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Biofilms are complex communities of microorganisms encased in a self-produced extracellular polysaccharide matrix attached to surfaces. These microbial consortia can include single or multiple species, providing enhanced survival benefits by forming organized, multilayered structures.The formation of biofilms occurs through four key stages: attachment, colonization, development, and dispersal.During attachment, free-swimming planktonic cells adhere to a surface, often facilitated by...
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An Intestinal Gut Organ Culture System for Analyzing Host-Microbiota Interactions
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Structural host-microbiota interaction networks.

Emine Guven-Maiorov1, Chung-Jung Tsai1, Ruth Nussinov1,2

  • 1Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America.

Plos Computational Biology
|October 13, 2017
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Summary
This summary is machine-generated.

Understanding host-microbiota interactions (HMIs) is key to deciphering health and disease. Computational approaches can map these interactions to reveal how microbes influence host immunity and signaling pathways.

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Area of Science:

  • Microbiology
  • Systems Biology
  • Computational Biology

Background:

  • Multicellular organisms host numerous microbial species, forming complex metaorganisms.
  • Host-microbiota interactions (HMIs) play a critical role in maintaining health and influencing disease states.
  • Understanding HMIs at a molecular level is crucial for insights into infection mechanisms and microbial roles in health and disease.

Purpose of the Study:

  • To explore the potential of computational methods for characterizing host-microbiota interactions.
  • To map HMIs onto host cellular networks to understand microbial modulation of host signaling.
  • To identify microbial effectors and host targets involved in HMIs.

Main Methods:

  • Developing and applying computational frameworks to predict cross-kingdom interactions.
  • Integrating host and microbiota data into network models.
  • Analyzing structural HMI networks to identify key microbial effectors and host regulatory nodes.

Main Results:

  • Structural HMI networks can identify microbial effectors targeting host nodes and how mutations impact interactions.
  • These networks can reveal master host cell regulator nodes manipulated by microbes.
  • The approach aids in delineating pathogenic mechanisms and potential therapeutic targets.

Conclusions:

  • Characterizing HMIs computationally offers a promising avenue for understanding complex host-microbe relationships.
  • Mapping HMIs on host networks can elucidate microbial influence on immune surveillance and signaling.
  • This approach can accelerate the discovery of therapeutic strategies by maximizing beneficial microbial effects.