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Hudson EP. The Calvin Benson cycle in bacteria: New insights from systems biology. Semin Cell Dev Biol 2024; 155:71-83. [PMID: 37002131 DOI: 10.1016/j.semcdb.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/21/2023] [Accepted: 03/16/2023] [Indexed: 03/31/2023]
Abstract
The Calvin Benson cycle in phototrophic and chemolithoautotrophic bacteria has ecological and biotechnological importance, which has motivated study of its regulation. I review recent advances in our understanding of how the Calvin Benson cycle is regulated in bacteria and the technologies used to elucidate regulation and modify it, and highlight differences between and photoautotrophic and chemolithoautotrophic models. Systems biology studies have shown that in oxygenic phototrophic bacteria, Calvin Benson cycle enzymes are extensively regulated at post-transcriptional and post-translational levels, with multiple enzyme activities connected to cellular redox status through thioredoxin. In chemolithoautotrophic bacteria, regulation is primarily at the transcriptional level, with effector metabolites transducing cell status, though new methods should now allow facile, proteome-wide exploration of biochemical regulation in these models. A biotechnological objective is to enhance CO2 fixation in the cycle and partition that carbon to a product of interest. Flux control of CO2 fixation is distributed over multiple enzymes, and attempts to modulate gene Calvin cycle gene expression show a robust homeostatic regulation of growth rate, though the synthesis rates of products can be significantly increased. Therefore, de-regulation of cycle enzymes through protein engineering may be necessary to increase fluxes. Non-canonical Calvin Benson cycles, if implemented with synthetic biology, could have reduced energy demand and enzyme loading, thus increasing the attractiveness of these bacteria for industrial applications.
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Affiliation(s)
- Elton P Hudson
- Department of Protein Science, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
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2
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Habibpour M, Razaghi-Moghadam Z, Nikoloski Z. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metab Eng 2024; 82:216-224. [PMID: 38367764 DOI: 10.1016/j.ymben.2024.02.008] [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: 11/30/2023] [Revised: 01/13/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.
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Affiliation(s)
- Mahdis Habibpour
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
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3
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Li G, Liu L, Du W, Cao H. Local flux coordination and global gene expression regulation in metabolic modeling. Nat Commun 2023; 14:5700. [PMID: 37709734 PMCID: PMC10502109 DOI: 10.1038/s41467-023-41392-6] [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: 11/05/2020] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
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Affiliation(s)
- Gaoyang Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Li Liu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Huansheng Cao
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.
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4
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Naz S, Liu P, Liu C, Cui M, Ma H. In silico prediction of mutation sites for anthranilate synthase from Serratia marcesens to deregulate tryptophan feedback inhibition. J Biomol Struct Dyn 2023:1-11. [PMID: 37676253 DOI: 10.1080/07391102.2023.2253910] [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: 04/09/2023] [Accepted: 08/24/2023] [Indexed: 09/08/2023]
Abstract
Allosteric feedback inhibition of the committed step in amino acid biosynthetic pathways is a major concern for production of amino acids at industrial scale. Anthranilate synthase (AS) catalyzes the first reaction of tryptophan biosynthetic pathway found in microorganisms and is feedback inhibited by its own product i.e. tryptophan. Here, we identified new mutant sites in AS using computational mutagenesis approach. MD simulations (20 ns) followed by MMPBSA and per residue decomposition energy analysis identified seven amino acid residues with best binding affinity for tryptophan. All 19 mutant structures were generated for each identified amino acid residue followed by simulation to evaluate effect of mutation on protein stability. Later, molecular docking studies were employed to generate mutant-tryptophan complex and structures with binding energies (kcal/mol) much higher than wild-type AS were selected. Finally, two mutants i.e., S37W and S37H were identified on the basis of positive binding scores and loss of tryptophan binding inside pocket. Further, MD simulations run for 200 ns were performed over these mutant-tryptophan complexes followed by RMSD, RMSF, radius of gyration , solvent accessible surface area , intra-protein hydrogen bond numbers, principal component analysis, free energy landscape (FEL) and secondary structure analysis to rationale effect of mutations on stability of protein. Cross correlation analysis of mutant site amino acids (S37W) with key residues of catalytic site (G325, T326, H395 and G482) was done to evaluate the effect of mutations on catalytic site conformation. Current computational mutagenesis approach predicted two mutants S37W and S37H with proposed deregulated feedback inhibition by tryptophan and retained catalytic activity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sadia Naz
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Pi Liu
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Cui Liu
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Mengfei Cui
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
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Potter AD, Baiocco CM, Papin JA, Criss AK. Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture. mSystems 2023; 8:e0126522. [PMID: 37387581 PMCID: PMC10470122 DOI: 10.1128/msystems.01265-22] [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] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/19/2023] [Indexed: 07/01/2023] Open
Abstract
The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.
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Affiliation(s)
- Aimee D. Potter
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M. Baiocco
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Alison K. Criss
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
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6
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Liska O, Boross G, Rocabert C, Szappanos B, Tengölics R, Papp B. Principles of metabolome conservation in animals. Proc Natl Acad Sci U S A 2023; 120:e2302147120. [PMID: 37603743 PMCID: PMC10468614 DOI: 10.1073/pnas.2302147120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/16/2023] [Indexed: 08/23/2023] Open
Abstract
Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.
