1
|
Bi S, Kargeti M, Colin R, Farke N, Link H, Sourjik V. Dynamic fluctuations in a bacterial metabolic network. Nat Commun 2023; 14:2173. [PMID: 37061520 PMCID: PMC10105761 DOI: 10.1038/s41467-023-37957-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/06/2023] [Indexed: 04/17/2023] Open
Abstract
The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.
Collapse
Affiliation(s)
- Shuangyu Bi
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Manika Kargeti
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
| | - Remy Colin
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
| | - Niklas Farke
- University of Tübingen, D-72076, Tübingen, Germany
| | - Hannes Link
- University of Tübingen, D-72076, Tübingen, Germany
| | - Victor Sourjik
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany.
| |
Collapse
|
2
|
Blöbaum L, Haringa C, Grünberger A. Microbial lifelines in bioprocesses: From concept to application. Biotechnol Adv 2023; 62:108071. [PMID: 36464144 DOI: 10.1016/j.biotechadv.2022.108071] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022]
Abstract
Bioprocesses are scaled up for the production of large product quantities. With larger fermenter volumes, mixing becomes increasingly inefficient and environmental gradients get more prominent than in smaller scales. Environmental gradients have an impact on the microorganism's metabolism, which makes the prediction of large-scale performance difficult and can lead to scale-up failure. A promising approach for improved understanding and estimation of dynamics of microbial populations in large-scale bioprocesses is the analysis of microbial lifelines. The lifeline of a microbe in a bioprocess is the experience of environmental gradients from a cell's perspective, which can be described as a time series of position, environment and intracellular condition. Currently, lifelines are predominantly determined using models with computational fluid dynamics, but new technical developments in flow-following sensor particles and microfluidic single-cell cultivation open the door to a more interdisciplinary concept. We critically review the current concepts and challenges in lifeline determination and application of lifeline analysis, as well as strategies for the integration of these techniques into bioprocess development. Lifelines can contribute to a successful scale-up by guiding scale-down experiments and identifying strain engineering targets or bioreactor optimisations.
Collapse
Affiliation(s)
- Luisa Blöbaum
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Bielefeld, Germany; CeBiTec, Bielefeld University, Bielefeld, Germany
| | - Cees Haringa
- Bioprocess Engineering, Applied Sciences/Biotechnology, TU, Delft, Netherlands
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Bielefeld, Germany; CeBiTec, Bielefeld University, Bielefeld, Germany; Microsystems in Bioprocess Engineering, Institute of Process Engineering in Life Sciences, Karlsruhe Institute of Technology, Karlsruhe, Germany.
| |
Collapse
|
3
|
Nakatani RJ, Itabashi M, Yamada TG, Hiroi NF, Funahashi A. Intercellular interaction mechanisms promote diversity in intracellular ATP concentration in Escherichia coli populations. Sci Rep 2022; 12:17946. [PMID: 36289258 PMCID: PMC9605964 DOI: 10.1038/s41598-022-22189-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 10/11/2022] [Indexed: 01/24/2023] Open
Abstract
In fluctuating environments, many microorganisms acquire phenotypic heterogeneity as a survival tactic to increase the likelihood of survival of the overall population. One example of this interindividual heterogeneity is the diversity of ATP concentration among members of Escherichia coli populations under glucose deprivation. Despite the importance of such environmentally driven phenotypic heterogeneity, how the differences in intracellular ATP concentration emerge among individual E. coli organisms is unknown. In this study, we focused on the mechanism through which individual E. coli achieve high intracellular ATP concentrations. First, we measured the ATP retained by E. coli over time when cultured at low (0.1 mM) and control (22.2 mM) concentrations of glucose and obtained the chronological change in ATP concentrations. Then, by comparing these chronological change of ATP concentrations and analyzing whether stochastic state transitions, periodic oscillations, cellular age, and intercellular communication-which have been reported as molecular biological mechanisms for generating interindividual heterogeneity-are involved, we showed that the appearance of high ATP-holding individuals observed among E. coli can be explained only by intercellular transmission. By performing metabolomic analysis of post-culture medium, we revealed a significant increase in the ATP, especially at low glucose, and that the number of E. coli that retain significantly higher ATP can be controlled by adding large amounts of ATP to the medium, even in populations cultured under control glucose concentrations. These results reveal for the first time that ATP-mediated intercellular transmission enables some individuals in E. coli populations grown at low glucose to retain large amounts of ATP.
