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The regulatory landscape of the yeast phosphoproteome

Nature Structural & Molecular Biology volume 30, pages 1761–1773 (2023)Cite this article

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Abstract

The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. Here, to gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 min of stress exposure. We identify shared and perturbation-specific stress response programs, uncover loss of phosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase–substrate networks, as we exemplify with target of rapamycin signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.

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Fig. 1: MS-based atlas of phosphoproteomic responses to 101 cellular perturbations.
Fig. 2: Structural, regulatory and functional features of the yeast phosphoproteome.
Fig. 3: Dissecting stress-responsive phosphorylation sites.
Fig. 4: Shared phosphorylation stress response program.
Fig. 5: Regulatory dynamics of the TOR signaling network.
Fig. 6: Functional organization of the stress responsive phosphoproteome revealed through dimensionality reduction and co-regulation analysis.

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Data availability

The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE82 partner repository with the dataset identifiers PXD035029 for the ultradeep reference phosphoproteomic DDA data, PXD035050 for the quantitative phosphoproteomic DIA data, and PXD034997 for the quantitative proteomics DIA data.

Code availability

The code for processing and analyzing the phosphoproteomic data is available at https://github.com/Villen-Lab/YeastPhosphoAtlasAnalysis. The code for the web resource is available at https://github.com/Villen-Lab/YeastPhosphoAtlas.

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Acknowledgements

We thank M. Berg, K. Hess, A. Hogrebe, J. Ramos, I. Smith, M. Dunham and members of the Villén Lab for useful discussions and feedback. We thank Life Science Editors for editing services (www.lifescienceeditors.com). These studies were supported by the National Institutes of Health grants R35GM119536 and R01AG056359. M.L. was supported by the Swiss National Science Foundation grants P2ZHP3_181503, P400PB_194379 and P5R5PB_211122. A.S.B. was supported by the NIH training grant T32LM012419. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Mario Leutert, Anthony S. Barente, Noelle K. Fukuda, Ricard A. Rodriguez-Mias & Judit Villén

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  1. Mario Leutert
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  3. Noelle K. Fukuda
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Contributions

M.L. and J.V. conceived the project. M.L. conducted experiments with assistance from N.K.F. and R.A.R.-M. M.L. and A.S.B. analyzed the data. A.S.B. created the website. M.L. and J.V. wrote the manuscript and all authors edited it.

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Correspondence to Mario Leutert or Judit Villén.

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Nature Structural & Molecular Biology thanks Danielle Swaney and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Dimitris Typas, in collaboration with the Nature Structural & Molecular Biology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Experimental setup and quality control of sample preparation and mass spectrometry measurements.

(a) Sample randomization and quality control: Cell lysates were scrambled across 96-well plates and biological replicates were assigned to different 96-well plates. On each plate, 4 samples containing the same pooled lysate were included to assess sample preparation reproducibility between 96-well plate batches. Quantitative LC-MS/MS measurement: Proteomic and phosphoproteomic sample preparation controls in each batch were assessed first and then individual sample batches were measured. Performance of LC-MS/MS was regularly assessed between and within batches using a pooled phosphopeptide sample. DIA-MS was performed on an Orbitrap Exploris mass spectrometer using a method with a 60-min effective gradient and staggered wide-window DIA as depicted. (b) Overview of data processing workflow for DIA files that includes spectral library searches, application of a global precursor FDR ( < 0.01) and a PTM localization filter (>0.75) using Spectronaut. Quantifications were aggregated to the phosphopeptide and phosphosite level. (c) Overview of different quality control parameters tracked across all DIA-MS runs for phosphoproteomic sample injections in chronological order. The dashed red lines show all injection medians. The black line shows injection mean values and the gray area shows the 25th–75th percentile. Numbers of phosphopeptide identification are shown as a bar plot. The two bottom panels show sample preparation batches and different colors in heatmap denote different cycles of LC-MS/MS maintenance (for example the analytical column was changed 6 times). (d) Missed cleavage rates of identified peptides and phosphopeptides across the different sample preparation batches. (e) Boxplots of Pearson’s correlation coefficients from pairwise comparisons of individual injections. Pooled lysate controls processed within (n = 36 intra batch comparisons) or between different 96-well plates (n = 240 inter batch comparisons) for the proteome (left) and phosphoproteome (middle) and phosphoproteomic control measurements of the same pooled sample across the whole experiment (right) (n = 240 MS control comparisons) are shown. Hinges represent the 25th and 75th percentiles, the bar denotes the median, and whiskers extend up to 1.5 times the interquartile range from the hinges.

