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The Mycobacterium tuberculosis protein O-phosphorylation landscape

Nature Microbiology volume 8, pages 548–561 (2023)Cite this article

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Abstract

Bacterial phosphosignalling has been synonymous with two-component systems and their histidine kinases, but many bacteria, including Mycobacterium tuberculosis (Mtb), also code for Ser/Thr protein kinases (STPKs). STPKs are the main phosphosignalling enzymes in eukaryotes but the full extent of phosphorylation on protein Ser/Thr and Tyr (O-phosphorylation) in bacteria is untested. Here we explored the global signalling capacity of the STPKs in Mtb using a panel of STPK loss-of-function and overexpression strains combined with mass spectrometry-based phosphoproteomics. A deep phosphoproteome with >14,000 unique phosphosites shows that O-phosphorylation in Mtb is a vastly underexplored protein modification that affects >80% of the proteome and extensively interfaces with the transcriptional machinery. Mtb O-phosphorylation gives rise to an expansive, distributed and cooperative network of a complexity that has not previously been seen in bacteria and that is on par with eukaryotic phosphosignalling networks. A resource of >3,700 high-confidence direct substrate–STPK interactions and their transcriptional effects provides signalling context for >80% of Mtb proteins and allows the prediction and assembly of signalling pathways for mycobacterial physiology.

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Fig. 1: A deep Mtb phosphoproteome.
Fig. 2: STPK perturbation defines kinase–substrate interactions.
Fig. 3: Mtb protein O-phosphorylation establishes a network.
Fig. 4: O-phosphorylation has large transcriptional effects.
Fig. 5: Prediction of functional TF phosphorylation events.
Fig. 6: Overview of the Mtb STPK O-phosphoproteome and transcriptional network.

Data availability

The mass spectrometry data reported in this paper are available in Supplementary Tables 1 and 2. Raw files are available under MassIVE accession no. MSV000088254 at https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=e7a33adf4a424aef9eaad14ec5ddc5b4. The mass spectrometry data were searched against the Mtb database RefSeq H37Rv_uid57777_2014-08-14 with 20,198 entries. RNA-seq data are available in Supplementary Table 3. The RNA-seq data are deposited in the Gene Expression Omnibus under accession no. GSE195959. Sequencing reads were mapped to the Mtb (NCBI GenBank accession no. AL123456.3). The Mtb STPK mutant strains are available upon request. Source data are provided with this paper.

Code availability

To assess the relative abundance of transcripts, we implemented a custom analysis pipeline in R v.3.3.0. The code used to process the RNA-seq reads is available at https://github.com/robertdouglasmorrison/DuffyNGS and https://github.com/robertdouglasmorrison/DuffyTools.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grant nos. R01AI117023, R01AI158159, R21AI137571 and R03AI131223 and by a grant from the American Lung Association to C.G. A.F. was supported by the Interdisciplinary Program in Bacterial Pathogenesis no. 5T32AI053396. Portions of this research were supported by NIH National Institute of General Medical Sciences GM103493. Some of the work was performed in the Environmental Molecular Sciences Laboratory, a US Department of Energy Office of Biological and Environmental Research national scientific user facility located at Pacific Northwest National Laboratory. The Pacific Northwest National Laboratory is operated by Battelle for the US Department of Energy under contract no. DE-AC05-76RLO 1830.

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Authors and Affiliations

  1. Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA, USA

    Andrew Frando, Vishant Boradia, Claude Beltejar, Shuyi Ma & Christoph Grundner

  2. Department of Global Health, University of Washington, Seattle, WA, USA

    Andrew Frando, Shuyi Ma & Christoph Grundner

  3. Pacific Northwest National Laboratory, Richland, WA, USA

    Marina Gritsenko, Le Day & Jon M. Jacobs

  4. Department of Microbiology, University of Washington, Seattle, WA, USA

    David R. Sherman

  5. Department of Chemical Engineering, University of Washington, Seattle, WA, USA

    Shuyi Ma

  6. Department of Pediatrics, University of Washington, Seattle, WA, USA

    Shuyi Ma & Christoph Grundner

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  1. Andrew Frando
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Contributions

A.F. designed the work, was involved in sample generation, data acquisition, analysis and interpretation, and wrote the manuscript. V.B., M.G. and L.D. were involved in data acquisition. C.B., D.R.S. and S.M. were involved in data interpretation and discussion. J.M.J. was involved in data acquisition, analysis, interpretation and discussion. C.G. designed the work, was involved in data interpretation, organized the ideas, wrote the manuscript and was involved in the discussion.

