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An E. coli display method for characterization of peptide–sensor kinase interactions

Nature Chemical Biology volume 19, pages 451–459 (2023)Cite this article

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Abstract

Bacteria use two-component system (TCS) signaling pathways to sense and respond to peptides involved in host defense, quorum sensing and inter-bacterial warfare. However, little is known about the broad peptide-sensing capabilities of TCSs. In this study, we developed an Escherichia coli display method to characterize the effects of human antimicrobial peptides (AMPs) on the pathogenesis-regulating TCS PhoPQ of Salmonella Typhimurium with much higher throughput than previously possible. We found that PhoPQ senses AMPs with diverse sequences, structures and biological functions. We further combined thousands of displayed AMP variants with machine learning to identify peptide sub-domains and biophysical features linked to PhoPQ activation. Most of the newfound AMP activators induce PhoPQ in S. Typhimurium, suggesting possible roles in virulence regulation. Finally, we present evidence that PhoPQ peptide-sensing specificity has evolved across commensal and pathogenic bacteria. Our method enables new insights into the specificities, mechanisms and evolutionary dynamics of TCS-mediated peptide sensing in bacteria.

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Fig. 1: Porting S. Typhimurium PhoPQ into E. coli.
Fig. 2: Surface-displayed AMPs activate PhoPQ in E. coli.
Fig. 3: Discovery and characterization of PhoPQ-activating human AMPs.
Fig. 4: Validation of SLAY-TCS hits and follow-up truncation studies.
Fig. 5: Non-cathelicidin AMPs activate PhoQ in S. Typhimurium.
Fig. 6: S. Typhimurium, ExPEC and K. pneumoniae PhoPQ exhibit different sensitivities to surface-displayed AMPs.

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Data availability

The raw and processed NGS datasets generated in this study (reported in Fig. 3c–f, Extended Data Figs. 4a, 5 and 7 and Supplementary Dataset 1) have been deposited in the Gene Expression Omnibus (GEO) database with series accession ID GSE174191. Raw flow cytometry data for Figs. 1c,d, 2d, 3b, 4b, 5a–c and 6b, Supplementary Figs. 6b–d and 7, Extended Data Figs. 1, 6, 9a–h and 10a–l, raw OD600 data for Extended Data Fig. 2, raw CD data for Extended Data Fig. 8a–g and raw Western blot data for Supplementary Fig. 4b,c are available on figshare (https://doi.org/10.6084/m9.figshare.21173701). Source data for Fig. 3 are available on GitHub at https://github.com/krbrink/PhoPQ_hAMP_sort-seq. Human AMP sequences were downloaded from the Antimicrobial Peptide Database; see Supplementary Dataset 1 for accession numbers. Peptide structures were downloaded from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) (Figs. 2b,c and 4a and Supplementary Fig. 2; references are provided in Supplementary Table 11; PDB IDs: 3GNY, 2N92, 1PG1, 6DST, 2K6O, 2JYO, 3U69, 2MP1, 1KJ6, 1RON and 1L9L). Tissue RNA expression data (Supplementary Fig. 3) were downloaded from the Human Protein Atlas RNA consensus tissue gene data dataset (http://v20.proteinatlas.org). Single PBMC expression data were downloaded from the GEO database under accession numbers GSM3454528 (naive cells) and GSM3454529 (Salmonella-exposed cells) (Supplementary Fig. 4a). Plasmids are available through Addgene with accession IDs listed in Supplementary Table 8. Strains are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Code for the analysis and visualization of sort-seq data, except for the cathelicidin machine learning model, is available on GitHub at https://github.com/krbrink/PhoPQ_hAMP_sort-seq. Code for the cathelicidin machine learning model is available on GitHub at https://github.com/kennygrosz/PhoPQ_Activation_model. Details of publically available software tools used in this study are provided in Supplementary Table 12.

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Acknowledgements

We thank B. Davies for providing SLAY source plasmids and insights into using SLAY in E. coli. Salmonella enterica subsp. enterica, strain 14028s ΔphoQ (serovar Typhimurium), NR-40554 was obtained through BEI Resources, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH). We thank J. Moake for the use of his cytometer. This work was supported by the Welch Foundation (C-1856 to J.J.T.), the National Science Foundation (CAREER 1553317 to J.J.T. and Graduate Research Fellowship 1842494 to B.P.) and NIH/NIAID (1-R01-AI-155586-01A1 to J.J.T.). K.R.B. was supported by the Nettie S. Autrey Fellowship from Rice University. K.V.H. and J.G. were supported by funds from Nationwide Children’s Hospital.

