1
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Xu X, Stellacci F. Amino Acids and Their Biological Derivatives Modulate Protein-Protein Interactions in an Additive Way. J Phys Chem Lett 2024; 15:7154-7160. [PMID: 38967372 PMCID: PMC11261602 DOI: 10.1021/acs.jpclett.4c01175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/06/2024]
Abstract
Protein-protein interactions (PPIs) differ when measured in test tubes and cells due to the complexity of the intracellular environment. Free amino acids (AAs) and their derivatives constitute a significant fraction of the intracellular volume and mass. Recently, we have found that AAs have a generic property of rendering protein dispersions more stable by reducing the net attractive part of PPIs. Here, we study the effects on PPIs of different AA derivatives, AA mixtures, and short peptides. We find that all the tested AA derivatives modulate PPIs in solution as effectively as AAs. Furthermore, we show that the modulation effect is additive when AAs form mixtures or are bound into short peptides. Therefore, this study demonstrates the additive effects of a class of small molecules (i.e., AAs and their biological derivatives) on PPIs and provides insights into rationally designing biocompatible molecules for stabilizing protein interactions and consequently tuning protein functions.
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Affiliation(s)
- Xufeng Xu
- Institute
of Materials, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Francesco Stellacci
- Institute
of Materials, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
- Bioengineering
Institute, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
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2
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Jasinska W, Dindo M, Cordoba SMC, Serohijos AWR, Laurino P, Brotman Y, Bershtein S. Non-consecutive enzyme interactions within TCA cycle supramolecular assembly regulate carbon-nitrogen metabolism. Nat Commun 2024; 15:5285. [PMID: 38902266 PMCID: PMC11189929 DOI: 10.1038/s41467-024-49646-7] [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: 11/29/2023] [Accepted: 06/14/2024] [Indexed: 06/22/2024] Open
Abstract
Enzymes of the central metabolism tend to assemble into transient supramolecular complexes. However, the functional significance of the interactions, particularly between enzymes catalyzing non-consecutive reactions, remains unclear. Here, by co-localizing two non-consecutive enzymes of the TCA cycle from Bacillus subtilis, malate dehydrogenase (MDH) and isocitrate dehydrogenase (ICD), in phase separated droplets we show that MDH-ICD interaction leads to enzyme agglomeration with a concomitant enhancement of ICD catalytic rate and an apparent sequestration of its reaction product, 2-oxoglutarate. Theory demonstrates that MDH-mediated clustering of ICD molecules explains the observed phenomena. In vivo analyses reveal that MDH overexpression leads to accumulation of 2-oxoglutarate and reduction of fluxes flowing through both the catabolic and anabolic branches of the carbon-nitrogen intersection occupied by 2-oxoglutarate, resulting in impeded ammonium assimilation and reduced biomass production. Our findings suggest that the MDH-ICD interaction is an important coordinator of carbon-nitrogen metabolism.
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Affiliation(s)
- Weronika Jasinska
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Mirco Dindo
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
- Department of Medicine and Surgery, Section of Physiology and Biochemistry, University of Perugia, Perugia, Italy
| | - Sandra M C Cordoba
- Max-Planck-Institut fur Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany
| | - Adrian W R Serohijos
- Departement de Biochimie, Universite de Montreal, Quebec, Canada
- Centre Robert-Cedergren en Bio-informatique et Genomique, Universite de Montreal, Quebec, Canada
| | - Paola Laurino
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
- Institute for Protein Research, Osaka University, Suita, Japan.
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Shimon Bershtein
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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3
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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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4
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Alam S, Doherty E, Ortega-Prieto P, Arizanova J, Fets L. Membrane transporters in cell physiology, cancer metabolism and drug response. Dis Model Mech 2023; 16:dmm050404. [PMID: 38037877 PMCID: PMC10695176 DOI: 10.1242/dmm.050404] [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: 12/02/2023] Open
Abstract
By controlling the passage of small molecules across lipid bilayers, membrane transporters influence not only the uptake and efflux of nutrients, but also the metabolic state of the cell. With more than 450 members, the Solute Carriers (SLCs) are the largest transporter super-family, clustering into families with different substrate specificities and regulatory properties. Cells of different types are, therefore, able to tailor their transporter expression signatures depending on their metabolic requirements, and the physiological importance of these proteins is illustrated by their mis-regulation in a number of disease states. In cancer, transporter expression is heterogeneous, and the SLC family has been shown to facilitate the accumulation of biomass, influence redox homeostasis, and also mediate metabolic crosstalk with other cell types within the tumour microenvironment. This Review explores the roles of membrane transporters in physiological and malignant settings, and how these roles can affect drug response, through either indirect modulation of sensitivity or the direct transport of small-molecule therapeutic compounds into cells.
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Affiliation(s)
- Sara Alam
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Emily Doherty
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Paula Ortega-Prieto
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Julia Arizanova
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Louise Fets
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
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5
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Wuyts S, Alves R, Zimmermann‐Kogadeeva M, Nishijima S, Blasche S, Driessen M, Geyer PE, Hercog R, Kartal E, Maier L, Müller JB, Garcia Santamarina S, Schmidt TSB, Sevin DC, Telzerow A, Treit PV, Wenzel T, Typas A, Patil KR, Mann M, Kuhn M, Bork P. Consistency across multi-omics layers in a drug-perturbed gut microbial community. Mol Syst Biol 2023; 19:e11525. [PMID: 37485738 PMCID: PMC10495815 DOI: 10.15252/msb.202311525] [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: 01/09/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/25/2023] Open
Abstract
Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.
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Affiliation(s)
- Sander Wuyts
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Renato Alves
- European Molecular Biology LaboratoryHeidelbergGermany
| | | | | | - Sonja Blasche
- European Molecular Biology LaboratoryHeidelbergGermany
- Medical Research Council Toxicology UnitCambridgeUK
| | | | - Philipp E Geyer
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Rajna Hercog
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Ece Kartal
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Lisa Maier
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Johannes B Müller
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Sarela Garcia Santamarina
- European Molecular Biology LaboratoryHeidelbergGermany
- Present address:
MOSTMICRO Unit, Instituto de Tecnologia Quimica e BiologicaUniversidade Nova de LisboaOeirasPortugal
| | | | | | - Anja Telzerow
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Peter V Treit
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Tobias Wenzel
- European Molecular Biology LaboratoryHeidelbergGermany
- Present address:
Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological SciencesPontificia Universidad Catolica de ChileSantiagoChile
| | | | - Kiran R Patil
- European Molecular Biology LaboratoryHeidelbergGermany
- Medical Research Council Toxicology UnitCambridgeUK
| | - Matthias Mann
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
- Proteomics Program, NNF Center for Protein Research, Faculty of Health SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Michael Kuhn
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Peer Bork
- European Molecular Biology LaboratoryHeidelbergGermany
- Max Delbrück Centre for Molecular MedicineBerlinGermany
- Yonsei Frontier Lab (YFL)Yonsei UniversitySeoulSouth Korea
- Department of Bioinformatics, BiocenterUniversity of WürzburgWürzburgGermany
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6
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Mitosch K, Beyß M, Phapale P, Drotleff B, Nöh K, Alexandrov T, Patil KR, Typas A. A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella. PLoS Biol 2023; 21:e3002198. [PMID: 37594988 PMCID: PMC10468081 DOI: 10.1371/journal.pbio.3002198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/30/2023] [Accepted: 06/16/2023] [Indexed: 08/20/2023] Open
Abstract
Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.