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Affiliation(s)
- Orsolya Liska
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, 6728Szeged, Hungary
- National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
- Doctoral School of Biology, University of Szeged, 6726Szeged, Hungary
| | - Gábor Boross
- National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
- Department of Biology, Stanford University, Stanford, City of Palo Alto, CA94305-5020
| | - Charles Rocabert
- National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
- Inria, 78150Rocquencourt, 69100Villeurbanne, France
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, 00014Helsinki, Finland
- Institute for Computational Cell Biology, Heinrich-Heine Universität, 40225Düsseldorf, Germany
| | - Balázs Szappanos
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, 6728Szeged, Hungary
- National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
- Department of Biotechnology, University of Szeged, 6726Szeged, Hungary
| | - Roland Tengölics
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, 6728Szeged, Hungary
- National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
- Metabolomics Lab, Core facilities, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
| | - Balázs Papp
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, 6728Szeged, Hungary
- National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
- National Laboratory for Health Security, Biological Research Centre, Eötvös Loránd Research Network, 6726Szeged, Hungary
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7
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Naz S, Liu P, Farooq U, Ma H. Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method. Microb Cell Fact 2023; 22:161. [PMID: 37612753 PMCID: PMC10464499 DOI: 10.1186/s12934-023-02178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/13/2023] [Indexed: 08/25/2023] Open
Abstract
Regulation of amino acid's biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology. Deregulation of feedback inhibition has been achieved for various enzymes using in vitro and in silico mutagenesis techniques. As enzyme's function, its substrate binding capacity, catalysis activity, regulation and stability are dependent on its structural characteristics, here, we provide detailed structural analysis of all feedback sensitive enzyme targets in amino acid biosynthetic pathways. Current review summarizes information regarding structural characteristics of various enzyme targets and effect of mutations on their structures and functions especially in terms of deregulation of feedback inhibition. Furthermore, applicability of various experimental as well as computational mutagenesis techniques to accomplish feedback resistance has also been discussed in detail to have an insight into various aspects of research work reported in this particular field of study.
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Affiliation(s)
- Sadia Naz
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Pi Liu
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Umar Farooq
- Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Islamabad, 22060, Pakistan
| | - Hongwu Ma
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
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8
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Soleymani Babadi F, Razaghi-Moghadam Z, Zare-Mirakabad F, Nikoloski Z. Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling. BIOINFORMATICS ADVANCES 2023; 3:vbad098. [PMID: 37521309 PMCID: PMC10374491 DOI: 10.1093/bioadv/vbad098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 06/28/2023] [Accepted: 07/15/2023] [Indexed: 08/01/2023]
Abstract
Motivation Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning. Results By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite-protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite-protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules. Availability and implementation https://github.com/fayazsoleymani/SARTRE.
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Affiliation(s)
- Fayaz Soleymani Babadi
- Departement of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Biology, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Fatemeh Zare-Mirakabad
- Departement of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Zoran Nikoloski
- Corresponding author. Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany. E-mail:
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9
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Noell SE, Hellweger FL, Temperton B, Giovannoni SJ. A Reduction of Transcriptional Regulation in Aquatic Oligotrophic Microorganisms Enhances Fitness in Nutrient-Poor Environments. Microbiol Mol Biol Rev 2023; 87:e0012422. [PMID: 36995249 PMCID: PMC10304753 DOI: 10.1128/mmbr.00124-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023] Open
Abstract
In this review, we consider the regulatory strategies of aquatic oligotrophs, microbial cells that are adapted to thrive under low-nutrient concentrations in oceans, lakes, and other aquatic ecosystems. Many reports have concluded that oligotrophs use less transcriptional regulation than copiotrophic cells, which are adapted to high nutrient concentrations and are far more common subjects for laboratory investigations of regulation. It is theorized that oligotrophs have retained alternate mechanisms of regulation, such as riboswitches, that provide shorter response times and smaller amplitude responses and require fewer cellular resources. We examine the accumulated evidence for distinctive regulatory strategies in oligotrophs. We explore differences in the selective pressures copiotrophs and oligotrophs encounter and ask why, although evolutionary history gives copiotrophs and oligotrophs access to the same regulatory mechanisms, they might exhibit distinctly different patterns in how these mechanisms are used. We discuss the implications of these findings for understanding broad patterns in the evolution of microbial regulatory networks and their relationships to environmental niche and life history strategy. We ask whether these observations, which have emerged from a decade of increased investigation of the cell biology of oligotrophs, might be relevant to recent discoveries of many microbial cell lineages in nature that share with oligotrophs the property of reduced genome size.
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Affiliation(s)
- Stephen E. Noell
- Department of Microbiology, Oregon State University, Corvallis, Oregon, USA
| | | | - Ben Temperton
- School of Biosciences, University of Exeter, Exeter, United Kingdom
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10
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Wendering P, Nikoloski Z. Toward mechanistic modeling and rational engineering of plant respiration. PLANT PHYSIOLOGY 2023; 191:2150-2166. [PMID: 36721968 PMCID: PMC10069892 DOI: 10.1093/plphys/kiad054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.
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Affiliation(s)
- Philipp Wendering
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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11
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Kurbatov I, Dolgalev G, Arzumanian V, Kiseleva O, Poverennaya E. The Knowns and Unknowns in Protein-Metabolite Interactions. Int J Mol Sci 2023; 24:ijms24044155. [PMID: 36835565 PMCID: PMC9964805 DOI: 10.3390/ijms24044155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.
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12
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Iyer MS, Pal A, Venkatesh KV. A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli. Microbiol Spectr 2023; 11:e0210122. [PMID: 36749045 PMCID: PMC10100776 DOI: 10.1128/spectrum.02101-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 01/19/2023] [Indexed: 02/08/2023] Open
Abstract
Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.
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Affiliation(s)
- Mahesh S. Iyer
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ankita Pal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - K. V. Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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13
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Muralidhara P, Ewald JC. Protein-Metabolite Interactions Shape Cellular Metabolism and Physiology. Methods Mol Biol 2023; 2554:1-10. [PMID: 36178616 DOI: 10.1007/978-1-0716-2624-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Protein-metabolite interactions regulate many important cellular processes but still remain understudied. Recent technological advancements are gradually uncovering the complexity of the protein-metabolite interactome. Here, we highlight some classic and recent examples of how protein metabolite interactions regulate metabolism, both locally and globally, and how this contributes to cellular physiology. We also discuss the importance of these interactions in diseases such as cancer.
