Proteins Deleterious on Overexpression Are Associated with High Intrinsic Disorder, Specific Interaction Domains, and Low Abundance Liang Ma, Chi Nam Ignatius Pang, Simone S. Li, and Marc R. Wilkins* Systems Biology Initiative and School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia Received August 4, 2009
In proteomics, there is a major challenge in how the functional significance of overexpressed proteins can be interpreted. This is particularly the case when examining proteins in cells or tissues. Here we have analyzed the physicochemical parameters, abundance level, half-life and degree of intrinsic disorder of proteins previously overexpressed in the yeast Saccharomyces cerevisiae. We also examined the interaction domains present and the manner in which overexpressed proteins are, or are not, associated with known complexes. We found a number of protein characteristics were strongly associated with deleterious phenotypes. These included protein abundance (where low-abundance proteins tend to be deleterious on overexpression), intrinsic disorder (where a striking association was seen between percent disorder and degree of deleterious effect), and the number of likely domain-domain interactions. Furthermore, we found a number of domain types, for example, DUF221 and the ubiquitin interaction motif, that were present predominantly in proteins that are deleterious on overexpression. Together, these results provide strong evidence that particular types of proteins are deleterious on overexpression whereas others are not. These factors can be considered in the interpretation of protein expression differences in proteomic experiments. Keywords: Intrinsic disorder • domain-domain interactions • protein overexpression • proteomics
Introduction Proteomic analysis is frequently applied to case-control studies. An aim, and indeed a result of many of these studies, is the detection of proteins that display significant changes in expression in association with a phenotype. A major challenge with over- or underexpressed proteins, notably those found in cells or tissues, has been the interpretation of their functional significance. It remains difficult to predict if a change in protein expression, while associated with a phenotype, is actually deleterious and/or causative and if it will affect cellular homeostasis. Recently, Sopko et al.1 explored this issue but from a different perspective. Instead of examining a phenotype with proteomic approaches, they systematically overexpressed 5032 proteins, one per strain, in Saccharomyces cerevisiae and examined each overexpressor for phenotypic effect. One of the most striking observations is that 15% of proteins (768 proteins out of 5032 tested) generated a discernible deleterious phenotype on overexpression. All phenotypes showed slow growth in the presence of galactose, and included those with abnormal morphology or showing cell cycle arrest. As a group, they contained a high number of signaling molecules, transcription factors and cell cycle regulators. An exploration of whether deleterious proteins were overrepresented in known complexes * Address for Correspondence: Prof. Marc Wilkins, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia. E-mail:
[email protected], Fax: +61-2-9385-1483.
1218 Journal of Proteome Research 2010, 9, 1218–1225 Published on Web 01/07/2010
suggested no significant difference, compared to proteins that had no discernible phenotype on overexpression. Here we have further analyzed the systematic overexpression experiments from Sopko et al.1 We show that protein abundance level, some physicochemical characteristics, the type of interaction domains and the degree of intrinsic disorder of proteins are clearly associated with deleterious phenotype on overexpression. This suggests that these factors should be considered as part of the interpretation of proteomic analyses, especially for proteins that are overexpressed.
