOI: 10.3389/fgene.2024.1292394] [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: 09/12/2023] [Accepted: 01/25/2024] [Indexed: 02/29/2024] Open
Abstract
Automating the recreation of gene and mixed gene-compound networks from Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) files is challenging because the data structure does not preserve the independent or loosely connected neighborhoods in which they were originally derived, referred to here as its topological environment. Identical accession numbers may overlap, causing neighborhoods to artificially collapse based on duplicated identifiers. This causes current parsers to create misleading or erroneous graphical representations when mixed gene networks are converted to gene-only networks. To overcome these challenges we created a python-based KEGG NetworkX Topological (KNeXT) parser that allows users to accurately recapitulate genetic networks and mixed networks from KGML map data. The software, archived as a python package index (PyPI) file to ensure broad application, is designed to ingest KGML files through built-in APIs and dynamically create high-fidelity topological representations. The utilization of NetworkX's framework to generate tab-separated files additionally ensures that KNeXT results may be imported into other graph frameworks and maintain programmatic access to the original x-y axis positions to each node in the KEGG pathway. KNeXT is a well-described Python 3 package that allows users to rapidly download and aggregate specific KGML files and recreate KEGG pathways based on a range of user-defined settings. KNeXT is platform-independent, distinctive, and it is not written on top of other Python parsers. Furthermore, KNeXT enables users to parse entire local folders or single files through command line scripts and convert the output into NCBI or UniProt IDs. KNeXT provides an ability for researchers to generate pathway visualizations while persevering the original context of a KEGG pathway. Source code is freely available at https://github.com/everest-castaneda/knext.
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Affiliation(s)
- Everest Uriel Castaneda
- Department of Biology, Baylor University, Waco, TX, United States
- School of Engineering and Computer Science, Baylor University, Waco, TX, United States
| | - Erich J Baker
- Department of Mathematics and Computer Science, Belmont University, Nashville, TN, United States
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15
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Lyu X, Nuhu M, Candry P, Wolfanger J, Betenbaugh M, Saldivar A, Zuniga C, Wang Y, Shrestha S. Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass. J Ind Microbiol Biotechnol 2024; 51:kuae025. [PMID: 39003244 PMCID: PMC11287213 DOI: 10.1093/jimb/kuae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 07/12/2024] [Indexed: 07/15/2024]
Abstract
Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.
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Affiliation(s)
- Xuejiao Lyu
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mujaheed Nuhu
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Pieter Candry
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE Wageningen, The Netherlands
| | - Jenna Wolfanger
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Michael Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alexis Saldivar
- Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA
| | - Cristal Zuniga
- Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA
| | - Ying Wang
- Department of Soil and Crop Sciences, Texas A&M University, TX 77843, USA
| | - Shilva Shrestha
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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16
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Uatay A, Gall L, Irons L, Tewari SG, Zhu XS, Gibbs M, Kimko H. Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model. J Pharm Sci 2024; 113:11-21. [PMID: 37898164 DOI: 10.1016/j.xphs.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/21/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.
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Affiliation(s)
- Aydar Uatay
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom.
| | - Louis Gall
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom
| | - Linda Irons
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Shivendra G Tewari
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States
| | - Xu Sue Zhu
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Megan Gibbs
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States.
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17
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Cerk K, Ugalde‐Salas P, Nedjad CG, Lecomte M, Muller C, Sherman DJ, Hildebrand F, Labarthe S, Frioux C. Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing. Microb Biotechnol 2024; 17:e14396. [PMID: 38243750 PMCID: PMC10832553 DOI: 10.1111/1751-7915.14396] [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: 01/09/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024] Open
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Affiliation(s)
- Klara Cerk
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Chabname Ghassemi Nedjad
- Inria, University of Bordeaux, INRAETalenceFrance
- University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Maxime Lecomte
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE STLO¸University of RennesRennesFrance
| | | | | | - Falk Hildebrand
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Simon Labarthe
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE, University of Bordeaux, BIOGECO, UMR 1202CestasFrance
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18
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Kaste JA, Shachar-Hill Y. Model validation and selection in metabolic flux analysis and flux balance analysis. Biotechnol Prog 2024; 40:e3413. [PMID: 37997613 PMCID: PMC10922127 DOI: 10.1002/btpr.3413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/25/2023]
Abstract
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.
