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Nontargeted in vitro metabolomics for high-throughput identification of novel enzymes in Escherichia coli

Abstract

Our understanding of metabolism is limited by a lack of knowledge about the functions of many enzymes. Here, we develop a high-throughput mass spectrometry approach to comprehensively profile proteins for in vitro enzymatic activity. Overexpressed or purified proteins are incubated in a supplemented metabolome extract containing hundreds of biologically relevant candidate substrates, and accumulating and depleting metabolites are determined by nontargeted mass spectrometry. By combining chemometrics and database approaches, we established an automated pipeline for unbiased annotation of the functions of novel enzymes. In screening all 1,275 functionally uncharacterized Escherichia coli proteins, we discovered 241 potential novel enzymes, 12 of which we experimentally validated. Our high-throughput in vitro metabolomics method is generally applicable to any purified protein or crude cell lysate of its overexpression host and enables performing up to 1,200 nontargeted enzyme assays per working day.

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Figure 1: Enzyme discovery by nontargeted metabolomics.
Figure 2: Nontargeted metabolomics reveals numerous novel enzymes in E. coli.
Figure 3: Functional validation of 12 diverse novel metabolic enzymes.

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Acknowledgements

We thank S. Suter and J. Schmitz for technical assistance with validation enzyme assays. Funding was provided by the MetaNetX project of the Swiss Initiative for Systems Biology (SystemsX.ch; http://metanetx.org; evaluated by the Swiss National Science Foundation) and the Swiss Federal Government through the Federal Office of Education and Science.

Author information

Authors and Affiliations

Authors

Contributions

D.C.S. and T.F. performed the experiments and analyzed the data. D.C.S., T.F. and N.Z. developed data analysis software and algorithms. D.C.S., N.Z. and U.S. designed the research and wrote the paper.

Corresponding author

Correspondence to Uwe Sauer.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–21 (PDF 6949 kb)

Supplementary Table 1

Annotation of ions as metabolites based on matching their accurate mass with compounds in the KEGG eco database. (XLSX 899 kb)

Supplementary Table 2

Annotation of ions as metabolites based on correlation with ions annotated by accurate mass. (XLSX 35 kb)

Supplementary Table 3

All strains of the ASKA library used in this work for His-tagged protein expression and purification. (XLSX 100 kb)

Supplementary Table 4

Known metabolic enzymes with ions passing the Z-score cutoff of 5.0. (XLSX 30 kb)

Supplementary Table 5

Functionally uncharacterized proteins with ions passing the Z-score cutoff of 5.0. (XLSX 53 kb)

Supplementary Table 6

Enzymatic reactions predicted to be catalyzed by uncharacterized proteins, based on matching differential metabolites of opposing change direction with the KEGG main reactant pair database. (XLSX 16 kb)

Supplementary Table 7

Predicted reactant pairs based on molecular similarity among two differential metabolites of opposing change direction. (XLSX 17 kb)

Supplementary Table 8

Predicted reactant pairs based on mass difference among two ions of opposing change direction. (XLSX 16 kb)

Supplementary Table 9

List of experimentally tested reaction predictions and outcomes. (XLSX 14 kb)

Supplementary Table 10

Reactant ions that show consistent changes in respective deletion mutants of predicted enzymes. (XLSX 15 kb)

Supplementary Table 11

Growth phenotyping of E. coli single-gene deletion mutants each lacking one of the 12 experimentally validated enzymes. (XLSX 13 kb)

Supplementary Data Set 1

Z-scores of ions in assays of purified functionally uncharacterized proteins. Proteins with concentrations < 50 mg/L were not analyzed. (CSV 26690 kb)

Supplementary Data Set 2

Z-scores of ions in cell lysate assays of strains overexpressing functionally uncharacterized proteins. (CSV 19446 kb)

Supplementary Data Set 3

Metabolomics of 223 viable single-gene deletion mutants each lacking a predicted enzyme discovered in this study. (CSV 8592 kb)

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Sévin, D., Fuhrer, T., Zamboni, N. et al. Nontargeted in vitro metabolomics for high-throughput identification of novel enzymes in Escherichia coli. Nat Methods 14, 187–194 (2017). https://doi.org/10.1038/nmeth.4103

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