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

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 Maria, Hai He, Steffen N. Lindner, Elad Noor, Arren Bar-Even, Tobias J. Erb and Pablo I. Nikel ()
Additional contact information
Enrico Orsi: Technical University of Denmark
Helena Schulz-Mirbach: Max Planck Institute for Terrestrial Microbiology
Charles A. R. Cotton: Max Planck Institute of Molecular Plant Physiology
Ari Satanowski: Max Planck Institute for Terrestrial Microbiology
Henrik M. Petri: Max Planck Institute for Terrestrial Microbiology
Susanne L. Arnold: Max Planck Institute for Terrestrial Microbiology
Natalia Grabarczyk: Technical University of Denmark
Rutger Verbakel: Technical University of Denmark
Karsten S. Jensen: Technical University of Denmark
Stefano Donati: Technical University of Denmark
Nicole Paczia: Max Planck Institute for Terrestrial Microbiology
Timo Glatter: Max Planck Institute for Terrestrial Microbiology
Andreas M. Küffner: Max Planck Institute for Terrestrial Microbiology
Tanguy Chotel: Max Planck Institute for Terrestrial Microbiology
Farah Schillmüller: Max Planck Institute for Terrestrial Microbiology
Alberto Maria: Technical University of Denmark
Hai He: Max Planck Institute for Terrestrial Microbiology
Steffen N. Lindner: Max Planck Institute of Molecular Plant Physiology
Elad Noor: Weizmann Institute of Science
Arren Bar-Even: Max Planck Institute of Molecular Plant Physiology
Tobias J. Erb: Max Planck Institute for Terrestrial Microbiology
Pablo I. Nikel: Technical University of Denmark

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-57407-3

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