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Affiliation(s)
- Karin Mitosch
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
- RWTH Aachen University, Computational Systems Biotechnology, Aachen, Germany
| | - Prasad Phapale
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Bernhard Drotleff
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Theodore Alexandrov
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- BioInnovation Institute, Copenhagen, Denmark
| | - Kiran R. Patil
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Athanasios Typas
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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7
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Zhang Y, Chen R, Zhang D, Qi S, Liu Y. Metabolite interactions between host and microbiota during health and disease: Which feeds the other? Biomed Pharmacother 2023; 160:114295. [PMID: 36709600 DOI: 10.1016/j.biopha.2023.114295] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/30/2023] Open
Abstract
Metabolites produced by the host and microbiota play a crucial role in how human bodies develop and remain healthy. Most of these metabolites are produced by microbiota and hosts in the digestive tract. Metabolites in the gut have important roles in energy metabolism, cellular communication, and host immunity, among other physiological activities. Although numerous host metabolites, such as free fatty acids, amino acids, and vitamins, are found in the intestine, metabolites generated by gut microbiota are equally vital for intestinal homeostasis. Furthermore, microbiota in the gut is the sole source of some metabolites, including short-chain fatty acids (SCFAs). Metabolites produced by microbiota, such as neurotransmitters and hormones, may modulate and significantly affect host metabolism. The gut microbiota is becoming recognized as a second endocrine system. A variety of chronic inflammatory disorders have been linked to aberrant host-microbiota interplays, but the precise mechanisms underpinning these disturbances and how they might lead to diseases remain to be fully elucidated. Microbiome-modulated metabolites are promising targets for new drug discovery due to their endocrine function in various complex disorders. In humans, metabolotherapy for the prevention or treatment of various disorders will be possible if we better understand the metabolic preferences of bacteria and the host in specific tissues and organs. Better disease treatments may be possible with the help of novel complementary therapies that target host or bacterial metabolism. The metabolites, their physiological consequences, and functional mechanisms of the host-microbiota interplays will be highlighted, summarized, and discussed in this overview.
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Affiliation(s)
- Yan Zhang
- Department of Anethesiology, China-Japan Union Hospital of Jilin University, Changchun 130033, People's Republic of China.
| | - Rui Chen
- Department of Pediatrics, China-Japan Union Hospital of Jilin University, Changchun 130033, People's Republic of China.
| | - DuoDuo Zhang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin Province 130021, People's Republic of China.
| | - Shuang Qi
- Department of Anethesiology, China-Japan Union Hospital of Jilin University, Changchun 130033, People's Republic of China.
| | - Yan Liu
- Department of Hand and Foot Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People's Republic of China.
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8
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Chang PK, Scharfenstein LL, Mahoney N, Kong Q. Kojic Acid Gene Clusters and the Transcriptional Activation Mechanism of Aspergillus flavus KojR on Expression of Clustered Genes. J Fungi (Basel) 2023; 9:jof9020259. [PMID: 36836373 PMCID: PMC9961346 DOI: 10.3390/jof9020259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/08/2023] [Accepted: 02/11/2023] [Indexed: 02/18/2023] Open
Abstract
Kojic acid (KA) is a fungal metabolite and has a variety of applications in the cosmetics and food industries. Aspergillus oryzae is a well-known producer of KA, and its KA biosynthesis gene cluster has been identified. In this study, we showed that nearly all section Flavi aspergilli except for A. avenaceus had complete KA gene clusters, and only one Penicillium species, P. nordicum, contained a partial KA gene cluster. Phylogenetic inference based on KA gene cluster sequences consistently grouped section Flavi aspergilli into clades as prior studies. The Zn(II)2Cys6 zinc cluster regulator KojR transcriptionally activated clustered genes of kojA and kojT in Aspergillus flavus. This was evidenced by the time-course expression of both genes in kojR-overexpressing strains whose kojR expression was driven by a heterologous Aspergillus nidulans gpdA promoter or a homologous A. flavus gpiA promoter. Using sequences from the kojA and kojT promoter regions of section Flavi aspergilli for motif analyses, we identified a consensus KojR-binding motif to be an 11-bp palindromic sequence of 5'-CGRCTWAGYCG-3' (R = A/G, W = A/T, Y = C/T). A CRISPR/Cas9-mediated gene-targeting technique showed that the motif sequence, 5'-CGACTTTGCCG-3', in the kojA promoter was critical for KA biosynthesis in A. flavus. Our findings may facilitate strain improvement and benefit future kojic acid production.
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Affiliation(s)
- Perng-Kuang Chang
- Southern Regional Research Center, Agricultural Research Service, U. S. Department of Agriculture, 1100 Allen Toussaint Boulevard, New Orleans, LA 70124, USA
- Correspondence: ; Tel.: +1-504-286-4208; Fax: +1-504-286-4419
| | - Leslie L. Scharfenstein
- Southern Regional Research Center, Agricultural Research Service, U. S. Department of Agriculture, 1100 Allen Toussaint Boulevard, New Orleans, LA 70124, USA
| | - Noreen Mahoney
- Western Regional Research Center, Agricultural Research Service, U. S. Department of Agriculture, 800 Buchanan Street, Albany, CA 94710, USA
| | - Qing Kong
- School of Food Science and Engineering, Ocean University of China, Qingdao 266003, China
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9
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Campana P, Nikoloski Z. Self- and cross-attention accurately predicts metabolite-protein interactions. NAR Genom Bioinform 2023; 5:lqad008. [PMID: 36733400 PMCID: PMC9887643 DOI: 10.1093/nargab/lqad008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/20/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Abstract
Metabolites regulate activity of proteins and thereby affect cellular processes in all organisms. Despite extensive efforts to catalogue the metabolite-protein interactome in different organisms by employing experimental and computational approaches, the coverage of such interactions remains fragmented, particularly for eukaryotes. Here, we make use of two most comprehensive collections, BioSnap and STITCH, of metabolite-protein interactions from seven eukaryotes as gold standards to train a deep learning model that relies on self- and cross-attention over protein sequences. This innovative protein-centric approach results in interaction-specific features derived from protein sequence alone. In addition, we designed and assessed a first double-blind evaluation protocol for metabolite-protein interactions, demonstrating the generalizability of the model. Our results indicated that the excellent performance of the proposed model over simpler alternatives and randomized baselines is due to the local and global features generated by the attention mechanisms. As a results, the predictions from the deep learning model provide a valuable resource for studying metabolite-protein interactions in eukaryotes.
