Assigning functions to the vast array of proteins present in eukaryotic cells remains challenging. To identify relationships between proteins, and thereby enable functional annotation of proteins, we determined changes in abundance of 10,323 human proteins in response to 294 biological perturbations using isotope-labeling mass spectrometry. We applied the machine learning algorithm treeClust to reveal functional associations between co-regulated human proteins from ProteomeHD, a compilation of our own data and datasets from the Proteomics Identifications database. This produced a co-regulation map of the human proteome. Co-regulation was able to capture relationships between proteins that do not physically interact or colocalize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover an organelle interface between peroxisomes and mitochondria in mammalian cells. We also predicted the functions of microproteins that are difficult to study with traditional methods. The co-regulation map can be explored at www.proteomeHD.net.
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All mass spectrometry raw files generated in-house have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository36 with the dataset identifier PXD008888. The co-regulation map is hosted on our website at www.proteomeHD.net, and pair-wise co-regulation scores are available through STRING (https://string-db.org). A network of the top 0.5% co-regulated protein pairs can be explored interactively on NDEx (https://doi.org/10.18119/N9N30Q).
Data analysis was performed in R 3.5.1. R scripts and input files required to reproduce the results of this manuscript are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/ProteomeHD. R scripts related specifically to the benchmarking of the treeClust algorithm using synthetic data are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/treeClust-benchmarking. The R package data.table was used for fast data processing. Figures were prepared using ggplot2, gridExtra, cowplot and viridis.
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We are grateful to D. Szklarczyk for providing the mRNA Pearson correlation data used by STRING and the STRING team for testing our coregulation data and adding them as a novel evidence type to STRING 11. We also thank K. Wills, K. Nakamura, C. Alabert and A. Groth for contributing chromatin enrichment experiments, and A.S. Azadi for support with live-cell imaging. This work was supported by the Wellcome Trust through a Senior Research Fellowship to J.R. (grant no. 103139) and by the Biotechnology and Biological Sciences Research Council (grant nos. BB/N01541X/1 and BB/R016844/1 to M.S.) and grant no. H2020-MSCA-ITN-2018 812968 PERICO (to M.S.). The Wellcome Centre for Cell Biology is supported by core funding from the Wellcome Trust (grant no. 203149).
The authors declare no competing interests.
Integrated supplementary information
(a) Histogram showing the number of peptides identified per protein in ProteomeHD (10,323 proteins, light blue) and in the subset of ProteomeHD used to make the co-regulation map (5,013 proteins, dark blue). Dashed lines show the average number of peptides per protein. (b) Number of peptides per protein broken down by experiment. The average peptide number for the proteins detected in each experiment is shown. (c) Average number of SILAC ratio counts (independent observations) per protein, broken down into the 294 input experiments. (d) Sequence coverage of proteins in ProteomeHD. Dashed lines indicate the average. (e) Average sequence coverage of proteins in each input experiment. (f) The number of proteins that were quantified in the 294 experiments of ProteomeHD ranges from 817 to 6,080. The average is 3,928 proteins per SILAC ratio. (g) Number of experiments, i.e. SILAC ratios, in which proteins were quantified. Only proteins that were quantified in at least 95 experiments were used for the co-regulation analysis. On average, proteins in ProteomeHD were quantified in 112 input experiments. The average rises to 190 if only proteins used for the co-regulation analysis are considered. (h) Bar chart showing which fraction of proteins have been detected in which fraction of experiments. For example, 100% of proteins in the co-regulation map have been quantified in at least 30% of the 294 experiments. About 15% of the proteins have been quantified in at least 90% of the experiments.
Performance comparison of a standard treeClust application on ProteomeHD with two types of co-occurrence measures. Jaccard scores are an established co-occurrence measure (protein pairs observed in the same set of experiments would get a Jaccard score of 1, while protein pairs without any overlapping experiments would get a score of 0). We also applied treeClust to a “binary” version of ProteomeHD, where all SILAC ratios were set to 1 and all missing values were set to 0. The precision recall curve uses Reactome as a gold standard. It shows that Jaccard and “binary treeClust” work equally well but both are outperformed by the standard co-regulation analysis. Therefore, while co-occurrence of proteins across ProteomeHD does provide some information about functional associations, quantitative up- and down regulation is a far better indicator of shared protein function, at least for ProteomeHD. Notably, this also shows that treeClust can detect co-occurrence, in principle, if the data are transformed into a binary format.