Materials and Methods Deleterious and Neutral Proteins. A total of 5032 unique genes were overexpressed by Sopko et al.1 and of these, 768 were deleterious on overexpression while 4264 showed no discernible negative phenotype. The deleterious proteins were further classified as ‘lethal’, ‘abnormal morphology’ and ‘cell cycle arrest’. While it was of interest to analyze these separately, there was considerable overlap among these classes; for example, 111 proteins were classified as both ‘abnormal morphology’ and ‘cell cycle arrest’ and 21 proteins fell into all three classes. Statistical analysis relies on independence between the samples; however, disregarding proteins shared among classes left only 61 ‘lethal’ proteins, 60 ‘abnormal morphology’ proteins and 7 ‘cell cycle arrest’ proteins. Owing to these small sample sizes and thus weak statistical power, all proteins that were toxic on overexpression were analyzed as one group of ‘deleterious’ proteins and compared to the 10.1021/pr900693e
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Proteins Deleterious on Overexpression ‘neutral’ proteins of no discernible phenotype. The two groups are referred to by these names throughout this study. We note that, since the publication of Sopko et al.,1 three deleterious genes (ordered locus names YKL157W, YCL014W, YLL016W) have been merged with three other genes in the Saccharomyces Genome Database2 and Swiss-Prot3 databases (APE2, BUD3, SDC25, respectively). To keep our analyses consistent with Sopko et al.,1 we have maintained these as separate genes, as previously described. Databases and Calculation of Protein Parameters. For the analysis of all proteins, sequence data was from Swiss-Prot release 54.3 Prior to calculation of protein parameters, protein sequences were processed to their mature forms according to Swiss-Prot annotation. Protein isoelectric point was calculated according to Bjellqvist et al.4 and grand average hydropathy (GRAVY) calculated according to Kyte and Doolittle.5 Protein domains and interaction domains were from iPfam release 20;6 for proteins carrying interaction domains, putative domaindomain interactions were calculated by determining the number of proteins which carry complementary interaction domains in the proteome, as in Pang et al.7 Protein abundance data, in copies per cell, was from Ghaemmaghami et al.,8 protein half-life estimates from Belle et al.9 and protein complex data from Gavin et al.10 Estimates of protein structural disorder, also known as intrinsic disorder, were from Kim et al.11 whereby a score and classifier were assigned to each residue in a protein and the percent disorder computed by dividing the number of disordered residues by protein length. Structural disorder is the tendency of a protein to lack a unique 3-D structure, instead existing in a dynamic ensemble of conformations. Data Transformation. Log10 transformations were applied to protein molecular weight, abundance, half-life and data such as the number of domain-domain interactions. Inverse-sine (arcsin) transformations were applied to percentage data. Neither log transformations nor inverse-sine transformations affect the outcome of hypothesis testing,12 and in most cases, they were used only to assist in visualization of data distributions. The log transformation was also used to improve linearity prior to regression analysis. Statistical Analyses and Graphs. Wilcoxon rank-sum tests were used to compare the distributions of protein molecular weight, pI, GRAVY scores, abundance, half-life, intrinsic disorder and the number of domain-domain interactions between deleterious proteins and neutral proteins. The Wilcoxon rank-sum test is a nonparametric test for assessing whether two independent samples of observations come from the same distribution. All tests were performed with a significance level of 5%. Covariances of pairs of parameters were studied through calculating Pearson correlation coefficients. For covariance analysis, F-value of less than 0.3 was considered to indicate little or no association between pairs of parameters. All statistical analyses, as well as box and whisker plots, density estimates, scatter plots and bar plots were undertaken with R.13 Density estimates of all distributions were based on a Gaussian kernel.14
Results In their generation and analysis of the deleterious and neutral overexpressed proteins, Sopko et al.1 focused their investigation predominantly on the functions of proteins. Here, we are instead investigating the characteristics of the deleterious proteins and the manner in which they interact with other proteins. We hypothesize that the characteristics of the proteins
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Figure 1. Physicochemical properties of neutral and deleterious proteins. (a) Protein molecular weight, log scale (b) protein pI and (c) protein grand average hydropathy (GRAVY). Vertical lines in all graphs indicate median values.
that are deleterious on overexpression are likely to be different to those that are neutral on overexpression in the yeast cell. Proteins Deleterious on Overexpression Show Different Physicochemical Characteristics to Those of Neutral Proteins. We examined protein size, isoelectric point and hydropathy to determine if deleterious proteins are different in these parameters to neutral proteins. It was found that deleterious proteins tended to be larger than neutral proteins. They showed less proteins of mass 10-20 kDa (Figure 1a), with median values Journal of Proteome Research • Vol. 9, No. 3, 2010 1219
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Figure 2. Box and whisker plot of protein abundance, for proteins that are deleterious on overexpression and those that are neutral on overexpression. The abundance of proteins that are deleterious is lower than that of proteins that are neutral on overexpression. Note that copies per cell is a represented on a logarithmic scale.
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Figure 3. Proteins deleterious on overexpression have shorter half-lives than neutral overexpressed proteins. Note that halflife in this graph is plotted on a logarithmic scale.
in a cell compared to its normal wild-type level is associated with the likelihood of a protein being deleterious. of 54 and 39 kDa, respectively. This is a significant difference (Wilcoxon rank-sum test, p ) 2.2 × 10-16). In both cases, a bimodal distribution of protein size was evident. For isoelectric point, it was found that deleterious proteins showed a reduced degree of very acidic proteins, in association with a slightly larger proportion of proteins of neutral pI (Figure 1b). The distribution of pI was not significantly different between neutral and deleterious overexpressors (Wilcoxon rank-sum test, p ) 0.12). Examination of protein hydropathy (Figure 1c) showed that deleterious proteins as a group are more hydrophilic than neutral proteins, having median GRAVY scores of -0.47 and -0.36, respectively. This is a significant difference between these two groups of proteins (Wilcoxon rank-sum test, p ) 2.9 × 10-9).
