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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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Mashabela MD, Piater LA, Dubery IA, Tugizimana F, Mhlongo MI. Rhizosphere Tripartite Interactions and PGPR-Mediated Metabolic Reprogramming towards ISR and Plant Priming: A Metabolomics Review. BIOLOGY 2022; 11:346. [PMID: 35336720 PMCID: PMC8945280 DOI: 10.3390/biology11030346] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023]
Abstract
Plant growth-promoting rhizobacteria (PGPR) are beneficial microorganisms colonising the rhizosphere. PGPR are involved in plant growth promotion and plant priming against biotic and abiotic stresses. Plant-microbe interactions occur through chemical communications in the rhizosphere and a tripartite interaction mechanism between plants, pathogenic microbes and plant-beneficial microbes has been defined. However, comprehensive information on the rhizosphere communications between plants and microbes, the tripartite interactions and the biochemical implications of these interactions on the plant metabolome is minimal and not yet widely available nor well understood. Furthermore, the mechanistic nature of PGPR effects on induced systemic resistance (ISR) and priming in plants at the molecular and metabolic levels is yet to be fully elucidated. As such, research investigating chemical communication in the rhizosphere is currently underway. Over the past decades, metabolomics approaches have been extensively used in describing the detailed metabolome of organisms and have allowed the understanding of metabolic reprogramming in plants due to tripartite interactions. Here, we review communication systems between plants and microorganisms in the rhizosphere that lead to plant growth stimulation and priming/induced resistance and the applications of metabolomics in understanding these complex tripartite interactions.
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Affiliation(s)
- Manamele D. Mashabela
- Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa; (M.D.M.); (L.A.P.); (I.A.D.); (F.T.)
| | - Lizelle A. Piater
- Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa; (M.D.M.); (L.A.P.); (I.A.D.); (F.T.)
| | - Ian A. Dubery
- Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa; (M.D.M.); (L.A.P.); (I.A.D.); (F.T.)
| | - Fidele Tugizimana
- Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa; (M.D.M.); (L.A.P.); (I.A.D.); (F.T.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
| | - Msizi I. Mhlongo
- Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa; (M.D.M.); (L.A.P.); (I.A.D.); (F.T.)
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Baidoo EEK, Teixeira Benites V. Mass Spectrometry-Based Microbial Metabolomics: Techniques, Analysis, and Applications. Methods Mol Biol 2019; 1859:11-69. [PMID: 30421222 DOI: 10.1007/978-1-4939-8757-3_2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The demand for understanding the roles genes play in biological systems has steered the biosciences into the direction the metabolome, as it closely reflects the metabolic activities within a cell. The importance of the metabolome is further highlighted by its ability to influence the genome, transcriptome, and proteome. Consequently, metabolomic information is being used to understand microbial metabolic networks. At the forefront of this work is mass spectrometry, the most popular metabolomics measurement technique. Mass spectrometry-based metabolomic analyses have made significant contributions to microbiological research in the environment and human disease. In this chapter, we break down the technical aspects of mass spectrometry-based metabolomics and discuss its application to microbiological research.
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Affiliation(s)
- Edward E K Baidoo
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
- Joint BioEnergy Institute, Emeryville, California, USA.
| | - Veronica Teixeira Benites
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Joint BioEnergy Institute, Emeryville, California, USA
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Kempa EE, Hollywood KA, Smith CA, Barran PE. High throughput screening of complex biological samples with mass spectrometry – from bulk measurements to single cell analysis. Analyst 2019; 144:872-891. [DOI: 10.1039/c8an01448e] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We review the state of the art in HTS using mass spectrometry with minimal sample preparation from complex biological matrices. We focus on industrial and biotechnological applications.
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Affiliation(s)
- Emily E. Kempa
- Michael Barber Centre for Collaborative Mass Spectrometry
- Manchester Institute of Biotechnology
- The University of Manchester
- Manchester
- UK
| | - Katherine A. Hollywood
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM)
- Manchester Institute of Biotechnology
- The University of Manchester
- Manchester M1 7DN
- UK
| | - Clive A. Smith
- Sphere Fluidics Limited
- The Jonas-Webb Building
- Babraham Research Campus
- Cambridge
- UK
| | - Perdita E. Barran
- Michael Barber Centre for Collaborative Mass Spectrometry
- Manchester Institute of Biotechnology
- The University of Manchester
- Manchester
- UK
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Costello Z, Martin HG. A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Syst Biol Appl 2018; 4:19. [PMID: 29872542 PMCID: PMC5974308 DOI: 10.1038/s41540-018-0054-3] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 04/11/2018] [Accepted: 04/20/2018] [Indexed: 02/01/2023] Open
Abstract
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
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Affiliation(s)
- Zak Costello
- 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA.,DOE Agile Biofoundry, Emeryville, CA USA.,3DOE Joint BioEnergy Institute, Emeryville, CA USA
| | - Hector Garcia Martin
- 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA.,DOE Agile Biofoundry, Emeryville, CA USA.,3DOE Joint BioEnergy Institute, Emeryville, CA USA.,4BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
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Heinemann J, Noon B, Willems D, Budeski K, Bothner B. Analysis of Raw Biofluids by Mass Spectrometry Using Microfluidic Diffusion-Based Separation. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2017; 9:385-392. [PMID: 28713441 PMCID: PMC5509350 DOI: 10.1039/c6ay02827f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Elucidation and monitoring of biomarkers continues to expand because of their medical value and potential to reduce healthcare costs. For example, biomarkers are used extensively to track physiology associated with drug addiction, disease progression, aging, and industrial processes. While longitudinal analyses are of great value from a biological or healthcare perspective, the cost associated with replicate analyses is preventing the expansion of frequent routine testing. Frequent testing could deepen our understanding of disease emergence and aid adoption of personalized healthcare. To address this need, we have developed a system for measuring metabolite abundance from raw biofluids. Using a metabolite extraction chip (MEC), based upon diffusive extraction of small molecules and metabolites from biofluids using microfluidics, we show that biologically relevant markers can be measured in blood and urine. Previously it was shown that the MEC could be used to track metabolic changes in real-time. We now demonstrate that the device can be adapted to high-throughput screening using standard liquid chromatography mass spectrometry instrumentation (LCMS). The results provide insight into the sensitivity of the system and its application for the analysis of human biofluids. Quantitative analysis of clinical predictors including nicotine, caffeine, and glutathione are described.
