1
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Holbrook-Smith D, Trouillon J, Sauer U. Metabolomics and Microbial Metabolism: Toward a Systematic Understanding. Annu Rev Biophys 2024; 53:41-64. [PMID: 38109374 DOI: 10.1146/annurev-biophys-030722-021957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.
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Affiliation(s)
| | - Julian Trouillon
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
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2
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Zhu XG, Treves H, Zhao H. Mechanisms controlling metabolite concentrations of the Calvin Benson Cycle. Semin Cell Dev Biol 2024; 155:3-9. [PMID: 36858897 DOI: 10.1016/j.semcdb.2023.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 01/28/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023]
Abstract
Maintaining proper metabolite levels in a complex metabolic network is crucial for maintaining a high flux through the network. In this paper, we discuss major regulatory mechanisms over the Calvin Benson Cycle (CBC) with regard to their roles in conferring homeostasis of metabolite levels in CBC. These include: 1) Redox regulation of enzymes in the CBC on one hand ensures that metabolite levels stay above certain lower bounds under low light while on the other hand increases the flux through the CBC under high light. 2) Metabolite regulations, especially allosteric regulations of major regulatory enzymes, ensure the rapid up-regulation of fluxes to ensure sufficient amount of triose phosphate is available for end product synthesis and concurrently avoid phosphate limitation. 3) A balanced activities of enzymes in the CBC help maintain balanced flux through CBC; some innate product feedback mechanisms, in particular the ADP feedback regulation of GAPDH and F6P feedback regulation of FBPase, exist in CBC to achieve such a balanced enzyme activities and hence flux distribution in the CBC for greater photosynthetic efficiency. Transcriptional regulation and natural variations of enzymes controlling CBC metabolite homeostasis should be further explored to maximize the potential of engineering CBC for greater efficiency.
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Affiliation(s)
- Xin-Guang Zhu
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Science, Shanghai 200032, China.
| | - Haim Treves
- School of Plant Sciences and Food Security, Tel-Aviv University, 6997801, Israel
| | - Honglong Zhao
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Science, Shanghai 200032, China
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3
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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4
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Mattei G, Gan Z, Ramazzotti M, Palsson BO, Zielinski DC. Differential Expression Analysis Utilizing Condition-Specific Metabolic Pathways. Metabolites 2023; 13:1127. [PMID: 37999223 PMCID: PMC10672963 DOI: 10.3390/metabo13111127] [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: 07/01/2023] [Revised: 10/19/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023] Open
Abstract
Pathway analysis is ubiquitous in biological data analysis due to the ability to integrate small simultaneous changes in functionally related components. While pathways are often defined based on either manual curation or network topological properties, an attractive alternative is to generate pathways around specific functions, in which metabolism can be defined as the production and consumption of specific metabolites. In this work, we present an algorithm, termed MetPath, that calculates pathways for condition-specific production and consumption of specific metabolites. We demonstrate that these pathways have several useful properties. Pathways calculated in this manner (1) take into account the condition-specific metabolic role of a gene product, (2) are localized around defined metabolic functions, and (3) quantitatively weigh the importance of expression to a function based on the flux contribution of the gene product. We demonstrate how these pathways elucidate network interactions between genes across different growth conditions and between cell types. Furthermore, the calculated pathways compare favorably to manually curated pathways in predicting the expression correlation between genes. To facilitate the use of these pathways, we have generated a large compendium of pathways under different growth conditions for E. coli. The MetPath algorithm provides a useful tool for metabolic network-based statistical analyses of high-throughput data.
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Affiliation(s)
- Gianluca Mattei
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50121 Florence, Italy; (G.M.)
| | - Zhuohui Gan
- School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou 325035, China;
| | - Matteo Ramazzotti
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50121 Florence, Italy; (G.M.)
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA
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5
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Schramm T, Lubrano P, Pahl V, Stadelmann A, Verhülsdonk A, Link H. Mapping temperature-sensitive mutations at a genome scale to engineer growth switches in Escherichia coli. Mol Syst Biol 2023; 19:e11596. [PMID: 37642940 PMCID: PMC10568205 DOI: 10.15252/msb.202311596] [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: 02/14/2023] [Revised: 08/14/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
Temperature-sensitive (TS) mutants are a unique tool to perturb and engineer cellular systems. Here, we constructed a CRISPR library with 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. 1,269 of these mutants showed temperature-sensitive growth in a time-resolved competition assay. We reconstructed 94 TS mutants and measured their metabolism under growth arrest at 42°C using metabolomics. Metabolome changes were strong and mutant-specific, showing that metabolism of nongrowing E. coli is perturbation-dependent. For example, 24 TS mutants of metabolic enzymes overproduced the direct substrate metabolite due to a bottleneck in their associated pathway. A strain with TS homoserine kinase (ThrBF267D ) produced homoserine for 24 h, and production was tunable by temperature. Finally, we used a TS subunit of DNA polymerase III (DnaXL289Q ) to decouple growth from arginine overproduction in engineered E. coli. These results provide a strategy to identify TS mutants en masse and demonstrate their large potential to produce bacterial metabolites with nongrowing cells.
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Affiliation(s)
- Thorben Schramm
- Interfaculty Institute of Microbiology and Infection MedicineUniversity of TübingenTübingenGermany
- Cluster of Excellence “Controlling Microbes to Fight Infections”University of TübingenTübingenGermany
- Present address:
Department of Biology, Institute of Molecular Systems BiologyETH ZurichZürichSwitzerland
| | - Paul Lubrano
- Interfaculty Institute of Microbiology and Infection MedicineUniversity of TübingenTübingenGermany
- Cluster of Excellence “Controlling Microbes to Fight Infections”University of TübingenTübingenGermany
| | - Vanessa Pahl
- Interfaculty Institute of Microbiology and Infection MedicineUniversity of TübingenTübingenGermany
- Cluster of Excellence “Controlling Microbes to Fight Infections”University of TübingenTübingenGermany
| | - Amelie Stadelmann
- Interfaculty Institute of Microbiology and Infection MedicineUniversity of TübingenTübingenGermany
- Cluster of Excellence “Controlling Microbes to Fight Infections”University of TübingenTübingenGermany
| | - Andreas Verhülsdonk
- Interfaculty Institute of Microbiology and Infection MedicineUniversity of TübingenTübingenGermany
- Cluster of Excellence “Controlling Microbes to Fight Infections”University of TübingenTübingenGermany
| | - Hannes Link
- Interfaculty Institute of Microbiology and Infection MedicineUniversity of TübingenTübingenGermany
- Cluster of Excellence “Controlling Microbes to Fight Infections”University of TübingenTübingenGermany
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6
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Campbell RP, Whittington AC, Zorio DAR, Miller BG. Recruitment of a Middling Promiscuous Enzyme Drives Adaptive Metabolic Evolution in Escherichia coli. Mol Biol Evol 2023; 40:msad202. [PMID: 37708398 PMCID: PMC10519446 DOI: 10.1093/molbev/msad202] [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: 04/18/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/16/2023] Open
Abstract
A key step in metabolic pathway evolution is the recruitment of promiscuous enzymes to perform new functions. Despite the recognition that promiscuity is widespread in biology, factors dictating the preferential recruitment of one promiscuous enzyme over other candidates are unknown. Escherichia coli contains four sugar kinases that are candidates for recruitment when the native glucokinase machinery is deleted-allokinase (AlsK), manno(fructo)kinase (Mak), N-acetylmannosamine kinase (NanK), and N-acetylglucosamine kinase (NagK). The catalytic efficiencies of these enzymes are 103- to 105-fold lower than native glucokinases, ranging from 2,400 M-1 s-1 for the most active candidate, NagK, to 15 M-1 s-1 for the least active candidate, AlsK. To investigate the relationship between catalytic activities of promiscuous enzymes and their recruitment, we performed adaptive evolution of a glucokinase-deficient E. coli strain to restore glycolytic metabolism. We observed preferential recruitment of NanK via a trajectory involving early mutations that facilitate glucose uptake and amplify nanK transcription, followed by nonsynonymous substitutions in NanK that enhance the enzyme's promiscuous glucokinase activity. These substitutions reduced the native activity of NanK and reduced organismal fitness during growth on an N-acetylated carbon source, indicating that enzyme recruitment comes at a cost for growth on other substrates. Notably, the two most active candidates, NagK and Mak, were not recruited, suggesting that catalytic activity alone does not dictate evolutionary outcomes. The results highlight our lack of knowledge regarding biological drivers of enzyme recruitment and emphasize the need for a systems-wide approach to identify factors facilitating or constraining this important adaptive process.