Proteins Deleterious on Overexpression Have Shorter Half-Lives. Sopko et al.1 noted that many proteins deleterious on overexpression were cell-cycle associated, implying that the mistiming of protein expression can be of consequence in the cell. To explore whether the dynamics of protein turnover is also different between neutral and overexpressed proteins, we studied protein half-lives using data from Belle et al.9 Proteins that are deleterious on overexpression (total 452) were found to have a shorter half-life (median of 34 min) as compared to the half-life of neutral proteins (total 2267, median of 45 min) (Figure 3). This difference is significant (Wilcoxon rank-sum test, p ) 1.7 × 10-7) and suggests that proteins which show tight regulation have a tendency to be deleterious when overexpressed.
Proteins Deleterious on Overexpression Are of Lower Abundance in the Wild-Type Cell. To understand if deleterious effects are associated with the degree of overexpression of a protein, we investigated the wild-type abundance levels of deleterious and neutral proteins. Abundance data was available for a total of 504 deleterious and 2601 neutral proteins from a large-scale study.8 The abundance of deleterious proteins ranged from 57 to 524 000 copies per cell with a median of 1970 copies per cell, while abundance of neutral proteins ranged from 41 to 883 000 copies per cell with a median of 2350 copies per cell. This difference was statistically significant (Wilcoxon rank-sum test on log-transformed data, p ) 0.003). The most abundant deleterious protein was the histone H4 protein, while a ketol-acid reductoisomerase ILV5 was the most abundant neutral protein. There was substantial overlap between the distributions of deleterious and neutral proteins (Figure 2); however, higher abundance proteins tended to have a neutral overexpression phenotype. For example, of the 59 proteins in yeast with >100 000 copies per cell, only 6 out of an expected 11 were deleterious. On the other hand, of the 24 proteins with 0.3. A weak positive correlation was seen between protein abundance and half-life (F-value 0.34 and 0.29 for deleterious and neutral proteins, respectively). A strong negative correlation was seen between protein hydropathy and structural disorder (F-value -0.68 and -0.61 for deleterious and neutral proteins, respectively). The latter correlation shows that highly hydrophilic proteins have a tendency to be structurally disordered and that highly hydrophobic proteins have a tendency to be structurally ordered (Figure 7). For both correlations, F-values were similar for proteins deleterious or neutral on overexpression.
Discussion In their landmark study, Sopko et al.1 overexpressed 5032 proteins in S. cerevisiae and showed that 15% of these were of deleterious phenotype. They noted that the deleterious proteins were enriched for proteins of specific function, namely, those involved in signaling, regulation of transcription or under cellcycle control. Here, we have extended the analysis of results from this large overexpression experiment to better understand why certain proteins were deleterious on overexpression. We believe this is of relevance to proteomic researchers who, having found overexpressed proteins, are seeking to understand if this might lead to deleterious effects in the system they are studying. Physicochemical Properties of Deleterious Proteins. The size of proteins deleterious on overexpression was, as a group, larger than those that were neutral on overexpression. A lower quantity of proteins of mass 10-20 kDa was clearly seen, corresponding to those composed of a single domain.16 A higher quantity of deleterious proteins with two or more domains was evident. It was also seen that deleterious proteins were collectively more hydrophilic than neutral proteins. While average hydropathy is an imprecise measure of protein type, where some membrane proteins are of high hydrophobicity but others are not,17 this observation suggests that overexpression-associated deleterious effects are not due to an overabundance of hydrophobic proteins interacting promiscuously in the cell. We also noted that there was no ‘spike’ of proteins deleterious on overexpression of pI 6-6.5, corresponding to
Proteins Deleterious on Overexpression
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Figure 6. Some complexes contain no proteins that are deleterious on overexpression, while others contain many. (a) The number of known complexes which contain no deleterious proteins versus the number of complexes that contain one or more deleterious proteins. (b) For complexes that contain proteins which are deleterious on overexpression, the number of deleterious proteins tends to increase with the size of a complex (line of best fit, r ) 0.72).