We tested which types of relationships treeClust detects by using a synthetic dataset consisting of 100 variables and 200 proteins, where 0.5% of all possible protein - protein combination have a defined relationship. (a) Precision - recall (PR) analyses show that treeClust separates linear from random relationships perfectly, resulting in an area under the PR curve (AUPRC) of 1. The same result is observed for the three tested correlation-based metrics: PCC, Spearman’s rho and biweight midcorrelation (bicor). The four PR curves overlap fully. (b) TreeClust completely fails to detect exponential or logistic relationships (AUPRC = 0). In contrast, although these pairs receive lower correlation coefficients than linear pairs, they still score high enough with PCC, rho and bicor to be completely separated from the pool of random associations. No metric detects quadratic relationships. (c) Anti-correlations are not identified well by treeClust.
We used synthetic data to assess the impact of various data characteristics on treeClust performance. This figure complements Fig. 2. (a) Synthetic datasets of 50 samples and 500 proteins were created with increasing percentage of defined linear relationships. This has no impact on the three correlation metrics (PCC, rho and bicor), so their curves overlap fully at AUPRC 1. Treeclust performance needs > 0.3% linear relationships in the data in order to detect them successfully. Synthetic datasets were created in triplicate. Points show the average area under the precision recall curve (AUPRC) obtained for each setting. Error bars show the standard error of the mean. (b) Combinatorial impact of the number of samples and the percentage of defined linear relationships (N proteins = 500). Note that for larger datasets lower percentages of “coexpressed” proteins can be detected. (c) TreeClust, but not the three correlation metrics, is also affected by the number of available observations (proteins). N samples = 20, 0.3% linear associations. (d, e) Adding missing values to a small (n = 50 samples, n = 500 proteins) and medium (n = 100 samples, n = 1,000 proteins) dataset, respectively, has a different impact on treeClust performance. (f) Combinatorial impact of missing values and the number of proteins, showing that for large datasets with many proteins a larger percentage of missing values can be tolerated (N samples = 150).
Supplementary Figure 5 Illustration of changing the goodness-of-fit and outlier occurrence in synthetic data.
This figure illustrates the different conditions tested in Fig. 2d, e. (a) Scatterplots illustrating the effect of increasing the difference between variables, which decreases treeClust performance but not that of correlation metrics. (b) Scatterplots illustrating the effect of adding outlier data points, which decreases treeClust performance less than that of the correlation metrics.
(a) Co-regulated protein pairs in ProteomeHD were divided into those detected by treeClust but not by PCC and vice versa. Separate comparisons were made for pairs detected by treeClust but not rho, and treeClust but not bicor. The pairs in the resulting groups were annotated using Reactome into known, biologically relevant interactions (true positives) and pairs that were unlikely to have any biological associations (false positives). Note that treeClust-specific pairs tend to be true positives, whereas correlation-specific pairs tend to be false positives. (b) This panel complements Fig. 2f. Outliers were detected in ProteomeHD via their Mahalanobis distance, i.e. these outliers are located far from the bulk of the data, but can be close to the regression line. The boxplots show that Mahalanobis outliers are more frequent in protein pairs detected specifically by rho or bicor as opposed to pairs detected specifically by treeClust. The number of protein pairs shown corresponds to n for each group as indicated in (a). (c) Removing these Mahalanobis outliers has little impact on the PCC of treeClust-, rho- or bicor-specific protein pairs, in contrast to what was observed for Pearson’s correlation (see Fig. 2g). For number of proteins shown, see panel (a). (d) A second type of outlier - regression outliers - were detected in ProteomeHD via studentized residuals. These outliers are located far away from the regression line and will decrease correlation coefficients. An example of a true association is shown, where regression outliers affect the resulting correlation. Fold-changes have been scaled to lie between 0 and 1. (e) The percentage of regression outliers is very similar in all six groups. See panel (a) for number of proteins shown. (f) Removing regression outliers increases the correlation coefficient (PCC) of protein pairs that were previously detected only by treeClust, suggesting PCC missed some of these pairs because of regression outliers. This is not the case for pairs missed by rho or bicor. See panel (a) for number of proteins shown. For boxplots, lower and upper hinges correspond to the first and third quartiles, and lower and upper whiskers extend to the smallest or largest value no further than 1.5 * IQR (inter-quartile range) from the hinge, respectively. Notches give roughly a 95% confidence interval for comparing medians.
Supplementary Figure 7 Goodness-of-fit partially explains different performance of PCC and treeClust.
This figure complements Fig. 2i. Systematic comparison of mean absolute errors (MAEs) from protein pairs that scored high with either treeClust or with PCC (see Supplementary Fig. 6a; n = 8,786 treeClust-specific protein pairs, n = 9,593 PCC-specific protein pairs). Protein pairs exclusively detected by PCC tend to have somewhat higher MAEs, possibly explaining why they are predominantly false-positive hits, in addition to the impact of outliers.