Collapse
Affiliation(s)
- Ryo J. Nakatani
- grid.26091.3c0000 0004 1936 9959Graduate School of Fundamental Science and Technology, Center for Biosciences and Informatics, Keio University, Yokohama, Kanagawa 223-8522 Japan
| | - Masahiro Itabashi
- grid.26091.3c0000 0004 1936 9959Graduate School of Fundamental Science and Technology, Center for Biosciences and Informatics, Keio University, Yokohama, Kanagawa 223-8522 Japan
| | - Takahiro G. Yamada
- grid.26091.3c0000 0004 1936 9959Graduate School of Fundamental Science and Technology, Center for Biosciences and Informatics, Keio University, Yokohama, Kanagawa 223-8522 Japan ,grid.26091.3c0000 0004 1936 9959Present Address: Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa 223-8522 Japan
| | - Noriko F. Hiroi
- grid.26091.3c0000 0004 1936 9959School of Medicine, Keio University, Shinjuku-ku, Tokyo 160-8582 Japan ,grid.419709.20000 0004 0371 3508Faculty of Creative Engineering, Kanagawa Institute of Technology, Atsugi, Kanagawa 243-0292 Japan
| | - Akira Funahashi
- grid.26091.3c0000 0004 1936 9959Graduate School of Fundamental Science and Technology, Center for Biosciences and Informatics, Keio University, Yokohama, Kanagawa 223-8522 Japan ,grid.26091.3c0000 0004 1936 9959Present Address: Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa 223-8522 Japan
| |
Collapse
|
4
|
Lin WH, Jacobs-Wagner C. Connecting single-cell ATP dynamics to overflow metabolism, cell growth, and the cell cycle in Escherichia coli. Curr Biol 2022; 32:3911-3924.e4. [PMID: 35961315 DOI: 10.1016/j.cub.2022.07.035] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/21/2022] [Accepted: 07/14/2022] [Indexed: 10/15/2022]
Abstract
Adenosine triphosphate (ATP) is an abundant and essential metabolite that cells consume and regenerate in large amounts to support growth. Although numerous studies have inferred the intracellular concentration of ATP in bacterial cultures, what happens in individual bacterial cells under stable growth conditions is less clear. Here, we use the QUEEN-2m biosensor to quantify ATP dynamics in single Escherichia coli cells in relation to their growth rate, metabolism, cell cycle, and cell lineage. We find that ATP dynamics are more complex than expected from population studies and are associated with growth-rate variability. Under stable nutrient-rich condition, cells can display large fluctuations in ATP level that are partially coordinated with the cell cycle. Abrogation of aerobic acetate fermentation (overflow metabolism) through genetic deletion considerably reduces both the amplitude of ATP level fluctuations and the cell-cycle trend. Similarly, growth in media in which acetate fermentation is lower or absent results in the reduction of ATP level fluctuation and cell-cycle trend. This suggests that overflow metabolism exhibits temporal dynamics, which contributes to fluctuating ATP levels during growth. Remarkably, at the single-cell level, growth rate negatively correlates with the amplitude of ATP fluctuation for each tested condition, linking ATP dynamics to growth-rate heterogeneity in clonal populations. Our work highlights the importance of single-cell analysis in studying metabolism and its implication to phenotypic diversity and cell growth.
Collapse
Affiliation(s)
- Wei-Hsiang Lin
- Department of Biology, Stanford University, Palo Alto, CA 94305, USA; Chemistry, Engineering, Medicine for Human Health Institute, Stanford University, Palo Alto, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305, USA
| | - Christine Jacobs-Wagner
- Department of Biology, Stanford University, Palo Alto, CA 94305, USA; Chemistry, Engineering, Medicine for Human Health Institute, Stanford University, Palo Alto, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305, USA.