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Affiliation(s)
- Joshua A.M. Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd, East Lansing, MI 48823
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
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19
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Pan DT, Wang P, Wang XL, Sun YQ, Xiu ZL. Dynamic flux balance analysis of 1,3-propanediol production by clostridium butyricum fermentation. Biotechnol Prog 2024; 40:e3411. [PMID: 37985220 DOI: 10.1002/btpr.3411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 10/23/2023] [Accepted: 11/01/2023] [Indexed: 11/22/2023]
Abstract
To study the relationship between the yield of 1,3-propanediol (1,3-PDO) and the flux change of the Clostridium butyricum metabolic pathway, an optimized calculation method based on dynamic flux balance analysis was used by combining genome-scale flux balance analysis with a kinetic model. A more comprehensive and extensive metabolic pathway was obtained by optimization calculations. The primary extended branches include: the dihydroxyacetone node, which enters the pentose phosphate pathway; the α-oxoglutarate node, which has synthetic metabolic pathways for glutamic acid and amino acids; and the serine and homocysteine nodes, which produce cystathionine before homocysteine enters the methionine cycle pathway. According to the expanded metabolic network, the flux distribution of key nodes in the metabolic pathway and the relationship between the flux distribution ratio of nodes and the yield of 1,3-PDO were analyzed. At the dihydroxyacetone node, the flux of dihydroxyacetone converted to dihydroxyacetone phosphate was positively correlated with the yield of 1,3-PDO. As an important intermediate product, the flux change in the metabolic pathway of α-oxoglutarate reacting with amino acids to produce glutamic acid is positively correlated with the yield. When pyruvate was used as the central node to convert into lactic acid and α-oxoglutarate, the proportion of branch flux was negatively correlated with the yield of 1,3-PDO. These studies provide a theoretical basis for the optimization and further study of the metabolic pathway of C. butyricum.
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Affiliation(s)
- Duo-Tao Pan
- Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang, PR China
| | - Pan Wang
- Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang, PR China
| | - Xiao-Li Wang
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian, PR China
| | - Ya-Qin Sun
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian, PR China
| | - Zhi-Long Xiu
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian, PR China
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20
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Zhao ZM, Liu ZH, Zhang T, Meng R, Gong Z, Li Y, Hu J, Ragauskas AJ, Li BZ, Yuan YJ. Unleashing the capacity of Rhodococcus for converting lignin into lipids. Biotechnol Adv 2024; 70:108274. [PMID: 37913947 DOI: 10.1016/j.biotechadv.2023.108274] [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: 05/10/2023] [Revised: 09/11/2023] [Accepted: 10/22/2023] [Indexed: 11/03/2023]
Abstract
Bioconversion of bioresources/wastes (e.g., lignin, chemical pulping byproducts) represents a promising approach for developing a bioeconomy to help address growing energy and materials demands. Rhodococcus, a promising microbial strain, utilizes numerous carbon sources to produce lipids, which are precursors for synthesizing biodiesel and aviation fuels. However, compared to chemical conversion, bioconversion involves living cells, which is a more complex system that needs further understanding and upgrading. Various wastes amenable to bioconversion are reviewed herein to highlight the potential of Rhodococci for producing lipid-derived bioproducts. In light of the abundant availability of these substrates, Rhodococcus' metabolic pathways converting them to lipids are analyzed from a "beginning-to-end" view. Based on an in-depth understanding of microbial metabolic routes, genetic modifications of Rhodococcus by employing emerging tools (e.g., multiplex genome editing, biosensors, and genome-scale metabolic models) are presented for promoting the bioconversion. Co-solvent enhanced lignocellulose fractionation (CELF) strategy facilitates the generation of a lignin-derived aromatic stream suitable for the Rhodococcus' utilization. Novel alkali sterilization (AS) and elimination of thermal sterilization (ETS) approaches can significantly enhance the bioaccessibility of lignin and its derived aromatics in aqueous fermentation media, which promotes lipid titer significantly. In order to achieve value-added utilization of lignin, biodiesel and aviation fuel synthesis from lignin and lipids are further discussed. The possible directions for unleashing the capacity of Rhodococcus through synergistically modifying microbial strains, substrates, and fermentation processes are proposed toward a sustainable biological lignin valorization.