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Affiliation(s)
- Ryan P Campbell
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, USA
| | - A Carl Whittington
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, USA
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Diego A R Zorio
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Brian G Miller
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, USA
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7
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Mirveis Z, Howe O, Cahill P, Patil N, Byrne HJ. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives. Metabolomics 2023; 19:67. [PMID: 37482587 PMCID: PMC10363518 DOI: 10.1007/s11306-023-02031-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge. AIM This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway. KEY SCIENTIFIC CONCEPTS Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.
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Affiliation(s)
- Zohreh Mirveis
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland.
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological, Health and Sport Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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8
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Mortazavi Farsani SS, Verma V. Lactate mediated metabolic crosstalk between cancer and immune cells and its therapeutic implications. Front Oncol 2023; 13:1175532. [PMID: 37234972 PMCID: PMC10206240 DOI: 10.3389/fonc.2023.1175532] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Metabolism is central to energy generation and cell signaling in all life forms. Cancer cells rely heavily on glucose metabolism wherein glucose is primarily converted to lactate even in adequate oxygen conditions, a process famously known as "the Warburg effect." In addition to cancer cells, Warburg effect was found to be operational in other cell types, including actively proliferating immune cells. According to current dogma, pyruvate is the end product of glycolysis that is converted into lactate in normal cells, particularly under hypoxic conditions. However, several recent observations suggest that the final product of glycolysis may be lactate, which is produced irrespective of oxygen concentrations. Traditionally, glucose-derived lactate can have three fates: it can be used as a fuel in the TCA cycle or lipid synthesis; it can be converted back into pyruvate in the cytosol that feeds into the mitochondrial TCA; or, at very high concentrations, accumulated lactate in the cytosol may be released from cells that act as an oncometabolite. In immune cells as well, glucose-derived lactate seems to play a major role in metabolism and cell signaling. However, immune cells are much more sensitive to lactate concentrations, as higher lactate levels have been found to inhibit immune cell function. Thus, tumor cell-derived lactate may serve as a major player in deciding the response and resistance to immune cell-directed therapies. In the current review, we will provide a comprehensive overview of the glycolytic process in eukaryotic cells with a special focus on the fate of pyruvate and lactate in tumor and immune cells. We will also review the evidence supporting the idea that lactate, not pyruvate, is the end product of glycolysis. In addition, we will discuss the impact of glucose-lactate-mediated cross-talk between tumor and immune cells on the therapeutic outcomes after immunotherapy.
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Affiliation(s)
- Seyedeh Sahar Mortazavi Farsani
- Section of Cancer Immunotherapy and Immune Metabolism, The Hormel Institute, University of Minnesota, Austin, MN, United States
| | - Vivek Verma
- Section of Cancer Immunotherapy and Immune Metabolism, The Hormel Institute, University of Minnesota, Austin, MN, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States
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9
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Abstract
Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.
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Affiliation(s)
- Sofia Moco
- Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Joerg M Buescher
- Metabolomics Core Facility, Max Planck Institute of Immunobiology and Epigenetics, Freiburg im Breisgau, Germany.
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10
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Cherkaoui S, Durot S, Bradley J, Critchlow S, Dubuis S, Masiero MM, Wegmann R, Snijder B, Othman A, Bendtsen C, Zamboni N. A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs. Mol Syst Biol 2022; 18:e11033. [PMID: 36321552 PMCID: PMC9627673 DOI: 10.15252/msb.202211033] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 10/03/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, we profiled the intracellular metabolome of 180 pan-cancer cell lines grown in identical conditions. For each cell line, we estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, we discovered a convergence into only two major metabolic types. These were functionally confirmed by 13 C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. They revealed that the major differences in cancers are associated with lipid, TCA cycle, and carbohydrate metabolism. Thorough integration of these types with multiomics highlighted little association with genetic alterations but a strong association with markers of epithelial-mesenchymal transition. Our analysis indicates that in absence of variations imposed by the microenvironment, cancer cells adopt distinct metabolic programs which serve as vulnerabilities for therapy.
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Affiliation(s)
- Sarah Cherkaoui
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland,PhD Program in Systems BiologyLife Science ZürichZürichSwitzerland
| | - Stephan Durot
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland,PhD Program in Systems BiologyLife Science ZürichZürichSwitzerland
| | | | | | - Sebastien Dubuis
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland
| | - Mauro Miguel Masiero
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland,PhD Program in Systems BiologyLife Science ZürichZürichSwitzerland
| | - Rebekka Wegmann
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland,PhD Program in Systems BiologyLife Science ZürichZürichSwitzerland
| | - Berend Snijder
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland
| | - Alaa Othman
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland,PHRT Swiss Multi‐OMICS Center / smoc.ethz.chZürichSwitzerland
| | | | - Nicola Zamboni
- Institute of Molecular Systems BiologyETH ZürichZürichSwitzerland,PHRT Swiss Multi‐OMICS Center / smoc.ethz.chZürichSwitzerland
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11
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Liebermeister W. Structural Thermokinetic Modelling. Metabolites 2022; 12:metabo12050434. [PMID: 35629936 PMCID: PMC9144996 DOI: 10.3390/metabo12050434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/16/2022] Open
Abstract
To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.
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12
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Zhao H, Wang Y, Lyu MJA, Zhu XG. Two major metabolic factors for an efficient NADP-malic enzyme type C4 photosynthesis. PLANT PHYSIOLOGY 2022; 189:84-98. [PMID: 35166833 PMCID: PMC9070817 DOI: 10.1093/plphys/kiac051] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/15/2022] [Indexed: 06/02/2023]
Abstract
Compared to the large number of studies focused on the factors controlling C3 photosynthesis efficiency, there are relatively fewer studies of the factors controlling photosynthetic efficiency in C4 leaves. Here, we used a dynamic systems model of C4 photosynthesis based on maize (Zea mays) to identify features associated with high photosynthetic efficiency in NADP-malic enzyme (NADP-ME) type C4 photosynthesis. We found that two additional factors related to coordination between C4 shuttle metabolism and C3 metabolism are required for efficient C4 photosynthesis: (1) accumulating a high concentration of phosphoenolpyruvate through maintaining a large PGA concentration in the mesophyll cell chloroplast and (2) maintaining a suitable oxidized status in bundle sheath cell chloroplasts. These identified mechanisms are in line with the current cellular location of enzymes/proteins involved in the starch synthesis, the Calvin-Benson cycle and photosystem II of NADP-ME type C4 photosynthesis. These findings suggested potential strategies for improving C4 photosynthesis and engineering C4 rice.