| |
Collapse
|
5
|
Zhou S, Alper HS, Zhou J, Deng Y. Intracellular biosensor-based dynamic regulation to manipulate gene expression at the spatiotemporal level. Crit Rev Biotechnol 2022; 43:646-663. [PMID: 35450502 DOI: 10.1080/07388551.2022.2040415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The use of intracellular, biosensor-based dynamic regulation strategies to regulate and improve the production of useful compounds have progressed significantly over previous decades. By employing such an approach, it is possible to simultaneously realize high productivity and optimum growth states. However, industrial fermentation conditions contain a mixture of high- and low-performance non-genetic variants, as well as young and aged cells at all growth phases. Such significant individual variations would hinder the precise controlling of metabolic flux at the single-cell level to achieve high productivity at the macroscopic population level. Intracellular biosensors, as the regulatory centers of metabolic networks, can real-time sense intra- and extracellular conditions and, thus, could be synthetically adapted to balance the biomass formation and overproduction of compounds by individual cells. Herein, we highlight advances in the designing and engineering approaches to intracellular biosensors. Then, the spatiotemporal properties of biosensors associated with the distribution of inducers are compared. Also discussed is the use of such biosensors to dynamically control the cellular metabolic flux. Such biosensors could achieve single-cell regulation or collective regulation goals, depending on whether or not the inducer distribution is only intracellular.
Collapse
Affiliation(s)
- Shenghu Zhou
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Hal S Alper
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA.,McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Jingwen Zhou
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Yu Deng
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, Jiangsu, China
| |
Collapse
|
6
|
Norris D, Yang P, Shin SY, Kearney AL, Kim HJ, Geddes T, Senior AM, Fazakerley DJ, Nguyen LK, James DE, Burchfield JG. Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression. iScience 2021; 24:102118. [PMID: 33659881 PMCID: PMC7892930 DOI: 10.1016/j.isci.2021.102118] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/07/2021] [Accepted: 01/22/2021] [Indexed: 12/12/2022] Open
Abstract
Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells. Insulin signaling is heterogeneous between cells in the same population The temporal response of signaling components within a cell is highly reproducible Upstream responses (Akt) can only partially predict downstream response (GLUT4) Protein expression variance is a driver of intercellular signaling heterogeneity
Collapse
Affiliation(s)
- Dougall Norris
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Pengyi Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Sung-Young Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Alison L Kearney
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Hani Jieun Kim
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Thomas Geddes
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Alistair M Senior
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - James G Burchfield
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| |
Collapse
|
7
|
Tomi-Andrino C, Norman R, Millat T, Soucaille P, Winzer K, Barrett DA, King J, Kim DH. Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions. PLoS Comput Biol 2021; 17:e1007694. [PMID: 33493151 PMCID: PMC7861524 DOI: 10.1371/journal.pcbi.1007694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 02/04/2021] [Accepted: 12/28/2020] [Indexed: 12/11/2022] Open
Abstract
Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed. Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.
Collapse
Affiliation(s)
- Claudio Tomi-Andrino
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Rupert Norman
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Thomas Millat
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Philippe Soucaille
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
- INSA, UPS, INP, Toulouse Biotechnology Institute, (TBI), Université de Toulouse, Toulouse, France
- INRA, UMR792, Toulouse, France
- CNRS, UMR5504, Toulouse, France
| | - Klaus Winzer
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - David A. Barrett
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
| | - John King
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Dong-Hyun Kim
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
- * E-mail:
| |
Collapse
|
8
|
Bandekar AC, Subedi S, Ioerger TR, Sassetti CM. Cell-Cycle-Associated Expression Patterns Predict Gene Function in Mycobacteria. Curr Biol 2020; 30:3961-3971.e6. [PMID: 32916109 PMCID: PMC7578119 DOI: 10.1016/j.cub.2020.07.070] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 05/26/2020] [Accepted: 07/22/2020] [Indexed: 12/20/2022]
Abstract
Although the major events in prokaryotic cell cycle progression are likely to be coordinated with transcriptional and metabolic changes, these processes remain poorly characterized. Unlike many rapidly growing bacteria, DNA replication and cell division are temporally resolved in mycobacteria, making these slow-growing organisms a potentially useful system to investigate the prokaryotic cell cycle. To determine whether cell-cycle-dependent gene regulation occurs in mycobacteria, we characterized the temporal changes in the transcriptome of synchronously replicating populations of Mycobacterium tuberculosis (Mtb). By enriching for genes that display a sinusoidal expression pattern, we discover 485 genes that oscillate with a period consistent with the cell cycle. During cytokinesis, the timing of gene induction could be used to predict the timing of gene function, as mRNA abundance was found to correlate with the order in which proteins were recruited to the developing septum. Similarly, the expression pattern of primary metabolic genes could be used to predict the relative importance of these pathways for different cell cycle processes. Pyrimidine synthetic genes peaked during DNA replication, and their depletion caused a filamentation phenotype that phenocopied defects in this process. In contrast, the inosine monophasphate dehydrogenase dedicated to guanosine synthesis, GuaB2, displayed the opposite expression pattern and its depletion perturbed septation. Together, these data imply obligate coordination between primary metabolism and cell division and identify periodically regulated genes that can be related to specific cell biological functions.