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Affiliation(s)
- Zhi-Min Zhao
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China; Department of Chemical & Biomolecular Engineering, University of Tennessee, Knoxville, TN 37996, United States; Key Laboratory of Ecology and Resource Use of the Mongolian Plateau (Ministry of Education), School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Zhi-Hua Liu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Tongtong Zhang
- Key Laboratory of Ecology and Resource Use of the Mongolian Plateau (Ministry of Education), School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Rongqian Meng
- Key Laboratory of Ecology and Resource Use of the Mongolian Plateau (Ministry of Education), School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Zhiqun Gong
- Key Laboratory of Ecology and Resource Use of the Mongolian Plateau (Ministry of Education), School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Yibing Li
- Key Laboratory of Ecology and Resource Use of the Mongolian Plateau (Ministry of Education), School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Jing Hu
- Key Laboratory of Ecology and Resource Use of the Mongolian Plateau (Ministry of Education), School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Arthur J Ragauskas
- Department of Chemical & Biomolecular Engineering, University of Tennessee, Knoxville, TN 37996, United States; Joint Institute of Biological Science, Biosciences Division, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37831, United States; Department of Forestry, Wildlife, and Fisheries, Center for Renewable Carbon, University of Tennessee Institute of Agriculture, Knoxville, TN 37996, United States.
| | - Bing-Zhi Li
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
| | - Ying-Jin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
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21
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Sun X, Xie J, Zheng D, Xia R, Wang W, Xun W, Huang Q, Zhang R, Kovács ÁT, Xu Z, Shen Q. Metabolic interactions affect the biomass of synthetic bacterial biofilm communities. mSystems 2023; 8:e0104523. [PMID: 37971263 PMCID: PMC10734490 DOI: 10.1128/msystems.01045-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Co-occurrence network analysis is an effective tool for predicting complex networks of microbial interactions in the natural environment. Using isolates from a rhizosphere, we constructed multi-species biofilm communities and investigated co-occurrence patterns between microbial species in genome-scale metabolic models and in vitro experiments. According to our results, metabolic exchanges and resource competition may partially explain the co-occurrence network analysis results found in synthetic bacterial biofilm communities.
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Affiliation(s)
- Xinli Sun
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jiyu Xie
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Daoyue Zheng
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Riyan Xia
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Wei Wang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Weibing Xun
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Qiwei Huang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Ruifu Zhang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Ákos T. Kovács
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
- Institute of Biology Leiden, Leiden University, Leiden, the Netherlands
| | - Zhihui Xu
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Qirong Shen
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
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22
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Bernstein DB, Akkas B, Price MN, Arkin AP. Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data. Mol Syst Biol 2023; 19:e11566. [PMID: 37888487 DOI: 10.15252/msb.202311566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 09/23/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.
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Affiliation(s)
- David B Bernstein
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Batu Akkas
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Morgan N Price
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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23
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Gonçalves OS, Creevey CJ, Santana MF. Designing a synthetic microbial community through genome metabolic modeling to enhance plant-microbe interaction. ENVIRONMENTAL MICROBIOME 2023; 18:81. [PMID: 37974247 PMCID: PMC10655421 DOI: 10.1186/s40793-023-00536-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynComs, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and carefully selecting compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed multi-genome metabolic modeling of 270 previously described metagenome-assembled genomes from Campos rupestres to design a synthetic microbial community to improve the yield of important crop plants. RESULTS We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, exopolysaccharide production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our in-silico selection for the SymComs, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. CONCLUSION Overall, the study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.
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Affiliation(s)
- Osiel S Gonçalves
- Grupo de Genômica Eco-evolutiva Microbiana, Laboratório de Genética Molecular de Microrganismos, Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Christopher J Creevey
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, BT9 5DL, UK
| | - Mateus F Santana
- Grupo de Genômica Eco-evolutiva Microbiana, Laboratório de Genética Molecular de Microrganismos, Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
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24
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Basile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion: from black to glass box. Curr Opin Microbiol 2023; 75:102363. [PMID: 37542746 DOI: 10.1016/j.mib.2023.102363] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/20/2023] [Accepted: 07/10/2023] [Indexed: 08/07/2023]
Abstract
Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.
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Affiliation(s)
- Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
| | - Guido Zampieri
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
| | - Adam Kovalovszki
- Department of Environmental and Resource Engineering, Technical University of Denmark, Building 115, Bygningstorvet, 2800 Kgs. Lyngby, Denmark
| | - Behzad Karkaria
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Laura Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy.
