ArticlePloS one2014
High-throughput screening of a Corynebacterium glutamicum mutant library on genomic and metabolic level.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transposon-based genome editing of industrial microorganisms: advances, challenges, and prospects.Synthetic and systems biotechnology · 2026Review
- Influence of L-lactate and low glucose concentrations on the metabolism and the toxin formation of Clostridioides difficile.PloS one · 2021Article
- Characterization of Clostridioides difficile DSM 101085 with A-B-CDT+ Phenotype from a Late Recurrent Colonization.Genome biology and evolution · 2020Article
- Metabolic Reprogramming ofFrontiers in microbiology · 2018Article
- The fate of lysine: Non-targeted stable isotope analysis reveals parallel ways for lysine catabolization in Phaeobacter inhibens.PloS one · 2017Article
- Time-resolved amino acid uptake of Clostridium difficile 630Δerm and concomitant fermentation product and toxin formation.BMC microbiology · 2015Article
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Authors and funding
4 authors.
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Abstract
Due to impressive achievements in genomic research, the number of genome sequences has risen quickly, followed by an increasing number of genes with unknown or hypothetical function. This strongly calls for development of high-throughput methods in the fields of transcriptomics, proteomics and metabolomics. Of these platforms, metabolic profiling has the strongest correlation with the phenotype. We previously published a high-throughput metabolic profiling method for C. glutamicum as well as the automatic GC/MS processing software MetaboliteDetector. Here, we added a high-throughput transposon insertion determination for our C. glutamicum mutant library. The combination of these methods allows the parallel analysis of genotype/phenotype correlations for a large number of mutants. In a pilot project we analyzed the insertion points of 722 transposon mutants and found that 36% of the affected genes have unknown functions. This underlines the need for further information gathered by high-throughput techniques. We therefore measured the metabolic profiles of 258 randomly chosen mutants. The MetaboliteDetector software processed this large amount of GC/MS data within a few hours with a low relative error of 11.5% for technical replicates. Pairwise correlation analysis of metabolites over all genotypes showed dependencies of known and unknown metabolites. For a first insight into this large data set, a screening for interesting mutants was done by a pattern search, focusing on mutants with changes in specific pathways. We show that our transposon mutant library is not biased with respect to insertion points. A comparison of the results for specific mutants with previously published metabolic results on a deletion mutant of the same gene confirmed the concept of high-throughput metabolic profiling. Altogether the described method could be applied to whole mutant libraries and thereby help to gain comprehensive information about genes with unknown, hypothetical and known functions.
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