Polymicrobial Infection
Polymicrobial Infection - Related Videos
03:57
An Assay to Determine Polymicrobial Biofilm Initiation on Virus-Infected Cells
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2025
This video illustrates the initiation of polymicrobial biofilm formation on virus-infected cells by bacteria and fungi. Herpes simplex virus infection impedes bacterial attachment to the cells, preventing bacterial-fungal co-localization. In contrast, uninfected cells display bacterial-fungal co-localization and initiation of biofilm formation.
14:54
A Controlled Mouse Model for Neonatal Polymicrobial Sepsis
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Cited by 16 •
2019
This protocol provides the necessary steps to establish and evaluate neonatal sepsis in 7-day-old mice.
12:33
Determination of Biofilm Initiation on Virus-infected Cells by Bacteria and Fungi
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Cited by 3 •
2016
A method is described herein for the determination of inter-Kingdom association and competition (bacterial and fungal) for adherence to virus-infected HeLa cell monolayers. This protocol can be extended to multiple combinations of prokaryotes, eukaryotes, and viruses.
13:50
Sequencing of Bacterial Microflora in Peripheral Blood: our Experience with HIV-infected Patients
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Cited by 7 •
2011
Our experiment will show how to perform a sequencing analysis of bacterial species translocating in peripheral blood of HIV positive patients.
07:16
Development of a Polymicrobial Colony Biofilm Model to Test Antimicrobials in Cystic Fibrosis
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Cited by 3 •
2024
This methodology allows for the establishment of a polymicrobial biofilm model in cystic fibrosis for antimicrobial sensitivity testing in research and clinical laboratories. This model provides accurate and reliable results over a range of outputs.
FAQs
Microbial interactions can shift the balance between competing organisms and allow some species to support others through cooperation or metabolic exchange. These relationships may also modify the local environment, changing which organisms persist and how strongly they affect host tissues. Consequently, disease behavior may differ from that produced by any one member studied separately, making community-level analysis important.
A mixed microbial community can present the immune system with multiple signals at the same time, while interactions among organisms may change the surrounding environment. These effects can influence how infection is recognized and how inflammation develops. In immunology research, examining the community rather than a single pathogen helps explain immune responses that are not fully reproduced in single-species models.
Antimicrobial susceptibility may change when organisms grow or persist together because community interactions can modify the local environment and the behavior of individual species. This means responses observed for one pathogen alone may not predict the response of the mixed infection. Studying the combined community therefore provides information relevant to interpreting treatment challenges and designing more appropriate antimicrobial strategies.
A single-pathogen model cannot fully represent competition, cooperation, metabolic exchange, or environmental changes created by multiple organisms. It may therefore miss interactions that influence persistence, severity, immune recognition, inflammation, or antimicrobial susceptibility. Polymicrobial models are valuable when the research question concerns these community effects or aims to reflect clinical disease more realistically.
Researchers should determine which microbial members and interactions are relevant to the disease process being studied, then examine how their combined presence affects the local environment and host response. The model should support assessment of persistence, inflammation, immune recognition, and antimicrobial susceptibility. Such planning helps distinguish effects caused by community interactions from effects attributable to individual organisms.
This approach is particularly useful when disease severity, persistence, immune responses, or treatment susceptibility cannot be explained adequately by examining one pathogen. Community-focused studies can reveal how microbial interactions shape host inflammation and recognition, while also supporting models that better reflect clinical disease. Their findings may inform diagnostic strategies and the development of treatments suited to mixed infections.