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Bacterial sensors define intracellular free energies for correct enzyme metalation

Nature Chemical Biology volume 15, pages 241–249 (2019)Cite this article

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Abstract

There is a challenge for metalloenzymes to acquire their correct metals because some inorganic elements form more stable complexes with proteins than do others. These preferences can be overcome provided some metals are more available than others. However, while the total amount of cellular metal can be readily measured, the available levels of each metal have been more difficult to define. Metal-sensing transcriptional regulators are tuned to the intracellular availabilities of their cognate ions. Here we have determined the standard free energy for metal complex formation to which each sensor, in a set of bacterial metal sensors, is attuned: the less competitive the metal, the less favorable the free energy and hence the greater availability to which the cognate allosteric mechanism is tuned. Comparing these free energies with values derived from the metal affinities of a metalloprotein reveals the mechanism of correct metalation exemplified here by a cobalt chelatase for vitamin B12.

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Fig. 1: Metal binding and DNA binding are coupled to enable Salmonella to sense different metals.
Fig. 2: Metal affinities that complete a set of values for Salmonella metal sensors.
Fig. 3: Metals change the abundance of some sensors to modify regulation.
Fig. 4: Sensing is tuned to the Irving–Williams series.
Fig. 5: Metalation of CbiK and sirohydrochlorin.

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Equation derivations, template Excel spreadsheet (with instructions) and MATLAB codes (with instructions) are available in Supplementary Note 2, Supplementary Dataset 1 and Supplementary Note 3, respectively.

Data availability

All data are available within the article and its Supplementary Information files or from the corresponding author upon request.

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Acknowledgements

This work was supported by Biotechnology and Biological Sciences Research Council awards nos. BB/J017787/1, BB/R002118/1 and BB/L009226/1. Interactions with industrial partners were supported by Biotechnology and Biological Sciences Research Council (BBSRC) award no. BB/L013711/1 plus a financial contribution from Procter and Gamble (in association with an Industrial Partnership Award no. BB/J017787/1). K. Svedaite, Department of Biosciences, Durham University, provided technical assistance in the measurements of in vitro DNA affinities of ZntR and CueR. E. Pohl and C. Bain, both of Durham University Department of Chemistry, assisted with structure homology modeling and consideration of standard free-energy changes, respectively. Salmonella enterica serovar Typhimurium strain SL1344 was provided by J.S. Cavet, School of Biological Sciences, University of Manchester, Manchester, UK. E. Fioravanti, Mathematical Institute, University of Oxford, assisted with derivations shown in the Supplementary Note 2. All DNA sequencing was conducted by DBS genomics, Durham University.

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  1. These authors contributed equally: Deenah Osman, Maria Alessandra Martini, Andrew W. Foster.

Authors and Affiliations

  1. Department of Biosciences, Durham University, Durham, UK

    Deenah Osman, Maria Alessandra Martini, Andrew W. Foster, Andrew J. P. Scott, Elena Lurie-Luke, Peter T. Chivers & Nigel J. Robinson

  2. Department of Chemistry, Durham University, Durham, UK

    Deenah Osman, Andrew W. Foster, Jonathan W. Steed, Peter T. Chivers & Nigel J. Robinson

  3. Procter and Gamble, Mason Business Center, Cincinnati, OH, USA

    Junjun Chen & Thomas G. Huggins

  4. Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle-upon-Tyne, UK

    Richard J. Morton

  5. School of Biosciences, University of Kent, Canterbury, Kent, UK

    Andrew D. Lawrence, Evelyne Deery & Martin J. Warren

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Contributions

D.O. conducted the in vivo experiments, bioinformatics analyses and was involved in all in vitro measurements of sensor affinities. M.A.M. determined in vitro affinities of MntR and Fur. M.A.M., along with D.O., A.W.F. and J.W.S., developed computational methods to determine θD and θDM. R.J.M. along with D.O. generated the MATLAB code relating fractional sensor responses to buffered [M]. A.J.P.S. and P.T.C. determined the in vitro affinities of NikR. J.C. and T.G.H. performed the MRM tandem mass spectrometry. A.W.F. along with E.D., A.D.L., P.T.C. and M.J.W. performed and co-designed analyses of CbiK. N.J.R. and E.L.-L. conceived the program. N.J.R., D.O. and A.W.F. drafted the manuscript and, in conjunction with M.A.M., interpreted the significance of the data. N.J.R., with input from P.T.C., had overall responsibility for the design, coordination and management of the project. All authors reviewed the results and edited and approved the final version of the manuscript.

Corresponding authors

Correspondence to Peter T. Chivers or Nigel J. Robinson.

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Competing interests

J.C. and T.G.H. are employees of Procter and Gamble. The collaboration was supported by an Industrial Partnership Award from the BBSRC plus a financial contribution from Procter and Gamble (in association with BBSRC award no. BB/J017787/1).

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Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–5, Supplementary Figures 1–22

Reporting Summary

Supplementary Dataset 1

Excel Spreadsheet (with instructions) to enable calculation of fractional DNA occupancy

Supplementary Note 1

The Dynafit scripts

Supplementary Note 2

The supplementary equations and unique Supplementary Note 2 references

Supplementary Note 3

The MATLAB codes (with instructions), to determine the buffered metal concentration from given value(s) of θD or θDM

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Osman, D., Martini, M.A., Foster, A.W. et al. Bacterial sensors define intracellular free energies for correct enzyme metalation. Nat Chem Biol 15, 241–249 (2019). https://doi.org/10.1038/s41589-018-0211-4

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