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Affiliation(s)
- Honglong Zhao
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Yu Wang
- The Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, USA
| | - Ming-Ju Amy Lyu
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China
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13
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Wang Z, Yang R, Zhang Y, Hui X, Yan L, Zhang R, Li X, Abliz Z. Ratiometric Mass Spectrometry Imaging for Stain-Free Delineation of Ischemic Tissue and Spatial Profiling of Ischemia-Related Molecular Signatures. Front Chem 2022; 9:807868. [PMID: 34993178 PMCID: PMC8724055 DOI: 10.3389/fchem.2021.807868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022] Open
Abstract
Mass spectrometry imaging (MSI) serves as an emerging tool for spatial profiling of metabolic dysfunction in ischemic tissue. Prior to MSI data analysis, commonly used staining methods, e.g., triphenyltetrazole chloride (TTC) staining, need to be implemented on the adjacent tissue for delineating lesion area and evaluating infarction, resulting in extra consumption of the tissue sample as well as morphological mismatch. Here, we propose an in situ ratiometric MSI method for simultaneous demarcation of lesion border and spatial annotation of metabolic and enzymatic signatures in ischemic tissue on identical tissue sections. In this method, the ion abundance ratio of a reactant pair in the TCA cycle, e.g., fumarate to malate, is extracted pixel-by-pixel from an ambient MSI dataset of ischemic tissue and used as a surrogate indicator for metabolic activity of mitochondria to delineate lesion area as if the tissue has been chemically stained. This method is shown to be precise and robust in identifying lesions in brain tissues and tissue samples from different ischemic models including heart, liver, and kidney. Furthermore, the proposed method allows screening and predicting metabolic and enzymatic alterations which are related to mitochondrial dysfunction. Being capable of concurrent lesion identification, in situ metabolomics analysis, and screening of enzymatic alterations, the ratiometric MSI method bears great potential to explore ischemic damages at both metabolic and enzymatic levels in biological research.
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Affiliation(s)
- Zixuan Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ran Yang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yaxin Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangyi Hui
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liuyan Yan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Center for Imaging and Systems Biology, Minzu University of China, Beijing, China
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14
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Karlstaedt A. Stable Isotopes for Tracing Cardiac Metabolism in Diseases. Front Cardiovasc Med 2021; 8:734364. [PMID: 34859064 PMCID: PMC8631909 DOI: 10.3389/fcvm.2021.734364] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/18/2021] [Indexed: 12/28/2022] Open
Abstract
Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level in vivo. The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.
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Affiliation(s)
- Anja Karlstaedt
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.,Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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15
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Dourado H, Mori M, Hwa T, Lercher MJ. On the optimality of the enzyme-substrate relationship in bacteria. PLoS Biol 2021; 19:e3001416. [PMID: 34699521 PMCID: PMC8547704 DOI: 10.1371/journal.pbio.3001416] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/17/2021] [Indexed: 11/28/2022] Open
Abstract
Much recent progress has been made to understand the impact of proteome allocation on bacterial growth; much less is known about the relationship between the abundances of the enzymes and their substrates, which jointly determine metabolic fluxes. Here, we report a correlation between the concentrations of enzymes and their substrates in Escherichia coli. We suggest this relationship to be a consequence of optimal resource allocation, subject to an overall constraint on the biomass density: For a cellular reaction network composed of effectively irreversible reactions, maximal reaction flux is achieved when the dry mass allocated to each substrate is equal to the dry mass of the unsaturated (or “free”) enzymes waiting to consume it. Calculations based on this optimality principle successfully predict the quantitative relationship between the observed enzyme and metabolite abundances, parameterized only by molecular masses and enzyme–substrate dissociation constants (Km). The corresponding organizing principle provides a fundamental rationale for cellular investment into different types of molecules, which may aid in the design of more efficient synthetic cellular systems. This study shows that in E. coli, the cellular mass of each metabolite approximately equals the combined mass of the free enzymes waiting to consume it; this simple relationship arises from the optimal utilization of cellular dry mass, and quantitatively describes available experimental data.
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Affiliation(s)
- Hugo Dourado
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Matteo Mori
- Department of Physics, University of California at San Diego, La Jolla, California, United States of America
| | - Terence Hwa
- Department of Physics, University of California at San Diego, La Jolla, California, United States of America
| | - Martin J. Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany
- * E-mail:
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16
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Schastnaya E, Raguz Nakic Z, Gruber CH, Doubleday PF, Krishnan A, Johns NI, Park J, Wang HH, Sauer U. Extensive regulation of enzyme activity by phosphorylation in Escherichia coli. Nat Commun 2021; 12:5650. [PMID: 34561442 PMCID: PMC8463566 DOI: 10.1038/s41467-021-25988-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/03/2021] [Indexed: 02/08/2023] Open
Abstract
Protein serine/threonine/tyrosine (S/T/Y) phosphorylation is an essential and frequent post-translational modification in eukaryotes, but historically has been considered less prevalent in bacteria because fewer proteins were found to be phosphorylated and most proteins were modified to a lower degree. Recent proteomics studies greatly expanded the phosphoproteome of Escherichia coli to more than 2000 phosphorylation sites (phosphosites), yet mechanisms of action were proposed for only six phosphosites and fitness effects were described for 38 phosphosites upon perturbation. By systematically characterizing functional relevance of S/T/Y phosphorylation in E. coli metabolism, we found 44 of the 52 mutated phosphosites to be functional based on growth phenotypes and intracellular metabolome profiles. By effectively doubling the number of known functional phosphosites, we provide evidence that protein phosphorylation is a major regulation process in bacterial metabolism. Combining in vitro and in vivo experiments, we demonstrate how single phosphosites modulate enzymatic activity and regulate metabolic fluxes in glycolysis, methylglyoxal bypass, acetate metabolism and the split between pentose phosphate and Entner-Doudoroff pathways through mechanisms that include shielding the substrate binding site, limiting structural dynamics, and disrupting interactions relevant for activity in vivo.
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Affiliation(s)
- Evgeniya Schastnaya
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Zrinka Raguz Nakic
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
- Institute of Chemistry and Biotechnology, ZHAW Zurich University of Applied Sciences, Wädenswil, Switzerland
| | - Christoph H Gruber
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | | | - Aarti Krishnan
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Nathan I Johns
- Department of Systems Biology, Columbia University, New York, NY, USA
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Jimin Park
- Department of Systems Biology, Columbia University, New York, NY, USA
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Harris H Wang
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
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17
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Cloutier M, Xiang D, Gao P, Kochian LV, Zou J, Datla R, Wang E. Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes. FRONTIERS IN PLANT SCIENCE 2021; 12:642938. [PMID: 33889166 PMCID: PMC8056077 DOI: 10.3389/fpls.2021.642938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.
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Affiliation(s)
- Mathieu Cloutier
- Laboratory of Bioinformatics and Systems Biology, National Research Council Canada, Montreal, QC, Canada
| | - Daoquan Xiang
- Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada
| | - Peng Gao
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Leon V. Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Jitao Zou
- Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada
| | - Raju Datla
- Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Edwin Wang
- Laboratory of Bioinformatics and Systems Biology, National Research Council Canada, Montreal, QC, Canada
- Centre for Health Genomics and Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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18
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Kochanowski K, Okano H, Patsalo V, Williamson J, Sauer U, Hwa T. Global coordination of metabolic pathways in Escherichia coli by active and passive regulation. Mol Syst Biol 2021; 17:e10064. [PMID: 33852189 PMCID: PMC8045939 DOI: 10.15252/msb.202010064] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 12/15/2022] Open
Abstract
Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.
