Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Interactions: Cooperation01:26

Microbial Interactions: Cooperation

44
Microbial cooperation involves beneficial interactions in which different species work together for individual or mutual advantage. These interactions can profoundly influence ecological dynamics and evolutionary processes, and they are essential to many pathogenic and symbiotic relationships.Nematode–Bacteria CooperationA striking example is the relationship between the Gram-negative bacterium Xenorhabdus nematophila and the parasitic nematode Steinernema carpocapsae. Juvenile nematodes...
44
Clinical Significance of Antibiotic Resistance01:25

Clinical Significance of Antibiotic Resistance

55
Methicillin-resistant Staphylococcus aureus (MRSA) presents a critical public health threat, arising from its capacity to resist β-lactam antibiotics due to acquisition of the mecA gene within the staphylococcal cassette chromosome mec (SCCmec). This gene encodes penicillin-binding protein 2a (PBP2a), which impairs binding efficacy of methicillin and other β-lactams. MRSA has evolved into distinct clonal lineages impacting humans and animals alike, reinforcing its significance within...
55
Gene Regulation in Microbial Communities: Quorum Sensing01:28

Gene Regulation in Microbial Communities: Quorum Sensing

917
Quorum sensing is a mechanism of bacterial communication that enables coordinated gene expression in response to changes in population density. This facilitates collective behaviors that enhance survival, resource acquisition, and ecological adaptation. This process relies on small signaling molecules called autoinducers that accumulate as bacterial populations grow. When a critical threshold concentration of autoinducers is reached, bacterial cells collectively modify gene expression,...
917
Introduction to Microbial Ecology01:28

Introduction to Microbial Ecology

150
Microbial ecology examines the complex web of interactions and diversity among microorganisms within various ecosystems. This field seeks to understand how microbial populations adapt to and influence their environments and how these interactions shape broader ecological processes. Microbes are integral to ecosystem function, participating in nutrient cycling, energy flow, and the maintenance of environmental homeostasis.An ecosystem represents a dynamic interaction between living organisms...
150
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

7.9K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
7.9K
Microbial Interactions: Competition01:26

Microbial Interactions: Competition

69
Microbial competition is an ecological interaction in which microorganisms vie for limited resources within shared environments. These resources may include nutrients, space, or light, depending on the system. The intensity and outcome of competition are influenced by the environmental context, such as nutrient availability, spatial constraints, and the diversity of microbial species present. These competitive interactions significantly influence the structure, function, and resilience of...
69

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genomic analysis of eight clinical <i>Rothia</i> isolates.

Microbiology resource announcements·2025
Same author

Infections as ecosystems: community metabolic interactions in microbial pathogenesis.

Infection and immunity·2025
Same author

Extended time, elevated expectations: The unappreciated downsides of pausing the tenure clock.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Metabolic diversity of human macrophages: potential influence on <i>Staphylococcus aureus</i> intracellular survival.

Infection and immunity·2024
Same author

Defining microbial community functions in chronic human infection with metatranscriptomics.

mSystems·2023
Same author

Precise spatial structure impacts antimicrobial susceptibility of <i>S. aureus</i> in polymicrobial wound infections.

Proceedings of the National Academy of Sciences of the United States of America·2022

Related Experiment Video

Updated: Apr 14, 2026

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions
08:33

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Published on: August 5, 2020

7.7K

Revealing community dynamics in polymicrobial infections through a quantitative framework.

Aanuoluwa E Adekoya1, Tyler E Boggs1, Carolyn B Ibberson1

  • 1Department of Microbiology, University of Tennessee, Knoxville, TN 37996, United States.

ISME Communications
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

This study adapted a framework to accurately model polymicrobial infections, like chronic wounds, using synthetic bacterial communities. This advances understanding of complex microbial ecosystems and treatment strategies.

Keywords:
bacterial pathogenesischronic woundsmetatranscriptomicsmicrobe-microbe interactionmicrobial ecologypolymicrobial infection

More Related Videos

Growing a Cystic Fibrosis-Relevant Polymicrobial Biofilm to Probe Community Phenotypes
03:53

Growing a Cystic Fibrosis-Relevant Polymicrobial Biofilm to Probe Community Phenotypes

Published on: April 19, 2024

1.3K
Development of a Polymicrobial Colony Biofilm Model to Test Antimicrobials in Cystic Fibrosis
07:16

Development of a Polymicrobial Colony Biofilm Model to Test Antimicrobials in Cystic Fibrosis

Published on: September 20, 2024

2.0K

Related Experiment Videos

Last Updated: Apr 14, 2026

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions
08:33

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Published on: August 5, 2020

7.7K
Growing a Cystic Fibrosis-Relevant Polymicrobial Biofilm to Probe Community Phenotypes
03:53

Growing a Cystic Fibrosis-Relevant Polymicrobial Biofilm to Probe Community Phenotypes

Published on: April 19, 2024

1.3K
Development of a Polymicrobial Colony Biofilm Model to Test Antimicrobials in Cystic Fibrosis
07:16

Development of a Polymicrobial Colony Biofilm Model to Test Antimicrobials in Cystic Fibrosis

Published on: September 20, 2024

2.0K

Area of Science:

  • Microbiology
  • Systems Biology
  • Infectious Diseases

Background:

  • Laboratory models are crucial in microbiology but their accuracy in mimicking real biological environments is often unknown.
  • Existing quantitative frameworks assess single-species physiology but not community-level functions in polymicrobial infections.
  • Chronic wounds (CWs) involve complex bacterial communities with poorly understood microbe-microbe interactions.

Purpose of the Study:

  • To extend a quantitative framework for evaluating laboratory model accuracy in capturing polymicrobial community functions.
  • To apply this framework to a chronic wound infection model.
  • To develop an accurate, ecologically relevant polymicrobial model for CW infections.

Main Methods:

  • Adapted a quantitative framework to assess laboratory models for polymicrobial infections.
  • Applied the extended framework to a human chronic wound (CW) infection model.
  • Utilized prior metagenomic and metatranscriptomic data to construct a synthetic bacterial community.

Main Results:

  • Demonstrated the adapted framework's utility in developing accurate polymicrobial models.
  • Showcased the framework's capability to evaluate microbe-microbe interactions within communities.
  • Proposed a 6-member synthetic bacterial community model representative of CW infections.

Conclusions:

  • The extended framework enables the development and evaluation of accurate polymicrobial models.
  • This approach supports the creation of ecologically relevant models for complex infections.
  • Findings facilitate better understanding and treatment strategies for chronic wound infections.