Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli

Despite progress in automated gene annotation, many deficiencies and knowledge gaps remain, even for well-studied organisms. Of particular concern is the accuracy and detail of annotations for transporters of various organic substrates and products of metabolism and for enzymes that do not share sequence homology with well-characterized strains. Unfortunately, annotation errors present in earlier genome-scale metabolic (GSM) models propagate to newer models with few opportunities for later correction. Here, we introduce a systematic computational procedure that applies the Escherichia coli genome-scale metabolic model iML1515, extended with transcriptional regulatory rules, to design auxotrophs that can grow on glucose but fail to grow on different carbon substrate(s) unless rescued with the addition of an ORF encoding a complementation metabolic function (transport and enzymatic reactions). Using the E. coli GSM model supplemented with regulatory rules that quantify growth/no growth outcomes on different organic substrates, we identified 258 distinct auxotrophic designs (97 single-gene, 142 double-gene, and 19 triple-gene knockouts) for which specific single functions can uniquely complement them. Experimental validation of 61 single-knockout strains demonstrated 59% confirmed auxotrophy and 28% partial auxotrophy. We envision that this collection of auxotrophic strains can be used to disambiguate the metabolic role of unannotated or poorly annotated genes.

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Publication Details

Journal
PLoS Computational Biology
Published
2026-06-29
DOI
https://doi.org/10.1371/journal.pcbi.1014469
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
0.00

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article

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli

Patrick F. Suthers, Brian F. Pfleger, Costas D. Maranas, Fang-Yu Chung et al.
PLoS Computational Biology
Microbial Metabolic Engineering and Bioproduction
article

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli

Patrick F. Suthers, Brian F. Pfleger, Costas D. Maranas, Fang-Yu Chung, Roghaye Mohammadbeygi
article en

Abstract

Despite progress in automated gene annotation, many deficiencies and knowledge gaps remain, even for well-studied organisms. Of particular concern is the accuracy and detail of annotations for transporters of various organic substrates and products of metabolism and for enzymes that do not share sequence homology with well-characterized strains. Unfortunately, annotation errors present in earlier genome-scale metabolic (GSM) models propagate to newer models with few opportunities for later correction. Here, we introduce a systematic computational procedure that applies the Escherichia coli genome-scale metabolic model iML1515, extended with transcriptional regulatory rules, to design auxotrophs that can grow on glucose but fail to grow on different carbon substrate(s) unless rescued with the addition of an ORF encoding a complementation metabolic function (transport and enzymatic reactions). Using the E. coli GSM model supplemented with regulatory rules that quantify growth/no growth outcomes on different organic substrates, we identified 258 distinct auxotrophic designs (97 single-gene, 142 double-gene, and 19 triple-gene knockouts) for which specific single functions can uniquely complement them. Experimental validation of 61 single-knockout strains demonstrated 59% confirmed auxotrophy and 28% partial auxotrophy. We envision that this collection of auxotrophic strains can be used to disambiguate the metabolic role of unannotated or poorly annotated genes.

PLoS Computational BiologyVol. 22(6)
Pennsylvania State University (US), University of Wisconsin–Madison (US)
National Science Foundation, Division of Molecular and Cellular Biosciences, Division of Integrative Organismal Systems
Openalex Percentile: Top 11%
Microbial Metabolic Engineering and Bioproduction
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