Evidence map›Paper›PMID 27604263›Full record

ArticleBMC systems biology2016

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

Poonam Phalak, Jin Chen, Ross P Carlson, Michael A Henson

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 64 citations in OpenAlex.

  1. Review
  2. Article
  3. Towards improved biofilm models.Nature reviews. Microbiology · 2025
    Review
  4. Antioxidants (Basel, Switzerland) · 2024
    Article
  5. Article
  6. From microbiome composition to functional engineering, one step at a time.Microbiology and molecular biology reviews : MMBR · 2023
    Review
  7. Article
  8. Review
  9. Milieu matters: AnBiofilm · 2021
    Article
  10. Review
  11. Article
  12. Metabolic modelling approaches for describing and engineering microbial communities.Computational and structural biotechnology journal · 2021
    Review
  13. Review
  14. Review
  15. Staphylococcal Biofilm Development: Structure, Regulation, and Treatment Strategies.Microbiology and molecular biology reviews : MMBR · 2020
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Poonam PhalakDepartment of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA.
Jin ChenDepartment of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA.
Ross P CarlsonDepartment of Chemical and Biological Engineering and Center for Biofilm Engineering, Montana State University, Bozeman, MT, 59717, USA.
Michael A HensonDepartment of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA. henson@ecs.umass.edu.
University of Massachusetts Amherst · USMontana State University · US

Funding

Predictive Multiscale Modeling of Microbial Consortia BiofilmsU01EB019416 · NIBIB · MONTANA STATE UNIVERSITY - BOZEMAN · PI CARLSON, ROSS P., FIELDS, MATTHEW W · 2014 to 2017
$1.3M
Cellular Engineering Biotechnology Training ProgramT32GM108556 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI HARDY, JEANNE ANN, PEYTON, SHELLY R. · 2015 to 2019
$766k
NIBIB NIH HHS U01 EB019416NIGMS NIH HHS T32 GM108556
6 · The paper itself

Abstract

backgroundChronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia.

resultsWe developed spatiotemporal models to investigate the multispecies metabolism of a biofilm consortium comprised of two common chronic wound isolates: the aerobe Pseudomonas aeruginosa and the facultative anaerobe Staphylococcus aureus. By combining genome-scale metabolic reconstructions with partial differential equations for metabolite diffusion, the models were able to provide both temporal and spatial predictions with genome-scale resolution. The models were used to analyze the metabolic differences between single species and two species biofilms and to demonstrate the tendency of the two bacteria to spatially partition in the multispecies biofilm as observed experimentally. Nutrient gradients imposed by supplying glucose at the bottom and oxygen at the top of the biofilm induced spatial partitioning of the two species, with S. aureus most concentrated in the anaerobic region and P. aeruginosa present only in the aerobic region. The two species system was predicted to support a maximum biofilm thickness much greater than P. aeruginosa alone but slightly less than S. aureus alone, suggesting an antagonistic metabolic effect of P. aeruginosa on S. aureus. When each species was allowed to enhance its growth through consumption of secreted metabolic byproducts assuming identical uptake kinetics, the competitiveness of P. aeruginosa was further reduced due primarily to the more efficient lactate metabolism of S. aureus. Lysis of S. aureus by a small molecule inhibitor secreted from P. aeruginosa and/or P. aeruginosa aerotaxis were predicted to substantially increase P. aeruginosa competitiveness in the aerobic region, consistent with in vitro experimental studies.

conclusionsOur biofilm modeling approach allows the prediction of individual species metabolism and interspecies interactions in both time and space with genome-scale resolution. This study yielded new insights into the multispecies metabolism of a chronic wound biofilm, in particular metabolic factors that may lead to spatial partitioning of the two bacterial species. We believe that P. aeruginosa lysis of S. aureus combined with nutrient competition is a particularly relevant scenario for which model predictions could be tested experimentally.

Indexed as

BiofilmsModels, BiologicalMetabolic Flux AnalysisPseudomonas aeruginosaStaphylococcus aureusWounds and InjuriesBiofilmsMetabolic modelingWound infections

Identifiers

PMID27604263
PMCPMC5015247
OpenAlexW2517239615

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.