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Li B, Srivastava S, Shaikh M, Mereddy G, Garcia MR, Shah A, Ofori-Anyinam N, Chu T, Cheney N, Yang JH. Bioenergetic stress potentiates antimicrobial resistance and persistence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603336. [PMID: 39026737 PMCID: PMC11257553 DOI: 10.1101/2024.07.12.603336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Antimicrobial resistance (AMR) is a global health crisis and there is an urgent need to better understand AMR mechanisms. Antibiotic treatment alters several aspects of bacterial physiology, including increased ATP utilization, carbon metabolism, and reactive oxygen species (ROS) formation. However, how the "bioenergetic stress" induced by increased ATP utilization affects treatment outcomes is unknown. Here we utilized a synthetic biology approach to study the direct effects of bioenergetic stress on antibiotic efficacy. We engineered a genetic system that constitutively hydrolyzes ATP or NADH in Escherichia coli. We found that bioenergetic stress potentiates AMR evolution via enhanced ROS production, mutagenic break repair, and transcription-coupled repair. We also find that bioenergetic stress potentiates antimicrobial persistence via potentiated stringent response activation. We propose a unifying model that antibiotic-induced antimicrobial resistance and persistence is caused by antibiotic-induced. This has important implications for preventing or curbing the spread of AMR infections.
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2
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Kujawinski EB, Braakman R, Longnecker K, Becker JW, Chisholm SW, Dooley K, Kido Soule MC, Swarr GJ, Halloran K. Metabolite diversity among representatives of divergent Prochlorococcus ecotypes. mSystems 2023; 8:e0126122. [PMID: 37815355 PMCID: PMC10654061 DOI: 10.1128/msystems.01261-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 08/31/2023] [Indexed: 10/11/2023] Open
Abstract
IMPORTANCE Approximately half of the annual carbon fixation on Earth occurs in the surface ocean through the photosynthetic activities of phytoplankton such as the ubiquitous picocyanobacterium Prochlorococcus. Ecologically distinct subpopulations (or ecotypes) of Prochlorococcus are central conduits of organic substrates into the ocean microbiome, thus playing important roles in surface ocean production. We measured the chemical profile of three cultured ecotype strains, observing striking differences among them that have implications for the likely chemical impact of Prochlorococcus subpopulations on their surroundings in the wild. Subpopulations differ in abundance along gradients of temperature, light, and nutrient concentrations, suggesting that these chemical differences could affect carbon cycling in different ocean strata and should be considered in models of Prochlorococcus physiology and marine carbon dynamics.
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Affiliation(s)
- Elizabeth B. Kujawinski
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Rogier Braakman
- Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Krista Longnecker
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Jamie W. Becker
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Science Department, Alvernia University, Reading, Pennsylvania, USA
| | - Sallie W. Chisholm
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Keven Dooley
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
| | - Melissa C. Kido Soule
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Gretchen J. Swarr
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Kathryn Halloran
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
- MIT/WHOI Joint Program in Oceanography/Applied Ocean Sciences and Engineering, Department of Marine Chemistry & Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
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3
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Cosetta CM, Niccum B, Kamkari N, Dente M, Podniesinski M, Wolfe BE. Bacterial-fungal interactions promote parallel evolution of global transcriptional regulators in a widespread Staphylococcus species. THE ISME JOURNAL 2023; 17:1504-1516. [PMID: 37524910 PMCID: PMC10432416 DOI: 10.1038/s41396-023-01462-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/06/2023] [Accepted: 06/15/2023] [Indexed: 08/02/2023]
Abstract
Experimental studies of microbial evolution have largely focused on monocultures of model organisms, but most microbes live in communities where interactions with other species may impact rates and modes of evolution. Using the cheese rind model microbial community, we determined how species interactions shape the evolution of the widespread food- and animal-associated bacterium Staphylococcus xylosus. We evolved S. xylosus for 450 generations alone or in co-culture with one of three microbes: the yeast Debaryomyces hansenii, the bacterium Brevibacterium aurantiacum, and the mold Penicillium solitum. We used the frequency of colony morphology mutants (pigment and colony texture phenotypes) and whole-genome sequencing of isolates to quantify phenotypic and genomic evolution. The yeast D. hansenii strongly promoted diversification of S. xylosus. By the end of the experiment, all populations co-cultured with the yeast were dominated by pigment and colony morphology mutant phenotypes. Populations of S. xylosus grown alone, with B. aurantiacum, or with P. solitum did not evolve novel phenotypic diversity. Whole-genome sequencing of individual mutant isolates across all four treatments identified numerous unique mutations in the operons for the SigB, Agr, and WalRK global regulators, but only in the D. hansenii treatment. Phenotyping and RNA-seq experiments highlighted altered pigment and biofilm production, spreading, stress tolerance, and metabolism of S. xylosus mutants. Fitness experiments revealed antagonistic pleiotropy, where beneficial mutations that evolved in the presence of the yeast had strong negative fitness effects in other biotic environments. This work demonstrates that bacterial-fungal interactions can have long-term evolutionary consequences within multispecies microbiomes by facilitating the evolution of strain diversity.