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Affiliation(s)
| | - Jennifer C Ewald
- Interfaculty Institute of Cell Biology, University of Tübingen, Tübingen, Germany
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14
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Kim YE, Cho KH, Bang I, Kim CH, Ryu YS, Kim Y, Choi EM, Nong LK, Kim D, Lee SK. Characterization of an Entner-Doudoroff pathway-activated Escherichia coli. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2022; 15:120. [PMID: 36352474 PMCID: PMC9648032 DOI: 10.1186/s13068-022-02219-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Escherichia coli have both the Embden-Meyerhof-Parnas pathway (EMPP) and Entner-Doudoroff pathway (EDP) for glucose breakdown, while the EDP primarily remains inactive for glucose metabolism. However, EDP is a more favorable route than EMPP for the production of certain products. RESULTS EDP was activated by deleting the pfkAB genes in conjunction with subsequent adaptive laboratory evolution (ALE). The evolved strains acquired mutations in transcriptional regulatory genes for glycolytic process (crp, galR, and gntR) and in glycolysis-related genes (gnd, ptsG, and talB). The genotypic, transcriptomic and phenotypic analyses of those mutations deepen our understanding of their beneficial effects on cellulosic biomass bio-conversion. On top of these scientific understandings, we further engineered the strain to produce higher level of lycopene and 3-hydroxypropionic acid. CONCLUSIONS These results indicate that the E. coli strain has innate capability to use EDP in lieu of EMPP for glucose metabolism, and this versatility can be harnessed to further engineer E. coli for specific biotechnological applications.
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Affiliation(s)
- Ye Eun Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Kyung Hyun Cho
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Ina Bang
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Chang Hee Kim
- Department of Biomedical Engineering, UNIST, Ulsan, 44919, Republic of Korea
| | - Young Shin Ryu
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Yuchan Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Eun Mi Choi
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Linh Khanh Nong
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Donghyuk Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.
- Department of Biomedical Engineering, UNIST, Ulsan, 44919, Republic of Korea.
| | - Sung Kuk Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.
- Department of Biomedical Engineering, UNIST, Ulsan, 44919, Republic of Korea.
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15
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Hurbain J, Thommen Q, Anquez F, Pfeuty B. Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy. iScience 2022; 25:104681. [PMID: 35856027 PMCID: PMC9287814 DOI: 10.1016/j.isci.2022.104681] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/12/2022] [Accepted: 06/23/2022] [Indexed: 01/22/2023] Open
Abstract
Living cells use signaling and regulatory mechanisms to adapt to environmental stresses. Adaptation to oxidative stress involves the regulation of many enzymes in both glycolysis and pentose phosphate pathways (PPP), so as to support PPP-driven NADPH recycling for antioxidant defense. The underlying regulatory logic is investigated by developing a kinetic modeling approach fueled with metabolomics and 13C-fluxomics datasets from human fibroblast cells. Bayesian parameter estimation and phenotypic analysis of models highlight complementary roles for several metabolite-enzyme regulations. Specifically, carbon flux rerouting into PPP involves a tight coordination between the upregulation of G6PD activity concomitant to a decreased NADPH/NADP+ ratio and the differential control of downward and upward glycolytic fluxes through the joint inhibition of PGI and GAPD enzymes. Such functional interplay between distinct regulatory feedbacks promotes efficient detoxification and homeostasis response over a broad range of stress level, but can also explain paradoxical pertubation phenotypes for instance reported for 6PGD modulation in mammalian cells.
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Affiliation(s)
- Julien Hurbain
- CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, University of Lille, 59000 Lille, France
| | - Quentin Thommen
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, University of Lille, 59000 Lille, France
| | - Francois Anquez
- CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, University of Lille, 59000 Lille, France
| | - Benjamin Pfeuty
- CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, University of Lille, 59000 Lille, France
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16
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Uematsu S, Ohno S, Tanaka KY, Hatano A, Kokaji T, Ito Y, Kubota H, Hironaka KI, Suzuki Y, Matsumoto M, Nakayama KI, Hirayama A, Soga T, Kuroda S. Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism. iScience 2022; 25:103787. [PMID: 35243212 PMCID: PMC8859528 DOI: 10.1016/j.isci.2022.103787] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/01/2021] [Accepted: 01/13/2022] [Indexed: 02/07/2023] Open
Abstract
Glucose homeostasis is maintained by modulation of metabolic flux. Enzymes and metabolites regulate the involved metabolic pathways. Dysregulation of glucose homeostasis is a pathological event in obesity. Analyzing metabolic pathways and the mechanisms contributing to obesity-associated dysregulation in vivo is challenging. Here, we introduce OMELET: Omics-Based Metabolic Flux Estimation without Labeling for Extended Trans-omic Analysis. OMELET uses metabolomic, proteomic, and transcriptomic data to identify relative changes in metabolic flux, and to calculate contributions of metabolites, enzymes, and transcripts to the changes in metabolic flux. By evaluating the livers of fasting ob/ob mice, we found that increased metabolic flux through gluconeogenesis resulted primarily from increased transcripts, whereas that through the pyruvate cycle resulted from both increased transcripts and changes in substrates of metabolic enzymes. With OMELET, we identified mechanisms underlying the obesity-associated dysregulation of metabolic flux in the liver. We developed OMELET to infer metabolic flux from label-free multi-omic data Contributions of metabolites, enzymes, and transcripts for flux were inferred Gluconeogenic flux increased in fasting ob/ob mice by increased transcripts Increased pyruvate cycle fluxes were led by increased transcripts and substrates
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Affiliation(s)
- Saori Uematsu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Satoshi Ohno
- Molecular Genetic Research Laboratory, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Kaori Y Tanaka
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Atsushi Hatano
- Department of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, 757 Ichibancho, Asahimachi-dori, Chuo-ku, Niigata City, Niigata 951-8510, Japan
| | - Toshiya Kokaji
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yuki Ito
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.,Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Ken-Ichi Hironaka
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Masaki Matsumoto
- Department of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, 757 Ichibancho, Asahimachi-dori, Chuo-ku, Niigata City, Niigata 951-8510, Japan