(a) Exponential and logistic (sigmoid) models were fitted to all protein pairs that scored high with treeClust or the three correlation metrics. Model fit was compared through their residual sum of squares (RSS). Exponential models only fitted better than linear ones in rare cases, but logistic models often did. Around half of the protein pairs detected specifically by PCC are better explained by a logistic than a linear model. However, this is mainly driven by Mahalanobis-type outliers. Removing those strongly reduces the number of logistic models outfitting the linear ones. (b) Two example regressions where an exponential (left) or logistic (right) model fits better than a linear one. Note that this clearly reflects overfitting due to outliers rather than genuine non-linear relationships.
Supplementary Figure 9 The protein co-regulation network satisfies scale-free topology but is difficult to visualize as an interaction network.
(a) The “scale free plot” produced by the WGCNA R package using the treeClust-derived adjacency matrix. The log of the connectivity k is plotted against the log of the frequency of this connectivity. There is a linear relationship between these two variables, as indicated by the square of the Pearson correlation, R2, being 0.91. This shows that the protein co-regulation network derived from ProteomeHD using treeClust is at least approximately scale free. (b) Visualization of a weighted, undirected network with 5,013 nodes (proteins detected in at least 95 experiments) and 62,812 edges (top scoring 0.5% of links), based on the co-regulation score. Four common algorithms were used to create different network layouts, but with so many edges it is difficult to avoid the “hairball” problem.
(a) Histogram showing the number of peptides identified per microprotein (proteins < 15 kDa) in ProteomeHD and the subset of ProteomeHD used to make the co-regulation map. Dashed lines show the average number of peptides per microprotein. (b) Average number of SILAC ratio counts (independent observations) per microprotein, broken down into the 294 input experiments. (c) Histogram showing the cumulative SILAC ratio counts per microprotein across all experiments in ProteomeHD. (d) Sequence coverage of microproteins in ProteomeHD. Dashed lines indicate the average. (e) The actual peptides for one example microprotein, MP68. The numbers in brackets indicate in how many different experiments each peptide was observed. (f) Microproteins tend to have more co-regulation partners in ProteomeHD than larger proteins (median 27 vs 10 associations; n = 206 microproteins, n = 2505 other proteins). Microproteins also have more functional protein - protein associations according to STRING (median 23 vs 14; n = 521 microproteins, n = 9,261 other proteins). However, larger proteins have considerably more physical interaction partners than microproteins, according to BioGRID (median 10 vs 17; n = 815 microproteins, n = 14,918 other proteins). (g) The number of interaction partners of microproteins identified by STRING and BioGRID, broken down by the evidence type available in each resource (n = 362 microproteins for mRNA coexpression, 481 for curated databases, 505 for experimental, 908 for text mining, 636 for affinity capture, 251 for co-fractionation, 367 for in vitro and 533 for two-hybrid. We considered STRING interactions with a minimum score of 400 in the individual evidence channels (e.g. mRNA coexpression). Two STRING evidence channels (gene neighborhood and evolutionary co-occurrence) were omitted because they contribute very little. For panel (f) we considered only the most reliable STRING interactions, i.e. those with a combined interaction score above 900. For boxplots, lower and upper hinges correspond to the first and third quartiles, and lower and upper whiskers extend to the smallest or largest value no further than 1.5 * IQR (inter-quartile range) from the hinge, respectively. Notches give roughly a 95% confidence interval for comparing medians.
Screenshot of the core page of www.proteomeHD.net, an interactive web-based app to explore co-regulation data. The basic elements are highlighted and explained. Note that the page also contains help and download sections.
A typical protein - protein association network in STRING, containing the Arp2/3 complex and tropomyosin 3, both of which are involved in actin cytoskeleton regulation. Network edges are colour-coded by the type of evidence available for the association. Protein co-regulation information is embedded in the gene coexpression channel. The channel view shows the channel-specific STRING score, a re-calibrated version of our co-regulation score. It also contains a pre-computed link to www.proteomeHD.net, which uses the first protein as ProteomeHD query and highlights the second protein in the results. If more than one protein isoform is available in ProteomeHD, STRING will link to the alphabetically first isoform, which is generally the main one. The link also contains a cut-off setting to match the ProteomeHD cut-off to the equivalent one selected by the user in STRING. In cases where both mRNA coexpression and protein co-regulation evidence is available for an association, their relative contribution to the STRING coexpression score is indicated (shown here as point 3).