Extended Data Fig. 2 Missing data imputation and batch correction of the core phosphoproteome.

(a) Proportion of all samples where a phosphosite was detected vs the median log2 intensity of the phosphosite in the remaining samples before imputation and batch correction. Color indicates phosphosite count. The weak logistic trend suggests low peptide abundance contributes to missing quantifications. Red line indicates imputation threshold. (b) Number of phosphosites in the dataset after increasingly strict cutoffs on the percent of samples with phosphosites. Red line indicates imputation threshold. (c) Percentage of imputed phosphosites across batches after filtering out phosphosites which were not present in at least 50% of all samples. (d) Distribution of phosphosite intensities before and after imputation for each sample batch, with the median intensity for the batch displayed as a horizontal line. (e) PCA of phosphosite quantifications per sample colored by sample batch after phosphosites were filtered for missingness and imputed. (f) Principal Variance Component Analysis (PVCA) on the same data as E) demonstrating the proportion of explainable variance. (g) Same as E) after ComBat correction. (h) Same as F) after correction. (i) Scatter plot of median Pearson correlation between samples from the same perturbation across batches before (imputed data) versus after batch correction (corrected data). (j) Same as I) for coefficient of variation across phosphosites for each perturbation. (k) Volcano plot displaying the negative log10 of the Benjamini-Hochberg corrected p-values by the log2 fold change determined by LIMMA for each phosphosite in each perturbation. (l) Same as (K) for protein abundances. (m) Significantly regulated proteins in each selected perturbation. 2,185 quantified proteins on average, 257 regulated proteins and 791 regulated perturbation-protein pairs. Most perturbations showed regulation of less than 3% of all measured proteins. Intracellular pH changes had the strongest impact, affecting 5%-7% of measured proteins. (n) Regulated phosphosites across perturbations grouped by range of imputed values. (o) Proportion of phosphosites that are down-regulated versus up-regulated binned by a range of imputed values. Over-representation of down regulation vs up regulation is anti-correlated with imputation, which implies that this is a biological effect affecting highly abundant sites.

Extended Data Fig. 3 Structural, regulatory, and functional features of the yeast phosphoproteome.

(a) Protein abundance for all yeast proteins (n = 7,147), all identified phosphoproteins (n = 3,857) and all regulated phosphoproteins (n = 1,204). Two-sided Wilcoxon test p-value: *** p < 0.001. For boxplots, hinges represent the 25th and 75th percentiles, the bar denotes the median, and whiskers extend up to 1.5 times the interquartile range from the hinges. (b) Histograms depicting the relative positions of non-modified peptides within proteins derived from proteomic datasets for all measured proteins, proteins with regulated phosphosites and proteins that have a regulated N-terminal phosphosite. No bias towards increased detection of N-terminal peptides was identified. C-terminal peptides are depleted, which is expected due to their increased probability of having a lower charge state. (c) Distribution of paralog pair sequence identity across phosphosites that occur at conserved residues in both parlogs (n = 41), in only one paralog (n = 183) and paralog pairs with conserved S/T sites that were not identified to be phosphorylated in either paralog (n = 971). On average, paralog pairs with conserved phosphorylation sites have a statistically higher sequence identity and therefore likely diverged more recently. Two-sided Wilcoxon test p-value: ** p < 0.01. For boxplots, hinges represent the 25th and 75th percentiles, the bar denotes the median, and whiskers extend up to 1.5 times the interquartile range from the hinges.

Extended Data Fig. 4 Differential expression of phosphosites and proteins.

(a) Heatmap and hierarchical clustering of regulated proteins across different perturbations. (b) Visualization of significantly enriched biological process terms within the 4 annotated clusters from (D). Most of the 123 proteins that increased in abundance showed a strong enrichment for amino acid metabolic processes, likely as a response to pH changes or adjusted metabolism. Down-regulated proteins were enriched for ribosome biogenesis, translation, and the oxidative stress response, all indicative of the early onset of the environmental stress response gene expression program. (c) Left: Dotplot of log2 fold changes of perturbations versus untreated for indicated phosphosites. Data points are color coded according to perturbation type (as in Fig. 1) if the indicated phosphosite is significantly regulated. Middle: count of perturbations where the phosphosite is regulated. Right: count of significant phenotypes when exposing indicated phospho-inhibitory mutants to different stresses13. Conserved phosphosites in human homologs are listed on the right.

