Literature DB >> 33446818

Pseudomonas aeruginosa reverse diauxie is a multidimensional, optimized, resource utilization strategy.

S Lee McGill1,2, Yeni Yung3, Kristopher A Hunt1,4, Michael A Henson5, Luke Hanley3, Ross P Carlson6,7.   

Abstract

Pseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an 'overflow' metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as 'reverse diauxie'. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence including its potentially, mutualistic interactions with microorganisms found commonly in the environment and in medical infections.

Entities:  

Year:  2021        PMID: 33446818      PMCID: PMC7809481          DOI: 10.1038/s41598-020-80522-8

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  95 in total

Review 1.  Mathematical models for explaining the Warburg effect: a review focussed on ATP and biomass production.

Authors:  Stefan Schuster; Daniel Boley; Philip Möller; Heiko Stark; Christoph Kaleta
Journal:  Biochem Soc Trans       Date:  2015-12       Impact factor: 5.407

2.  Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model.

Authors:  Stephanie DeLeon; Allie Clinton; Haley Fowler; Jake Everett; Alexander R Horswill; Kendra P Rumbaugh
Journal:  Infect Immun       Date:  2014-08-25       Impact factor: 3.441

3.  Multiple stable states in microbial communities explained by the stable marriage problem.

Authors:  Akshit Goyal; Veronika Dubinkina; Sergei Maslov
Journal:  ISME J       Date:  2018-07-19       Impact factor: 10.302

4.  Antagonism correlates with metabolic similarity in diverse bacteria.

Authors:  Jakob Russel; Henriette L Røder; Jonas S Madsen; Mette Burmølle; Søren J Sørensen
Journal:  Proc Natl Acad Sci U S A       Date:  2017-09-18       Impact factor: 11.205

5.  Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection.

Authors:  Matthew A Oberhardt; Joanna B Goldberg; Michael Hogardt; Jason A Papin
Journal:  J Bacteriol       Date:  2010-08-13       Impact factor: 3.490

6.  Distribution, organization, and ecology of bacteria in chronic wounds.

Authors:  Klaus Kirketerp-Møller; Peter Ø Jensen; Mustafa Fazli; Kit G Madsen; Jette Pedersen; Claus Moser; Tim Tolker-Nielsen; Niels Høiby; Michael Givskov; Thomas Bjarnsholt
Journal:  J Clin Microbiol       Date:  2008-05-28       Impact factor: 5.948

7.  Economics of membrane occupancy and respiro-fermentation.

Authors:  Kai Zhuang; Goutham N Vemuri; Radhakrishnan Mahadevan
Journal:  Mol Syst Biol       Date:  2011-06-21       Impact factor: 11.429

8.  Genetically and Phenotypically Distinct Pseudomonas aeruginosa Cystic Fibrosis Isolates Share a Core Proteomic Signature.

Authors:  Anahit Penesyan; Sheemal S Kumar; Karthik Kamath; Abdulrahman M Shathili; Vignesh Venkatakrishnan; Christoph Krisp; Nicolle H Packer; Mark P Molloy; Ian T Paulsen
Journal:  PLoS One       Date:  2015-10-02       Impact factor: 3.240

9.  Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species.

Authors:  Poonam Phalak; Jin Chen; Ross P Carlson; Michael A Henson
Journal:  BMC Syst Biol       Date:  2016-09-07

10.  Overflow metabolism in Escherichia coli results from efficient proteome allocation.

Authors:  Markus Basan; Sheng Hui; Hiroyuki Okano; Zhongge Zhang; Yang Shen; James R Williamson; Terence Hwa
Journal:  Nature       Date:  2015-12-03       Impact factor: 49.962

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  5 in total

1.  Complementary resource preferences spontaneously emerge in diauxic microbial communities.

Authors:  Zihan Wang; Akshit Goyal; Veronika Dubinkina; Ashish B George; Tong Wang; Yulia Fridman; Sergei Maslov
Journal:  Nat Commun       Date:  2021-11-18       Impact factor: 14.919

2.  Antibacterial mechanism of the Asp-Asp-Asp-Tyr peptide.

Authors:  Shanshan Zhuang; Yao Bao; Yaxin Zhang; Huangyou Zhang; Jianliang Liu; Huifan Liu
Journal:  Food Chem X       Date:  2022-01-29

3.  Environment Constrains Fitness Advantages of Division of Labor in Microbial Consortia Engineered for Metabolite Push or Pull Interactions.

Authors:  Ashley E Beck; Kathryn Pintar; Diana Schepens; Ashley Schrammeck; Timothy Johnson; Alissa Bleem; Martina Du; William R Harcombe; Hans C Bernstein; Jeffrey J Heys; Tomas Gedeon; Ross P Carlson
Journal:  mSystems       Date:  2022-06-28       Impact factor: 7.324

Review 4.  A Review of Recent Advances in Flexible Wearable Sensors for Wound Detection Based on Optical and Electrical Sensing.

Authors:  Xianyou Sun; Yanchi Zhang; Chiyu Ma; Qunchen Yuan; Xinyi Wang; Hao Wan; Ping Wang
Journal:  Biosensors (Basel)       Date:  2021-12-23

Review 5.  Carbon catabolite regulation of secondary metabolite formation, an old but not well-established regulatory system.

Authors:  Beatriz Ruiz-Villafán; Rodrigo Cruz-Bautista; Monserrat Manzo-Ruiz; Ajit Kumar Passari; Karen Villarreal-Gómez; Romina Rodríguez-Sanoja; Sergio Sánchez
Journal:  Microb Biotechnol       Date:  2021-03-06       Impact factor: 5.813

  5 in total

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