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Affiliation(s)
- Karl Kochanowski
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Life Science Zurich PhD Program on Systems BiologyZurichSwitzerland
| | - Hiroyuki Okano
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
| | - Vadim Patsalo
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical BiologyThe Scripps Research InstituteLa JollaCAUSA
| | - James Williamson
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical BiologyThe Scripps Research InstituteLa JollaCAUSA
| | - Uwe Sauer
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Terence Hwa
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
- Institute for Theoretical ScienceETH ZurichZurichSwitzerland
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19
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Shin M, Park H, Kim S, Oh EJ, Jeong D, Florencia C, Kim KH, Jin YS, Kim SR. Transcriptomic Changes Induced by Deletion of Transcriptional Regulator GCR2 on Pentose Sugar Metabolism in Saccharomyces cerevisiae. Front Bioeng Biotechnol 2021; 9:654177. [PMID: 33842449 PMCID: PMC8027353 DOI: 10.3389/fbioe.2021.654177] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Being a microbial host for lignocellulosic biofuel production, Saccharomyces cerevisiae needs to be engineered to express a heterologous xylose pathway; however, it has been challenging to optimize the engineered strain for efficient and rapid fermentation of xylose. Deletion of PHO13 (Δpho13) has been reported to be a crucial genetic perturbation in improving xylose fermentation. A confirmed mechanism of the Δpho13 effect on xylose fermentation is that the Δpho13 transcriptionally activates the genes in the non-oxidative pentose phosphate pathway (PPP). In the current study, we found a couple of engineered strains, of which phenotypes were not affected by Δpho13 (Δpho13-negative), among many others we examined. Genome resequencing of the Δpho13-negative strains revealed that a loss-of-function mutation in GCR2 was responsible for the phenotype. Gcr2 is a global transcriptional factor involved in glucose metabolism. The results of RNA-seq confirmed that the deletion of GCR2 (Δgcr2) led to the upregulation of PPP genes as well as downregulation of glycolytic genes, and changes were more significant under xylose conditions than those under glucose conditions. Although there was no synergistic effect between Δpho13 and Δgcr2 in improving xylose fermentation, these results suggested that GCR2 is a novel knockout target in improving lignocellulosic ethanol production.
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Affiliation(s)
- Minhye Shin
- Department of Agricultural Biotechnology, Research Institute of Agriculture and Life Science, Seoul National University, Seoul, South Korea
| | - Heeyoung Park
- School of Food Science and Biotechnology, Kyungpook National University, Daegu, South Korea
| | - Sooah Kim
- Department of Environment Science and Biotechnology, Jeonju University, Jeonju, South Korea
| | - Eun Joong Oh
- Department of Food Science, Purdue University, West Lafayette, IN, United States
| | - Deokyeol Jeong
- School of Food Science and Biotechnology, Kyungpook National University, Daegu, South Korea
| | - Clarissa Florencia
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Kyoung Heon Kim
- Department of Biotechnology, Graduate School, Korea University, Seoul, South Korea
| | - Yong-Su Jin
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Soo Rin Kim
- School of Food Science and Biotechnology, Kyungpook National University, Daegu, South Korea
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20
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Abstract
It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes. IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today’s whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli. Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.
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21
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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22
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Mass Spectrometry-based Metabolomics in Translational Research. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1310:509-531. [PMID: 33834448 DOI: 10.1007/978-981-33-6064-8_19] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.
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23
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Donati S, Kuntz M, Pahl V, Farke N, Beuter D, Glatter T, Gomes-Filho JV, Randau L, Wang CY, Link H. Multi-omics Analysis of CRISPRi-Knockdowns Identifies Mechanisms that Buffer Decreases of Enzymes in E. coli Metabolism. Cell Syst 2020; 12:56-67.e6. [PMID: 33238135 DOI: 10.1016/j.cels.2020.10.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/23/2020] [Accepted: 10/29/2020] [Indexed: 12/21/2022]
Abstract
Enzymes maintain metabolism, and their concentration affects cellular fitness: high enzyme levels are costly, and low enzyme levels can limit metabolic flux. Here, we used CRISPR interference (CRISPRi) to study the consequences of decreasing E. coli enzymes below wild-type levels. A pooled CRISPRi screen with 7,177 strains demonstrates that metabolism buffers fitness defects for hours after the induction of CRISPRi. We characterized the metabolome and proteome responses in 30 CRISPRi strains and elucidated three gene-specific buffering mechanisms: ornithine buffered the knockdown of carbamoyl phosphate synthetase (CarAB) by increasing CarAB activity, S-adenosylmethionine buffered the knockdown of homocysteine transmethylase (MetE) by de-repressing expression of the methionine pathway, and 6-phosphogluconate buffered the knockdown of 6-phosphogluconate dehydrogenase (Gnd) by activating a bypass. In total, this work demonstrates that CRISPRi screens can reveal global sources of metabolic robustness and identify local regulatory mechanisms that buffer decreases of specific enzymes. A record of this paper's transparent peer review process is included in the Supplemental Information.
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Affiliation(s)
- Stefano Donati
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Vanessa Pahl
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Dominik Beuter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | | | - Lennart Randau
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Chun-Ying Wang
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
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Balcerczyk A, Damblon C, Elena-Herrmann B, Panthu B, Rautureau GJP. Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures. Int J Mol Sci 2020; 21:E6843. [PMID: 32961865 PMCID: PMC7554780 DOI: 10.3390/ijms21186843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/12/2022] Open
Abstract
Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.
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Affiliation(s)
- Aneta Balcerczyk
- Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland;
| | - Christian Damblon
- Unité de Recherche MolSys, Faculté des sciences, Université de Liège, 4000 Liège, Belgium;
| | | | - Baptiste Panthu
- CarMeN Laboratory, INSERM, INRA, INSA Lyon, Univ Lyon, Université Claude Bernard Lyon 1, 69921 Oullins CEDEX, France;
- Hospices Civils de Lyon, Faculté de Médecine, Hôpital Lyon Sud, 69921 Oullins CEDEX, France
| | - Gilles J. P. Rautureau
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs (CRMN FRE 2034 CNRS, UCBL, ENS Lyon), Université Claude Bernard Lyon 1, 69100 Villeurbanne, France
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25
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Misra BB. Data normalization strategies in metabolomics: Current challenges, approaches, and tools. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2020; 26:165-174. [PMID: 32276547 DOI: 10.1177/1469066720918446] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.
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Affiliation(s)
- Biswapriya B Misra
- Center for Precision Medicine, Section of Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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26
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Sun C, Wang F, Zhang Y, Yu J, Wang X. Mass spectrometry imaging-based metabolomics to visualize the spatially resolved reprogramming of carnitine metabolism in breast cancer. Theranostics 2020; 10:7070-7082. [PMID: 32641979 PMCID: PMC7330837 DOI: 10.7150/thno.45543] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/19/2020] [Indexed: 01/08/2023] Open
Abstract
New insights into tumor-associated metabolic reprogramming have provided novel vulnerabilities that can be targeted for cancer therapy. Here, we propose a mass spectrometry imaging (MSI)-based metabolomic strategy to visualize the spatially resolved reprogramming of carnitine metabolism in heterogeneous breast cancer. Methods: A wide carnitine coverage MSI method was developed to investigate the spatial alternations of carnitines in cancer tissues of xenograft mouse models and human samples. Spatial expression of key metabolic enzymes that are closely associated with the altered carnitines was examined in adjacent cancer tissue sections. Results: A total of 17 carnitines, including L-carnitine, 6 short-chain acylcarnitines, 3 middle-chain acylcarnitines, and 7 long-chain acylcarnitines were imaged. L-carnitine and short-chain acylcarnitines are significantly reprogrammed in breast cancer. A classification model based on the carnitine profiles of 170 cancer samples and 128 normal samples enables an accurate identification of breast cancer. CPT 1A, CPT 2, and CRAT, which are extensively involved in carnitine system-mediated fatty acid β-oxidation pathway were also found to be abnormally expressed in breast cancer. Remarkably, the expressions of CPT 2 and CRAT were found for the first time to be altered in breast cancer. Conclusion: These data not only expand our understanding of the complex tumor metabolic reprogramming, but also provide the first evidence that carnitine metabolism is reprogrammed at both the metabolite and enzyme levels in breast cancer.