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Stefano Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
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25
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Wendering P, Nikoloski Z. Model-driven insights into the effects of temperature on metabolism. Biotechnol Adv 2023; 67:108203. [PMID: 37348662 DOI: 10.1016/j.biotechadv.2023.108203] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023]
Abstract
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
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26
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Gelbach PE, Finley SD. Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. iScience 2023; 26:107569. [PMID: 37664588 PMCID: PMC10474475 DOI: 10.1016/j.isci.2023.107569] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/24/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Abstract
Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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27
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Heinken A, Hertel J, Acharya G, Ravcheev DA, Nyga M, Okpala OE, Hogan M, Magnúsdóttir S, Martinelli F, Nap B, Preciat G, Edirisinghe JN, Henry CS, Fleming RMT, Thiele I. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. Nat Biotechnol 2023; 41:1320-1331. [PMID: 36658342 PMCID: PMC10497413 DOI: 10.1038/s41587-022-01628-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/30/2022] [Indexed: 01/21/2023]
Abstract
The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.
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Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- INSERM UMRS 1256, Nutrition, Genetics, and Environmental Risk Exposure (NGERE), University of Lorraine, Nancy, France
| | - Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Geeta Acharya
- Integrated BioBank of Luxembourg, Dudelange, Luxembourg
| | - Dmitry A Ravcheev
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | | | | | - Marcus Hogan
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Stefanía Magnúsdóttir
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - German Preciat
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Janaka N Edirisinghe
- Computation Institute, University of Chicago, Chicago, IL, USA
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Ronan M T Fleming
- School of Medicine, University of Galway, Galway, Ireland
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland.
- Ryan Institute, University of Galway, Galway, Ireland.
- Division of Microbiology, University of Galway, Galway, Ireland.
- APC Microbiome Ireland, Cork, Ireland.
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28
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Boelts J, Harth P, Gao R, Udvary D, Yáñez F, Baum D, Hege HC, Oberlaender M, Macke JH. Simulation-based inference for efficient identification of generative models in computational connectomics. PLoS Comput Biol 2023; 19:e1011406. [PMID: 37738260 PMCID: PMC10550169 DOI: 10.1371/journal.pcbi.1011406] [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: 03/06/2023] [Revised: 10/04/2023] [Accepted: 08/01/2023] [Indexed: 09/24/2023] Open
Abstract
Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.
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Affiliation(s)
- Jan Boelts
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Philipp Harth
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Richard Gao
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Daniel Udvary
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
| | - Felipe Yáñez
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
| | - Daniel Baum
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Hans-Christian Hege
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Marcel Oberlaender
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Free University Amsterdam, Amsterdam, Netherlands
| | - Jakob H. Macke
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
- Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
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29
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Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
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Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
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30
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Pismennõi D, Kattel A, Belouah I, Nahku R, Vilu R, Kobrin EG. The Quantitative Measurement of Peptidoglycan Components Obtained from Acidic Hydrolysis in Gram-Positive and Gram-Negative Bacteria via Hydrophilic Interaction Liquid Chromatography Coupled with Mass Spectrometry. Microorganisms 2023; 11:2134. [PMID: 37763978 PMCID: PMC10534856 DOI: 10.3390/microorganisms11092134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/21/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
The high throughput in genome sequencing and metabolic model (MM) reconstruction has democratised bioinformatics approaches such as flux balance analysis. Fluxes' prediction accuracy greatly relates to the deepness of the MM curation for a specific organism starting from the cell composition. One component is the cell wall, which is a functional barrier (cell shape, exchanges) with the environment. The bacterial cell wall (BCW), including its thickness, structure, and composition, has been extensively studied in Escherichia coli but poorly described for other organisms. The peptidoglycan (PG) layer composing the BCW is usually thinner in Gram- bacteria than in Gram+ bacteria. In both bacteria groups, PG is a polymeric mesh-like structure of amino acids and sugars, including N-acetylglucosamine, N-acetylmuramic acid, and amino acids. In this study, we propose a high-throughput method to characterise and quantify PG in Gram-positive and Gram-negative bacteria using acidic hydrolysis and hydrophilic interaction liquid chromatography coupled with mass spectrometry (HILIC-MS). The method showed a relatively short time frame (11 min analytical run), low inter- and intraday variability (3.2% and 4%, respectively), and high sensitivity and selectivity (limits of quantification in the sub mg/L range). The method was successfully applied on two Gram-negative bacteria (Escherichia coli K12 MG1655, Bacteroides thetaiotaomicron DSM 2079) and one Gram-positive bacterium (Streptococcus salivarius ssp. thermophilus DSM20259). The PG concentration ranged from 1.6% w/w to 14% w/w of the dry cell weight. The results were in good correlation with previously published results. With further development, the PG concentration provided by this newly developed method could reinforce the curation of MM.
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Affiliation(s)
- Dmitri Pismennõi
- Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618 Tallinn, Estonia; (D.P.); (A.K.); (I.B.); (R.N.); (R.V.)