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Affiliation(s)
- Pedro Alonso Campana
- Machine Learning, Department of Computer Science, University of Potsdam, 14476 Potsdam, Germany,Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- To whom correspondence should be addressed. Tel: +49 331 9776305; Fax: +49 331 977 702214;
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10
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El-Mansi M. Control of central metabolism’s architecture in Escherichia coli: An overview. Microbiol Res 2023; 266:127224. [DOI: 10.1016/j.micres.2022.127224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 10/05/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
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11
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Kuwabara S, Landers ER, Fisher DJ. Impact of nutrients on the function of the chlamydial Rsb partner switching mechanism. Pathog Dis 2022; 80:6831632. [PMID: 36385643 DOI: 10.1093/femspd/ftac044] [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: 07/26/2022] [Revised: 10/27/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
The obligate intracellular bacterial pathogen Chlamydia trachomatis is a leading cause of sexually transmitted infections and infectious blindness. Chlamydia undergo a biphasic developmental cycle alternating between the infectious elementary body (EB) and the replicative reticulate body (RB). The molecular mechanisms governing RB growth and RB-EB differentiation are unclear. We hypothesize that the bacterium senses host cell and bacterial energy levels and metabolites to ensure that development and growth coincide with nutrient availability. We predict that a partner switching mechanism (PSM) plays a key role in the sensing and response process acting as a molecular throttle sensitive to metabolite levels. Using purified wild type and mutant PSM proteins, we discovered that metal type impacts enzyme activity and the substrate specificity of RsbU and that RsbW prefers ATP over GTP as a phosphate donor. Immunoblotting analysis of RsbV1/V2 demonstrated the presence of both proteins beyond 20 hours post infection and we observed that an RsbV1-null strain has a developmental delay and exhibits differential growth attenuation in response to glucose levels. Collectively, our data support that the PSM regulates growth in response to metabolites and further defines biochemical features governing PSM-component interactions which could help in the development of novel PSM-targeted therapeutics.
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Affiliation(s)
- Shiomi Kuwabara
- Molecular Biology, Microbiology and Biochemistry Graduate Program, Southern Illinois University, Carbondale, IL 62901, United States
| | - Evan R Landers
- Molecular Biology, Microbiology and Biochemistry Graduate Program, Southern Illinois University, Carbondale, IL 62901, United States
| | - Derek J Fisher
- Molecular Biology, Microbiology and Biochemistry Graduate Program, Southern Illinois University, Carbondale, IL 62901, United States.,School of Biological Sciences, Southern Illinois University, Carbondale, IL 62901, United States
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12
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Schultz D, Stevanovic M, Tsimring LS. Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures. Biophys J 2022; 121:4137-4152. [PMID: 36168291 PMCID: PMC9675034 DOI: 10.1016/j.bpj.2022.09.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/22/2022] [Accepted: 09/23/2022] [Indexed: 11/17/2022] Open
Abstract
Cellular responses to the presence of toxic compounds in their environment require prompt expression of the correct levels of the appropriate enzymes, which are typically regulated by transcription factors that control gene expression for the duration of the response. The characteristics of each response dictate the choice of regulatory parameters such as the affinity of the transcription factor to its binding sites and the strength of the promoters it regulates. Although much is known about the dynamics of cellular responses, we still lack a framework to understand how different regulatory strategies evolved in natural systems relate to the selective pressures acting in each particular case. Here, we analyze a dynamical model of a typical antibiotic response in bacteria, where a transcriptionally repressed enzyme is induced by a sudden exposure to the drug that it processes. We identify strategies of gene regulation that optimize this response for different types of selective pressures, which we define as a set of costs associated with the drug, enzyme, and repressor concentrations during the response. We find that regulation happens in a limited region of the regulatory parameter space. While responses to more costly (toxic) drugs favor the usage of strongly self-regulated repressors, responses where expression of enzyme is more costly favor the usage of constitutively expressed repressors. Only a very narrow range of selective pressures favor weakly self-regulated repressors. We use this framework to determine which costs and benefits are most critical for the evolution of a variety of natural cellular responses that satisfy the approximations in our model and to analyze how regulation is optimized in new environments with different demands.
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Affiliation(s)
- Daniel Schultz
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
| | - Mirjana Stevanovic
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Lev S Tsimring
- Synthetic Biology Institute, University of California, San Diego, La Jolla, California
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13
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Haddadi K, Ahmed Barghout R, Mahadevan R. KinMod database: a tool for investigating metabolic regulation. Database (Oxford) 2022; 2022:6759124. [PMID: 36222201 PMCID: PMC9554645 DOI: 10.1093/database/baac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/08/2022] [Accepted: 10/07/2022] [Indexed: 11/05/2022]
Abstract
The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz
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Affiliation(s)
- Kiandokht Haddadi
- Laboratory for Metabolic Systems Engineering, BioZone, Center for Applied Biosciences and Bioengineering, Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON M5T 3A1, Canada
| | - Rana Ahmed Barghout
- *Correspondence to: Rana Ahmed Barghout Laboratory for Metabolic Systems Engineering, BioZone, Center for Applied Biosciences and Bioengineering, Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON M5T 3A1, Canada
| | - Radhakrishnan Mahadevan
- Laboratory for Metabolic Systems Engineering, BioZone, Center for Applied Biosciences and Bioengineering, Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON M5T 3A1, Canada
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14
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Luzia L, Lao‐Martil D, Savakis P, van Heerden J, van Riel N, Teusink B. pH dependencies of glycolytic enzymes of yeast under in vivo-like assay conditions. FEBS J 2022; 289:6021-6037. [PMID: 35429225 PMCID: PMC9790636 DOI: 10.1111/febs.16459] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/29/2022] [Accepted: 04/13/2022] [Indexed: 12/30/2022]
Abstract
Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S. cerevisiae under in vivo-like conditions at different pH. We demonstrate that glycolytic enzymes exhibit differential pH dependencies, and optima, in the pH range observed during carbon source transitions. The forward reaction of GAPDH shows the highest decrease in activity, 83%, during a simulated feast/famine regime upon glucose removal (cytosolic pH drop from 7.1 to 6.4). We complement our biochemical characterization of the glycolytic enzymes by fitting the Vmax to the progression curves of product formation or decay over time. The fitting analysis shows that the observed changes in enzyme activities require changes in Vmax , but changes in Km cannot be excluded. Our study highlights the relevance of pH as a key player in metabolic regulation and provides a large set of quantitative data that can be explored to improve our understanding of metabolism in dynamic environments.
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Affiliation(s)
| | | | | | | | - Natal van Riel
- Department of Biomedical EngineeringTU EindhovenNetherlands
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15
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Verhagen KJA, Eerden SA, Sikkema BJ, Wahl SA. Predicting Metabolic Adaptation Under Dynamic Substrate Conditions Using a Resource-Dependent Kinetic Model: A Case Study Using Saccharomyces cerevisiae. Front Mol Biosci 2022; 9:863470. [PMID: 35651815 PMCID: PMC9149170 DOI: 10.3389/fmolb.2022.863470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/29/2022] [Indexed: 11/26/2022] Open
Abstract
Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism Saccharomyces cerevisiae by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in S. cerevisiae is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived “overcapacity” present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model’s ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.