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Affiliation(s)
- Chenglong Sun
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Fukai Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Yang Zhang
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Jinqian Yu
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Xiao Wang
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
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The choice of tissue fixative is a key determinant for mass spectrometry imaging based tumor metabolic reprogramming characterization. Anal Bioanal Chem 2020; 412:3123-3134. [PMID: 32236659 DOI: 10.1007/s00216-020-02562-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 02/03/2020] [Accepted: 02/28/2020] [Indexed: 12/12/2022]
Abstract
The application of mass spectrometry imaging (MSI) for the study of spatiotemporal alterations of the metabolites in tumors has brought a number of significant biological results. At present, metabolite profiling based on MSI is typically performed on frozen tissue sections; however, the majority of clinical specimens need to be fixed in tissue fixative to avoid autolysis and to preserve antigenicity. In this study, we present the global impacts of different fixatives on the MS imaging of gastric cancer tissue metabolites. The MSI performances of 17 kinds of metabolites, such as amino acids, polyamines, cholines, organic acids, polypeptides, nucleotides, nucleosides, nitrogen bases, cholesterols, fatty acids, and phospholipids, in untreated, 10% formalin-, 4% paraformaldehyde-, acetone-, and 95% ethanol-fixed gastric cancer tissues were thoroughly explored for the first time. Furthermore, we also investigated the spatial expressions of 6 metabolic enzymes, namely, GLS, FASN, CHKA, PLD2, cPLA2, and EGFR, closely related to tumor-associated metabolites. Immunohistochemical staining carried out on the same tissue sections' which have undergone MSI analysis' suggests that enzymatic characterization is feasible after metabolite imaging. Combining the spatial signatures of metabolites and pathway-related metabolic enzymes in heterogeneous tumor tissues offers an insight to understand the complex tumor metabolism. Graphical abstract.
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28
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Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 2020; 117:8494-8502. [PMID: 32229570 DOI: 10.1073/pnas.1915551117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Human tuberculosis is caused by members of the Mycobacterium tuberculosis complex (MTBC) that vary in virulence and transmissibility. While genome-wide association studies have uncovered several mutations conferring drug resistance, much less is known about the factors underlying other bacterial phenotypes. Variation in the outcome of tuberculosis infection and diseases has been attributed primarily to patient and environmental factors, but recent evidence indicates an additional role for the genetic diversity among MTBC clinical strains. Here, we used metabolomics to unravel the effect of genetic variation on the strain-specific metabolic adaptive capacity and vulnerability. To define the functionality of single-nucleotide polymorphisms (SNPs) systematically, we developed a constraint-based approach that integrates metabolomic and genomic data. Our model-based predictions correctly classify SNP effects in pyruvate kinase and suggest a genetic basis for strain-specific inherent baseline susceptibility to the antibiotic para-aminosalicylic acid. Our method is broadly applicable across microbial life, opening possibilities for the development of more selective treatment strategies.
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29
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Soleja N, Agrawal N, Nazir R, Ahmad M, Mohsin M. Enhanced sensitivity and detection range of FRET-based vitamin B 12 nanosensor. 3 Biotech 2020; 10:87. [PMID: 32089982 DOI: 10.1007/s13205-020-2073-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 01/18/2020] [Indexed: 11/29/2022] Open
Abstract
Vitamin B12 (cobalamin) is a cobalt-containing compound that acts as an essential co-factor for various enzymes involved in the metabolic processes of the living cells. The constructed FRET Sensor for Vitamin Anemia Linked (SenVitAL) displayed marginal FRET efficiency. Here, we report the development of a molecular SenVitAL containing enhanced cyan fluorescent protein (ECFP) and venus as FRET pair to improve the FRET efficiency for optical imaging and screening of already developed sensor by our group. The sensor is the improved version of previously reported SenVitAL and consists of ECFP/venus as FRET pair instead of the originally used pair CFP/YFP. To increase the physiological range of vitamin B12 measurement, affinity mutants were created. Compared to the wild type, SenVitAL-5 with W44Q mutation has higher affinity and displayed large dynamic detection range (0.10-480 µM) in response to vitamin B12 binding. For cell-based monitoring and dynamic measurement of vitamin B12 flux rates, SenVitAL-5 was successfully expressed in cytosol of yeast and mammalian cells. Changes in the emission intensities of the two fluorophores were detected using confocal microscopy in both cell types in response to vitamin B12. With the addition of 50 µM extracellular vitamin B12 to the cells, the emission intensity of venus increased and that of ECFP decreased over the time. Furthermore, the results show that the variant SenVitAL-5 measures the vitamin B12 in a concentration-dependent manner, showing the resulting increase in the FRET ratio and thus confirming its utility as an ideal fluorescent indicator for the detection of vitamin B12 in eukaryotic systems in real time.
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Affiliation(s)
- Neha Soleja
- 1Department of Biosciences, Metabolic Engineering Laboratory, Jamia Millia Islamia, New Delhi, 110025 India
| | - Neha Agrawal
- 1Department of Biosciences, Metabolic Engineering Laboratory, Jamia Millia Islamia, New Delhi, 110025 India
| | - Rahila Nazir
- 1Department of Biosciences, Metabolic Engineering Laboratory, Jamia Millia Islamia, New Delhi, 110025 India
| | - Mohd Ahmad
- 2Department of Physics, Syracuse University, New York, NY USA
| | - Mohd Mohsin
- 1Department of Biosciences, Metabolic Engineering Laboratory, Jamia Millia Islamia, New Delhi, 110025 India
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30
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Hogan JD, Keenan JL, Luo L, Ibn-Salem J, Lamba A, Schatzberg D, Piacentino ML, Zuch DT, Core AB, Blumberg C, Timmermann B, Grau JH, Speranza E, Andrade-Navarro MA, Irie N, Poustka AJ, Bradham CA. The developmental transcriptome for Lytechinus variegatus exhibits temporally punctuated gene expression changes. Dev Biol 2019; 460:139-154. [PMID: 31816285 DOI: 10.1016/j.ydbio.2019.12.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 10/25/2022]
Abstract
Embryonic development is arguably the most complex process an organism undergoes during its lifetime, and understanding this complexity is best approached with a systems-level perspective. The sea urchin has become a highly valuable model organism for understanding developmental specification, morphogenesis, and evolution. As a non-chordate deuterostome, the sea urchin occupies an important evolutionary niche between protostomes and vertebrates. Lytechinus variegatus (Lv) is an Atlantic species that has been well studied, and which has provided important insights into signal transduction, patterning, and morphogenetic changes during embryonic and larval development. The Pacific species, Strongylocentrotus purpuratus (Sp), is another well-studied sea urchin, particularly for gene regulatory networks (GRNs) and cis-regulatory analyses. A well-annotated genome and transcriptome for Sp are available, but similar resources have not been developed for Lv. Here, we provide an analysis of the Lv transcriptome at 11 timepoints during embryonic and larval development. Temporal analysis suggests that the gene regulatory networks that underlie specification are well-conserved among sea urchin species. We show that the major transitions in variation of embryonic transcription divide the developmental time series into four distinct, temporally sequential phases. Our work shows that sea urchin development occurs via sequential intervals of relatively stable gene expression states that are punctuated by abrupt transitions.
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Affiliation(s)
- John D Hogan
- Program in Bioinformatics, Boston University, Boston, MA, USA
| | | | - Lingqi Luo
- Program in Bioinformatics, Boston University, Boston, MA, USA
| | - Jonas Ibn-Salem
- Evolution and Development Group, Max-Planck Institute for Molecular Genetics, Berlin, Germany; Faculty of Biology, Johannes Gutenberg University of Mainz, Mainz, Germany
| | - Arjun Lamba
- Biology Department, Boston University, Boston, MA, USA
| | | | - Michael L Piacentino
- Program in Molecular and Cellular Biology and Biochemistry, Boston University, Boston, MA, USA
| | - Daniel T Zuch
- Program in Molecular and Cellular Biology and Biochemistry, Boston University, Boston, MA, USA
| | - Amanda B Core
- Biology Department, Boston University, Boston, MA, USA
| | | | - Bernd Timmermann
- Sequencing Core Facility, Max-Planck Institute for Molecular Genetics, Berlin, Germany
| | - José Horacio Grau
- Dahlem Centre for Genome Research and Medical Systems Biology, Environmental and Phylogenomics Group, Berlin, Germany; Museum für Naturkunde Berlin, Leibniz-Institut für Evolutions- und Biodiversitätsforschung, Berlin, Germany
| | - Emily Speranza
- Program in Bioinformatics, Boston University, Boston, MA, USA
| | | | - Naoki Irie
- Department of Biological Sciences, University of Tokyo, Tokyo, Japan
| | - Albert J Poustka
- Evolution and Development Group, Max-Planck Institute for Molecular Genetics, Berlin, Germany; Dahlem Centre for Genome Research and Medical Systems Biology, Environmental and Phylogenomics Group, Berlin, Germany
| | - Cynthia A Bradham
- Program in Bioinformatics, Boston University, Boston, MA, USA; Biology Department, Boston University, Boston, MA, USA; Program in Molecular and Cellular Biology and Biochemistry, Boston University, Boston, MA, USA.