(a-h) PEX5-deficient human skin fibroblasts were mock-treated (control), or transfected with Myc-Miro-PO, a peroxisome-targeted Miro1 variant, in the presence of control- or PEX11β-specific siRNA. Cells were processed for immunofluorescence using anti-Myc and anti-PEX14 antibodies (peroxisomal marker). Results are representative of three independent experiments. (b) Quantification of cells with peroxisomal protrusions. The average result of 3 independent experiments is shown, error bars indicate the mean +/- standard deviation. (a, b) Control cells occasionally contain peroxisomes with membrane protrusions (< 5 per cell; up to 5 µm in length). (c-e, b) Myc-Miro-PO induces the formation of peroxisomal membrane protrusions (> 5 per cell; > 5 µm in length). Results are representative of three independent experiments. (f-h, b) Silencing of PEX11β by siRNA significantly reduces the number of cells with peroxisomal membrane protrusions in controls and Myc-Miro-PO expressing cells. Results are representative of three independent experiments. Globular peroxisomes (arrows) with membrane protrusions (arrowheads) in (a) are highlighted. *** P < 0.001; ** P < 0.01 from a two-tailed unpaired t test; ns, not significant (p = 0.9695). Scale bars, 10 µm.
(a) treeClust was applied to the TMT-based cancer proteomics dataset from Lapek et al (Nature Biotechnology, 2017). It outperforms Pearson, Spearman and Bicor correlation, as shown by a Precision-Recall analysis using Reactome annotations as the gold standard. Note that treeClust builds only one decision tree per condition, i.e. 41 trees on this dataset, too few for a standard analysis. Therefore, treeClust was performed iteratively, obtaining the mean co-regulation score of 100 treeClust forests, each generated from 10 random experiments. (b) Co-regulation map for the Lapek et al dataset, made by t-SNE from treeClust scores. As in the correlation network of the original report (Fig. 2 in Lapek et al), CORUM protein complexes are colored. In contrast to a network, there is not a limited number of arbitrarily arranged, pairwise links, but the position of each protein reflects its similarity or dissimilarity to all other proteins in the map. This makes it possible to place all proteins in a functional context, not just those that are directly linked to members of the core network. It also allows for a hierarchical analysis of protein associations, with increasing distances indicating weaker co-regulation. For example, the subunits of the protein complexes in the enlarged map area (inset) are clustered together, and the distances between the complexes are larger. However, all complexes have roles in vesicular trafficking. n = 6,151 proteins shown in plot.
We randomly removed 5%, 10% and 15% of the data points across the ProteomeHD matrix, in triplicate, and repeated treeClust learning to predict protein associations. The Precision-Recall analysis shows that removing data points decreases performance proportionally to the amount of removed data, suggesting that adding additional data would likely enhance performance further.
Supplementary Figs. 1–15.
Interaction of peroxisomal membrane protrusions with mitochondria in COS-7 cells. See Fig. 4a–f. COS-7 cells were transfected with PEX11β-EGFP; mitochondria were stained with Mitotracker (red), and analysed by live-cell imaging using an IX81 microscope (Olympus) equipped with a CSUX1 spinning disk head (Yokogawa). A peroxisome interacts with a mitochondrion via its membrane protrusion, and follows it, occasionally detaching and re-establishing contact. Two hundred stacks of nine planes (0.5 µm thickness, 100 ms exposure) were taken in a continuous stream; 118 frames, 14× speed. Scale bar, 5 µm.
Note a peroxisome at the bottom, which interacts with a mitochondrion via its membrane protrusion and then wraps around it, possibly to increase the membrane contact area. Two hundred stacks of nine planes (0.5 µm thickness, 100 ms exposure) were taken in a continuous stream; 200 frames, 14× speed. Scale bar, 5 µm
A mitochondrion, which moves to the left, is dragging a peroxisome with a membrane protrusion with it, indicating that the organelles are tightly tethered to each other. Two hundred stacks of nine planes (0.5 µm thickness, 100 ms exposure) were taken in a continuous stream; 100 frames, 14× speed. Scale bar, 5 µm.
The table provides the 294 SILAC ratios for 10,323 proteins that make up ProteomeHD.
Contains PRIDE identifiers and brief description of each project
Contains the top-scoring 0.5% of protein pairs in the co-regulation analysis. A complete set of all 12.5 million pair-wise associations is available in our PRIDE repository (PXD008888).
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Kustatscher, G., Grabowski, P., Schrader, T.A. et al. Co-regulation map of the human proteome enables identification of protein functions. Nat Biotechnol 37, 1361–1371 (2019). https://doi.org/10.1038/s41587-019-0298-5
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