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Akadeemia Tee 15, 12618 Tallinn, Estonia
| | - Anna Kattel
- Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618 Tallinn, Estonia; (D.P.); (A.K.); (I.B.); (R.N.); (R.V.)
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Akadeemia Tee 15, 12618 Tallinn, Estonia
| | - Isma Belouah
- Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618 Tallinn, Estonia; (D.P.); (A.K.); (I.B.); (R.N.); (R.V.)
| | - Ranno Nahku
- Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618 Tallinn, Estonia; (D.P.); (A.K.); (I.B.); (R.N.); (R.V.)
| | - Raivo Vilu
- Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618 Tallinn, Estonia; (D.P.); (A.K.); (I.B.); (R.N.); (R.V.)
| | - Eeva-Gerda Kobrin
- Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618 Tallinn, Estonia; (D.P.); (A.K.); (I.B.); (R.N.); (R.V.)
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Jadebeck JF, Wiechert W, Nöh K. Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance. PLoS Comput Biol 2023; 19:e1011378. [PMID: 37566638 PMCID: PMC10446239 DOI: 10.1371/journal.pcbi.1011378] [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: 01/04/2023] [Revised: 08/23/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling constraint-based models, a highly relevant use-case in systems biology, we here demonstrate that thinning boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. By benchmarking CHRR with thinning with simplices and genome-scale metabolic networks of up to thousands of dimensions, we find a substantial increase in computational efficiency compared to unthinned CHRR, in our examples by orders of magnitude, as measured by the effective sample size per time (ESS/t), with performance gains growing with polytope (effective network) dimension. Using a set of benchmark models we derive a ready-to-apply guideline for tuning thinning to efficient and effective use of compute resources without requiring additional coding effort. Our guideline is validated using three (out-of-sample) large-scale networks and we show that it allows sampling convex polytopes uniformly to convergence in a fraction of time, thereby unlocking the rigorous investigation of hitherto intractable models. The derivation of our guideline is explained in detail, allowing future researchers to update it as needed as new model classes and more training data becomes available. CHRR with deliberate utilization of thinning thereby paves the way to keep pace with progressing model sizes derived with the constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.
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Affiliation(s)
- Johann F. Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
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Bartmanski BJ, Rocha M, Zimmermann-Kogadeeva M. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Curr Opin Chem Biol 2023; 75:102324. [PMID: 37207402 PMCID: PMC10410306 DOI: 10.1016/j.cbpa.2023.102324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
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Affiliation(s)
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus of Gualtar, Braga, Portugal
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33
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Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Res 2023; 33:972-987. [PMID: 37468308 PMCID: PMC10629481 DOI: 10.1101/gr.277056.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
| | - Jeanne Got
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
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Schäfer M, Pacheco AR, Künzler R, Bortfeld-Miller M, Field CM, Vayena E, Hatzimanikatis V, Vorholt JA. Metabolic interaction models recapitulate leaf microbiota ecology. Science 2023; 381:eadf5121. [PMID: 37410834 DOI: 10.1126/science.adf5121] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/18/2023] [Indexed: 07/08/2023]
Abstract
Resource allocation affects the structure of microbiomes, including those associated with living hosts. Understanding the degree to which this dependency determines interspecies interactions may advance efforts to control host-microbiome relationships. We combined synthetic community experiments with computational models to predict interaction outcomes between plant-associated bacteria. We mapped the metabolic capabilities of 224 leaf isolates from Arabidopsis thaliana by assessing the growth of each strain on 45 environmentally relevant carbon sources in vitro. We used these data to build curated genome-scale metabolic models for all strains, which we combined to simulate >17,500 interactions. The models recapitulated outcomes observed in planta with >89% accuracy, highlighting the role of carbon utilization and the contributions of niche partitioning and cross-feeding in the assembly of leaf microbiomes.
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Affiliation(s)
- Martin Schäfer
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Alan R Pacheco
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Rahel Künzler
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | | | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
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Chen C, Liao C, Liu YY. Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning. Nat Commun 2023; 14:2375. [PMID: 37185345 PMCID: PMC10130184 DOI: 10.1038/s41467-023-38110-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method - CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) - to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes.
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Affiliation(s)
- Can Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
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Borer B, Magnúsdóttir S. The media composition as a crucial element in high-throughput metabolic network reconstruction. Interface Focus 2023; 13:20220070. [PMID: 36789238 PMCID: PMC9912011 DOI: 10.1098/rsfs.2022.0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/11/2023] [Indexed: 02/12/2023] Open
Abstract
In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.