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31
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Real time quantification of intracellular nickel using genetically encoded FRET-based nanosensor. Int J Biol Macromol 2019; 138:648-657. [DOI: 10.1016/j.ijbiomac.2019.07.115] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/13/2019] [Accepted: 07/19/2019] [Indexed: 12/19/2022]
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32
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Houston S. Sarah-Maria Fendt: Driving scientific discovery through collaboration. J Exp Med 2019; 216:1467-1468. [PMID: 31213487 PMCID: PMC6605742 DOI: 10.1084/jem.20191037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Sarah-Maria Fendt: Driving scientific discovery through collaboration Sarah-Maria Fendt is a principal investigator at the Flemish Institute for Biotechnology (VIB) in Belgium, and her laboratory focuses on cellular metabolism and metabolic regulation. Recent work from Sarah’s laboratory has shown that pyruvate available in the metastatic niche enables cancer cells to shape their environment and promote metastatic outgrowth. We contacted Sarah to find out more about her journey in science so far.
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33
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Dikicioglu D, Oliver SG. Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements. Biotechnol Bioeng 2019; 116:610-621. [PMID: 30578666 PMCID: PMC6492170 DOI: 10.1002/bit.26905] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 11/21/2018] [Accepted: 12/20/2018] [Indexed: 12/15/2022]
Abstract
Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor‐utilising enzymes and transporters through constraint‐based modelling. The first eukaryotic genome‐scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron‐sulphur (Fe‐S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron‐recruiting enzyme at a rate of 3.02 × 10
−11 mmol·(g biomass)
−1·h
−1.
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Affiliation(s)
- Duygu Dikicioglu
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.,Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Stephen G Oliver
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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34
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Zeng H, Yang A. Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways. BMC SYSTEMS BIOLOGY 2019; 13:3. [PMID: 30630470 PMCID: PMC6329140 DOI: 10.1186/s12918-018-0677-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 12/28/2018] [Indexed: 11/22/2022]
Abstract
Background The formation of acetate by fast-growing Escherichia coli (E. coli) is a commonly observed phenomenon, often referred to as overflow metabolism. Among various studies that have been carried over decades, a recent work (Basan, M. et al. Nature528, 99–104, 2015) suggested and validated that it is the differential proteomic efficiencies in energy biogenesis between fermentation and respiration that lead to the production of acetate at rapid growth conditions, as the consequence of optimally allocating the limited proteomic resource. In the current work, we attempt to incorporate this newly developed proteome allocation theory into flux balance analysis (FBA) to capture quantitatively the extent of overflow metabolism in different E. coli strains. Results A concise constraint was introduced into a FBA-based model with three proteomic cost parameters to represent constrained allocation of proteome over two energy (respiration and fermentation) pathways and biomass synthesis. Linear relationships were shown to exist between the three proteomic cost parameters. Tests with three different strains revealed that the proteomic cost of fermentation was consistently lower than that of respiration. A slow-growing strain appeared to have a higher proteomic cost for biomass synthesis than fast-growing strains. Different assumed levels of carbon flowing into pentose phosphate pathway affected the absolute value of model parameters, but had no qualitative impact on the comparative proteomic costs. For the prediction of biomass yield, significant errors that occurred for one of the tested strains (ML308) were rectified by adjusting the cellular energy demand according to literature data. Conclusions With the aid of a concise proteome allocation constraint, our FBA-based model is able to quantitatively predict the onset and extent of the overflow metabolism in various E. coli strains. Such prediction is enabled by three linearly-correlated (as opposed to uniquely determinable) proteomic cost parameters. The linear relationships between these parameters, when determined using data from cell culturing experiments, render biologically meaningful comparative proteomic costs between fermentation and respiration pathways and between the biomass synthesis sectors of slow- and fast-growing species. Simultaneous prediction of acetate production and biomass yield in the overflow region requires the use of reliable cellular energy demand data. Electronic supplementary material The online version of this article (10.1186/s12918-018-0677-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hong Zeng
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
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35
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Sander T, Farke N, Diehl C, Kuntz M, Glatter T, Link H. Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance. Cell Syst 2019; 8:66-75.e8. [PMID: 30638812 PMCID: PMC6345581 DOI: 10.1016/j.cels.2018.12.005] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 10/08/2018] [Accepted: 12/10/2018] [Indexed: 02/06/2023]
Abstract
Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli. Amino acid biosynthesis enzymes do not normally operate at maximum capacity Allosteric feedback inhibition ensures that enzymes are overabundant Enzyme overabundance provides robustness against decreases in gene expression
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Affiliation(s)
- Timur Sander
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Christoph Diehl
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg 35043, Germany.
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36
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Abstract
Tumor cells reprogram their metabolism to support cell growth, proliferation, and differentiation, thus driving cancer progression. Profiling of the metabolic signatures in heterogeneous tumors facilitates the understanding of tumor metabolism and introduces potential metabolic vulnerabilities that might be targeted therapeutically. We proposed a spatially resolved metabolomics method for high-throughput discovery of tumor-associated metabolite and enzyme alterations using ambient mass spectrometry imaging. Metabolic pathway-related metabolites and metabolic enzymes that are associated with tumor metabolism were efficiently discovered and visualized in heterogeneous esophageal cancer tissues. Spatially resolved metabolic alterations hold the key to defining the dependencies of metabolism that are most limiting for cancer growth and exploring metabolic targeted strategies for better cancer treatment. Characterization of tumor metabolism with spatial information contributes to our understanding of complex cancer metabolic reprogramming, facilitating the discovery of potential metabolic vulnerabilities that might be targeted for tumor therapy. However, given the metabolic variability and flexibility of tumors, it is still challenging to characterize global metabolic alterations in heterogeneous cancer. Here, we propose a spatially resolved metabolomics approach to discover tumor-associated metabolites and metabolic enzymes directly in their native state. A variety of metabolites localized in different metabolic pathways were mapped by airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) in tissues from 256 esophageal cancer patients. In combination with in situ metabolomics analysis, this method provided clues into tumor-associated metabolic pathways, including proline biosynthesis, glutamine metabolism, uridine metabolism, histidine metabolism, fatty acid biosynthesis, and polyamine biosynthesis. Six abnormally expressed metabolic enzymes that are closely associated with the altered metabolic pathways were further discovered in esophageal squamous cell carcinoma (ESCC). Notably, pyrroline-5-carboxylate reductase 2 (PYCR2) and uridine phosphorylase 1 (UPase1) were found to be altered in ESCC. The spatially resolved metabolomics reveal what occurs in cancer at the molecular level, from metabolites to enzymes, and thus provide insights into the understanding of cancer metabolic reprogramming.
