Abstract
Bacterial pathogens rapidly change and adapt their proteome to cope with the environment in host cells and secrete effector proteins to hijack host targets and ensure their survival and proliferation during infection. Excessive host proteins make it difficult to profile pathogens’ proteome dynamics by conventional proteomics. It is even more challenging to map pathogen–host protein–protein interactions in real time, given the low abundance of bacterial effectors and weak and transient interactions in which they may be involved. Here we report a method for selectively labeling bacterial proteomes using a bifunctional amino acid, photo-ANA, equipped with a bio-orthogonal handle and a photoreactive warhead, which enables simultaneous analysis of bacterial proteome reprogramming and pathogen–host protein interactions of Salmonella enterica serovar Typhimurium (S. Typhimurium) during infection. Using photo-ANA, we identified FLOT1/2 as host interactors of S. Typhimurium effector PipB2 in late-stage infection and globally profiled the extensive interactions between host proteins and pathogens during infection.
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Data availability
The generated proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with accession no. PXD033036. Source data are provided with this paper.
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Acknowledgements
We thank H. Hang (Scripps Research) for the MetRS plasmids, A. Yan (University of Hong Kong) and A. Typas (European Molecular Biology Laboratory) for S. Typhimurium strains. This work was supported by the Excellent Young Scientists Fund of China (Hong Kong and Macau) (grant no. 21922708 to X.D.L.), Shenzhen-Hong Kong-Macau Technology Research Program (Type C) (grant no. SGDX2020110309520101 to X.D.L.), Hong Kong Research Grants Council Collaborative Research Fund (grant no. C7028-19G to X.D.L.), Areas of Excellence Scheme (grant no. AoE/P-705/16 to X.D.L.) and General Research Fund (granst nos. 17310122 and 17121120 to X.D.L.)
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X.D.L. supervised the project. X.-M.L. and X.D.L. conceived the ideas and designed the experiments. S.H. screened the active MetRS and optimized and characterized photo-ANA labeling in vitro. X.-M.L. performed the optimization and characterization of photo-ANA labeling during infection, immunofluorescence, sample preparation for quantitative proteomics, data acquisition and analysis of MS. X.-M.L. and X.D.L wrote the manuscript.
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Extended data
Extended Data Fig. 1
Chemical structures for methionine, ANL, AOA, and photo-ANA.
Extended Data Fig. 2 Characterization of photo-ANA labeling of S. Typhimurium proteome.
a, In-gel fluorescence analysis (representative images from n = 2 independent experiments) of cell lysate from 2 mM photo-ANA-labeled S. Typhimurium expressing different MetRS mutants. b, In-gel fluorescence analysis (representative images from n = 2 independent experiments) shows the comparable labeling of photo-ANA by MetRS (L13G) to labeling of AOA by MetRS (NLL). c, In-gel fluorescence analysis (representative images from n = 2 independent experiments) of concentration-dependent labeling of photo-ANA for 2 hours. d, In-gel fluorescence analysis (representative images from n = 2 independent experiments) of time-dependent labeling of photo-ANA at concentration of 2 mM. CB, Coomassie blue staining, indicates equal loadings.
Extended Data Fig. 3 Characterization of photo-ANA-labeled S. Typhimurium proteins.
a, Pathway analysis of photo-ANA-incorporated proteins. b, In-gel fluorescence analysis (representative images from n = 2 independent experiments) of cell lysate from 2 mM photo-ANA-labeled HeLa cells, S. Typhimurium, or S. Typhimurium expressing MetRS L13G mutant. CB, Coomassie blue staining.
Extended Data Fig. 4
Experimental design to investigate nascent proteome change of S. Typhimurium in different infection stages.
Extended Data Fig. 5 Quantification of synthesis of S. Typhimurium proteins during infection.
Quantification of synthesis of proteins related to secretion system and virulence (a), metabolism of branched-chain amino acids (b) and cysteine (c), TCA cycle and oxidative phosphorylation (d), and glycolysis (e).
Extended Data Fig. 6 Photo-ANA enables the identification of host-pathogen protein-protein interactions during infection.
a, Schematic of ‘forward’ experiment to identify PipB2-interacting host proteins during infection using photo-ANA. In ‘reverse’ experiments, ‘heavy’ and ‘light’ cells were swapped. b, PPI network of identified PipB2-interacting host proteins generated by STRING v.11.5. Thickness of edges denotes confidence of interaction. Node color depicts k-mean clusters. Edges in dash line indicate interactions between clusters. c, Colocalization analysis of PipB2 and FLOT1-GFP signal in Fig. 4e. R indicate Pearson’s correlation coefficient. M1 and M2 indicate Mander’s overlap coefficient for PipB2 and FLOT1-GFP, respectively.