Collapse
Affiliation(s)
- Aditya C Bandekar
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA
| | - Sishir Subedi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Thomas R Ioerger
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
| | - Christopher M Sassetti
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA.
| |
Collapse
|
9
|
Nishida H. Factors That Affect the Enlargement of Bacterial Protoplasts and Spheroplasts. Int J Mol Sci 2020; 21:E7131. [PMID: 32992574 PMCID: PMC7582836 DOI: 10.3390/ijms21197131] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 11/21/2022] Open
Abstract
Cell enlargement is essential for the microinjection of various substances into bacterial cells. The cell wall (peptidoglycan) inhibits cell enlargement. Thus, bacterial protoplasts/spheroplasts are used for enlargement because they lack cell wall. Though bacterial species that are capable of gene manipulation are limited, procedure for bacterial cell enlargement does not involve any gene manipulation technique. In order to prevent cell wall resynthesis during enlargement of protoplasts/spheroplasts, incubation media are supplemented with inhibitors of peptidoglycan biosynthesis such as penicillin. Moreover, metal ion composition in the incubation medium affects the properties of the plasma membrane. Therefore, in order to generate enlarged cells that are suitable for microinjection, metal ion composition in the medium should be considered. Experiment of bacterial protoplast or spheroplast enlargement is useful for studies on bacterial plasma membrane biosynthesis. In this paper, we have summarized the factors that influence bacterial cell enlargement.
Collapse
Affiliation(s)
- Hiromi Nishida
- Department of Biotechnology, Toyama Prefectural University, 5180 Kurokawa, Imizu, Toyama 939-0398, Japan
| |
Collapse
|
10
|
Ahl PJ, Hopkins RA, Xiang WW, Au B, Kaliaperumal N, Fairhurst AM, Connolly JE. Met-Flow, a strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations. Commun Biol 2020; 3:305. [PMID: 32533056 PMCID: PMC7292829 DOI: 10.1038/s42003-020-1027-9] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/20/2020] [Indexed: 12/25/2022] Open
Abstract
A complex interaction of anabolic and catabolic metabolism underpins the ability of leukocytes to mount an immune response. Their capacity to respond to changing environments by metabolic reprogramming is crucial to effector function. However, current methods lack the ability to interrogate this network of metabolic pathways at single-cell level within a heterogeneous population. We present Met-Flow, a flow cytometry-based method capturing the metabolic state of immune cells by targeting key proteins and rate-limiting enzymes across multiple pathways. We demonstrate the ability to simultaneously measure divergent metabolic profiles and dynamic remodeling in human peripheral blood mononuclear cells. Using Met-Flow, we discovered that glucose restriction and metabolic remodeling drive the expansion of an inflammatory central memory T cell subset. This method captures the complex metabolic state of any cell as it relates to phenotype and function, leading to a greater understanding of the role of metabolic heterogeneity in immune responses.
Collapse
Affiliation(s)
- Patricia J Ahl
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore
| | - Richard A Hopkins
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
- Tessa Therapeutics Pte Ltd, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
| | - Wen Wei Xiang
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
- Tessa Therapeutics Pte Ltd, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
| | - Bijin Au
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
| | - Nivashini Kaliaperumal
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
| | - Anna-Marie Fairhurst
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore
| | - John E Connolly
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, 138673, Singapore.
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore.
- Institute of Biomedical Studies, Baylor University, Waco, TX, 76712, USA.