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Affiliation(s)
- Casey M Cosetta
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - Brittany Niccum
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - Nick Kamkari
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - Michael Dente
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | | | - Benjamin E Wolfe
- Department of Biology, Tufts University, Medford, MA, 02155, USA.
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4
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de Leeuw M, Matos MRA, Nielsen LK. Omics data for sampling thermodynamically feasible kinetic models. Metab Eng 2023; 78:41-47. [PMID: 37209863 DOI: 10.1016/j.ymben.2023.05.002] [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: 11/10/2022] [Revised: 05/03/2023] [Accepted: 05/07/2023] [Indexed: 05/22/2023]
Abstract
Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.
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Affiliation(s)
- Marina de Leeuw
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Marta R A Matos
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Lars Keld Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark; Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, 4072, Brisbane QLD, Australia.
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5
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Donati S, Mattanovich M, Hjort P, Jacobsen SAB, Blomquist SD, Mangaard D, Gurdo N, Pastor FP, Maury J, Hanke R, Herrgård MJ, Wulff T, Jakočiūnas T, Nielsen LK, McCloskey D. An automated workflow for multi-omics screening of microbial model organisms. NPJ Syst Biol Appl 2023; 9:14. [PMID: 37208327 DOI: 10.1038/s41540-023-00277-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 04/19/2023] [Indexed: 05/21/2023] Open
Abstract
Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.
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Affiliation(s)
- Stefano Donati
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Matthias Mattanovich
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen, Denmark
| | - Pernille Hjort
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | | | - Sarah Dina Blomquist
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Drude Mangaard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Nicolas Gurdo
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Felix Pacheco Pastor
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Jérôme Maury
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Rene Hanke
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Markus J Herrgård
- BioInnovation Institute, Ole Maaløes Vej 3, 2200, København, Denmark
| | - Tune Wulff
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Tadas Jakočiūnas
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Lars Keld Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark.
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark.
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6
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Li S, Zhang Z, Liu FL, Yuan BF, Liu TG, Feng YQ. Comprehensive Profiling of Phosphomonoester Metabolites in Saccharomyces cerevisiae by the Chemical Isotope Labeling-LC-MS Method. J Proteome Res 2023; 22:114-122. [PMID: 36484485 DOI: 10.1021/acs.jproteome.2c00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Phosphomonoesters are important biosynthetic and energy metabolism intermediates in microorganisms. A comprehensive analysis of phosphomonoester metabolites is of great significance for the understanding of their metabolic phosphorylation process and inner mechanism. In this study, we established a pair of isotope reagent d0/d5-2-diazomethyl-N-methyl-phenyl benzamide-labeling-based LC-MS method for the comprehensive analysis of phosphomonoester metabolites. By this method, the labeled phosphomonoester metabolites specifically produced characteristic isotope paired peaks with an m/z difference of 5.0314 in the MS1 spectra and a pair of diagnostic ions (m/z 320.0693/325.1077) in the MS2 spectra. Based on this, a diagnostic ion-based strategy was established for the rapid screening, identification, and relative quantification of phosphomonoester metabolites. Using this strategy, 42 phosphomonoester metabolites were highly accurately identified fromSaccharomyces cerevisiae (S. cerevisiae). Notably, two phosphomonoesters were first detected fromS. cerevisiae. The relative quantification results indicated that the contents of nine phosphomonoester metabolites including two intermediates (Ru5P and S7P) in the pentose phosphate pathway (PPP) were significantly different between lycopene-producible and wild-type S. cerevisiae. A further enzyme assay indicated that the activity of the PPP was closely related to the production of lycopene. Our findings provide new perspectives for the related mechanism study and valuable references for making informed microbial engineering decisions.