| | - Keiichi I Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
| | - Shinya Kuroda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.,Molecular Genetic Research Laboratory, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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17
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Pal A, Iyer MS, Srinivasan S, Narain Seshasayee AS, Venkatesh KV. Global pleiotropic effects in adaptively evolved Escherichia coli lacking CRP reveal molecular mechanisms that define the growth physiology. Open Biol 2022; 12:210206. [PMID: 35167766 PMCID: PMC8846999 DOI: 10.1098/rsob.210206] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Evolution facilitates emergence of fitter phenotypes by efficient allocation of cellular resources in conjunction with beneficial mutations. However, system-wide pleiotropic effects that redress the perturbations to the apex node of the transcriptional regulatory networks remain unclear. Here, we elucidate that absence of global transcriptional regulator CRP in Escherichia coli results in alterations in key metabolic pathways under glucose respiratory conditions, favouring stress- or hedging-related functions over growth-enhancing functions. Further, we disentangle the growth-mediated effects from the CRP regulation-specific effects on these metabolic pathways. We quantitatively illustrate that the loss of CRP perturbs proteome efficiency, as evident from metabolic as well as ribosomal proteome fractions, that corroborated with intracellular metabolite profiles. To address how E. coli copes with such systemic defect, we evolved Δcrp mutant in the presence of glucose. Besides acquiring mutations in the promoter of glucose transporter ptsG, the evolved populations recovered the metabolic pathways to their pre-perturbed state coupled with metabolite re-adjustments, which altogether enabled increased growth. By contrast to Δcrp mutant, the evolved strains remodelled their proteome efficiency towards biomass synthesis, albeit at the expense of carbon efficiency. Overall, we comprehensively illustrate the genetic and metabolic basis of pleiotropic effects, fundamental for understanding the growth physiology.
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Affiliation(s)
- Ankita Pal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Mahesh S. Iyer
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Sumana Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | | | - K. V. Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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18
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Jahn M, Crang N, Janasch M, Hober A, Forsström B, Kimler K, Mattausch A, Chen Q, Asplund-Samuelsson J, Hudson EP. Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator. eLife 2021; 10:69019. [PMID: 34723797 PMCID: PMC8591527 DOI: 10.7554/elife.69019] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/30/2021] [Indexed: 12/12/2022] Open
Abstract
Bacteria must balance the different needs for substrate assimilation, growth
functions, and resilience in order to thrive in their environment. Of all
cellular macromolecules, the bacterial proteome is by far the most important
resource and its size is limited. Here, we investigated how the highly versatile
'knallgas' bacterium Cupriavidus necator reallocates protein
resources when grown on different limiting substrates and with different growth
rates. We determined protein quantity by mass spectrometry and estimated enzyme
utilization by resource balance analysis modeling. We found that C.
necator invests a large fraction of its proteome in functions that
are hardly utilized. Of the enzymes that are utilized, many are present in
excess abundance. One prominent example is the strong expression of CBB cycle
genes such as Rubisco during growth on fructose. Modeling and mutant competition
experiments suggest that CO2-reassimilation through Rubisco does not
provide a fitness benefit for heterotrophic growth, but is rather an investment
in readiness for autotrophy.
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Affiliation(s)
- Michael Jahn
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Nick Crang
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Markus Janasch
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Andreas Hober
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Björn Forsström
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Kyle Kimler
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Alexander Mattausch
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Qi Chen
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Johannes Asplund-Samuelsson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Elton Paul Hudson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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19
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Gruber CH, Diether M, Sauer U. Conservation of metabolic regulation by phosphorylation and non-covalent small-molecule interactions. Cell Syst 2021; 12:538-546. [PMID: 34004157 DOI: 10.1016/j.cels.2021.04.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/04/2021] [Accepted: 04/21/2021] [Indexed: 12/25/2022]
Abstract
Here, we review extant observations of protein phosphorylation and small-molecule interactions in metabolism and ask which of their specific regulatory functions are conserved in Escherichia coli and Homo sapiens. While the number of phosphosites is dramatically higher in humans, the number of metabolite-protein interactions remains largely constant. Moreover, we found the regulatory logic of metabolite-protein interactions, and in many cases also the effector molecules, to be conserved. Post-translational regulation through phosphorylation does not appear to replace this regulation in human but rather seems to add additional opportunities for fine-tuning and more complex responses. The abundance of metabolite-protein interactions in metabolism, their conserved cross-species abundance, and the apparent conservation of regulatory logic across enormous phylogenetic distance demonstrate their relevance for maintaining cellular homeostasis in these ancient biological processes.
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Affiliation(s)
- Christoph H Gruber
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Maren Diether
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland.
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20
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Razaghi-Moghadam Z, Sokolowska EM, Sowa MA, Skirycz A, Nikoloski Z. Combination of network and molecule structure accurately predicts competitive inhibitory interactions. Comput Struct Biotechnol J 2021; 19:2170-2178. [PMID: 34136091 PMCID: PMC8172118 DOI: 10.1016/j.csbj.2021.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/01/2021] [Accepted: 04/03/2021] [Indexed: 11/30/2022] Open
Abstract
Mining of metabolite-protein interaction networks
facilitates the identification of design principles underlying the regulation of
different cellular processes. However, identification and characterization of
the regulatory role that metabolites play in interactions with proteins on a
genome-scale level remains a pressing task. Based on availability of
high-quality metabolite-protein interaction networks and genome-scale metabolic
networks, here we propose a supervised machine learning approach, called CIRI
that determines whether or not a metabolite is involved in a
competitive inhibitory
regulatory interaction with an enzyme.