| |
Collapse
|
11
|
Santos ARS, Gerhardt ECM, Parize E, Pedrosa FO, Steffens MBR, Chubatsu LS, Souza EM, Passaglia LMP, Sant'Anna FH, de Souza GA, Huergo LF, Forchhammer K. NAD + biosynthesis in bacteria is controlled by global carbon/nitrogen levels via PII signaling. J Biol Chem 2020; 295:6165-6176. [PMID: 32179648 PMCID: PMC7196632 DOI: 10.1074/jbc.ra120.012793] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 03/10/2020] [Indexed: 01/01/2023] Open
Abstract
NAD+ is a central metabolite participating in core metabolic redox reactions. The prokaryotic NAD synthetase enzyme NadE catalyzes the last step of NAD+ biosynthesis, converting nicotinic acid adenine dinucleotide (NaAD) to NAD+. Some members of the NadE family use l-glutamine as a nitrogen donor and are named NadEGln. Previous gene neighborhood analysis has indicated that the bacterial nadE gene is frequently clustered with the gene encoding the regulatory signal transduction protein PII, suggesting a functional relationship between these proteins in response to the nutritional status and the carbon/nitrogen ratio of the bacterial cell. Here, using affinity chromatography, bioinformatics analyses, NAD synthetase activity, and biolayer interferometry assays, we show that PII and NadEGln physically interact in vitro, that this complex relieves NadEGln negative feedback inhibition by NAD+. This mechanism is conserved in distantly related bacteria. Of note, the PII protein allosteric effector and cellular nitrogen level indicator 2-oxoglutarate (2-OG) inhibited the formation of the PII-NadEGln complex within a physiological range. These results indicate an interplay between the levels of ATP, ADP, 2-OG, PII-sensed glutamine, and NAD+, representing a metabolic hub that may balance the levels of core nitrogen and carbon metabolites. Our findings support the notion that PII proteins act as a dissociable regulatory subunit of NadEGln, thereby enabling the control of NAD+ biosynthesis according to the nutritional status of the bacterial cell.
Collapse
Affiliation(s)
- Adrian Richard Schenberger Santos
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil; Interfakultäres Institut für Mikrobiologie und Infektionsmedizin der Eberhard-Karls Universität Tübingen, Auf der Morgenstelle 28, Tübingen 72076, Germany
| | | | - Erick Parize
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil
| | - Fabio Oliveira Pedrosa
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil
| | - Maria Berenice Reynaud Steffens
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil
| | - Leda Satie Chubatsu
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil
| | - Emanuel Maltempi Souza
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil
| | - Luciane Maria Pereira Passaglia
- Departamento de Genética, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, CEP:91501-970 CP 15053 Brazil
| | - Fernando Hayashi Sant'Anna
- Departamento de Genética, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, CEP:91501-970 CP 15053 Brazil
| | - Gustavo Antônio de Souza
- Departamento de Bioquímica, Universidade Federal do Rio Grande do Norte, Natal/RN, CEP: 59072-970 Brazil
| | - Luciano Fernandes Huergo
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná (UFPR), Curitiba, Paraná, CEP: 81531-980 Brazil; Interfakultäres Institut für Mikrobiologie und Infektionsmedizin der Eberhard-Karls Universität Tübingen, Auf der Morgenstelle 28, Tübingen 72076, Germany; Setor Litoral, UFPR, Matinhos, Paraná, CEP: 83260-000 Brazil.
| | - Karl Forchhammer
- Interfakultäres Institut für Mikrobiologie und Infektionsmedizin der Eberhard-Karls Universität Tübingen, Auf der Morgenstelle 28, Tübingen 72076, Germany.
| |
Collapse
|
12
|
Evers TMJ, Hochane M, Tans SJ, Heeren RMA, Semrau S, Nemes P, Mashaghi A. Deciphering Metabolic Heterogeneity by Single-Cell Analysis. Anal Chem 2019; 91:13314-13323. [PMID: 31549807 PMCID: PMC6922888 DOI: 10.1021/acs.analchem.9b02410] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Single-cell analysis provides insights into cellular heterogeneity and dynamics of individual cells. This Feature highlights recent developments in key analytical techniques suited for single-cell metabolic analysis with a special focus on mass spectrometry-based analytical platforms and RNA-seq as well as imaging techniques that reveal stochasticity in metabolism.