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Affiliation(s)
- Sha Li
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Zheng Zhang
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Fei-Long Liu
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Bi-Feng Yuan
- School of Public Health, Wuhan University, Wuhan 430071, China
| | - Tian-Gang Liu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
| | - Yu-Qi Feng
- Department of Chemistry, Wuhan University, Wuhan 430072, China.,School of Public Health, Wuhan University, Wuhan 430071, China
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7
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Calero P, Gurdo N, Nikel PI. Role of the CrcB transporter of Pseudomonas putida in the multi-level stress response elicited by mineral fluoride. Environ Microbiol 2022; 24:5082-5104. [PMID: 35726888 PMCID: PMC9796867 DOI: 10.1111/1462-2920.16110] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/16/2022] [Accepted: 06/19/2022] [Indexed: 01/07/2023]
Abstract
The presence of mineral fluoride (F- ) in the environment has both a geogenic and anthropogenic origin, and the halide has been described to be toxic in virtually all living organisms. While the evidence gathered in different microbial species supports this notion, a systematic exploration of the effects of F- salts on the metabolism and physiology of environmental bacteria remained underexplored thus far. In this work, we studied and characterized tolerance mechanisms deployed by the model soil bacterium Pseudomonas putida KT2440 against NaF. By adopting systems-level omic approaches, including functional genomics and metabolomics, we gauged the impact of this anion at different regulatory levels under conditions that impair bacterial growth. Several genes involved in halide tolerance were isolated in a genome-wide Tn-Seq screening-among which crcB, encoding an F- -specific exporter, was shown to play the predominant role in detoxification. High-resolution metabolomics, combined with the assessment of intracellular and extracellular pH values and quantitative physiology experiments, underscored the key nodes in central carbon metabolism affected by the presence of F- . Taken together, our results indicate that P. putida undergoes a general, multi-level stress response when challenged with NaF that significantly differs from that caused by other saline stressors. While microbial stress responses to saline and oxidative challenges have been extensively studied and described in the literature, very little is known about the impact of fluoride (F- ) on bacterial physiology and metabolism. This state of affairs contrasts with the fact that F- is more abundant than other halides in the Earth crust (e.g. in some soils, the F- concentration can reach up to 1 mg gsoil -1 ). Understanding the global effects of NaF treatment on bacterial physiology is not only relevant to unveil distinct mechanisms of detoxification but it could also guide microbial engineering approaches for the target incorporation of fluorine into value-added organofluorine molecules. In this regard, the soil bacterium P. putida constitutes an ideal model to explore such scenarios, since this species is particularly known for its high level of stress resistance against a variety of physicochemical perturbations.
