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Related Concept Videos

Lytic Cycle of Bacteriophages01:30

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Bacteriophages, also known as phages, are specialized viruses that infect bacteria. A key characteristic of phages is their distinctive “head-tail” morphology. A phage begins the infection process (i.e., lytic cycle) by attaching to the outside of a bacterial cell. Attachment is accomplished via proteins in the phage tail that bind to specific receptor proteins on the outer surface of the bacterium. The tail injects the phage’s DNA genome into the bacterial cytoplasm. In the...
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In contrast to the lytic cycle, phages infecting bacteria via the lysogenic cycle do not immediately kill their host cell. Instead, they combine their genome with the host genome, allowing the bacteria to replicate the phage DNA along with the bacterial genome. The incorporated copy of the phage genome is called the prophage. Some prophages can re-activate and enter the lytic cycle. This often occurs in response to a perturbation, such as DNA damage, but can also transpire in the absence of...
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Digital phagograms: predicting phage infectivity through a multilayer machine learning approach.

Cédric Lood1, Dimitri Boeckaerts2, Michiel Stock3

  • 1Laboratory of Gene Technology, Department of Biosystems, KU Leuven, Leuven, Belgium; Centre of Microbial and Plant Genetics, Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium.

Current Opinion in Virology
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning aids phage biology by predicting phage-host interactions. A new multilayer model integrating omics and systems biology data can predict phage infectivity and support phage engineering.

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Area of Science:

  • * Computational biology and bioinformatics
  • * Microbial genetics and molecular biology

Background:

  • * Machine learning (ML) is increasingly used in biological systems research.
  • * Phage biology utilizes ML for predicting phage-host interactions, including receptor binding, defense systems, prophage presence, and life cycle.
  • * Integrating omics data with systems biology offers potential for deeper understanding of phage-host dynamics.

Purpose of the Study:

  • * To highlight the potential of integrating omics data and systems biology for understanding phage-host interactions.
  • * To conceptualize a multilayer model for predicting phage infectivity.
  • * To explore the application of digital phagograms in phage engineering and cocktail design.

Main Methods:

  • * Conceptualization of a multilayer model mirroring the phage infection process.
  • * Integration of omics data and systems biology principles.
  • * Focus on adsorption, bacterial pan-immune components, and metabolic hijacking.

Main Results:

  • * The proposed multilayer model has the potential to predict phage infectivity.
  • * Integration of diverse data types enhances the understanding of phage-host interactions.
  • * Digital phagograms can aid in phage cocktail design and engineering.

Conclusions:

  • * A multilayer model integrating omics and systems biology can predict phage infectivity.
  • * This approach offers insights into phage infection mechanisms.
  • * Digital phagograms can advance phage cocktail design and phage engineering.