Extended Data Fig. 5 Dephosphorylation is a major stress response.

(a) Summary of known regulation and functions of selected phosphatases49. (b) Count of stress-resistant and stress-sensitive growth phenotypes for deletion strains of selected kinases and phosphatases (indicated in Fig. 4c) as determined by (ref. 13). Average phenotypes for all assessed kinase and phosphatase deletion strains are indicated by dashed lines. (c) Line plots of counts of down- and up-regulated phosphosites or phosphopeptides upon different perturbations of S. cerevisiae over time as identified previously20,21,22. (d) Same plot as (C) for different perturbations in C. elegans and in the human MCF7 epithelial breast cancer cell line as previously identified50,51. (e) Bar plot of down- and up-regulated phosphosite counts upon different perturbations in rat L7 myotubes as previously identified52.

Extended Data Fig. 6 Analysis of the TOR signaling network.

(a) Numbers of target phosphosites associated with kinases in the TOR cascade that we considered in our analysis. (b) Signed R2 of the scale free topology model fit (left) and the mean connectivity (right) of the underlying adjacency matrix produced by raising the Pearson correlation matrix to a soft threshold power. (c) Number of phosphosites in the TOR cascade assigned to each subnetwork discovered by WGCNA. (d) Hierarchical clustering of the Pearson correlation between module eigensites. (e) Linear motif enrichment of phosphosites assigned to each TOR subnetwork. (f) Significantly enriched (Fisher exact test q-value < 0.01) GO biological processes (top) and kinase-protein interactions (bottom) for phosphoproteins within each subnetwork.

Extended Data Fig. 7 Co-regulation Analysis.

(a) Heatmap showing the aggregated relative intensity of phosphosites within modules. The intensity is scaled across individual perturbations. Hierarchical clustering is performed on rows and columns. Perturbation types and phosphosites contained within a module are color-coded. (b) Aggregated relative intensity of all phosphosites within a module across the sample UMAP faceted for all modules. (c) Phosphosite motif enrichment analysis for all phosphosites within a module using all phosphosites assigned to modules as a background. (d) UMAP embedding of 2,191 phosphosites based on their quantitative profiles across samples. Color-coded by the number of perturbations where phosphosite is up-regulated subtracted by the number of perturbations where phosphosite is downregulated.

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Supplementary Table 1

Treatment, sample preparation and measurement information for all analyzed samples, related to all figures.

Supplementary Table 2

Ultradeep reference yeast phosphoproteome, related to Figs. 1 and 2.

Supplementary Table 3

Complete and uncorrected, quantitative phosphosite datasets, related to Fig. 1.

Supplementary Table 4

Proteome quantitation of 30 selected perturbations and differential expression of proteins versus untreated. Differential expression was determined using LIMMA on all samples at once. Significant differential expression was calculated for each treatment against the untreated samples, and P values were corrected globally using Benjamini–Hochberg correction. Related to Fig. 3.

Supplementary Table 5

Corrected quantitative core phosphoproteome dataset, related to Figs. 1, 5 and 6.

Supplementary Table 6

Differential expression of phosphosites in all perturbations versus the untreated control. Differential expression was determined using LIMMA on all samples at once. Significant differential expression was calculated for each treatment against the untreated samples, and P values were corrected globally using Benjamini–Hochberg correction. Related to Figs. 2, 3 and 4.

Supplementary Table 7

Perturbation-specific phosphosites. Related to Fig. 4, Supplementary Fig. 3 and Supplementary Text 5.

Supplementary Table 8

Phosphosites associated with TOR subnetworks. Related to Fig. 5.

Supplementary Table 9

Catalog of characterized, co-regulated phosphosites, assembled in modules. Related to Fig. 6.

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Leutert, M., Barente, A.S., Fukuda, N.K. et al. The regulatory landscape of the yeast phosphoproteome. Nat Struct Mol Biol 30, 1761–1773 (2023). https://doi.org/10.1038/s41594-023-01115-3

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