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Affiliation(s)
- K. J. A. Verhagen
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
| | - S. A. Eerden
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
| | - B. J. Sikkema
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
| | - S. A. Wahl
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, Erlangen, Germany
- *Correspondence: S. A. Wahl,
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16
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Muntoni AP, Braunstein A, Pagnani A, De Martino D, De Martino A. Relationship between fitness and heterogeneity in exponentially growing microbial populations. Biophys J 2022; 121:1919-1930. [DOI: 10.1016/j.bpj.2022.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/13/2021] [Accepted: 04/08/2022] [Indexed: 11/02/2022] Open
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17
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Quantitative metabolic fluxes regulated by trans-omic networks. Biochem J 2022; 479:787-804. [PMID: 35356967 PMCID: PMC9022981 DOI: 10.1042/bcj20210596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/21/2022]
Abstract
Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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18
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Chung CH, Lin DW, Eames A, Chandrasekaran S. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites 2021; 11:606. [PMID: 34564422 PMCID: PMC8470976 DOI: 10.3390/metabo11090606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 12/18/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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Affiliation(s)
- Carolina H. Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Da-Wei Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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19
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Currin A, Parker S, Robinson CJ, Takano E, Scrutton NS, Breitling R. The evolving art of creating genetic diversity: From directed evolution to synthetic biology. Biotechnol Adv 2021; 50:107762. [PMID: 34000294 PMCID: PMC8299547 DOI: 10.1016/j.biotechadv.2021.107762] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 04/21/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022]
Abstract
The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.
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Affiliation(s)
- Andrew Currin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom.
| | - Steven Parker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom.
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20
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Diederen T, Delabrière A, Othman A, Reid ME, Zamboni N. Metabolomics. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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21
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Chowdhury S, Hepper S, Lodi MK, Saier MH, Uetz P. The Protein Interactome of Glycolysis in Escherichia coli. Proteomes 2021; 9:proteomes9020016. [PMID: 33917325 PMCID: PMC8167557 DOI: 10.3390/proteomes9020016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/31/2021] [Accepted: 04/02/2021] [Indexed: 11/16/2022] Open
Abstract
Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Cary Street, Richmond, VA 23284, USA; or
| | - Stephen Hepper
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA; (S.H.); (M.K.L.)
| | - Mudassir K. Lodi
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA; (S.H.); (M.K.L.)
| | - Milton H. Saier
- Department of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093, USA;
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA; (S.H.); (M.K.L.)
- Correspondence:
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22
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Millard P, Enjalbert B, Uttenweiler-Joseph S, Portais JC, Létisse F. Control and regulation of acetate overflow in Escherichia coli. eLife 2021; 10:63661. [PMID: 33720011 PMCID: PMC8021400 DOI: 10.7554/elife.63661] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/12/2021] [Indexed: 12/21/2022] Open
Abstract
Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.
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Affiliation(s)
- Pierre Millard
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Brice Enjalbert
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
| | | | - Jean-Charles Portais
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France.,RESTORE, Université de Toulouse, INSERM U1031, CNRS 5070, Université Toulouse III - Paul Sabatier, EFS, Toulouse, France
| | - Fabien Létisse
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,Université Toulouse III - Paul Sabatier, Toulouse, France
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23
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Vasdekis AE, Singh A. Microbial metabolic noise. WIREs Mech Dis 2020; 13:e1512. [PMID: 33225608 DOI: 10.1002/wsbm.1512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 09/23/2020] [Accepted: 10/26/2020] [Indexed: 11/06/2022]
Abstract
From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.
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Affiliation(s)
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA
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24
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Wang B, Lv W, Chang M, Zhao C, Shi X, Xu G. Untargeted Defining Protein-Metabolites Interaction Based on Label-Free Kinetic Size Exclusion Chromatography-Mass Spectrometry. Anal Chem 2020; 92:7657-7665. [PMID: 32384235 DOI: 10.1021/acs.analchem.0c00495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The specific interactions between protein and metabolites (PMIs) are closely related to many cellular processes and play a vital role in signal transduction and regulating material and energy metabolism. However, most of the available analytical strategies for PMIs involve chemical modification of metabolites or immobilization of protein, which has restricted current PMIs study mainly to lipid-protein and hydrophobic metabolites. In this work, a label-free online kinetic size exclusion chromatography-mass spectrometry (KSEC-MS) method combined with untargeted metabolomics was developed to define PMIs in a complex system. The metabolite mixture and target protein were injected into the SEC column sequentially without preincubation, and the separation results of KSEC were monitored by global metabolite profiling with mass spectrometry. The potential ligands in the metabolite mixture can be discovered if their migration patterns were affected by the target protein and the variation was positively correlated with the concentration of target protein. To verify this approach, carbonic anhydrase was first selected as a test protein, and acetazolamide as its known inhibitor was successfully defined. Furthermore, human serum albumin (HSA) as the common transport carrier of metabolites was selected as a target protein to demonstrate the usefulness of this approach. Multiple endogenous ligands of HSA were simultaneously defined from the extracted metabolites of human serum; most of them are polar metabolites rather than nonpolar lipids. This approach can provide a novel way for mapping and identifying unknown PMIs in a complex system, especially for polar metabolites-protein interactions.
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Affiliation(s)
- Bohong Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wangjie Lv
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengmeng Chang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chunxia Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xianzhe Shi
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
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25
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Coussement P, Bauwens D, Peters G, Maertens J, De Mey M. Mapping and refactoring pathway control through metabolic and protein engineering: The hexosamine biosynthesis pathway. Biotechnol Adv 2020; 40:107512. [DOI: 10.1016/j.biotechadv.2020.107512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 08/07/2019] [Accepted: 09/30/2019] [Indexed: 01/14/2023]
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26
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Jacoby RP, Succurro A, Kopriva S. Nitrogen Substrate Utilization in Three Rhizosphere Bacterial Strains Investigated Using Proteomics. Front Microbiol 2020; 11:784. [PMID: 32411116 PMCID: PMC7198800 DOI: 10.3389/fmicb.2020.00784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 04/01/2020] [Indexed: 12/25/2022] Open
Abstract
Nitrogen metabolism in the rhizosphere microbiome plays an important role in mediating plant nutrition, particularly under low inputs of mineral fertilizers. However, there is relatively little mechanistic information about which genes and metabolic pathways are induced by rhizosphere bacterial strains to utilize diverse nitrogen substrates. Here we investigate nitrogen substrate utilization in three taxonomically diverse bacterial strains previously isolated from Arabidopsis roots. The three strains represent taxa that are consistently detected as core members of the plant microbiome: Pseudomonas, Streptomyces, and Rhizobium. We use phenotype microarrays to determine the nitrogen substrate preferences of these strains, and compare the experimental results vs. computational simulations of genome-scale metabolic network models obtained with EnsembleFBA. Results show that all three strains exhibit generalistic nitrogen substrate preferences, with substrate utilization being well predicted by EnsembleFBA. Using label-free quantitative proteomics, we document hundreds of proteins in each strain that exhibit differential abundance values following cultivation on five different nitrogen sources: ammonium, glutamate, lysine, serine, and urea. The proteomic response to these nitrogen sources was strongly strain-dependent, with lysine nutrition eliciting widespread protein-level changes in Pseudomonas sp. Root9, whereas Rhizobium sp. Root491 showed relatively stable proteome composition across different nitrogen sources. Our results give new protein-level information about the specific transporters and enzymes induced by diverse rhizosphere bacterial strains to utilize organic nitrogen substrates.