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37
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Zelezniak A, Vowinckel J, Capuano F, Messner CB, Demichev V, Polowsky N, Mülleder M, Kamrad S, Klaus B, Keller MA, Ralser M. Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts. Cell Syst 2018; 7:269-283.e6. [PMID: 30195436 PMCID: PMC6167078 DOI: 10.1016/j.cels.2018.08.001] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 05/29/2018] [Accepted: 07/31/2018] [Indexed: 02/08/2023]
Abstract
A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. The proteome of kinase knockouts is dominated by enzyme abundance changes The enzyme expression profiles of kinase knockouts are non-redundant Metabolism is regulated by many expression changes acting in concert Machine learning accurately predicts the metabolome from enzyme abundance
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Affiliation(s)
- Aleksej Zelezniak
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jakob Vowinckel
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Biognosys AG, Schlieren, Switzerland
| | - Floriana Capuano
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Nicole Polowsky
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Michael Mülleder
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Stephan Kamrad
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Bernd Klaus
- Centre for Statistical Data Analysis, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Markus A Keller
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Medical University of Innsbruck, Innsbruck, Austria
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biochemistry, Charité Universitaetsmedizin Berlin, Berlin, Germany.
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38
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Younis T, Khan MI, Khan MR, Rasul A, Majid M, Adhami VM, Mukhtar H. Nummularic acid, a triterpenoid, from the medicinal plant Fraxinus xanthoxyloides, induces energy crisis to suppress growth of prostate cancer cells. Mol Carcinog 2018; 57:1267-1277. [PMID: 29802724 DOI: 10.1002/mc.22841] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 05/16/2018] [Accepted: 05/23/2018] [Indexed: 12/19/2022]
Abstract
We recently identified and characterized nummularic acid (NA) as a major chemical constituent of Fraxinus xanthoxyloides, a medicinal plant used for over hundred years in traditional medicine. In this study, we describe its potential anti-cancer activity using prostate cancer (PCa) cells as a model. We found that NA treatment (5-60 μM) significantly reduced the proliferation and colony formation capabilities of PCa DU145 and C4-2 cells in a time and dose dependent manner, reduced the migratory and invasive properties and increased apoptotic cell population. Mechanistically, we found that NA treatment to PCa cells resulted in a sustained activation of adenosine monophosphate-activated protein kinase (AMPK). NA simultaneously increased acetyl CoA carboxylase phosphorylation and decreased pS6 phosphorylation, the two major substrates of AMPK. Further, NA treatment significantly elevated the cellular ADP/ATP ratio and altered glycolytic rate. We further observed a reversible decrease in oxygen consumption rate in NA treated cells when compared to the control. Finally, we performed global untargeted metabolomics which showed that NA treatment alters PCa cell metabolism at multiple sites including glycolysis, tricarboxylic acid, and glutamine metabolism which supported our observation of a possible AMPK activation. In summary, we report NA as a novel small molecule activator of AMPK that alters cellular metabolism to induce energy crisis and ultimately cancer cell death. Because of its unique mechanism NA could be potentially applicable against other cancer types.
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Affiliation(s)
- Tahira Younis
- Department of Dermatology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.,Faculty of Biological Sciences, Department of Biochemistry, Quaid-i-Azam University Islamabad, Islamabad, Pakistan.,Faculty of Life Sciences, Department of Zoology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Mohammad I Khan
- Faculty of Science, Department of Biochemistry, King Abdulaziz University, Jeddah, Saudi Arabia.,Faculty of Science, Cancer Metabolism and Epigenetic Unit, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad R Khan
- Faculty of Biological Sciences, Department of Biochemistry, Quaid-i-Azam University Islamabad, Islamabad, Pakistan
| | - Azhar Rasul
- Faculty of Life Sciences, Department of Zoology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Muhammad Majid
- Faculty of Biological Sciences, Department of Biochemistry, Quaid-i-Azam University Islamabad, Islamabad, Pakistan
| | - Vaqar M Adhami
- Department of Dermatology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Hasan Mukhtar
- Department of Dermatology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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39
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Teoh ST, Ogrodzinski MP, Ross C, Hunter KW, Lunt SY. Sialic Acid Metabolism: A Key Player in Breast Cancer Metastasis Revealed by Metabolomics. Front Oncol 2018; 8:174. [PMID: 29892572 PMCID: PMC5985449 DOI: 10.3389/fonc.2018.00174] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 05/04/2018] [Indexed: 11/13/2022] Open
Abstract
Metastatic breast cancer is currently incurable. It has recently emerged that different metabolic pathways support metastatic breast cancer. To further uncover metabolic pathways enabling breast cancer metastasis, we investigated metabolic differences in mouse tumors of differing metastatic propensities using mass spectrometry-based metabolomics. We found that sialic acid metabolism is upregulated in highly metastatic breast tumors. Knocking out a key gene in sialic acid metabolism, Cmas, inhibits synthesis of the activated form of sialic acid, cytidine monophosphate-sialic acid and decreases the formation of lung metastases in vivo. Thus, the sialic acid pathway may be a new target against metastatic breast cancer.
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Affiliation(s)
- Shao Thing Teoh
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Martin P Ogrodzinski
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States.,Department of Physiology, Michigan State University, East Lansing, MI, United States
| | - Christina Ross
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Kent W Hunter
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Sophia Y Lunt
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States.,Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, United States
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40
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Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia. Biochem Soc Trans 2018; 46:269-284. [PMID: 29472366 DOI: 10.1042/bst20170242] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 12/21/2017] [Accepted: 01/01/2018] [Indexed: 01/24/2023]
Abstract
Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.
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41
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Abstract
Metabolism constitutes the basis of life, and the dynamics of metabolism dictate various cellular processes. However, exactly how metabolite dynamics are controlled remains poorly understood. By studying an engineered fatty acid-producing pathway as a model, we found that upon transcription activation a metabolic product from an unregulated pathway required seven cell cycles to reach to its steady state level, with the speed mostly limited by enzyme expression dynamics. To overcome this limit, we designed metabolic feedback circuits (MeFCs) with three different architectures, and experimentally measured and modeled their metabolite dynamics. Our engineered MeFCs could dramatically shorten the rise-time of metabolites, decreasing it by as much as 12-fold. The findings of this study provide a systematic understanding of metabolite dynamics in different architectures of MeFCs and have potentially immense applications in designing synthetic circuits to improve the productivities of engineered metabolic pathways.
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Affiliation(s)
- Di Liu
- Department of Energy, Environmental & Chemical Engineering, ‡Division of Biological & Biomedical Sciences, §Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Fuzhong Zhang
- Department of Energy, Environmental & Chemical Engineering, ‡Division of Biological & Biomedical Sciences, §Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
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Zampieri M, Sauer U. Metabolomics-driven understanding of genotype-phenotype relations in model organisms. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.08.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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43
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Christen S, Lorendeau D, Schmieder R, Broekaert D, Metzger K, Veys K, Elia I, Buescher JM, Orth MF, Davidson SM, Grünewald TGP, De Bock K, Fendt SM. Breast Cancer-Derived Lung Metastases Show Increased Pyruvate Carboxylase-Dependent Anaplerosis. Cell Rep 2017; 17:837-848. [PMID: 27732858 DOI: 10.1016/j.celrep.2016.09.042] [Citation(s) in RCA: 172] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 08/30/2016] [Accepted: 09/14/2016] [Indexed: 01/19/2023] Open
Abstract
Cellular proliferation depends on refilling the tricarboxylic acid (TCA) cycle to support biomass production (anaplerosis). The two major anaplerotic pathways in cells are pyruvate conversion to oxaloacetate via pyruvate carboxylase (PC) and glutamine conversion to α-ketoglutarate. Cancers often show an organ-specific reliance on either pathway. However, it remains unknown whether they adapt their mode of anaplerosis when metastasizing to a distant organ. We measured PC-dependent anaplerosis in breast-cancer-derived lung metastases compared to their primary cancers using in vivo 13C tracer analysis. We discovered that lung metastases have higher PC-dependent anaplerosis compared to primary breast cancers. Based on in vitro analysis and a mathematical model for the determination of compartment-specific metabolite concentrations, we found that mitochondrial pyruvate concentrations can promote PC-dependent anaplerosis via enzyme kinetics. In conclusion, we show that breast cancer cells proliferating as lung metastases activate PC-dependent anaplerosis in response to the lung microenvironment.