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Affiliation(s)
- Benedict Borer
- Earth, Atmospheric and Planetary Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Stefanía Magnúsdóttir
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig 04318, Germany
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37
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Ramon C, Stelling J. Functional comparison of metabolic networks across species. Nat Commun 2023; 14:1699. [PMID: 36973280 PMCID: PMC10043025 DOI: 10.1038/s41467-023-37429-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.
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Affiliation(s)
- Charlotte Ramon
- Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland
- Ph.D. Program Systems Biology, Life Science Zurich Graduate School, Zurich, Switzerland
| | - Jörg Stelling
- Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland.
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38
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Gelbach PE, Finley SD. Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.532000. [PMID: 36993493 PMCID: PMC10052244 DOI: 10.1101/2023.03.09.532000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
1Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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Rios Garza D, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: Advances and challenges. Cell Syst 2023; 14:109-121. [PMID: 36796330 DOI: 10.1016/j.cels.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 02/17/2023]
Abstract
The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.
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Affiliation(s)
- Daniel Rios Garza
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231, Université Libre de Bruxelles, Bvd du Triomphe, 1050 Bruxelles, Belgium
| | - Haris Zafeiropoulos
- Biology Department, University of Crete, Heraklion 700 13, Greece; Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
| | - Bin Liu
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
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Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability. Mater Today Bio 2023; 19:100560. [PMID: 36756210 PMCID: PMC9900623 DOI: 10.1016/j.mtbio.2023.100560] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 01/22/2023] Open
Abstract
Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.
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Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures. Metabolites 2023; 13:metabo13010126. [PMID: 36677051 PMCID: PMC9866716 DOI: 10.3390/metabo13010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.
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Dzofou Ngoumelah D, Harnisch F, Sulheim S, Heggeset TMB, Aune IH, Wentzel A, Kretzschmar J. A unified and simple medium for growing model methanogens. Front Microbiol 2023; 13:1046260. [PMID: 36704566 PMCID: PMC9871610 DOI: 10.3389/fmicb.2022.1046260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
Apart from their archetypic use in anaerobic digestion (AD) methanogenic archaea are targeted for a wide range of applications. Using different methanogenic archaea for one specific application requires the optimization of culture media to enable the growth of different strains under identical environmental conditions, e.g., in microbial electrochemical technologies (MET) for (bio)electromethanation. Here we present a new culture medium (BFS01) adapted from the DSM-120 medium by omitting resazurin, yeast extract, casitone, and using a low salt concentration, that was optimized for Methanosarcina barkeri, Methanobacterium formicicum, and Methanothrix soehngenii. The aim was to provide a medium for follow-up co-culture studies using specific methanogens and Geobacter spp. dominated biofilm anodes. All three methanogens showed growth and activity in the BFS01 medium. This was demonstrated by estimating the specific growth rates ( μ ) and doubling times ( t d ) of each methanogen. The μ and t d based on methane accumulation in the headspace showed values consistent with literature values for M. barkeri and M. soehngenii. However, μ and t d based on methane accumulation in the headspace differed from literature data for M. formicicum but still allowed sufficient growth. The lowered salt concentration and the omission of chemically complex organic components in the medium may have led to the observed deviation from μ and t d for M. formicicum as well as the changed morphology. 16S rRNA gene-based amplicon sequencing and whole genome nanopore sequencing further confirmed purity and species identity.