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Affiliation(s)
- Patricia Calero
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
| | - Nicolás Gurdo
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
| | - Pablo I. Nikel
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
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8
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Joffré E, Xiao X, Correia MSP, Nookaew I, Sasse S, Globisch D, Zhu B, Sjöling Å. Analysis of Growth Phases of Enterotoxigenic Escherichia coli Reveals a Distinct Transition Phase before Entry into Early Stationary Phase with Shifts in Tryptophan, Fucose, and Putrescine Metabolism and Degradation of Neurotransmitter Precursors. Microbiol Spectr 2022; 10:e0175521. [PMID: 35876501 PMCID: PMC9431495 DOI: 10.1128/spectrum.01755-21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 03/03/2022] [Indexed: 11/20/2022] Open
Abstract
Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in children and adults in endemic areas. Gene regulation of ETEC during growth in vitro and in vivo needs to be further evaluated, and here we describe the full transcriptome and metabolome of ETEC during growth from mid-logarithmic growth to early stationary phase in rich medium (LB medium). We identified specific genes and pathways subjected to rapid transient alterations in gene expression and metabolite production during the transition from logarithmic to stationary growth. The transient phase was found to be different from the subsequent induction of early stationary phase-induced genes. The transient phase was characterized by the repression of genes and metabolites involved in organic substance transport. Genes involved in fucose and putrescine metabolism were upregulated, and genes involved in iron transport were repressed. Expression of toxins and colonization factors were not changed, suggesting retained virulence from mid-logarithmic to the start of the stationary phase. Metabolomic analyses showed that the transient phase was characterized by a drop of intracellular amino acids, e.g., l-tyrosine, l-tryptophan, l-phenylalanine, l-leucine, and l-glutamic acid, followed by increased levels at induction of stationary phase. A pathway enrichment analysis of the entire combined transcriptome and metabolome revealed that significant pathways during progression from logarithmic to early stationary phase are involved in the degradation of neurotransmitters aminobutyrate (GABA) and precursors of 5-hydroxytryptamine (serotonin). This work provides a comprehensive framework for further studies on transcriptional and metabolic regulation in pathogenic E. coli. IMPORTANCE We show that E. coli, exemplified by the pathogenic subspecies enterotoxigenic E. coli (ETEC), undergoes a stepwise transcriptional and metabolic transition into the stationary phase. At a specific entry point, E. coli induces activation and repression of specific pathways. This leads to a rapid decrease of intracellular levels of certain amino acids. The resulting metabolic activity leads to an intense but short peak of indole production, suggesting that this is the previously described "indole peak," rapid decrease of intermediate molecules of bacterial neurotransmitters, increased putrescine and fucose uptake, increased glutathione levels, and decreased iron uptake. This specific transient shift in gene expression and metabolome is short-lived and disappears when bacteria enter the early stationary phase. We suggest that these changes mainly prepare bacteria for ceased growth, but based on the pathways involved, we could suggest that this transient phase substantially influences survival and virulence.
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Affiliation(s)
- Enrique Joffré
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Xue Xiao
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Mário S. P. Correia
- Department of Chemistry - BMC, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Intawat Nookaew
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Samantha Sasse
- Department of Chemistry - BMC, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Daniel Globisch
- Department of Chemistry - BMC, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Baoli Zhu
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Åsa Sjöling
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
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9
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Fernández-Cabezón L, Rosich I Bosch B, Kozaeva E, Gurdo N, Nikel PI. Dynamic flux regulation for high-titer anthranilate production by plasmid-free, conditionally-auxotrophic strains of Pseudomonas putida. Metab Eng 2022; 73:11-25. [PMID: 35659519 DOI: 10.1016/j.ymben.2022.05.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/05/2022] [Accepted: 05/29/2022] [Indexed: 10/18/2022]
Abstract
Anthranilate, an intermediate of the shikimate pathway, is a high-value aromatic compound widely used as a precursor in the production of dyes, fragrances, plastics and pharmaceuticals. Traditional strategies adopted for microbial anthranilate production rely on the implementation of auxotrophic strains-which requires aromatic amino acids or complex additives to be supplemented in the culture medium, negatively impacting production costs. In this work, we engineered the soil bacterium Pseudomonas putida for high-titer, glucose-dependent anthranilate production by repurposing elements of the Esa quorum sensing (QS) system of Pantoea stewartii. The PesaS promoter mediated a self-regulated transcriptional response that effectively knocked-down the expression of the trpDC genes. Next, we harnessed the synthetic QS elements to engineer a growth-to-anthranilate production switch. The resulting plasmid-free P. putida strain produced the target compound at 3.8 ± 0.3 mM in shaken-flask cultures after 72 h-a titer >2-fold higher than anthranilate levels reported thus far. Our results highlight the value of dynamic flux regulation for the production of intermediate metabolites within highly-regulated routes (such as the shikimate pathway), thereby circumventing the need of expensive additives.
