Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa

Dunphy, Laura J.; Yen, Phillip; Papin, Jason A.

doi:10.1016/j.cels.2018.12.002

Cited by 40 publications

(41 citation statements)

References 68 publications

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“…This functionality can then be formalized with a mathematical framework and constrained by known biological parameters to allow for simulation of metabolic processes. These powerful discovery platforms have enabled guided genetic engineering efforts, directed hypothesis generation for downstream laboratory testing, and investigation of metabolic responses of bacteria to antibiotic stress [5,6].…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

et al. 2020

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The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most costeffective usage of metabolism that also best reflects the cell's investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.

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Section: Introductionmentioning

confidence: 99%

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

et al. 2020

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“…A solution to tackling AMR might be in the observation that gain of resistance to an antibiotic is typically associated with loss of fitness 3 , which is restored through compensatory mutations 3 and changes in regulation and metabolism 15,16 . For example, Pseudomonas aeruginosa upregulates anaerobic nitrate respiration to quench intracellular protons and compensate for loss of fitness due to efflux-mediated resistance 17 .…”

Section: Introductionmentioning

confidence: 99%

Disrupting the ArcA regulatory network increases tetracycline susceptibility of Tet^REscherichia coli

Arrieta-Ortiz

Pan

Kaur

et al. 2020

Preprint

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There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including down-regulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Targeted knockout of ArcA, identified by network analysis as a master regulator among 25 transcription factors of this new compensatory physiological state, significantly increased the susceptibility of TetRE. coli to tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain also synergistically re-sensitized TetRE. coli to tetracycline. The potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that targeting mechanistic drivers of compensatory physiological states could be a generalizable strategy to re-sensitize AMR pathogens to lost antibiotics.

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“…This functionality can then be formalized with a mathematical framework and constrained by known biological parameters to allow for simulation of metabolic processes. These powerful discovery platforms have allowed for guided genetic engineering efforts, directed hypothesis generation for downstream laboratory testing, and deconvoluted metabolic responses of bacteria to antibiotic stress [5,6] .…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

Jenior

Moutinho

Dougherty

et al. 2019

Preprint

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AbstractThe metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.Author SummaryTranscriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.

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Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa

Cited by 40 publications

References 68 publications

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

Disrupting the ArcA regulatory network increases tetracycline susceptibility of Tet^REscherichia coli

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

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Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa

Cited by 40 publications

References 68 publications

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

Disrupting the ArcA regulatory network increases tetracycline susceptibility of TetREscherichia coli

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

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Disrupting the ArcA regulatory network increases tetracycline susceptibility of Tet^REscherichia coli