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Affiliation(s)
- Daniel Dzofou Ngoumelah
- Biochemical Conversion Department, DBFZ Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Leipzig, Germany,Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | - Falk Harnisch
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany,*Correspondence: Falk Harnisch, ✉
| | - Snorre Sulheim
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | | | - Ingvild Haugnes Aune
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Jörg Kretzschmar
- Biochemical Conversion Department, DBFZ Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Leipzig, Germany,Jörg Kretzschmar, ✉
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Du H, Pan J, Zou D, Huang Y, Liu Y, Li M. Microbial active functional modules derived from network analysis and metabolic interactions decipher the complex microbiome assembly in mangrove sediments. MICROBIOME 2022; 10:224. [PMID: 36510268 PMCID: PMC9746113 DOI: 10.1186/s40168-022-01421-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 11/09/2022] [Indexed: 05/24/2023]
Abstract
BACKGROUND The metabolic interactions of microbes significantly affect the assembly of microbial communities that play important roles in biogeochemical processes. However, most interspecies interactions between microorganisms in natural communities remain unknown, leading to a poor understanding of community assembly mechanisms. RESULTS Here, we used a genome-scale metabolic modeling-based approach to explore the potential interactions among bacteria and archaea in mangrove sediments. More than half of the assembled microbial species ([Formula: see text]) combined about 3000 pairwise metabolic interaction relationship with high potential. The examples of predicted interactions are consistent with the implications of studies based on microbial enrichment/culture, indicating the feasibility of our strategy for extracting diverse potential interactions from complex interspecies networks. Moreover, a substantial number of previously unknown microbial metabolic interactions were also predicted. We proposed a concept of microbial active functional module (mAFM), defined as a consortium constituted by a group of microbes possessing relatively high metabolic interactions via which they can actively realize certain dominant functions in element transformations. Based on the metabolic interactions and the transcript distribution of microorganisms, five mAFMs distributed in different layers of the sediments were identified. The whole group of mAFMs covered most of the principal pathways in the cycle of carbon, nitrogen, and sulfur, while each module possessed divergently dominant functions. According to thinctiis diston, we inferred that the mAFMs participated in the element cycles via their intra-cycle and the inter-exchange among them and the sediments. CONCLUSIONS The results of this study greatly expanded interaction potential of microbes in mangrove sediments, which could provide supports for prospective mutualistic system construction and microbial enrichment culture. Furthermore, the mAFMs can help to extract valuable microbial metabolic interactions from the whole community and to profile the functioning of the microbial community that promote biogeochemical cycling in mangrove sediments. Video Abstract.
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Affiliation(s)
- Huan Du
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
- Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
| | - Jie Pan
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
- Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
| | - Dayu Zou
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
- Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
| | - Yuhan Huang
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
- Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
| | - Yang Liu
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
- Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
| | - Meng Li
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
- Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, 518060 China
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Systems Biology: New Insight into Antibiotic Resistance. Microorganisms 2022; 10:microorganisms10122362. [PMID: 36557614 PMCID: PMC9781975 DOI: 10.3390/microorganisms10122362] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 11/26/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
Over the past few decades, antimicrobial resistance (AMR) has emerged as an important threat to public health, resulting from the global propagation of multidrug-resistant strains of various bacterial species. Knowledge of the intrinsic factors leading to this resistance is necessary to overcome these new strains. This has contributed to the increased use of omics technologies and their extrapolation to the system level. Understanding the mechanisms involved in antimicrobial resistance acquired by microorganisms at the system level is essential to obtain answers and explore options to combat this resistance. Therefore, the use of robust whole-genome sequencing approaches and other omics techniques such as transcriptomics, proteomics, and metabolomics provide fundamental insights into the physiology of antimicrobial resistance. To improve the efficiency of data obtained through omics approaches, and thus gain a predictive understanding of bacterial responses to antibiotics, the integration of mathematical models with genome-scale metabolic models (GEMs) is essential. In this context, here we outline recent efforts that have demonstrated that the use of omics technology and systems biology, as quantitative and robust hypothesis-generating frameworks, can improve the understanding of antibiotic resistance, and it is hoped that this emerging field can provide support for these new efforts.
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Simensen V, Seif Y, Almaas E. Phenotypic response of yeast metabolic network to availability of proteinogenic amino acids. Front Mol Biosci 2022; 9:963548. [PMID: 36072429 PMCID: PMC9441596 DOI: 10.3389/fmolb.2022.963548] [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: 06/07/2022] [Accepted: 07/25/2022] [Indexed: 11/20/2022] Open
Abstract
Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.
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Affiliation(s)
- Vetle Simensen
- Department of Biotechnology and Food Science, NTNU—Norwegian University of Science and Technology, Trondheim, Norway
| | - Yara Seif
- Department of Bioengineering, University of California San Diego, San Diego, CA, United States
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU—Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU—Norwegian University of Science and Technology, Trondheim, Norway
- *Correspondence: Eivind Almaas,
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Chung CH, Chandrasekaran S. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions. PNAS NEXUS 2022; 1:pgac132. [PMID: 36016709 PMCID: PMC9396445 DOI: 10.1093/pnasnexus/pgac132] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023]
Abstract
Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.
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Affiliation(s)
- Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Islam MM, Goertzen A, Singh PK, Saha R. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling. iScience 2022; 25:104483. [PMID: 35712079 PMCID: PMC9194136 DOI: 10.1016/j.isci.2022.104483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/08/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a major research focus because of its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism to the surrounding environment, often relying on diverse nutrient sources. Because traditional experimental techniques appear exhaustive to find a viable therapeutic strategy, a highly curated and omics-informed PDAC genome-scale metabolic model was reconstructed using patient-specific transcriptomics data. From the model-predictions, several new metabolic functions were explored as potential therapeutic targets in addition to the known metabolic hallmarks of PDAC. Significant downregulation in the peroxisomal beta oxidation pathway, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were correlated with potential drug combinations targeting genes with poor prognosis in PDAC. Overall, this study provides a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.