Collapse
Affiliation(s)
- Tom MJ Evers
- Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Mazène Hochane
- Leiden Institute of Physics, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Sander J Tans
- AMOLF Institute, Science Park 104 1098 XG Amsterdam, The Netherlands
| | - Ron MA Heeren
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Stefan Semrau
- Leiden Institute of Physics, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Alireza Mashaghi
- Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| |
Collapse
|
13
|
Stratford JP, Edwards CLA, Ghanshyam MJ, Malyshev D, Delise MA, Hayashi Y, Asally M. Electrically induced bacterial membrane-potential dynamics correspond to cellular proliferation capacity. Proc Natl Acad Sci U S A 2019; 116:9552-9557. [PMID: 31000597 PMCID: PMC6511025 DOI: 10.1073/pnas.1901788116] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Membrane-potential dynamics mediate bacterial electrical signaling at both intra- and intercellular levels. Membrane potential is also central to cellular proliferation. It is unclear whether the cellular response to external electrical stimuli is influenced by the cellular proliferative capacity. A new strategy enabling electrical stimulation of bacteria with simultaneous monitoring of single-cell membrane-potential dynamics would allow bridging this knowledge gap and further extend electrophysiological studies into the field of microbiology. Here we report that an identical electrical stimulus can cause opposite polarization dynamics depending on cellular proliferation capacity. This was demonstrated using two model organisms, namely Bacillus subtilis and Escherichia coli, and by developing an apparatus enabling exogenous electrical stimulation and single-cell time-lapse microscopy. Using this bespoke apparatus, we show that a 2.5-second electrical stimulation causes hyperpolarization in unperturbed cells. Measurements of intracellular K+ and the deletion of the K+ channel suggested that the hyperpolarization response is caused by the K+ efflux through the channel. When cells are preexposed to 400 ± 8 nm wavelength light, the same electrical stimulation depolarizes cells instead of causing hyperpolarization. A mathematical model extended from the FitzHugh-Nagumo neuron model suggested that the opposite response dynamics are due to the shift in resting membrane potential. As predicted by the model, electrical stimulation only induced depolarization when cells are treated with antibiotics, protonophore, or alcohol. Therefore, electrically induced membrane-potential dynamics offer a reliable approach for rapid detection of proliferative bacteria and determination of their sensitivity to antimicrobial agents at the single-cell level.
Collapse
Affiliation(s)
- James P Stratford
- School of Life Sciences, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, West Midlands, CV4 7AL,United Kingdom
| | - Conor L A Edwards
- School of Life Sciences, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom
| | - Manjari J Ghanshyam
- School of Life Sciences, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom
| | - Dmitry Malyshev
- School of Life Sciences, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom
| | - Marco A Delise
- School of Life Sciences, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom
| | - Yoshikatsu Hayashi
- Department of Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, Berkshire, RG6 6AH, United Kingdom
| | - Munehiro Asally
- School of Life Sciences, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom;
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, West Midlands, CV4 7AL,United Kingdom
- Bio-Electrical Engineering Innovation Hub, University of Warwick, Coventry, West Midlands, CV4 7AL, United Kingdom
| |
Collapse
|
14
|
Zerfaß C, Asally M, Soyer OS. Interrogating metabolism as an electron flow system. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 13:59-67. [PMID: 31008413 PMCID: PMC6472609 DOI: 10.1016/j.coisb.2018.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Metabolism is generally considered as a neatly organised system of modular pathways, shaped by evolution under selection for optimal cellular growth. This view falls short of explaining and predicting a number of key observations about the structure and dynamics of metabolism. We highlight these limitations of a pathway-centric view on metabolism and summarise studies suggesting how these could be overcome by viewing metabolism as a thermodynamically and kinetically constrained, dynamical flow system. Such a systems-level, first-principles based view of metabolism can open up new avenues of metabolic engineering and cures for metabolic diseases and allow better insights to a myriad of physiological processes that are ultimately linked to metabolism. Towards further developing this view, we call for a closer interaction among physical and biological disciplines and an increased use of electrochemical and biophysical approaches to interrogate cellular metabolism together with the microenvironment in which it exists.
Collapse
Affiliation(s)
- Christian Zerfaß
- Bio-Electrical Engineering (BEE) Innovation Hub, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Munehiro Asally
- Bio-Electrical Engineering (BEE) Innovation Hub, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
- Warwick Integrative Synthetic Biology Centre (WISB), University of Warwick, Coventry, CV4 7AL, UK
| | - Orkun S. Soyer
- Bio-Electrical Engineering (BEE) Innovation Hub, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
- Warwick Integrative Synthetic Biology Centre (WISB), University of Warwick, Coventry, CV4 7AL, UK
| |
Collapse
|
15
|
Metabolic heterogeneity in clonal microbial populations. Curr Opin Microbiol 2018; 45:30-38. [DOI: 10.1016/j.mib.2018.02.004] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 02/07/2018] [Accepted: 02/08/2018] [Indexed: 11/22/2022]
|