Corresponding author

Correspondence to Christoph Grundner.

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Nature Microbiology thanks Yossef Av-Gay, Simone Lemeer 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 Characterization of STPK LOF and OE strains.

(a) Quantitation of mean STPK protein levels in LOF and OE Mtb mutant strains by selected reaction monitoring (n = 3 biologically independent replicates). PknB LOF is a knockdown strain, all other LOF strains are knockouts. Error bars show standard error of the mean. (b) Growth analysis of strains show only small growth effects of STPK perturbation and viability of strains in stationary phase (shown by regrowth after dilution). Error bars show standard error of the mean (n = 2 biologically independent replicates).

Extended Data Fig. 2

Overview of the MS experimental workflow.

Extended Data Fig. 3 Correlation of phosphosites and STPK abundance and kinase-independent effects on phosphoproteome.

(a) The abundance of STPKs in the OE mutants does not overall correlate with the number of phosphosites detected in these strains. Statistically significant correlation was evaluated by calculating a Pearson correlation coefficient and comparing against a Student’s t-distribution. (b) PknK kinase-dead OE causes only few changes in the phosphoproteome. Green dots indicate PknK peptides and show OE of PknK. Significance was determined by one-way ANOVA.

Extended Data Fig. 4 Contribution of STPKs to Mtb protein Tyr phosphorylation.

Number of Tyr phosphosites changed by STPK perturbation compared to WT.

Extended Data Fig. 5 Phosphosite-level overview of STPK substrates and overlap in substrate phosphorylation.

The purple squares represent the STPKs. The size of the square is proportional to the number of that STPK’s substrates, which is also given in parentheses. The round nodes show the number of sites phosphorylated by one or multiple STPKs. The thickness of the edge corresponds to the number of sites phosphorylated by the respective STPK.

Extended Data Fig. 6 Transcriptional effects of STPK perturbation.

Volcano plots show the DEGs in STPK mutant strains, direction of change, magnitude, and P value associated with the change. Horizontal and vertical red lines show significance cutoffs (>4-fold, P < 0.01) used for further analysis of DEGs. The STPKs that were altered in the respective strain were removed from the data as they showed the largest changes and distorted the plots. Plots are shown with different scales to avoid compression of data points. Significance was determined by calculating the geometric mean of five DE tools.

Extended Data Fig. 7 Relationship between STPK induction, phosphosites, and DEGs.

(a) More phosphorylation sites in the OE mutant strains led to more DEGs. PknJ affects the expression of a large number of genes through few phosphosites. Red line shows linear regression if outliers PknE and PknF are excluded. (b) STPK abundance is not correlated with the number of DEGs in the OE strains. Statistically significant correlation was evaluated by calculating a Pearson correlation coefficient and comparing against a Student’s t-distribution.

Extended Data Fig. 8 Transcriptional effects of a PknL and PknK kinase-dead mutant.

(a) The PknL kinase-dead mutant reduces the DEGs compared to WT OE from 260 to 1. PknL WT and kinase-dead transcripts are highlighted in green, showing induction in both cases. (b) The PknK kinase-dead mutant reduces DEGs compared to WT OE from 181 to 13, suggesting a small transcriptional effect of the PknK malT domain in the OE. PknK WT and kinase-dead transcripts are highlighted in green, showing induction in both cases. Horizontal and vertical red lines show significance cutoffs (>4-fold, P < 0.01) used for further analysis of DEGs. Significance was determined by calculating the geometric mean of five DE tools.

Supplementary information

Reporting Summary

Supplementary Table 1

Total phosphosites detected in the label-free and TMT datasets.

Supplementary Table 2

Total differential phosphorylation between STPK mutants and WT.

Supplementary Table 3

Total differential gene expression between STPK mutants and WT.

Source data

Source Data Fig. 1

Unprocessed in vitro phosphorylation assay gel and EMSA gel from Fig. 5.

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Frando, A., Boradia, V., Gritsenko, M. et al. The Mycobacterium tuberculosis protein O-phosphorylation landscape. Nat Microbiol 8, 548–561 (2023). https://doi.org/10.1038/s41564-022-01313-7

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