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Affiliation(s)
- Lorena Fernández-Cabezón
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Berta Rosich I Bosch
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Ekaterina Kozaeva
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Nicolás Gurdo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Pablo Iván Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
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10
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Wong N, Jantama K. Engineering Escherichia coli for a high yield of 1,3-propanediol near the theoretical maximum through chromosomal integration and gene deletion. Appl Microbiol Biotechnol 2022; 106:2937-2951. [PMID: 35416488 DOI: 10.1007/s00253-022-11898-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 03/15/2022] [Accepted: 03/26/2022] [Indexed: 11/02/2022]
Abstract
Glycerol dehydratase (gdrAB-dhaB123) operon from Klebsiella pneumoniae and NADPH-dependent 1,3-propanediol oxidoreductase (yqhD) from Escherichia coli were stably integrated on the chromosomal DNA of E. coli under the control of the native-host ldhA and pflB constitutive promoters, respectively. The developed E. coli NSK015 (∆ldhA::gdrAB-dhaB123 ∆ackA::FRT ∆pflB::yqhD ∆frdABCD::cat-sacB) produced 1,3-propanediol (1,3-PDO) at the level of 36.8 g/L with a yield of 0.99 mol/mol of glycerol consumed when glucose was used as a co-substrate with glycerol. Co-substrate of glycerol and cassava starch was also utilized for 1,3-PDO production with the concentration and yield of 31.9 g/L and 0.84 mol/mol of glycerol respectively. This represents a work for efficient 1,3-PDO production in which the overexpression of heterologous genes on the E. coli host genome devoid of plasmid expression systems. Plasmids, antibiotics, IPTG, and rich nutrients were omitted during 1,3-PDO production. This may allow a further application of E. coli NSK015 for the efficient 1,3-PDO production in an economically industrial scale. KEY POINTS: • gdrAB-dhaB123 and yqhD were overexpressed in E. coli devoid of a plasmid system • E. coli NSK015 produced a high yield of 1,3-PDO at 99% theoretical maximum • Cassava starch was alternatively used as substrate for economical 1,3-PDO production.
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Affiliation(s)
- Nonthaporn Wong
- Metabolic Engineering Research Unit, School of Biotechnology, Institute of Agricultural Technology, Suranaree Sub-District, Suranaree University of Technology, 111 University Avenue, Muang district, Nakhon Ratchasima, 30000, Thailand
| | - Kaemwich Jantama
- Metabolic Engineering Research Unit, School of Biotechnology, Institute of Agricultural Technology, Suranaree Sub-District, Suranaree University of Technology, 111 University Avenue, Muang district, Nakhon Ratchasima, 30000, Thailand.
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11
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Reiter A, Asgari J, Wiechert W, Oldiges M. Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data. Metabolites 2022; 12:257. [PMID: 35323700 PMCID: PMC8949988 DOI: 10.3390/metabo12030257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/08/2022] [Accepted: 03/12/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.
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Affiliation(s)
- Alexander Reiter
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (A.R.); (J.A.); (W.W.)
- Institute of Biotechnology, RWTH Aachen University, 52062 Aachen, Germany
| | - Jian Asgari
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (A.R.); (J.A.); (W.W.)
- Institute of Biotechnology, RWTH Aachen University, 52062 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (A.R.); (J.A.); (W.W.)
- Computational Systems Biotechnology, RWTH Aachen University, 52062 Aachen, Germany
| | - Marco Oldiges
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (A.R.); (J.A.); (W.W.)