First, we show that CIRI outperforms the naive approach based on a structural
similarity threshold for a putative competitive inhibitor and the substrates of
a metabolic reaction. We also validate the performance of CIRI on several unseen
data sets and databases of metabolite-protein interactions not used in the
training, and demonstrate that the classifier can be effectively used to predict
competitive inhibitory interactions. Finally, we show that CIRI can be employed
to refine predictions about metabolite-protein interactions from a recently
proposed PROMIS approach that employs metabolomics and proteomics profiles from
size exclusion chromatography in E. coli to predict
metabolite-protein interactions. Altogether, CIRI fills a gap in cataloguing
metabolite-protein interactions and can be used in directing future machine
learning efforts to categorize the regulatory type of these
interactions.
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Affiliation(s)
- Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.,Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Ewelina M Sokolowska
- Department of Molecular Physiology, Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Marcin A Sowa
- Institute for Cardiovascular and Metabolic Research, School of Biological Sciences, University of Reading, Reading, United Kingdom
| | - Aleksandra Skirycz
- Department of Molecular Physiology, Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany.,Boyce Thompson Institute, Ithaca, NY, USA
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.,Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
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21
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Iyer MS, Pal A, Srinivasan S, Somvanshi PR, Venkatesh KV. Global Transcriptional Regulators Fine-Tune the Translational and Metabolic Efficiency for Optimal Growth of Escherichia coli. mSystems 2021; 6:e00001-21. [PMID: 33785570 PMCID: PMC8546960 DOI: 10.1128/msystems.00001-21] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 02/25/2021] [Indexed: 12/13/2022] Open
Abstract
Global transcriptional regulators coordinate complex genetic interactions that bestow better adaptability for an organism against external and internal perturbations. These transcriptional regulators are known to control an enormous array of genes with diverse functionalities. However, regulator-driven molecular mechanisms that underpin precisely tuned translational and metabolic processes conducive for rapid exponential growth remain obscure. Here, we comprehensively reveal the fundamental role of global transcriptional regulators FNR, ArcA, and IHF in sustaining translational and metabolic efficiency under glucose fermentative conditions in Escherichia coli By integrating high-throughput gene expression profiles and absolute intracellular metabolite concentrations, we illustrate that these regulators are crucial in maintaining nitrogen homeostasis, govern expression of otherwise unnecessary or hedging genes, and exert tight control on metabolic bottleneck steps. Furthermore, we characterize changes in expression and activity profiles of other coregulators associated with these dysregulated metabolic pathways, determining the regulatory interactions within the transcriptional regulatory network. Such systematic findings emphasize their importance in optimizing the proteome allocation toward metabolic enzymes as well as ribosomes, facilitating condition-specific phenotypic outcomes. Consequentially, we reveal that disruption of this inherent trade-off between ribosome and metabolic proteome economy due to the loss of regulators resulted in lowered growth rates. Moreover, our findings reinforce that the accumulations of intracellular metabolites in the event of proteome repartitions negatively affects the glucose uptake. Overall, by extending the three-partition proteome allocation theory concordant with multi-omics measurements, we elucidate the physiological consequences of loss of global regulators on central carbon metabolism restraining the organism to attain maximal biomass synthesis.IMPORTANCE Cellular proteome allocation in response to environmental or internal perturbations governs their eventual phenotypic outcome. This entails striking an effective balance between amino acid biosynthesis by metabolic proteins and its consumption by ribosomes. However, the global transcriptional regulator-driven molecular mechanisms that underpin their coordination remains unexplored. Here, we emphasize that global transcriptional regulators, known to control expression of a myriad of genes, are fundamental for precisely tuning the translational and metabolic efficiencies that define the growth optimality. Towards this, we systematically characterized the single deletion effect of FNR, ArcA, and IHF regulators of Escherichia coli on exponential growth under anaerobic glucose fermentative conditions. Their absence disrupts the stringency of proteome allocation, which manifests as impairment in key metabolic processes and an accumulation of intracellular metabolites. Furthermore, by incorporating an extension to the empirical growth laws, we quantitatively demonstrate the general design principles underlying the existence of these regulators in E. coli.
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Affiliation(s)
- Mahesh S Iyer
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ankita Pal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Sumana Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Pramod R Somvanshi
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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22
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Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Sci Rep 2021; 11:4787. [PMID: 33637852 PMCID: PMC7910480 DOI: 10.1038/s41598-021-84114-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023] Open
Abstract
Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 \documentclass[12pt]{minimal}
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\begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.
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23
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Britton S, Alber M, Cannon WR. Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell. J R Soc Interface 2020; 17:20200656. [PMID: 33050777 PMCID: PMC7653389 DOI: 10.1098/rsif.2020.0656] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 09/17/2020] [Indexed: 12/23/2022] Open
Abstract
Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization-reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism.
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Affiliation(s)
- Samuel Britton
- Department of Mathematics, University of California Riverside, Riverside, CA 92505, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA 92505, USA
| | - Mark Alber
- Department of Mathematics, University of California Riverside, Riverside, CA 92505, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA 92505, USA
| | - William R. Cannon
- Department of Mathematics, University of California Riverside, Riverside, CA 92505, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA 92505, USA
- Physical and Computational Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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24
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Kyle Bennett R, Agee A, Har JRG, von Hagel B, Antoniewicz MR, Papoutsakis ET. Regulatory interventions improve the biosynthesis of limiting amino acids from methanol carbon to improve synthetic methylotrophy in Escherichia coli. Biotechnol Bioeng 2020; 118:43-57. [PMID: 32876943 DOI: 10.1002/bit.27549] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/29/2020] [Accepted: 08/26/2020] [Indexed: 12/30/2022]
Abstract
Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methylotrophs have proved successful, autonomous methylotrophy, that is, the ability to utilize methane or methanol as sole carbon and energy substrates, has not yet been realized. Here, we address an important limitation of autonomous methylotrophy in E. coli: the inability of the organism to synthesize several amino acids when grown on methanol. We targeted global and local amino acid regulatory networks. Those include removal of amino acid allosteric feedback inhibition (argAH15Y , ilvAL447F , hisGE271K , leuAG462D , proBD107N , thrAS345F , trpES40F ), knockouts of transcriptional repressors (ihfA, metJ); and overexpression of amino acid biosynthetic operons (hisGDCBHAFI, leuABCD, thrABC, trpEDCBA) and transcriptional regulators (crp, purR). Compared to the parent methylotrophic E. coli strain that was unable to synthesize these amino acids from methanol carbon, these strategies resulted in improved biosynthesis of limiting proteinogenic amino acids (histidine, leucine, lysine, methionine, phenylalanine, threonine, tyrosine) from methanol carbon. In several cases, improved amino acid biosynthesis from methanol carbon led to improvements in methylotrophic growth in methanol minimal medium supplemented with a small amount of yeast extract. This study addresses a key limitation currently preventing autonomous methylotrophy in E. coli and possibly other synthetic methylotrophs and provides insight as to how this limitation can be alleviated via global and local regulatory modifications.