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Affiliation(s)
- Joshua Heinemann
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720
- Joint Bioenergy Institute, Emeryville, CA 94608
| | - Brigit Noon
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717
| | - Daniel Willems
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717
| | - Katherine Budeski
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717
| | - Brian Bothner
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717
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Heinemann J, Deng K, Shih SCC, Gao J, Adams PD, Singh AK, Northen TR. On-chip integration of droplet microfluidics and nanostructure-initiator mass spectrometry for enzyme screening. LAB ON A CHIP 2017; 17:323-331. [PMID: 27957569 DOI: 10.1039/c6lc01182a] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Biological assays often require expensive reagents and tedious manipulations. These shortcomings can be overcome using digitally operated microfluidic devices that require reduced sample volumes to automate assays. One particular challenge is integrating bioassays with mass spectrometry based analysis. Towards this goal we have developed μNIMS, a highly sensitive and high throughput technique that integrates droplet microfluidics with nanostructure-initiator mass spectrometry (NIMS). Enzyme reactions are carried out in droplets that can be arrayed on discrete NIMS elements at defined time intervals for subsequent mass spectrometry analysis, enabling time resolved enzyme activity assay. We apply the μNIMS platform for kinetic characterization of a glycoside hydrolase enzyme (CelE-CMB3A), a chimeric enzyme capable of deconstructing plant hemicellulose into monosaccharides for subsequent conversion to biofuel. This study reveals NIMS nanostructures can be fabricated into arrays for microfluidic droplet deposition, NIMS is compatible with droplet and digital microfluidics, and can be used on-chip to assay glycoside hydrolase enzyme in vitro.
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Affiliation(s)
- Joshua Heinemann
- Joint Bioenergy Institute, Emeryville, California 94608, USA and Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
| | - Kai Deng
- Joint Bioenergy Institute, Emeryville, California 94608, USA and Sandia National Laboratories, Livermore, California 94551, USA
| | - Steve C C Shih
- Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada
| | - Jian Gao
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
| | - Paul D Adams
- Joint Bioenergy Institute, Emeryville, California 94608, USA and Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA. and Department of Bioengineering, University of California, Berkeley, California, 94720, USA
| | - Anup K Singh
- Joint Bioenergy Institute, Emeryville, California 94608, USA and Sandia National Laboratories, Livermore, California 94551, USA
| | - Trent R Northen
- Joint Bioenergy Institute, Emeryville, California 94608, USA and Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA. and Joint Genome Institute, Walnut creek, California, 94598, USA
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Zampieri M, Sekar K, Zamboni N, Sauer U. Frontiers of high-throughput metabolomics. Curr Opin Chem Biol 2017; 36:15-23. [PMID: 28064089 DOI: 10.1016/j.cbpa.2016.12.006] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 11/30/2016] [Accepted: 12/05/2016] [Indexed: 02/06/2023]
Abstract
Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.
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Affiliation(s)
- Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, CH-8093 Zurich, Switzerland
| | - Karthik Sekar
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, CH-8093 Zurich, Switzerland
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, CH-8093 Zurich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, CH-8093 Zurich, Switzerland.
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Abstract
The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential “seismic” shift from the current “healthcare” model to a “wellness” paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human “data poor” to a human “data rich” environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.
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Affiliation(s)
- George D Loizou
- Health Risks, Health and Safety Laboratory, Health and Safety Executive Buxton, UK
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de Raad M, Fischer CR, Northen TR. High-throughput platforms for metabolomics. Curr Opin Chem Biol 2015; 30:7-13. [PMID: 26544850 DOI: 10.1016/j.cbpa.2015.10.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2015] [Accepted: 10/11/2015] [Indexed: 01/06/2023]
Abstract
Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis.
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Affiliation(s)
- Markus de Raad
- Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, United States
| | - Curt R Fischer
- Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, United States
| | - Trent R Northen
- Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, United States.
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