Abstract
Novel classes of broad-spectrum antibiotics have been extremely difficult to discover, largely due to the impermeability of the Gram-negative membranes coupled with a poor understanding of the physicochemical properties a compound should possess to promote its accumulation inside the cell. To address this challenge, numerous methodologies for assessing intracellular compound accumulation in Gram-negative bacteria have been established, including classic radiometric and fluorescence-based methods. The recent development of accumulation assays that utilize liquid chromatography–tandem mass spectrometry (LC-MS/MS) have circumvented the requirement for labeled compounds, enabling assessment of a substantially broader range of small molecules. Our unbiased study of accumulation trends in Escherichia coli using an LC-MS/MS-based assay led to the development of the eNTRy rules, which stipulate that a compound is most likely to accumulate in E. coli if it has an ionizable Nitrogen, has low Three-dimensionality and is relatively Rigid. To aid in the implementation of the eNTRy rules, we developed a complementary web tool, eNTRyway, which calculates relevant properties and predicts compound accumulation. Here we provide a comprehensive protocol for analysis and prediction of intracellular accumulation of small molecules in E. coli using an LC-MS/MS-based assay (which takes ~2 d) and eNTRyway, a workflow that is readily adoptable by any microbiology, biochemistry or chemical biology laboratory.
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Data availability
The main data discussed in this protocol are available in the supporting primary research papers (https://doi.org/10.1038/nature22308 and https://doi.org/10.1038/s41564-019-0604-5). Source data are provided with this paper. Additional requests should be addressed to the corresponding authors.
Code availability
Source code for eNTRyway for local use is available on GitHub (https://github.com/HergenrotherLab/entry-cli).
References
Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 2019. https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf (2019).
Tommasi, R., Brown, D. G., Walkup, G. K., Manchester, J. I. & Miller, A. A. ESKAPEing the labyrinth of antibacterial discovery. Nat. Rev. Drug. Discov. 14, 529–542 (2015).
Richter, M. F. & Hergenrother, P. J. The challenge of converting Gram-positive-only compounds into broad-spectrum antibiotics. Ann. NY Acad. Sci. 1435, 18–38 (2019).
Rice, L. B. Federal funding for the study of antimicrobial resistance in nosocomial pathogens: no ESKAPE. J. Infect. Dis. 197, 1079–1081 (2008).
Lewis, K. Platforms for antibiotic discovery. Nat. Rev. Drug Discov. 12, 371–387 (2013).
Gladki, A., Kaczanowski, S., Szczesny, P. & Zielenkiewicz, P. The evolutionary rate of antibacterial drug targets. BMC Bioinforma. 14, 36–36 (2013).
Krishnamoorthy, G. et al. Synergy between active efflux and outer membrane diffusion defines rules of antibiotic permeation into Gram-negative bacteria. mBio 8, e01172–172017 (2017).
Krishnamoorthy, G. et al. Breaking the permeability barrier of Escherichia coli by controlled hyperporination of the outer membrane. Antimicrob. Agents Chemother. 60, 7372–7381 (2016).
Bazile, S., Moreau, N., Bouzard, D. & Essiz, M. Relationships among antibacterial activity, inhibition of DNA gyrase, and intracellular accumulation of 11 fluoroquinolones. Antimicrob. Agents Chemother. 36, 2622–2627 (1992).
Cai, H., Rose, K., Liang, L. H., Dunham, S. & Stover, C. Development of a liquid chromatography/mass spectrometry-based drug accumulation assay in Pseudomonas aeruginosa. Anal. Biochem. 385, 321–325 (2009).
Capobianco, J. O. & Goldman, R. C. Macrolide transport in Escherichia coli strains having normal and altered OmpC and/or OmpF porins. Int. J. Antimicrob. Agents 4, 183–189 (1994).
Chopra, I. Transport of tetracyclines into Escherichia coli requires a carboxamide group at the C2 position of the molecule. J. Antimicrob. Chemother. 18, 661–666 (1986).
de Cristóbal, R. E., Vincent, P. A. & Salomón, R. A. Multidrug resistance pump AcrAB-TolC is required for high-level, Tet(A)-mediated tetracycline resistance in Escherichia coli. J. Antimicrob. Chemother. 58, 31–36 (2006).