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Affiliation(s)
- Mohammad Mazharul Islam
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Andrea Goertzen
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Pankaj K. Singh
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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Capela J, Lagoa D, Rodrigues R, Cunha E, Cruz F, Barbosa A, Bastos J, Lima D, Ferreira EC, Rocha M, Dias O. merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models. Nucleic Acids Res 2022; 50:6052-6066. [PMID: 35694833 PMCID: PMC9226533 DOI: 10.1093/nar/gkac459] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/10/2022] [Indexed: 01/18/2023] Open
Abstract
Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.
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Affiliation(s)
- João Capela
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Davide Lagoa
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Ruben Rodrigues
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Emanuel Cunha
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Fernando Cruz
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Ana Barbosa
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - José Bastos
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Diogo Lima
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Eugénio C Ferreira
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
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All Driven by Energy Demand? Integrative Comparison of Metabolism of Enterococcus faecalis Wildtype and a Glutamine Synthase Mutant. Microbiol Spectr 2022; 10:e0240021. [PMID: 35234500 PMCID: PMC8941932 DOI: 10.1128/spectrum.02400-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g., food industry and also in human health. Many LAB genera have developed a multidrug resistance in the past few years, causing a serious problem in controlling hospital germs worldwide. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important to promote the development of new therapeutic approaches. In this study, we investigated the effect of glutamine auxotrophy (ΔglnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of the organism's natural environment in the human body and play a role in the food industry. We compared the results with those of the wildtype. Using a genome-scale metabolic model constrained by metabolic and proteomic data, our integrative method allows us to understand the bigger picture of the adaptation strategies of this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that ΔglnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the nonspecificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models because it affects energy balance. IMPORTANCE The integration of new pH-dependent experimental data on metabolic uptake and release fluxes, as well as of proteome data with a genome-scale computational model of a glutamine synthetase mutant of E. faecalis is used and compared with those of the wildtype to understand why glutamine auxotrophy results in a less efficient metabolism and how-in comparison with the wildtype-the glutamine synthetase knockout impacts metabolic adjustments during acidification or simply exposure to lower pH. We show that forced glutamine auxotrophy causes more energy demand and that this is likely due to a disregulated glutamine uptake. Proteome changes during acidification observed for the mutant resemble those of the wildtype with the exception of glycolysis-related genes, as the mutant is already energetically stressed at a higher pH and the respective proteome changes were in effect.
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Palù M, Basile A, Zampieri G, Treu L, Rossi A, Silvia Morlino M, Campanaro S. KEMET - a python tool for KEGG Module evaluation and microbial genome annotation expansion. Comput Struct Biotechnol J 2022; 20:1481-1486. [PMID: 35422973 PMCID: PMC8976094 DOI: 10.1016/j.csbj.2022.03.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/23/2022] Open
Abstract
KEMET allows evaluating and expanding microbial genome annotations. KEMET can identify unannotated genes having partial sequence truncations. Annotation expansion can improve contextual draft genome-scale metabolic model reconstruction.
Background The rapid accumulation of sequencing data from metagenomic studies is enabling the generation of huge collections of microbial genomes, with new challenges for mapping their functional potential. In particular, metagenome-assembled genomes are typically incomplete and harbor partial gene sequences that can limit their annotation from traditional tools. New scalable solutions are thus needed to facilitate the evaluation of functional potential in microbial genomes. Methods To resolve annotation gaps in microbial genomes, we developed KEMET, an open-source Python library devised for the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units. KEMET focuses on the in-depth analysis of metabolic reaction networks to identify missing orthologs through hidden Markov model profiles. Results We evaluate the potential of KEMET for expanding functional annotations by simulating the effect of assembly issues on real gene sequences and showing that our approach can identify missing KEGG orthologs. Additionally, we show that recovered gene annotations can sensibly increase the quality of draft genome-scale metabolic models obtained from metagenome-assembled genomes, in some cases reaching the accuracy of models generated from complete genomes. Conclusions KEMET therefore allows expanding genome annotations by targeted searches for orthologous sequences, enabling a better qualitative and quantitative assessment of metabolic capabilities in novel microbial organisms.
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