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Affiliation(s)
- Richard P. Jacoby
- Institute for Plant Sciences and Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany
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27
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Vasconcelos e Sá J, Simão D, Terrasso AP, Silva MM, Brito C, Isidro IA, Alves PM, Carrondo MJT. Unveiling dynamic metabolic signatures in human induced pluripotent and neural stem cells. PLoS Comput Biol 2020; 16:e1007780. [PMID: 32298259 PMCID: PMC7188302 DOI: 10.1371/journal.pcbi.1007780] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 04/28/2020] [Accepted: 03/08/2020] [Indexed: 11/19/2022] Open
Abstract
Metabolism plays an essential role in cell fate decisions. However, the methods used for metabolic characterization and for finding potential metabolic regulators are still based on characterizing cellular metabolic steady-state which is dependent on the extracellular environment. In this work, we hypothesized that the response dynamics of intracellular metabolic pools to extracellular stimuli is controlled in a cell type-specific manner. We applied principles of process dynamics and control to human induced pluripotent stem cells (hiPSC) and human neural stem cells (hNSC) subjected to a sudden extracellular glutamine step. The fold-changes of steady-states and the transient profiles of metabolic pools revealed that dynamic responses were reproducible and cell type-specific. Importantly, many amino acids had conserved dynamics and readjusted their steady state concentration in response to the increased glutamine influx. Overall, we propose a novel methodology for systematic metabolic characterization and identification of potential metabolic regulators.
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Affiliation(s)
- João Vasconcelos e Sá
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Daniel Simão
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Ana P. Terrasso
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Marta M. Silva
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Catarina Brito
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Inês A. Isidro
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Paula M. Alves
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Manuel J. T. Carrondo
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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den Ridder M, Daran-Lapujade P, Pabst M. Shot-gun proteomics: why thousands of unidentified signals matter. FEMS Yeast Res 2019; 20:5682490. [DOI: 10.1093/femsyr/foz088] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 12/19/2019] [Indexed: 12/14/2022] Open
Abstract
ABSTRACT
Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.
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Affiliation(s)
- Maxime den Ridder
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Pascale Daran-Lapujade
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Martin Pabst
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
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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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30
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Qin W, Yang F, Wang C. Chemoproteomic profiling of protein-metabolite interactions. Curr Opin Chem Biol 2019; 54:28-36. [PMID: 31812894 DOI: 10.1016/j.cbpa.2019.11.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 10/23/2019] [Accepted: 11/03/2019] [Indexed: 12/29/2022]
Abstract
Small molecule metabolites play important roles in regulating protein functions, which are acted through either covalent non-enzymatic post-translational modifications or non-covalent binding interactions. Chemical proteomic strategies can help delineate global landscapes of cellular protein-metabolite interactions and provide molecular insights about their mechanisms of action. In this review, we summarized the recent progress in developments and applications of chemoproteomic strategies to profile protein-metabolite interactions.
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Affiliation(s)
- Wei Qin
- Synthetic and Functional Biomolecules Center, Peking University, Beijing, China; Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China; Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Fan Yang
- Synthetic and Functional Biomolecules Center, Peking University, Beijing, China; Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China; Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China; College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Chu Wang
- Synthetic and Functional Biomolecules Center, Peking University, Beijing, China; Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China; Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
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31
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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: 35] [Impact Index Per Article: 7.0] [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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32
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Hinzpeter F, Tostevin F, Gerland U. Regulation of reaction fluxes via enzyme sequestration and co-clustering. J R Soc Interface 2019; 16:20190444. [PMID: 31362617 DOI: 10.1098/rsif.2019.0444] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Experimental observations suggest that cells change the intracellular localization of key enzymes to regulate the reaction fluxes in enzymatic networks. In particular, cells appear to use sequestration and co-clustering of enzymes as spatial regulation strategies. These strategies should be equally useful to achieve rapid flux regulation in synthetic biomolecular systems. Here, we leverage a theoretical model to analyse the capacity of enzyme sequestration and co-clustering to control the reaction flux in a branch of a reaction-diffusion network. We find that in both cases, the response of the system is determined by two dimensionless parameters, the ratio of total activities of the competing enzymes and the ratio of diffusion to reaction timescales. Using these dependencies, we determine the parameter range for which sequestration and co-clustering can yield a biologically significant regulatory effect. Based on the known kinetic parameters of enzymes, we conclude that sequestration and co-clustering represent a viable regulation strategy for a large fraction of metabolic enzymes, and suggest design principles for reaction flux regulation in natural or synthetic systems.
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Affiliation(s)
- Florian Hinzpeter
- Physik-Department, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Filipe Tostevin
- Physik-Department, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Ulrich Gerland
- Physik-Department, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
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Macpherson JA, Theisen A, Masino L, Fets L, Driscoll PC, Encheva V, Snijders AP, Martin SR, Kleinjung J, Barran PE, Fraternali F, Anastasiou D. Functional cross-talk between allosteric effects of activating and inhibiting ligands underlies PKM2 regulation. eLife 2019; 8:e45068. [PMID: 31264961 PMCID: PMC6636998 DOI: 10.7554/elife.45068] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Accepted: 07/01/2019] [Indexed: 12/18/2022] Open
Abstract
Several enzymes can simultaneously interact with multiple intracellular metabolites, however, how the allosteric effects of distinct ligands are integrated to coordinately control enzymatic activity remains poorly understood. We addressed this question using, as a model system, the glycolytic enzyme pyruvate kinase M2 (PKM2). We show that the PKM2 activator fructose 1,6-bisphosphate (FBP) alone promotes tetramerisation and increases PKM2 activity, but addition of the inhibitor L-phenylalanine (Phe) prevents maximal activation of FBP-bound PKM2 tetramers. We developed a method, AlloHubMat, that uses eigenvalue decomposition of mutual information derived from molecular dynamics trajectories to identify residues that mediate FBP-induced allostery. Experimental mutagenesis of these residues identified PKM2 variants in which activation by FBP remains intact but cannot be attenuated by Phe. Our findings reveal residues involved in FBP-induced allostery that enable the integration of allosteric input from Phe and provide a paradigm for the coordinate regulation of enzymatic activity by simultaneous allosteric inputs.