Li, X. Z., Livermore, D. M. & Nikaido, H. Role of efflux pump(s) in intrinsic resistance of Pseudomonas aeruginosa: resistance to tetracycline, chloramphenicol, and norfloxacin. Antimicrob. Agents Chemother. 38, 1732–1741 (1994).
Piddock, L. J. V., Jin, Y.-F., Ricci, V. & Asuquo, A. E. Quinolone accumulation by Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli. J. Antimicrob. Chemother. 43, 61–70 (1999).
Williams, K. J. & Piddock, L. J. Accumulation of rifampicin by Escherichia coli and Staphylococcus aureus. J. Antimicrob. Chemother. 42, 597–603 (1998).
Richter, M. F. et al. Predictive compound accumulation rules yield a broad-spectrum antibiotic. Nature 545, 299–304 (2017).
Motika, S. E. et al. A Gram-negative antibiotic active through inhibition of an essential riboswitch. J. Am. Chem. Soc. 142, 10856–10862 (2020).
Parker, E. N. et al. Implementation of permeation rules leads to a FabI inhibitor with activity against Gram-negative pathogens. Nat. Microbiol. 5, 67–75 (2020).
Li, Y. et al. First-generation structure-activity relationship studies of 2,3,4,9-tetrahydro-1H-carbazol-1-amines as CpxA phosphatase inhibitors. Bioorg. Med. Chem. Lett. 29, 1836–1841 (2019).
Lukežič, T. et al. Engineering atypical tetracycline formation in Amycolatopsis sulphurea for the production of modified chelocardin antibiotics. ACS Chem. Biol. 14, 468–477 (2019).
Masci, D. et al. Switching on the activity of 1,5-diaryl-pyrrole derivatives against drug-resistant ESKAPE bacteria: structure-activity relationships and mode of action studies. Eur. J. Med. Chem. 178, 500–514 (2019).
Hu, Y. et al. Discovery of pyrido[2,3-b]indole derivatives with gram-negative activity targeting both DNA gyrase and topoisomerase IV. J. Med. Chem. 63, 9623–9649 (2020).
Andrews, L. D. et al. Optimization and mechanistic characterization of pyridopyrimidine inhibitors of bacterial biotin carboxylase. J. Med. Chem. 62, 7489–7505 (2019).
Cohen, F. et al. Optimization of LpxC inhibitors for antibacterial activity and cardiovascular safety. ChemMedChem 14, 1560–1572 (2019).
Perlmutter, S. J. et al. Compound uptake into E. coli can be facilitated by N-alkyl guanidiniums and pyridiniums. ACS Infect. Dis. 7, 162–173 (2021).
Munoz, K. A. & Hergenrother, P. J. Facilitating compound entry as a means to discover antibiotics for Gram-negative bacteria. Acc. Chem. Res. 54, 1322–1333 (2021).
O’Shea, R. & Moser, H. E. Physicochemical properties of antibacterial compounds: implications for drug discovery. J. Med. Chem. 51, 2871–2878 (2008).
Thanassi, D. G., Suh, G. S. B. & Nikaido, H. Role of outer membrane barrier in efflux-mediated tetracycline resistance in Escherichia coli. J. Bacteriol. 177, 998–1007 (1995).
Leive, L., Telesetsky, S., Coleman, W. G. J. & Carr, D. Tetracyclines of various hydrophobicities as a probe for permeability of Escherichia coli outer membranes. Antimicrob. Agents Chemother. 25, 539–544 (1984).
Lindley, E. V., Munske, G. R. & Magnuson, J. A. Kinetic analysis of tetracycline accumulation by Streptococcus faecalis. J. Bacteriol. 158, 334–336 (1984).
Sigler, A., Schubert, P., Hillen, W. & Niederweis, M. Permeation of tetracyclines through membranes of liposomes and Escherichia coli. Eur. J. Biochem. 267, 527–534 (2000).
Vergalli, J. et al. Spectrofluorimetric quantification of antibiotic drug concentration in bacterial cells for the characterization of translocation across bacterial membranes. Nat. Protoc. 13, 1348–1361 (2018).
Cinquin, B. et al. Microspectrometric insights on the uptake of antibiotics at the single bacterial cell level. Sci. Rep. 5, 17968 (2015).