Author information

Authors and Affiliations

  1. Ph.D. Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, USA

    Kathryn R. Brink, Maxwell G. Hunt & Jeffrey J. Tabor

  2. Department of Biosciences, Rice University, Houston, TX, USA

    Andrew M. Mu & Jeffrey J. Tabor

  3. Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ken Groszman

  4. Center for Microbial Pathogenesis, Nationwide Children’s Hospital, Columbus, OH, USA

    Ky V. Hoang & John S. Gunn

  5. Infectious Diseases Institute, The Ohio State University, Columbus, OH, USA

    Ky V. Hoang & John S. Gunn

  6. Department of Bioengineering, Rice University, Houston, TX, USA

    Kevin P. Lorch, Brett H. Pogostin & Jeffrey J. Tabor

  7. Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA

    John S. Gunn

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Contributions

K.R.B. and J.J.T. conceived of the project. J.J.T. and J.S.G. supervised the project. K.R.B. and A.M.M. (identification of PhoPQ reporter promoter and PhoPQ orthologs), M.G.H. (exogenous AMPs), K.V.H. (AMP expression in human cell lines), K.P.L. (K-12 PhoPQ display experiments), B.H.P. (CD experiments) and K.R.B. (all other experiments) designed and executed experiments. K.G. and K.R.B. designed, implemented and analyzed the cathelicidin machine learning model. K.R.B., M.G.H. and K.P.L. analyzed reported results and generated figures and tables. K.R.B., M.G.H. and J.J.T. wrote the manuscript.

Corresponding author

Correspondence to Jeffrey J. Tabor.

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Competing interests

Rice University has filed a patent application including the use of PhoPQ for host peptide biosensing. J.J.T. is a founder of Pana Bio, a company that aims to commercialize diagnostic and therapeutic bacteria. The remaining authors declare no competing interests.

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Nature Chemical Biology thanks Octavio Franco and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Identification of PvirK as the primary reporter of PhoPQ activity.

Transcriptional activity of each promoter in response to aTc-induced PhoP overexpression was measured using an sfGFP reporter protein (Supplementary Fig. 1b) and flow cytometry. Data was collected over n = 3 separate days, with results from each day shown as a separate marker. Annotated promoter sequences are provided in Supplementary Table 1.

Source data

Extended Data Fig. 2 Most PhoQ-activating AMPs are not toxic when displayed on the E. coli outer membrane.

Endpoint OD600 measurements for KB1 and KB1 PhoQ H277A in response to IPTG induction of surface-displayed AMPs. A negative control strain expressing the SLAY peptide display system without an appended peptide cargo (no-peptide) is shown. Measurements were performed after 4.5 h growth from an initial culture OD600 of 0.0001. Data was collected over n = 3 separate days, with results from each day shown as a separate marker.

Source data

Extended Data Fig. 3 Composition of the surface-displayed human AMP library.

(a) Length distribution of the 133 human AMPs identified in APD316. Peptides excluded from the human AMP library due to length considerations are indicated in light grey. (b) Charge, (c) grand average of hydropathicity (GRAVY), and (d) secondary structure distributions of AMPs in the human AMP library. (e) Distribution of cluster sizes for AMPs in the human AMP library.

Extended Data Fig. 4 Growth and fluorescence analysis of sort-seq results.

(a) Peptide frequency in the human AMP library before and after the sort-seq workflow. Pre- and post-sort frequency data was determined from single biological replicates (n = 1). (b) Fluorescence of KB1, KB1 with PvirK-mNG and no peptide control plasmids, KB1 with PvirK-mNG and C18G display plasmids, and KB1 with the human AMP (hAMP) library plasmids prior to sorting. Controls were grown as single-strain cultures alongside the human AMP library. Each histogram represents a single biological replicate (n = 1).

Extended Data Fig. 5 PhoPQ activation by human cathelicidin fragments of at least 3 amino acids in the sort-seq screen.

Amino acids 113 to 170 of full-length cathelicidin are shown. The α-helical region of LL-37 is indicated by the grey boxed region. Peptide names are shown for cathelicidin-derived peptides in the APD3 database. Asterisks (*) indicate truncation variants not present in the APD3 database. Data refer to fold-change in PvirK-mNG reporter expression and represent results from a single biological replicate (n = 1).

Extended Data Fig. 6 Single strain peptide display experiments validate non-cathelicidin activators identified in the sort-seq screen.

PhoPQ activation is reported via the PvirK-mNG plasmid. Strains in which human AMP activators are displayed in KB1 are shown in black. Control strains in which human AMP activators are displayed in KB1 PhoQ H277A are shown in grey. Positive (LL-37) and negative (no peptide) peptide display controls in KB1 are shown in red and blue, respectively. Each plot contains data collected over n = 3 separate days, with results from each day shown as a separate marker.

Source data

Extended Data Fig. 7 Training and test set results for the cathelicidin sparse robust linear model.

Coefficient of determination (r2) and Pearson correlation coefficient and its associated two-sided p-value (r, p) were calculated for log10-transformed fold change and predicted fold change values.

