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Synthetic Biology02:55

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression
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COMMBAT: a web platform for exploring expression control of biosynthetic gene clusters.

Silvia Ribeiro Monteiro1, Augustin Rigolet2, Clément Jeunehomme1

  • 1InBioS-Center for Protein Engineering, University of Liège, Institut de Chimie, Liège B-4000, Belgium.

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Summary

Predicting transcription factor (TF) regulation of bacterial biosynthetic gene clusters (BGCs) is challenging. COMMBAT integrates genomic data to predict TF-BGC interactions, aiding natural product discovery.

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Bacterial genomes harbor numerous biosynthetic gene clusters (BGCs) producing valuable natural products.
  • Transcription factors (TFs) regulate BGC expression in response to environmental signals.
  • Predicting TF-BGC regulatory interactions is difficult due to divergent TF binding sites (TFBSs).

Purpose of the Study:

  • To develop a framework for large-scale prediction of TF-BGC regulatory interactions.
  • To create a web platform for identifying TF-BGC regulatory relationships.
  • To facilitate natural product discovery by predicting regulatory inputs.

Main Methods:

  • Developed COMMBAT (COnditions for Microbial Metabolite Biosynthesis Activated Transcription) framework.
  • Integrated motif matching with genomic context and gene function data.
  • Utilized over 4000 TF position weight matrices and 400,000+ BGCs.

Main Results:

  • COMMBAT predicts functional TFBSs by integrating diverse data.
  • The COMMBAT web platform allows users to predict TF-BGC interactions.
  • Provides a scalable resource for understanding BGC regulation.

Conclusions:

  • COMMBAT offers a novel approach to predict TF-BGC regulatory interactions.
  • Enables systematic exploration of BGC regulation across bacterial genomes.
  • Guides strategies for natural product discovery and optimization.