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Affiliation(s)
- Robert Kyle Bennett
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.,The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Alec Agee
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.,The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Jie R G Har
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA
| | - Bryan von Hagel
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.,The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA
| | - Eleftherios T Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.,The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
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25
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Waller TC, Berg JA, Lex A, Chapman BE, Rutter J. Compartment and hub definitions tune metabolic networks for metabolomic interpretations. Gigascience 2020; 9:giz137. [PMID: 31972021 PMCID: PMC6977586 DOI: 10.1093/gigascience/giz137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/31/2019] [Accepted: 10/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. RESULTS We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. CONCLUSIONS Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.
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Affiliation(s)
- T Cameron Waller
- Division of Medical Genetics, Department of Medicine, School of Medicine, University of California San Diego, Room 1318A, 9500 Gilman Drive #0606, La Jolla, California 92093-0606, United States of America
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Jordan A Berg
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Alexander Lex
- School of Computing, University of Utah, Room 3190, 50 South Central Campus Drive, Salt Lake City, Utah 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Room 3750, 72 South Central Campus Drive, Salt Lake City, Utah 84112, USA
| | - Brian E Chapman
- Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Room 1A071, 30 North 1900 East, Salt Lake City, Utah 84132, USA
- Department of Biomedical Informatics, School of Medicine, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, Utah 84108, USA
| | - Jared Rutter
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
- Howard Hughes Medical Institute, School of Medicine, University of Utah, Room AC101, 30 North 1900 East, Salt Lake City, Utah 84132, USA
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26
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Wang G, Haringa C, Tang W, Noorman H, Chu J, Zhuang Y, Zhang S. Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses. Biotechnol Bioeng 2019; 117:844-867. [PMID: 31814101 DOI: 10.1002/bit.27243] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/13/2022]
Abstract
Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.
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Affiliation(s)
- Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Cees Haringa
- Transport Phenomena, Chemical Engineering Department, Delft University of Technology, Delft, The Netherlands.,DSM Biotechnology Center, Delft, The Netherlands
| | - Wenjun Tang
- DSM Biotechnology Center, Delft, The Netherlands
| | - Henk Noorman
- DSM Biotechnology Center, Delft, The Netherlands.,Bioprocess Engineering, Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Siliang Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
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27
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Monteiro F, Hubmann G, Takhaveev V, Vedelaar SR, Norder J, Hekelaar J, Saldida J, Litsios A, Wijma HJ, Schmidt A, Heinemann M. Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor. Mol Syst Biol 2019; 15:e9071. [PMID: 31885198 PMCID: PMC6920703 DOI: 10.15252/msb.20199071] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 11/28/2019] [Accepted: 11/29/2019] [Indexed: 12/17/2022] Open
Abstract
Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.
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Affiliation(s)
- Francisca Monteiro
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
- Present address:
cE3c‐Centre for Ecology, Evolution and Environmental ChangesFaculdade de CiênciasUniversidade de LisboaLisboaPortugal
| | - Georg Hubmann
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
- Present address:
Laboratory of Molecular Cell BiologyDepartment of BiologyInstitute of Botany and MicrobiologyKU Leuven, & Center for Microbiology, VIBHeverlee, FlandersBelgium
| | - Vakil Takhaveev
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | - Silke R Vedelaar
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | - Justin Norder
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | - Johan Hekelaar
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | - Joana Saldida
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | - Athanasios Litsios
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | - Hein J Wijma
- Biotechnology, Groningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
| | | | - Matthias Heinemann
- Molecular Systems BiologyGroningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
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28
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Lempp M, Farke N, Kuntz M, Freibert SA, Lill R, Link H. Systematic identification of metabolites controlling gene expression in E. coli. Nat Commun 2019; 10:4463. [PMID: 31578326 PMCID: PMC6775132 DOI: 10.1038/s41467-019-12474-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/11/2019] [Indexed: 01/07/2023] Open
Abstract
Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states. Interactions between metabolites and transcription factors are known to control gene expression but analyzing these events at genome-scale is challenging. Here, the authors integrate dynamic metabolome and transcriptome data from E.coli to predict regulatory metabolite-transcription factor interactions.
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Affiliation(s)
- Martin Lempp
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany
| | - Sven Andreas Freibert
- Institut für Zytobiologie und Zytopathologie, Philipps-Universität Marburg, 35033, Marburg, Germany
| | - Roland Lill
- Institut für Zytobiologie und Zytopathologie, Philipps-Universität Marburg, 35033, Marburg, Germany.,LOEWE Zentrum für Synthetische Mikrobiologie SYNMIKRO, Philipps-Universität Marburg, 35032, Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany. .,LOEWE Zentrum für Synthetische Mikrobiologie SYNMIKRO, Philipps-Universität Marburg, 35032, Marburg, Germany.
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29
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Foster CJ, Gopalakrishnan S, Antoniewicz MR, Maranas CD. From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. PLoS Comput Biol 2019; 15:e1007319. [PMID: 31504032 PMCID: PMC6759195 DOI: 10.1371/journal.pcbi.1007319] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 09/24/2019] [Accepted: 08/02/2019] [Indexed: 12/02/2022] Open
Abstract
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.