- Institute of Biotechnology, RWTH Aachen University, 52062 Aachen, Germany
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12
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Abram KJ, McCloskey D. A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning. Metabolites 2022; 12:202. [PMID: 35323644 PMCID: PMC8948616 DOI: 10.3390/metabo12030202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 12/04/2022] Open
Abstract
Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning known as deep learning has found applications in genomics, proteomics, and metabolomics. However, a thorough assessment of how the data preprocessing methods required for the analysis of life science data affect the performance of deep learning is lacking. This work contributes to filling that gap by assessing the impact of commonly used as well as newly developed methods employed in data preprocessing workflows for metabolomics that span from raw data to processed data. The results from these analyses are summarized into a set of best practices that can be used by researchers as a starting point for downstream classification and reconstruction tasks using deep learning.
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Affiliation(s)
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark;
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13
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Abstract
Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis. The testing of engineered enzymes represents a bottleneck. Here the authors report a screening method combining microfluidics and mass spectrometry, to map the catalysis of a mutated enzyme, characterise the range of products generated and recover the sequences of variants with desired activities.
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14
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Lauritsen I, Frendorf PO, Capucci S, Heyde SAH, Blomquist SD, Wendel S, Fischer EC, Sekowska A, Danchin A, Nørholm MHH. Temporal evolution of master regulator Crp identifies pyrimidines as catabolite modulator factors. Nat Commun 2021; 12:5880. [PMID: 34620864 PMCID: PMC8497467 DOI: 10.1038/s41467-021-26098-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 09/07/2021] [Indexed: 12/19/2022] Open
Abstract
The evolution of microorganisms often involves changes of unclear relevance, such as transient phenotypes and sequential development of multiple adaptive mutations in hotspot genes. Previously, we showed that ageing colonies of an E. coli mutant unable to produce cAMP when grown on maltose, accumulated mutations in the crp gene (encoding a global transcription factor) and in genes involved in pyrimidine metabolism such as cmk; combined mutations in both crp and cmk enabled fermentation of maltose (which usually requires cAMP-mediated Crp activation for catabolic pathway expression). Here, we study the sequential generation of hotspot mutations in those genes, and uncover a regulatory role of pyrimidine nucleosides in carbon catabolism. Cytidine binds to the cytidine regulator CytR, modifies the expression of sigma factor 32 (RpoH), and thereby impacts global gene expression. In addition, cytidine binds and activates a Crp mutant directly, thus modulating catabolic pathway expression, and could be the catabolite modulating factor whose existence was suggested by Jacques Monod and colleagues in 1976. Therefore, transcription factor Crp appears to work in concert with CytR and RpoH, serving a dual role in sensing both carbon availability and metabolic flux towards DNA and RNA. Our findings show how certain alterations in metabolite concentrations (associated with colony ageing and/or due to mutations in metabolic or regulatory genes) can drive the evolution in non-growing cells. Microbial evolution often involves transient phenotypes and sequential development of multiple mutations of unclear relevance. Here, the authors show that the evolution of non-growing E. coli cells can be driven by alterations in pyrimidine nucleoside levels associated with colony ageing and/or due to mutations in metabolic or regulatory genes.
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Affiliation(s)
- Ida Lauritsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Pernille Ott Frendorf
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Silvia Capucci
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Sophia A H Heyde
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Sarah D Blomquist
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Sofie Wendel
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Emil C Fischer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | | | | | - Morten H H Nørholm
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
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15
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Wang L, Upadhyay V, Maranas CD. dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. PLoS Comput Biol 2021; 17:e1009448. [PMID: 34570771 PMCID: PMC8496854 DOI: 10.1371/journal.pcbi.1009448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/07/2021] [Accepted: 09/13/2021] [Indexed: 11/19/2022] Open
Abstract
Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG′o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor). The standard Gibbs energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs energy change. Here, we introduce a molecular fingerprint-based thermodynamic tool, dGPredictor, that enables distinguishing between (stereo)isomers in metabolic reactions leading to improved reaction coverage and comparable prediction accuracy as GC methods. dGPredictor can also be used alongside de novo pathway design tools to ensure the correct directionality of chosen reaction steps. We applied and tested dGPredictor on reactions from the KEGG database and applied it to screen an isobutanol synthesis pathway design. An open-source, user-friendly web interface is provided to facilitate easy access for standard Gibbs energy change of reactions at different pH values. (https://github.com/maranasgroup/dGPredictor).