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Affiliation(s)
- Jamie A Macpherson
- Cancer Metabolism LaboratoryThe Francis Crick InstituteLondonUnited Kingdom
- Randall Centre for Cell and Molecular BiophysicsKing’s College LondonLondonUnited Kingdom
| | - Alina Theisen
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, School of ChemistryUniversity of ManchesterManchesterUnited Kingdom
| | - Laura Masino
- Structural Biology Science Technology PlatformThe Francis Crick InstituteLondonUnited Kingdom
| | - Louise Fets
- Cancer Metabolism LaboratoryThe Francis Crick InstituteLondonUnited Kingdom
| | - Paul C Driscoll
- Metabolomics Science Technology PlatformThe Francis Crick InstituteLondonUnited Kingdom
| | - Vesela Encheva
- Proteomics Science Technology PlatformThe Francis Crick InstituteLondonUnited Kingdom
| | - Ambrosius P Snijders
- Proteomics Science Technology PlatformThe Francis Crick InstituteLondonUnited Kingdom
| | - Stephen R Martin
- Structural Biology Science Technology PlatformThe Francis Crick InstituteLondonUnited Kingdom
| | - Jens Kleinjung
- Computational Biology Science Technology PlatformThe Francis Crick InstituteLondonUnited Kingdom
| | - Perdita E Barran
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, School of ChemistryUniversity of ManchesterManchesterUnited Kingdom
| | - Franca Fraternali
- Randall Centre for Cell and Molecular BiophysicsKing’s College LondonLondonUnited Kingdom
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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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35
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Lee HS, Im W. Stalis: A Computational Method for Template-Based Ab Initio Ligand Design. J Comput Chem 2019; 40:1622-1632. [PMID: 30829435 DOI: 10.1002/jcc.25813] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/23/2019] [Accepted: 02/17/2019] [Indexed: 12/20/2022]
Abstract
Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Hui Sun Lee
- Departments of Biological Sciences and Bioengineering, Lehigh University, 111 Research Drive, Bethlehem, Pennsylvania 18015
| | - Wonpil Im
- Departments of Biological Sciences and Bioengineering, Lehigh University, 111 Research Drive, Bethlehem, Pennsylvania 18015
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36
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Abstract
Synthetic biology has undergone dramatic advancements for over a decade, during which it has expanded our understanding on the systems of life and opened new avenues for microbial engineering. Many biotechnological and computational methods have been developed for the construction of synthetic systems. Achievements in synthetic biology have been widely adopted in metabolic engineering, a field aimed at engineering micro-organisms to produce substances of interest. However, the engineering of metabolic systems requires dynamic redistribution of cellular resources, the creation of novel metabolic pathways, and optimal regulation of the pathways to achieve higher production titers. Thus, the design principles and tools developed in synthetic biology have been employed to create novel and flexible metabolic pathways and to optimize metabolic fluxes to increase the cells’ capability to act as production factories. In this review, we introduce synthetic biology tools and their applications to microbial cell factory constructions.
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37
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Yang L, Yurkovich JT, King ZA, Palsson BO. Modeling the multi-scale mechanisms of macromolecular resource allocation. Curr Opin Microbiol 2018; 45:8-15. [PMID: 29367175 PMCID: PMC6419967 DOI: 10.1016/j.mib.2018.01.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/16/2022]
Abstract
As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.
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Affiliation(s)
- Laurence Yang
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA.
| | - James T Yurkovich
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Zachary A King
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Bioengineering Department, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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38
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Reznik E, Christodoulou D, Goldford JE, Briars E, Sauer U, Segrè D, Noor E. Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity. Cell Rep 2018; 20:2666-2677. [PMID: 28903046 DOI: 10.1016/j.celrep.2017.08.066] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 08/05/2017] [Accepted: 08/19/2017] [Indexed: 12/21/2022] Open
Abstract
Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.
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Affiliation(s)
- Ed Reznik
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Dimitris Christodoulou
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland
| | | | - Emma Briars
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, MA, USA; Bioinformatics Program, Boston University, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA
| | - Elad Noor
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
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Golubeva LI, Shupletsov MS, Mashko SV. Metabolic Flux Analysis Using 13C Isotopes (13C-MFA). 1. Experimental Basis of the Method and the Present State of Investigations. APPL BIOCHEM MICRO+ 2018. [DOI: 10.1134/s0003683817070031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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40
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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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41
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Chouhan S, Sharma K, Zha J, Guleria S, Koffas MAG. Recent Advances in the Recombinant Biosynthesis of Polyphenols. Front Microbiol 2017; 8:2259. [PMID: 29201020 PMCID: PMC5696593 DOI: 10.3389/fmicb.2017.02259] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 11/01/2017] [Indexed: 01/29/2023] Open
Abstract
Plants are the source of various natural compounds with pharmaceutical and nutraceutical importance which have shown numerous health benefits with relatively fewer side effects. However, extraction of these compounds from native producers cannot meet the ever-increasing demands of the growing population due to, among other things, the limited production of the active compound(s). Their production depends upon the metabolic demands of the plant and is also subjected to environmental conditions, abundance of crop species and seasonal variations. Moreover, their extraction from plants requires complex downstream processing and can also lead to the extinction of many useful plant varieties. Microbial engineering is one of the alternative approaches which can meet the global demand for natural products in an eco-friendly manner. Metabolic engineering of microbes or pathway reconstruction using synthetic biology tools and novel enzymes lead to the generation of a diversity of compounds (like flavonoids, stilbenes, anthocyanins etc.) and their natural and non-natural derivatives. Strain and pathway optimization, pathway regulation and tolerance engineering have produced microbial cell factories into which the metabolic pathway of plants can be introduced for the production of compounds of interest on an industrial scale in an economical and eco-friendly way. While microbial production of phytochemicals needs to further increase product titer if it is ever to become a commercial success. The present review covers the advancements made for the improvement of microbial cell factories in order to increase the product titer of recombinant polyphenolic compounds.
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Affiliation(s)
- Sonam Chouhan
- Natural Product Laboratory, Division of Biochemistry, Faculty of Basic Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu, India
| | - Kanika Sharma
- Natural Product Laboratory, Division of Biochemistry, Faculty of Basic Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu, India
| | - Jian Zha
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Sanjay Guleria
- Natural Product Laboratory, Division of Biochemistry, Faculty of Basic Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu, India
| | - Mattheos A G Koffas
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, United States.,Department of Biological Sciences, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, United States
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42
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Tuncil YE, Xiao Y, Porter NT, Reuhs BL, Martens EC, Hamaker BR. Reciprocal Prioritization to Dietary Glycans by Gut Bacteria in a Competitive Environment Promotes Stable Coexistence. mBio 2017; 8:e01068-17. [PMID: 29018117 PMCID: PMC5635687 DOI: 10.1128/mbio.01068-17] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 08/28/2017] [Indexed: 12/22/2022] Open
Abstract
When presented with nutrient mixtures, several human gut Bacteroides species exhibit hierarchical utilization of glycans through a phenomenon that resembles catabolite repression. However, it is unclear how closely these observed physiological changes, often measured by altered transcription of glycan utilization genes, mirror actual glycan depletion. To understand the glycan prioritization strategies of two closely related human gut symbionts, Bacteroides ovatus and Bacteroides thetaiotaomicron, we performed a series of time course assays in which both species were individually grown in a medium with six different glycans that both species can degrade. Disappearance of the substrates and transcription of the corresponding polysaccharide utilization loci (PULs) were measured. Each species utilized some glycans before others, but with different priorities per species, providing insight into species-specific hierarchical preferences. In general, the presence of highly prioritized glycans repressed transcription of genes involved in utilizing lower-priority nutrients. However, transcriptional sensitivity to some glycans varied relative to the residual concentration in the medium, with some PULs that target high-priority substrates remaining highly expressed even after their target glycan had been mostly depleted. Coculturing of these organisms in the same mixture showed that the hierarchical orders generally remained the same, promoting stable coexistence. Polymer length was found to be a contributing factor for glycan utilization, thereby affecting its place in the hierarchy. Our findings not only elucidate how B. ovatus and B. thetaiotaomicron strategically access glycans to maintain coexistence but also support the prioritization of carbohydrate utilization based on carbohydrate structure, advancing our understanding of the relationships between diet and the gut microbiome.IMPORTANCE The microorganisms that reside in the human colon fulfill their energy requirements mainly from diet- and host-derived complex carbohydrates. Members of this ecosystem possess poorly understood strategies to prioritize and compete for these nutrients. Based on direct carbohydrate measurements and corresponding transcriptional analyses, our findings showed that individual bacterial species exhibit different preferences for the same set of glycans and that this prioritization is maintained in a competitive environment, which may promote stable coexistence. Such understanding of gut bacterial glycan utilization will be essential to eliciting predictable changes in the gut microbiota to improve health through the diet.