Extended Data Fig. 7 Characterization of the interaction between FLOT1/2 and PipB2.
a, Western blot analysis (representative images from n = 2 independent experiments) of HA-tag pull-down experiment in HEK293T cells expressing full-length (FL) or LFNEF-deleted (Δ341-345) PipB2-HA. b, Fluorescence images (representative images from n = 2 independent experiments) of HeLa cells expressing FLOT1/2-GFP and PipB2-FLAG. PipB2 was visualized by anti-FLAG (red) antibody. DNA were detected by DAPI (blue). Rectangle indicates regions with magnified views shown in bottom right. Scale bar indicates 10 μm (5 μm for magnified views). c, Fluorescence images (representative images from n = 2 independent experiments) of S. Typhimurium (PipB2-STF)-infected HeLa cells expressing GFP-tagged FLOT2 at 20 hpi. PipB2 was visualized by anti-FLAG (red) antibody. Cell nuclei were stained with DAPI (blue). Bottom panel displays the magnified view of SCV region as indicated with rectangle. Scale bar indicates 5 μm.
Extended Data Fig. 8
Workflow to identify proteome adaptation and host interactome with S. Typhimurium during infection using photo-ANA.
Extended Data Fig. 9 Identified host proteins targeted by S. Typhimurium from buffer containing harsh detergent.
a, Volcano plot of host proteins identified from buffer containing harsh detergent using photo-ANA at 3 hpi, 8 hpi and 20 hpi. Proteins with P value < 0.05 and fold change > 1.5 are highlighted in red. Dot size denotes significance. P value was calculated using two-way Student’s t-test (n = 4) and adjusted by Benjamini–Hochberg procedure. The exact P value for each protein can be found in Supplementary Data 8. b, Gene set enrichment analysis (GSEA) against a manually curated gene set evaluating the identified proteins from (a). See Method for details. Left, high fold change in infection samples (combined 3 hpi, 8hpi and 20 hpi sample). Right, high fold change in no infection samples. FDR, false discovery rate. NES, normalized enrichment score.
Extended Data Fig. 10 Photo-ANA revealed the global host proteins interacting with S. Typhimurium during infection.
a, Protein interaction network of identified host interactors. Thickness of edges denotes confidence of interaction. Node size indicates number of neighbors. b-c, GO-term enrichment analysis (b) and Pathway enrichment analysis (c) of identified host interactors. The number in column in (b) indicates number of associated proteins in each term. Only top 10 (b) or 20 (c) significant terms are shown. Full list can be found in Supplementary Data 9.
Supplementary information
Supplementary Information
Supplementary Figs. 1–3, Note and Tables 1–2.
Supplementary Data 1
Identified photo-ANA-incorporated peptides with abundance values and estimated incorporation rates of photo-ANA. Peptides without abundances value were discarded. The abundances of different photo-ANA-incorporated peptides but with same photo-ANA site due to missed cleavages were summed to create unique incorporation rate for each site.
Supplementary Data 2
All identified peptides for the estimation of incorporation rates.
Supplementary Data 3
Quantification of identified S. Typhimurium proteins in vitro, 0–3 h.p.i., 5–8 h.p.i. and 17–20 h.p.i.
Supplementary Data 4
Raw quantification data of heatmap and k-mean clusters related to Fig. 3d.
Supplementary Data 5
Identified PipB2 peptides including photo-ANA-incorporated peptides. The abundances of different photo-ANA-incorporated peptides but with same photo-ANA site due to missed cleavages were summed to create unique incorporation rate for each methionine site.
Supplementary Data 6
All proteins identified from photo-ANA-based proteomics to identify PipB2–host interactions during infection. Proteins showing ratio (‘forward’) >1.5 and ratio (‘reverse’) <0.67 were considered to be PipB2 interactors and highlighted in light blue. Proteins showing ratio (‘forward’) >2 and ratio (‘reverse’) <0.5 were highlighted in deep blue.
Supplementary Data 7
The quantification of proteins identified from lysis buffer II in the profiling of host interactome with S. Typhimurium.
Supplementary Data 8
The quantification of proteins identified from lysis buffer III in the profiling of host interactome with S. Typhimurium.
Supplementary Data 9
Full list of GO terms and pathway enrichment analysis.
Source data
Source Data Fig. 1
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Source Data Fig. 2
Unprocessed images of blots or gels.
Source Data Fig. 4
Unprocessed images of blots or gels.
Source Data Extended Data Fig. 2
Unprocessed images of blots or gels.
Source Data Extended Data Fig. 3
Unprocessed images of blots or gels.
Source Data Extended Data Fig. 7
Unprocessed images of blots or gels.
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Li, XM., Huang, S. & Li, X.D. Photo-ANA enables profiling of host–bacteria protein interactions during infection. Nat Chem Biol 19, 614–623 (2023). https://doi.org/10.1038/s41589-022-01245-7
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DOI: https://doi.org/10.1038/s41589-022-01245-7