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Vikas Upadhyay
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
- * E-mail:
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16
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An ion-pair free LC-MS/MS method for quantitative metabolite profiling of microbial bioproduction systems. Talanta 2021; 222:121625. [DOI: 10.1016/j.talanta.2020.121625] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/31/2020] [Accepted: 09/02/2020] [Indexed: 11/23/2022]
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17
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Kutuzova S, Colaianni P, Röst H, Sachsenberg T, Alka O, Kohlbacher O, Burla B, Torta F, Schrübbers L, Kristensen M, Nielsen L, Herrgård MJ, McCloskey D. SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing. Anal Chem 2020; 92:15968-15974. [PMID: 33269929 DOI: 10.1021/acs.analchem.0c03421] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.
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Affiliation(s)
- Svetlana Kutuzova
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Pasquale Colaianni
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Hannes Röst
- Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto M5S 1A8, Canada.,Department of Computer Science, University of Toronto, 40 St. George Street, Toronto M5T 3A1, Canada
| | - Timo Sachsenberg
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Geschwister-Scholl-Platz, Tübingen 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72074, Germany
| | - Oliver Alka
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Geschwister-Scholl-Platz, Tübingen 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72074, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Geschwister-Scholl-Platz, Tübingen 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72074, Germany.,Biomolecular Interactions, MPI for Developmental Biology, Tübingen 72076, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen 72076, Germany
| | - Bo Burla
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 117456, Singapore
| | - Federico Torta
- Singapore Lipidomics Incubator (SLING), Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 117597, Singapore
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Mette Kristensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Lars Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Markus J Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark.,BioInnovation Institute, Ole Maaløes Vej 3, Copenhagen N 2830, Denmark
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
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18
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Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020; 10:E303. [PMID: 32722118 PMCID: PMC7465778 DOI: 10.3390/metabo10080303] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
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Affiliation(s)
| | | | | | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; (S.V.); (M.R.A.M.); (M.M.)
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19
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Leavell MD, Singh AH, Kaufmann-Malaga BB. High-throughput screening for improved microbial cell factories, perspective and promise. Curr Opin Biotechnol 2020; 62:22-28. [DOI: 10.1016/j.copbio.2019.07.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 07/24/2019] [Accepted: 07/27/2019] [Indexed: 01/11/2023]
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20
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Vavricka CJ, Hasunuma T, Kondo A. Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction. Trends Biotechnol 2020; 38:68-82. [DOI: 10.1016/j.tibtech.2019.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 12/15/2022]
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21
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Kopp J, Slouka C, Spadiut O, Herwig C. The Rocky Road From Fed-Batch to Continuous Processing With E. coli. Front Bioeng Biotechnol 2019; 7:328. [PMID: 31824931 PMCID: PMC6880763 DOI: 10.3389/fbioe.2019.00328] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 10/28/2019] [Indexed: 12/21/2022] Open
Abstract
Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.
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Affiliation(s)
- Julian Kopp
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna, Austria
| | - Christoph Slouka
- Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna, Austria
| | - Oliver Spadiut
- Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna, Austria
| | - Christoph Herwig
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna, Austria
- Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna, Austria
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22
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Yang JH, Wright SN, Hamblin M, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. Cell 2019; 177:1649-1661.e9. [PMID: 31080069 PMCID: PMC6545570 DOI: 10.1016/j.cell.2019.04.016] [Citation(s) in RCA: 188] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 03/19/2019] [Accepted: 04/08/2019] [Indexed: 12/13/2022]
Abstract
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.
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Affiliation(s)
- Jason H Yang
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah N Wright
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Meagan Hamblin
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Miguel A Alcantar
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Allison J Lopatkin
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Sangeeta Satish
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Amir Nili
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Graham C Walker
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James J Collins
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
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