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Affiliation(s)
- Charles J. Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Saratram Gopalakrishnan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Maciek R. Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware. Newark, Delaware, United States of America
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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30
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Long CP, Antoniewicz MR. Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of E. coli metabolism. Metab Eng 2019; 55:249-257. [DOI: 10.1016/j.ymben.2019.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 08/03/2019] [Accepted: 08/03/2019] [Indexed: 02/08/2023]
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31
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Christodoulou D, Kuehne A, Estermann A, Fuhrer T, Lang P, Sauer U. Reserve Flux Capacity in the Pentose Phosphate Pathway by NADPH Binding Is Conserved across Kingdoms. iScience 2019; 19:1133-1144. [PMID: 31536961 PMCID: PMC6831883 DOI: 10.1016/j.isci.2019.08.047] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 07/13/2019] [Accepted: 08/24/2019] [Indexed: 02/03/2023] Open
Abstract
All organisms evolved defense mechanisms to counteract oxidative stress and buildup of reactive oxygen species (ROS). To test whether a potentially conserved mechanism exists for the rapid response, we investigated immediate metabolic dynamics of Escherichia coli, yeast, and human dermal fibroblasts to oxidative stress that we found to be conserved between species. To elucidate the regulatory mechanisms that implement this metabolic response, we developed mechanistic kinetic models for each organism's central metabolism and systematically tested activation and inactivation of each irreversible reaction by each metabolite. This ensemble modeling predicts in vivo relevant metabolite-enzyme interactions based on their ability to quantitatively describe metabolite dynamics. All three species appear to inhibit their oxidative pentose phosphate pathway during normal growth by the redox cofactor NADPH and relieve this inhibition to increase the pathway flux for detoxification of ROS during stress, with the sole exception of yeast when exposed to high levels of stress. Characterization of immediate metabolic response to oxidative stress The metabolic response in glycolysis and PP pathway depends on stress severity Identification of NADPH feedback inhibition on G6PDH as key regulatory interaction The identified oxidative stress regulatory interaction is conserved across kingdoms
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Affiliation(s)
- Dimitris Christodoulou
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland
| | - Andreas Kuehne
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland
| | | | - Tobias Fuhrer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Paul Lang
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
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32
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Diether M, Nikolaev Y, Allain FHT, Sauer U. Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli. Mol Syst Biol 2019; 15:e9008. [PMID: 31464375 PMCID: PMC6706640 DOI: 10.15252/msb.20199008] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/18/2019] [Accepted: 07/31/2019] [Indexed: 01/30/2023] Open
Abstract
Metabolite binding to proteins regulates nearly all cellular processes, but our knowledge of these interactions originates primarily from empirical in vitro studies. Here, we report the first systematic study of interactions between water-soluble proteins and polar metabolites in an entire biological subnetwork. To test the depth of our current knowledge, we chose to investigate the well-characterized Escherichia coli central metabolism. Using ligand-detected NMR, we assayed 29 enzymes towards binding events with 55 intracellular metabolites. Focusing on high-confidence interactions at a false-positive rate of 5%, we detected 98 interactions, among which purine nucleotides accounted for one-third, while 50% of all metabolites did not interact with any enzyme. In contrast, only five enzymes did not exhibit any metabolite binding and some interacted with up to 11 metabolites. About 40% of the interacting metabolites were predicted to be allosteric effectors based on low chemical similarity to their target's reactants. For five of the eight tested interactions, in vitro assays confirmed novel regulatory functions, including ATP and GTP inhibition of the first pentose phosphate pathway enzyme. With 76 new candidate regulatory interactions that have not been reported previously, we essentially doubled the number of known interactions, indicating that the presently available information about protein-metabolite interactions may only be the tip of the iceberg.
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Affiliation(s)
- Maren Diether
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Life Science Zurich PhD Program on Systems BiologyZurichSwitzerland
| | - Yaroslav Nikolaev
- Institute of Molecular Biology and BiophysicsETH ZurichZurichSwitzerland
| | - Frédéric HT Allain
- Institute of Molecular Biology and BiophysicsETH ZurichZurichSwitzerland
| | - Uwe Sauer
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
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33
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Wang G, Chu J, Zhuang Y, van Gulik W, Noorman H. A dynamic model-based preparation of uniformly-13C-labeled internal standards facilitates quantitative metabolomics analysis of Penicillium chrysogenum. J Biotechnol 2019; 299:21-31. [DOI: 10.1016/j.jbiotec.2019.04.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/03/2019] [Accepted: 04/25/2019] [Indexed: 01/03/2023]
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34
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Ortmayr K, Dubuis S, Zampieri M. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism. Nat Commun 2019; 10:1841. [PMID: 31015463 PMCID: PMC6478870 DOI: 10.1038/s41467-019-09695-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/22/2019] [Indexed: 12/20/2022] Open
Abstract
Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs. Aberrant gene expression in cancer coincides with drastic changes in metabolism. Here, the authors combined metabolome, transcriptome and proteome data in 54 cancer cell lines to uncover a genome-scale network of associations between transcriptional regulators and metabolites.
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Affiliation(s)
- Karin Ortmayr
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland
| | - Sébastien Dubuis
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland.
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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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Dai Z, Locasale JW. Thermodynamic constraints on the regulation of metabolic fluxes. J Biol Chem 2018; 293:19725-19739. [PMID: 30361440 PMCID: PMC6314121 DOI: 10.1074/jbc.ra118.004372] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2018] [Revised: 09/17/2018] [Indexed: 12/20/2022] Open
Abstract
Nutrition and metabolism are fundamental to cellular function. Metabolic activity (i.e. rates of flow, most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis and computer simulations in several models of simplified metabolic pathways, we derive analytical expressions that define relationships between thermodynamics, enzyme activity, and flux control. The relationships are further analyzed in a mathematical model of glycolysis as an example of a complex biochemical pathway. We show that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, regulation of metabolic fluxes by an enzyme has a more adaptable pattern, which relies more on distribution of free energy among reaction steps in the pathway than on the thermodynamic properties of the given enzyme. These findings show how the control of metabolic pathways is shaped by thermodynamic constraints of the given pathway.
