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<p>Auxotrophic metabolic sensors (AMS) are microbial strains modified so that biomass formation correlates with the availability of specific metabolites. These sensors are essential for bioengineering (e.g., in growth-coupled designs) but creating them is often a time-consuming and low-throughput process that can be streamlined by in silico analysis. Here, we present a systematic workflow for designing, implementing, and testing versatile AMS based on <i>Escherichia coli</i>. Glyoxylate, a key metabolite in (synthetic) CO<sub>2</sub> fixation and carbon-conserving pathways, served as the test analyte. Through iterative screening of a compact metabolic model, we identify non-trivial growth-coupled designs that result in six AMS with a wide sensitivity range for glyoxylate, spanning three orders of magnitude in the detected analyte concentration. We further adapt these <i>E. coli</i> AMS for sensing glycolate and demonstrate their utility in both pathway engineering (testing a key metabolic module for carbon assimilation <i>via</i> glyoxylate) and environmental monitoring (quantifying glycolate produced by photosynthetic microalgae). Adapting this workflow to the sensing of different metabolites could facilitate the design and implementation of AMS for diverse biotechnological applications.</p>

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Computation-aided designs enable developing auxotrophic metabolic sensors for wide-range glyoxylate and glycolate detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月4日

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  • Enrico Orsi,
  • Helena Schulz-Mirbach,
  • Charles A. R. Cotton,
  • Ari Satanowski,
  • Henrik M. Petri,
  • Susanne L. Arnold,
  • Natalia Grabarczyk,
  • Rutger Verbakel,
  • Karsten S. Jensen,
  • Stefano Donati,
  • Nicole Paczia,
  • Timo Glatter,
  • Andreas M. Küffner,
  • Tanguy Chotel,
  • Farah Schillmüller,
  • Alberto De Maria,
  • Hai He,
  • Steffen N. Lindner,
  • Elad Noor,
  • Arren Bar-Even,
  • Tobias J. Erb,
  • Pablo I. Nikel

摘要

Auxotrophic metabolic sensors (AMS) are microbial strains modified so that biomass formation correlates with the availability of specific metabolites. These sensors are essential for bioengineering (e.g., in growth-coupled designs) but creating them is often a time-consuming and low-throughput process that can be streamlined by in silico analysis. Here, we present a systematic workflow for designing, implementing, and testing versatile AMS based on Escherichia coli. Glyoxylate, a key metabolite in (synthetic) CO2 fixation and carbon-conserving pathways, served as the test analyte. Through iterative screening of a compact metabolic model, we identify non-trivial growth-coupled designs that result in six AMS with a wide sensitivity range for glyoxylate, spanning three orders of magnitude in the detected analyte concentration. We further adapt these E. coli AMS for sensing glycolate and demonstrate their utility in both pathway engineering (testing a key metabolic module for carbon assimilation via glyoxylate) and environmental monitoring (quantifying glycolate produced by photosynthetic microalgae). Adapting this workflow to the sensing of different metabolites could facilitate the design and implementation of AMS for diverse biotechnological applications.