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Related Concept Videos

Gene Regulation in Microbial Communities: Quorum Sensing01:28

Gene Regulation in Microbial Communities: Quorum Sensing

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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,...
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Bacterial signaling can occur within bacteria (intracellular) or between bacteria (intercellular). At times, a group of bacteria behaves like a community. To achieve this, they engage in quorum sensing, the perception of higher cell density that causes changes in gene expression. Quorum sensing involves both extracellular and intracellular signaling. The signaling cascade starts with a molecule called an autoinducer (AI). Individual bacteria produce AIs that move out of the bacterial cell...
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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Types of Signaling Molecules01:32

Types of Signaling Molecules

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In multicellular organisms, many molecules transmit signals between cells to pass information. These signals vary in complexity and include small peptides, nucleotides, steroids, fatty acid derivatives, and dissolved gases such as nitric oxide. Some signaling molecules diffuse through the plasma membrane to act locally between neighboring cells or travel long distances. Others remain attached to the cell surface, transmitting information to other cells only when they make contact. In some...
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QSIdb: quorum sensing interference molecules.

Shengbo Wu1, Chunjiang Liu2, Jie Feng3

  • 1School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.

Briefings in Bioinformatics
|October 1, 2020
PubMed
Summary
This summary is machine-generated.

Quorum sensing interference (QSI) is crucial for synthetic biology and therapies. This study introduces QSIdb, a comprehensive database of quorum sensing interference molecules (QSIMs), aiding in the discovery of new broad-spectrum QSIMs.

Keywords:
AutoDock VinaSMILESantibioticsmultidrug-resistantquorum sensing analoguesquorum sensing interference

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Area of Science:

  • Microbiology
  • Synthetic Biology
  • Bioinformatics

Background:

  • Quorum sensing interference (QSI) is vital for controlling bacterial populations, with applications in synthetic biology and clinical therapies.
  • Existing quorum sensing interference molecules (QSIMs) are limited, hindering diverse QS system manipulations.
  • A need exists for expanded QSIM libraries and tools to discover novel molecules.

Purpose of the Study:

  • To develop QSIdb, a specialized repository of QSIMs.
  • To expand the known QSIM repertoire through computational mining.
  • To introduce novel methods for assessing QSIM potential and interactions.

Main Methods:

  • Compiled 633 reported QSIMs and 73,073 expanded QSIMs from literature.
  • Developed a computational pipeline using SMILES-based similarity and docking for QSIM discovery in PubChem.
  • Introduced 'pocketedit' for assessing protein pocket similarity and QSIM crosstalk.

Main Results:

  • QSIdb repository established with reported and expanded QSIMs.
  • Identified 273 potential broad-spectrum QSIMs through computational mining.
  • User-friendly interface for data retrieval and comparison.

Conclusions:

  • QSIdb provides a valuable resource for QSI research.
  • Facilitates the development of QS-related therapeutics and metabolic engineering tools.
  • Enables discovery of novel broad-spectrum QSIMs and ligands.