Masi, M. et al. Fluorescence enlightens RND pump activity and the intrabacterial concentration of antibiotics. Res. Microbiol. 169, 432–441 (2018).
Renggli, S., Keck, W., Jenal, U. & Ritz, D. Role of autofluorescence in flow cytometric analysis of Escherichia coli treated with bactericidal antibiotics. J. Bacteriol. 195, 4067–4073 (2013).
Six, D. A., Krucker, T. & Leeds, J. A. Advances and challenges in bacterial compound accumulation assays for drug discovery. Curr. Opin. Chem. Biol. 44, 9–15 (2018).
Davis, T. D., Gerry, C. J. & Tan, D. S. General platform for systematic quantitative evaluation of small-molecule permeability in bacteria. ACS Chem. Biol. 9, 2535–2544 (2014).
Volkmer, B. & Heinemann, M. Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling. PLoS One 6, e23126 (2011).
Prochnow, H. et al. Subcellular quantification of uptake in Gram-negative bacteria. Anal. Chem. 91, 1863–1872 (2019).
Iyer, R. et al. Evaluating LC–MS/MS to measure accumulation of compounds within bacteria. ACS Infect. Dis. 4, 1336–1345 (2018).
Smith, P. A. et al. Optimized arylomycins are a new class of Gram-negative antibiotics. Nature 561, 189–194 (2018).
Rhomberg, P. R., Sader, H. S. & Jones, R. N. Reproducibility of daptomycin MIC results using dry-form commercial trays with appropriate supplemental calcium content. Int. J. Antimicrob. Agents 25, 274–275 (2005).
O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminformatics 3, 33 (2011).
Acknowledgements
We thank M. Richter for optimization and development of the LC-MS/MS-based accumulation assay, and we thank B. Drown for the creation of the web tool eNTRyway. This work was supported by the University of Illinois and the NIH (R01AI136773).
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E.J.G. and Z.L. performed experiments. All authors wrote the manuscript and were involved in editing of the final manuscript.
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Peer review information Nature Protocols thanks Kim Lewis and the other, anonymous reviewer(s) for their contribution to the peer review of this work.
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Related links
Key references using this protocol:
Richter, M. F. et al. Nature 545, 299–304 (2017): https://doi.org/10.1038/nature22308
Parker, E. N. et al. Nat. Microbiol. 5, 67–75 (2020): https://doi.org/10.1038/s41564-019-0604-5
Motika, S. E. et al. J. Am. Chem. Soc. 142, 10856–10862 (2020): https://doi.org/10.1021/jacs.0c04427
Extended data
Extended Data Fig. 1 Importance of amine steric accessibility and amphiphilic moment (vsurf_A).
a, Primary amines demonstrate higher accumulation than the mono-methyl amine, di-methyl amine, tri-methyl amine and amide comparisons. Primary amines on primary carbons also show improved accumulation over primary amines on secondary or tertiary carbons. b, Increasing amphiphilic moment trends with increasing accumulation. Accumulation is reported in nmol/1012 CFUs. Data are taken from Richter et al.17 with permission.
Extended Data Fig. 2 Screenshot of the ‘input’ box for SMILES strings.
SMILES strings are canonicalized using Open Babel GUI.
Extended Data Fig. 3 Screenshots of the process of predicting accumulation using the web tool eNTRyway.
SMILES strings are submitted to eNTRyway, and compounds are prioritized for evaluation based on how closely they meet the eNTRy rules. In the example here, both ampicillin and 6-DNM-NH3 meet all of the criteria and are predicted to accumulate, whereas penicillin and DNM are not. A portion of this figure is taken from Parker et al.19 with permission.
Supplementary information
Source data
Source Data Fig. 4
Raw accumulation data.
Source Data Fig. 5
Calibration curve and mass spectrum.
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Geddes, E.J., Li, Z. & Hergenrother, P.J. An LC-MS/MS assay and complementary web-based tool to quantify and predict compound accumulation in E. coli. Nat Protoc 16, 4833–4854 (2021). https://doi.org/10.1038/s41596-021-00598-y
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DOI: https://doi.org/10.1038/s41596-021-00598-y
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