Extended Data Fig. 8 Circular dichroism spectra of exogenous peptides.

(a-g) Spectra were collected as the average of 5-10 accumulations for a single sample. For all peptides, the absence of a minimum between 196-200 nm is consistent with secondary structure formation. Minima at 210 and 225 nm are characteristic of α-helical character, while a minimum at 218 nm is characteristic of β-sheet character. The broad minima observed for all peptides between 205–225 nm is characteristic of α-helical character and may also be obscuring a weak β-sheet signal. These spectra indicate proper folding of the peptides in accordance with their previously-reported secondary structures.

Source data

Extended Data Fig. 9 Exogenous peptide activation of S. Typhimurium PhoPQ in engineered E. coli.

(a) PhoPQ activation by exogenous AMPs in pKB233-containing KB1 and KB1 PhoQ H277A strains, as measured by flow cytometry. TCP refers to the TCP C-terminal α-helix fragment. Data for each AMP was collected in n = 3 replicates; conditions where no AMP was added were collected alongside each AMP replicate for a total of n = 7 replicates. Each marker represents one biological replicate; bar heights correspond to the average of these replicates. Numbers above the bars are the mean fold changes between AMP-containing and control samples. (b) Flow cytometry histograms of the pKB233-containing KB1 strain in response to exogenous AMPs. TCP refers to the TCP C-terminal α-helix fragment. Each histogram is the combined fluorescence distribution for all biological replicates (n = 3 for +AMP groups and n = 7 for -AMP groups). Histograms are scaled to size. Black bars represent the arithmetic mean fluorescence of the combined populations. Concentration-dependent PhoPQ activation and toxicity for (c-d) GNLY, (e-f) CCL20, and (g-h) hBD3 as measured by flow cytometry. Flow rate depicts the number of intact bacteria (events) counted by the cytometer per second. Reduced flow rate is indicative of peptide-mediated growth inhibition. Data for each AMP was collected in n = 3 replicates; conditions where no AMP was added were collected alongside each AMP replicate in n = 3 replicates. Each marker represents one biological replicate; bar heights correspond to the average of these replicates. Numbers above the bars are the mean fold changes between AMP-containing and control samples.

Source data

Extended Data Fig. 10 Exogenous AMPs activate diverse PhoPQ-regulated promoters in E. coli and S. Typhimurium.

(a-d) Mg2+ limitation activates S. Typhimurium Pmig-14, PmgtA, PpmrD and PslyB in a PhoQ-dependent manner in E. coli. The activity of each promoter was measured by mNG expression from pKB233.1-4 in KB1 (colored bars) and KB1 PhoQ H277A mutant (white bars) in n = 3 replicates. (e-h) Exogenous AMPs activate diverse PhoPQ-activated promoters in engineered E. coli KB1 (colored bars) and KB1 PhoQ H277A mutant (white bars). TCP refers to the C-terminal α-helix fragment. (i-l) Exogenous AMPs activate diverse PhoPQ-activated promoters in S. Typhimurium and S. Typhimurium ∆phoQ. TCP refers to the C-terminal α-helix fragment. Data for each AMP was collected in n = 3 replicates; conditions where no AMP was added were collected alongside each AMP replicate in n = 7 or n = 5 replicates for E. coli and S. Typhimurium experiments respectively. Each marker represents one biological replicate; bar heights correspond to the average of these replicates. Numbers above the bars are the mean fold changes between AMP-containing and control samples.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Supplementary Tables 1–12, Source Data for Supplementary Fig.4c and References.

Reporting Summary.

Supplementary Data 1

Human AMP sequences, cluster assignments, basic biophysical and biochemical properties, primer sequences and fold changes in sort-seq experiment.

Supplementary Data 2

Source data for Supplementary Fig. 6.

Source data

Source Data Fig. 1

Processed flow cytometry data.

Source Data Fig. 2

Processed flow cytometry data.

Source Data Fig. 4

Processed flow cytometry data.

Source Data Fig. 5

Processed flow cytometry data.

Source Data Fig. 6

Processed flow cytometry data.

Source Data Extended Data Fig. 1

Processed flow cytometry data

Source Data Extended Data Fig. 2

Processed flow cytometry data.

Source Data Extended Data Fig. 6

Processed flow cytometry data.

Source Data Extended Data Fig. 8

Processed flow cytometry data.

Source Data Extended Data Fig. 9

Processed flow cytometry data.

Source Data Extended Data Fig. 10

Processed flow cytometry data.

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Brink, K.R., Hunt, M.G., Mu, A.M. et al. An E. coli display method for characterization of peptide–sensor kinase interactions. Nat Chem Biol 19, 451–459 (2023). https://doi.org/10.1038/s41589-022-01207-z

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