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Affiliation(s)
- Yunus E Tuncil
- Whistler Center for Carbohydrate Research, Food Science Department, Purdue University, West Lafayette, Indiana, USA
| | - Yao Xiao
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Nathan T Porter
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Bradley L Reuhs
- Whistler Center for Carbohydrate Research, Food Science Department, Purdue University, West Lafayette, Indiana, USA
| | - Eric C Martens
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Bruce R Hamaker
- Whistler Center for Carbohydrate Research, Food Science Department, Purdue University, West Lafayette, Indiana, USA
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43
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Schmitz AC, Hartline CJ, Zhang F. Engineering Microbial Metabolite Dynamics and Heterogeneity. Biotechnol J 2017; 12. [PMID: 28901715 DOI: 10.1002/biot.201700422] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Revised: 09/06/2017] [Indexed: 11/09/2022]
Abstract
As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.
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Affiliation(s)
- Alexander C Schmitz
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, USA
| | - Christopher J Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, USA.,Division of Biological and Biomedical Sciences, and Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, USA
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44
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Ledezma-Tejeida D, Ishida C, Collado-Vides J. Genome-Wide Mapping of Transcriptional Regulation and Metabolism Describes Information-Processing Units in Escherichia coli. Front Microbiol 2017; 8:1466. [PMID: 28824593 PMCID: PMC5540944 DOI: 10.3389/fmicb.2017.01466] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 07/20/2017] [Indexed: 11/13/2022] Open
Abstract
In the face of changes in their environment, bacteria adjust gene expression levels and produce appropriate responses. The individual layers of this process have been widely studied: the transcriptional regulatory network describes the regulatory interactions that produce changes in the metabolic network, both of which are coordinated by the signaling network, but the interplay between them has never been described in a systematic fashion. Here, we formalize the process of detection and processing of environmental information mediated by individual transcription factors (TFs), utilizing a concept termed genetic sensory response units (GENSOR units), which are composed of four components: (1) a signal, (2) signal transduction, (3) genetic switch, and (4) a response. We used experimentally validated data sets from two databases to assemble a GENSOR unit for each of the 189 local TFs of Escherichia coli K-12 contained in the RegulonDB database. Further analysis suggested that feedback is a common occurrence in signal processing, and there is a gradient of functional complexity in the response mediated by each TF, as opposed to a one regulator/one pathway rule. Finally, we provide examples of other GENSOR unit applications, such as hypothesis generation, detailed description of cellular decision making, and elucidation of indirect regulatory mechanisms.
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Affiliation(s)
- Daniela Ledezma-Tejeida
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de MéxicoCuernavaca, Mexico
| | - Cecilia Ishida
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de MéxicoCuernavaca, Mexico
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de MéxicoCuernavaca, Mexico
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Schatschneider S, Schneider J, Blom J, Létisse F, Niehaus K, Goesmann A, Vorhölter FJ. Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris. Microbiology (Reading) 2017; 163:1117-1144. [DOI: 10.1099/mic.0.000473] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Sarah Schatschneider
- Abteilung für Proteom und Metabolomforschung, Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Bielefeld, Germany
- Present address: Evonik Nutrition and Care GmbH, Kantstr. 2, 33790 Halle-Künsebeck, Germany
| | - Jessica Schneider
- Bioinformatics Resource Facility, Centrum für Biotechnologie, Universität Bielefeld, Germany
- Present address: Evonik Nutrition and Care GmbH, Kantstr. 2, 33790 Halle-Künsebeck, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, Germany
| | - Fabien Létisse
- LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France
| | - Karsten Niehaus
- Abteilung für Proteom und Metabolomforschung, Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Bielefeld, Germany
| | - Alexander Goesmann
- Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, Germany
| | - Frank-Jörg Vorhölter
- Institut für Genomforschung und Systembiologie, Centrum für Biotechnology (CeBiTec), Universität Bielefeld, Bielefeld, Germany
- Present address: MVZ Dr. Eberhard & Partner Dortmund, Dortmund, Germany
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46
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The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization. Nat Commun 2017; 8:16018. [PMID: 28691704 PMCID: PMC5508129 DOI: 10.1038/ncomms16018] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Accepted: 05/23/2017] [Indexed: 01/03/2023] Open
Abstract
Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism. Metabolites act as enzyme inhibitors, but their global impact on metabolism has scarcely been considered. Here, the authors generate a human genome-wide metabolite-enzyme inhibition network, and find that inhibition occurs largely due to limited structural diversity of metabolites, leading to a global constraint on metabolism which subcellular compartmentalization minimizes.
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Barbier T, Zúñiga-Ripa A, Moussa S, Plovier H, Sternon JF, Lázaro-Antón L, Conde-Álvarez R, De Bolle X, Iriarte M, Moriyón I, Letesson JJ. Brucella central carbon metabolism: an update. Crit Rev Microbiol 2017; 44:182-211. [PMID: 28604247 DOI: 10.1080/1040841x.2017.1332002] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The brucellae are facultative intracellular pathogens causing brucellosis, an important zoonosis. Here, we review the nutritional, genetic, proteomic and transcriptomic studies on Brucella carbon uptake and central metabolism, information that is needed for a better understanding of Brucella virulence. There is no uniform picture across species but the studies suggest primary and/or secondary transporters for unknown carbohydrates, lactate, glycerol phosphate, erythritol, xylose, ribose, glucose and glucose/galactose, and routes for their incorporation to central metabolism, including an erythritol pathway feeding the pentose phosphate cycle. Significantly, all brucellae lack phosphoenolpyruvate synthase and phosphofructokinase genes, which confirms previous evidence on glycolysis absence, but carry all Entner-Doudoroff (ED) pathway and Krebs cycle (and glyoxylate pathway) genes. However, glucose catabolism proceeds through the pentose phosphate cycle in the classical species, and the ED pathway operates in some rodent-associated brucellae, suggesting an ancestral character for this pathway in this group. Gluconeogenesis is functional but does not rely exclusively on classical fructose bisphosphatases. Evidence obtained using infection models is fragmentary but suggests the combined or sequential use of hexoses/pentoses, amino acids and gluconeogenic substrates. We also discuss the role of the phosphotransferase system, stringent reponse, quorum sensing, BvrR/S and sRNAs in metabolism control, an essential aspect of the life style of facultative intracellular parasites.