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Affiliation(s)
- Stefan Christen
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Doriane Lorendeau
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Roberta Schmieder
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Dorien Broekaert
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Kristine Metzger
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Koen Veys
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology (KU Leuven) and Vesalius Research Center (VIB), Herestraat 49, 3000 Leuven, Belgium
| | - Ilaria Elia
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Joerg Martin Buescher
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Martin Franz Orth
- Max-Eder Research Group for Pediatric Sarcoma Biology, Institute of Pathology, LMU Munich, Thalkirchner Strasse 36, 80337 Munich, Germany
| | - Shawn Michael Davidson
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main Street, Cambridge, MA 02139, USA
| | - Thomas Georg Philipp Grünewald
- Max-Eder Research Group for Pediatric Sarcoma Biology, Institute of Pathology, LMU Munich, Thalkirchner Strasse 36, 80337 Munich, Germany
| | - Katrien De Bock
- Laboratory of Exercise and Health, Department of Health Sciences and Technology, ETH Zurich, Schorenstrasse 16, 8603 Schwerzenbach, Switzerland
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, Vesalius Research Center, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium.
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44
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Abernathy MH, He L, Tang YJ. Channeling in native microbial pathways: Implications and challenges for metabolic engineering. Biotechnol Adv 2017. [DOI: 10.1016/j.biotechadv.2017.06.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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45
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Efficient protein production by yeast requires global tuning of metabolism. Nat Commun 2017; 8:1131. [PMID: 29070809 PMCID: PMC5656615 DOI: 10.1038/s41467-017-00999-2] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 08/09/2017] [Indexed: 01/20/2023] Open
Abstract
The biotech industry relies on cell factories for production of pharmaceutical proteins, of which several are among the top-selling medicines. There is, therefore, considerable interest in improving the efficiency of protein production by cell factories. Protein secretion involves numerous intracellular processes with many underlying mechanisms still remaining unclear. Here, we use RNA-seq to study the genome-wide transcriptional response to protein secretion in mutant yeast strains. We find that many cellular processes have to be attuned to support efficient protein secretion. In particular, altered energy metabolism resulting in reduced respiration and increased fermentation, as well as balancing of amino-acid biosynthesis and reduced thiamine biosynthesis seem to be particularly important. We confirm our findings by inverse engineering and physiological characterization and show that by tuning metabolism cells are able to efficiently secrete recombinant proteins. Our findings provide increased understanding of which cellular regulations and pathways are associated with efficient protein secretion. The contribution of metabolic pathways to protein secretion is largely unknown. Here, the authors find conserved metabolic patterns in yeast by examining genome-wide transcriptional responses in high protein secretion mutants and reveal critical factors that can be tuned for efficient protein secretion.
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46
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Yang HF, Zhang XN, Li Y, Zhang YH, Xu Q, Wei DQ. Theoretical Studies of Intracellular Concentration of Micro-organisms' Metabolites. Sci Rep 2017; 7:9048. [PMID: 28831069 PMCID: PMC5567373 DOI: 10.1038/s41598-017-08793-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 07/13/2017] [Indexed: 02/03/2023] Open
Abstract
With the rapid growth of micro-organism metabolic networks, acquiring the intracellular concentration of microorganisms’ metabolites accurately in large-batch is critical to the development of metabolic engineering and synthetic biology. Complementary to the experimental methods, computational methods were used as effective assessing tools for the studies of intracellular concentrations of metabolites. In this study, the dataset of 130 metabolites from E. coli and S. cerevisiae with available experimental concentrations were utilized to develop a SVM model of the negative logarithm of the concentration (-logC). In this statistic model, in addition to common descriptors of molecular properties, two special types of descriptors including metabolic network topologic descriptors and metabolic pathway descriptors were included. All 1997 descriptors were finally reduced into 14 by variable selections including genetic algorithm (GA). The model was evaluated through internal validations by 10-fold and leave-one-out (LOO) cross-validation, as well as external validations by predicting -logC values of the test set. The developed SVM model is robust and has a strong predictive potential (n = 91, m = 14, R2 = 0.744, RMSE = 0.730, Q2 = 0.57; R2p = 0.59, RMSEp = 0.702, Q2p = 0.58). An effective tool could be provided by this analysis for the large-batch prediction of the intracellular concentrations of the micro-organisms’ metabolites.
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Affiliation(s)
- Hai-Feng Yang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiao-Nan Zhang
- Chongqing key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, and College of Stomatology, Chongqing Medical University, Chongqing, China
| | - Yan Li
- Department of Chinese Traditional Medicine, Chongqing Medical University, Chongqing, China
| | - Yong-Hong Zhang
- Medicine Engineering Research Center, and College of Pharmacy, Chongqing Medical University, Chongqing, China.
| | - Qin Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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47
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Zampieri M, Enke T, Chubukov V, Ricci V, Piddock L, Sauer U. Metabolic constraints on the evolution of antibiotic resistance. Mol Syst Biol 2017; 13:917. [PMID: 28265005 PMCID: PMC5371735 DOI: 10.15252/msb.20167028] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.
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Affiliation(s)
- Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Tim Enke
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.,Institute of Biogeochemistry and Pollutant Dynamics (IBP), ETH Zürich, Zürich, Switzerland
| | - Victor Chubukov
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Vito Ricci
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Laura Piddock
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
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Fuhrer T, Zampieri M, Sévin DC, Sauer U, Zamboni N. Genomewide landscape of gene-metabolome associations in Escherichia coli. Mol Syst Biol 2017; 13:907. [PMID: 28093455 PMCID: PMC5293155 DOI: 10.15252/msb.20167150] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Metabolism is one of the best-understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non-enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single-celled organisms. Here, we systematically mapped the association between > 3,800 single-gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene-metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism-related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.
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Affiliation(s)
- Tobias Fuhrer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Daniel C Sévin
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
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49
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Du W, Jongbloets JA, Pineda Hernández H, Bruggeman FJ, Hellingwerf KJ, Branco dos Santos F. Photonfluxostat: A method for light-limited batch cultivation of cyanobacteria at different, yet constant, growth rates. ALGAL RES 2016. [DOI: 10.1016/j.algal.2016.10.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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50
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Eanes WF. New views on the selection acting on genetic polymorphism in central metabolic genes. Ann N Y Acad Sci 2016; 1389:108-123. [PMID: 27859384 DOI: 10.1111/nyas.13285] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 09/20/2016] [Accepted: 09/29/2016] [Indexed: 12/14/2022]
Abstract
Studies of the polymorphism of central metabolic genes as a source of fitness variation in natural populations date back to the discovery of allozymes in the 1960s. The unique features of these genes and their enzymes and our knowledge base greatly facilitates the systems-level study of this group. The expectation that pathway flux control is central to understanding the molecular evolution of genes is discussed, as well as studies that attempt to place gene-specific molecular evolution and polymorphism into a context of pathway and network architecture. There is an increasingly complex picture of the metabolic genes assuming additional roles beyond their textbook anabolic and catabolic reactions. In particular, this review emphasizes the potential role of these genes as part of the energy-sensing machinery. It is underscored that the concentrations of key cellular metabolites are the reflections of cellular energy status and nutritional input. These metabolites are the top-down signaling messengers that set signaling through signaling pathways that are involved in energy economy. I propose that the polymorphisms in central metabolic genes shift metabolite concentrations and in that fashion act as genetic modifiers of the energy-state coupling to the transcriptional networks that affect physiological trade-offs with significant fitness consequences.
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Affiliation(s)
- Walter F Eanes
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York
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