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Affiliation(s)
- Ziwei Dai
- From the Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina 27710
| | - Jason W Locasale
- From the Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina 27710
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Venayak N, von Kamp A, Klamt S, Mahadevan R. MoVE identifies metabolic valves to switch between phenotypic states. Nat Commun 2018; 9:5332. [PMID: 30552335 PMCID: PMC6294006 DOI: 10.1038/s41467-018-07719-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 11/02/2018] [Indexed: 01/29/2023] Open
Abstract
Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.
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Affiliation(s)
- Naveen Venayak
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106, Magdeburg, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada. .,Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164, College Street, Toronto, ON, M5S 3G9, Canada.
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Integrating -omics data into genome-scale metabolic network models: principles and challenges. Essays Biochem 2018; 62:563-574. [PMID: 30315095 DOI: 10.1042/ebc20180011] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/30/2018] [Accepted: 08/31/2018] [Indexed: 12/13/2022]
Abstract
At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available -omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of -omics data into CBMs focussing on the methods' assumptions and limitations. We argue that key assumptions - often derived from single-enzyme kinetics - do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for -omics data integration in a common framework to provide more accurate predictions.
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Abernathy MH, Zhang Y, Hollinshead WD, Wang G, Baidoo EEK, Liu T, Tang YJ. Comparative studies of glycolytic pathways and channeling under
in vitro
and
in vivo
modes. AIChE J 2018. [DOI: 10.1002/aic.16367] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Mary H. Abernathy
- Dept. of Energy, Environmental and Chemical Engineering Washington University St. Louis MO 63130
| | - Yuchen Zhang
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 China
| | - Whitney D. Hollinshead
- Dept. of Energy, Environmental and Chemical Engineering Washington University St. Louis MO 63130
| | - George Wang
- Lawrence Berkeley National Laboratory Emeryville CA 64608
| | | | - Tiangang Liu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 China
| | - Yinjie J. Tang
- Dept. of Energy, Environmental and Chemical Engineering Washington University St. Louis MO 63130
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40
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Davidi D, Longo LM, Jabłońska J, Milo R, Tawfik DS. A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations. Chem Rev 2018; 118:8786-8797. [DOI: 10.1021/acs.chemrev.8b00039] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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41
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Smith RW, van Rosmalen RP, Martins Dos Santos VAP, Fleck C. DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems. BMC SYSTEMS BIOLOGY 2018; 12:72. [PMID: 29914475 PMCID: PMC6006996 DOI: 10.1186/s12918-018-0584-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 05/14/2018] [Indexed: 12/21/2022]
Abstract
Background Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. Results In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. Conclusion The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future. Electronic supplementary material The online version of this article (10.1186/s12918-018-0584-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert W Smith
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands.,LifeGlimmer GmbH, Markelstrasse 38, Berlin, 12163, Germany
| | - Rik P van Rosmalen
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands.,LifeGlimmer GmbH, Markelstrasse 38, Berlin, 12163, Germany
| | - Christian Fleck
- Laboratory of Systems & Synthetic Biology, Wageningen UR, Stippeneng 4, Wageningen, 6708WE, The Netherlands.
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Reserve Flux Capacity in the Pentose Phosphate Pathway Enables Escherichia coli's Rapid Response to Oxidative Stress. Cell Syst 2018; 6:569-578.e7. [PMID: 29753645 DOI: 10.1016/j.cels.2018.04.009] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 01/19/2018] [Accepted: 04/10/2018] [Indexed: 01/01/2023]
Abstract
To counteract oxidative stress and reactive oxygen species (ROS), bacteria evolved various mechanisms, primarily reducing ROS through antioxidant systems that utilize cofactor NADPH. Cells must stabilize NADPH levels by increasing flux through replenishing metabolic pathways like pentose phosphate (PP) pathway. Here, we investigate the mechanism enabling the rapid increase in NADPH supply by exposing Escherichia coli to hydrogen peroxide and quantifying the immediate metabolite dynamics. To systematically infer active regulatory interactions governing this response, we evaluated ensembles of kinetic models of glycolysis and PP pathway, each with different regulation mechanisms. Besides the known inactivation of glyceraldehyde 3-phosphate dehydrogenase by ROS, we reveal the important allosteric inhibition of the first PP pathway enzyme by NADPH. This NADPH feedback inhibition maintains a below maximum-capacity PP pathway flux under non-stress conditions. Relieving this inhibition instantly increases PP pathway flux upon oxidative stress. We demonstrate that reducing cells' capacity to rapidly reroute their flux through the PP pathway increases their oxidative stress sensitivity.
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Tummler K, Klipp E. The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions? ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.coisb.2017.11.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Yugi K, Kuroda S. Metabolism as a signal generator across trans-omic networks at distinct time scales. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.coisb.2017.12.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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45
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Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, Sauer U, Picotti P. A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication. Cell 2018; 172:358-372.e23. [DOI: 10.1016/j.cell.2017.12.006] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 10/27/2017] [Accepted: 12/01/2017] [Indexed: 10/25/2022]
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Abstract
Glycolysis, the breakdown of glucose, is one of the most conserved and extensively studied biochemical pathways. Designing principles from chemistry and thermodynamics allow for energy production, biosynthesis and cellular communication. However, the kinetics or metabolic flux through the pathway also determines its function. Recently, there have been numerous developments that establish new allosteric interactions of glycolytic enzymes with small molecule metabolites and other mechanisms that may cooperate to allow for addition complex regulation of glycolysis. This review surveys these newfound sources of glycolysis regulation and discusses their possible roles in establishing kinetic design principles of glycolysis.
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Affiliation(s)
- Jason W Locasale
- Department of Pharmacology and Cancer Biology, Duke Cancer Institute, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC 27710, USA
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