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Affiliation(s)
- T Barbier
- a Unité de Recherche en Biologie des Microorganismes , Laboratoire d'Immunologie et de Microbiologie, NARILIS, Université de Namur , Namur , Belgium
| | - A Zúñiga-Ripa
- b Instituto de Salud Tropical (ISTUN), Instituto de Investigación Sanitaria de Navarra (IdISNA) and Depto. Microbiología y Parasitología , Universidad de Navarra, Edificio de Investigación , Pamplona , Spain
| | - S Moussa
- a Unité de Recherche en Biologie des Microorganismes , Laboratoire d'Immunologie et de Microbiologie, NARILIS, Université de Namur , Namur , Belgium
| | - H Plovier
- a Unité de Recherche en Biologie des Microorganismes , Laboratoire d'Immunologie et de Microbiologie, NARILIS, Université de Namur , Namur , Belgium
| | - J F Sternon
- a Unité de Recherche en Biologie des Microorganismes , Laboratoire d'Immunologie et de Microbiologie, NARILIS, Université de Namur , Namur , Belgium
| | - L Lázaro-Antón
- b Instituto de Salud Tropical (ISTUN), Instituto de Investigación Sanitaria de Navarra (IdISNA) and Depto. Microbiología y Parasitología , Universidad de Navarra, Edificio de Investigación , Pamplona , Spain
| | - R Conde-Álvarez
- b Instituto de Salud Tropical (ISTUN), Instituto de Investigación Sanitaria de Navarra (IdISNA) and Depto. Microbiología y Parasitología , Universidad de Navarra, Edificio de Investigación , Pamplona , Spain
| | - X De Bolle
- a Unité de Recherche en Biologie des Microorganismes , Laboratoire d'Immunologie et de Microbiologie, NARILIS, Université de Namur , Namur , Belgium
| | - M Iriarte
- b Instituto de Salud Tropical (ISTUN), Instituto de Investigación Sanitaria de Navarra (IdISNA) and Depto. Microbiología y Parasitología , Universidad de Navarra, Edificio de Investigación , Pamplona , Spain
| | - I Moriyón
- b Instituto de Salud Tropical (ISTUN), Instituto de Investigación Sanitaria de Navarra (IdISNA) and Depto. Microbiología y Parasitología , Universidad de Navarra, Edificio de Investigación , Pamplona , Spain
| | - J J Letesson
- b Instituto de Salud Tropical (ISTUN), Instituto de Investigación Sanitaria de Navarra (IdISNA) and Depto. Microbiología y Parasitología , Universidad de Navarra, Edificio de Investigación , Pamplona , Spain
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Kuehne A, Mayr U, Sévin DC, Claassen M, Zamboni N. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data. PLoS Comput Biol 2017; 13:e1005577. [PMID: 28598965 PMCID: PMC5482507 DOI: 10.1371/journal.pcbi.1005577] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 06/23/2017] [Accepted: 05/16/2017] [Indexed: 12/30/2022] Open
Abstract
In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes. Reciprocal crosstalk between metabolism and cellular signaling pathways plays a crucial role in cellular decision-making. In recent years, this premise has motivated several metabolomics studies that aimed to gain a mechanistic understanding of metabolic phenotypes. However, due to complex interactions within metabolic networks, it remains a challenge to infer mechanisms that underlie metabolome changes. We present the metabolic network segmentation approach, a novel method that aimed to identify the sites and sequential order of metabolic regulatory events, based merely on steady state or dynamic metabolomics data. This method employs probabilistic graphical models to partition the entire metabolic network into modules with correlated metabolites. It identifies fractures between modules (i.e., reactions that connect non-correlated metabolites) as sites of regulation. We performed validation and benchmark analyses in hundreds of E. coli knockout mutants deficient in enzymes and transcription factors. Moreover, we verified the capability of our method for identifying the sequential order of metabolic regulatory events by testing it on fibroblasts exposed to oxidative stress. Our metabolic network segmentation algorithm is widely applicable, and thus, it will enhance our mechanistic understanding of metabolic phenotypes and their connections to cellular signaling and regulatory processes.
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Affiliation(s)
- Andreas Kuehne
- Institute of Molecular Systems Biology, ETH Zurich, Switzerland.,PhD Program Systems Biology, Life Science Zurich Graduate School, Zurich, Switzerland
| | - Urs Mayr
- Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Daniel C Sévin
- Institute of Molecular Systems Biology, ETH Zurich, Switzerland.,PhD Program Systems Biology, Life Science Zurich Graduate School, Zurich, Switzerland
| | | | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Switzerland
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Wang Z, Danziger SA, Heavner BD, Ma S, Smith JJ, Li S, Herricks T, Simeonidis E, Baliga NS, Aitchison JD, Price ND. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast. PLoS Comput Biol 2017; 13:e1005489. [PMID: 28520713 PMCID: PMC5453602 DOI: 10.1371/journal.pcbi.1005489] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 06/01/2017] [Accepted: 03/30/2017] [Indexed: 01/24/2023] Open
Abstract
Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.
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Affiliation(s)
- Zhuo Wang
- Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Samuel A. Danziger
- Institute for Systems Biology, Seattle, Washington, United States of America
- Center for Infectious Disease Research, Seattle, Washington, United States of America
| | - Benjamin D. Heavner
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Shuyi Ma
- Institute for Systems Biology, Seattle, Washington, United States of America
- Center for Infectious Disease Research, Seattle, Washington, United States of America
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana-Champaign, Illinois, United States of America
| | - Jennifer J. Smith
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Song Li
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Thurston Herricks
- Institute for Systems Biology, Seattle, Washington, United States of America
| | | | - Nitin S. Baliga
- Institute for Systems Biology, Seattle, Washington, United States of America
- Departments of Biology and Microbiology & Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
- Lawrence Berkeley National Lab, Berkeley, California, United States of America
| | - John D. Aitchison
- Institute for Systems Biology, Seattle, Washington, United States of America
- Center for Infectious Disease Research, Seattle, Washington, United States of America
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
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50
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Allosteric regulation of metabolism in cancer: endogenous mechanisms and considerations for drug design. Curr Opin Biotechnol 2017; 48:102-110. [PMID: 28431259 DOI: 10.1016/j.copbio.2017.03.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 03/24/2017] [Indexed: 01/21/2023]
Abstract
Alterations in metabolic processes have been linked to various diseases, including cancer. Although gene expression can dictate long-term metabolic adaptation, many metabolic changes found in cancer are associated with altered allosteric properties of the underlying enzymes. Small molecule-protein interactions and intracellular signalling converge to orchestrate these allosteric mechanisms, which, emerging evidence suggests, constitute a promising therapeutic avenue. In this review we focus on glucose and energy metabolism to illustrate the role of allostery in cancer physiology and we discuss approaches to streamline the process of targeting aberrant allosteric pathways with small molecules.
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