the approximate pH of the yeast cytoplasm.18 This suggests that precipitation of proteins at their pI or formation of inclusion bodies, documented in association with heterologous overexpression in S. cerevisiae,19 is unlikely to be present or a reason for deleterious overexpression. Together, these data suggest that, while physicochemical parameters are clearly biased, they are not clear indicators which can be used in isolation to predict the functional impact of an overexpressed protein. Deleterious Proteins Show a High Degree of Intrinsic Disorder. A striking observation in our study was that deleterious proteins, as a group, had shorter half-life and showed enrichment in predicted structural disorder. A relationship of half-life and intrinsic disorder has been recently reported15,20,21 and intrinsic disorder is also reported to be associated with proteins under tight regulation.15,22 However, we also found that the severity of deleterious phenotype showed a strong positive association with percent of disorder. Intrinsic disorder is thus likely to be an informative protein parameter when investigating the functional impact of any overexpressed protein. It can be easily calculated with bioinformatics tools (e.g., Kim et al.11) for proteins of interest. Our observations on protein disorder and half-life would suggest that many deleterious proteins are regulatory. This was noted by Sopko et al.1 and is supported by other observations that regulatory proteins have short half-life,23 are enriched in disordered regions,24 are likely to cause deleterious phenotypes when overexpressed25 and are low in abundance.8 Indeed, we did note that deleterious proteins were, as a group, of lower abundance than those that were neutral on overexpression. For
low-abundance proteins, overexpression would cause a dramatic fold increase in copies per cell and could disrupt cellular homeostasis. By contrast, proteins that are normally of high abundance would show a smaller fold increase in copies per cell when overexpressed. This is more likely to be accommodated by compensatory mechanisms in the cell. Interestingly, recent quantitative analysis of protein abundances in individual yeast or human cells has shown that a range of expression levels is seen and can be tolerated for many, but certainly not all proteins.26,27 The Impact of Overexpression on Protein Complexes. There is considerable debate surrounding the effect of protein overexpression on complexes. Previous studies have suggested that overexpression of the components of complexes has a limited role in deleterious phenotypes.1,28,29 Neither the core or attachment units of yeast protein complexes10 were reported to be enriched for deleterious proteins29 and the topology of the protein complex was reported to have a limited role in determining which protein would be deleterious upon overexpression.28 However, we have found that proteins deleterious on overexpression were not present in 37% of the yeast complexes recently defined by Gavin et al.10 in their systematic study. This included some complexes with large numbers of subunits such as the exosome (17 subunits). It is interesting to consider that the exosome complex contains symmetrical components30 which can self-associate and form alternative species of the protein complex upon overexpression. This may reduce the likelihood of promiscuous binding with other proteins and a deleterious phenotype. It also raises the question Journal of Proteome Research • Vol. 9, No. 3, 2010 1223
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cally regulated proteins. The dynamic proteins are expressed ‘just-in-time’ to activate complexes, but only when the function of the whole complex is required. This was illustrated for the cell cycle, involving many cyclin proteins. This ‘just-in-time’ activation model can explain why deleterious proteins are enriched for many cell-cycle proteins;1 overexpression of the dynamic component activates the complex even when it is not required to act. In contrast, the overexpression of static subunits cannot activate the complex. This strongly suggests that the cell will tolerate ‘noise’ in expression levels for constitutive proteins of a complex but not their tightly regulated ‘just-in-time’ protein subunits. This observation is critical for the interpretation of proteomic expression data; however, it is acknowledged that there is relatively little information of this type available in databases. Domain-Domain Interactions and Types. Sopko et al.1 examined overexpressed proteins for enrichment of any domaintype.We,alternatively,focusedonpotentialdomain-domain interactions and showed that deleterious proteins have a much higher number of these than neutral proteins. The reason why proteins with a large number of domain-domain interactions are deleterious on overexpression is that they could form undesirable interactions15 with proteins that have a compatible interaction domain but usually interact with other proteins.25 The binding of an overexpressed protein with the native binding partners can become saturated, permitting proteins with the next highest affinity to interact with the overexpressed protein.32 Dissociation constants dictate that the more highly overexpressed a protein becomes, the more likely it is to show this effect. We further showed that some domains involved in protein-protein or protein-nucleic acid interactions were uniquely or almost uniquely associated with deleterious proteins in the yeast proteome. Interestingly, this has highlighted specific essential processes other than the cell cycle that are susceptible to overexpression. For example, the gelsolin domain and Sec23/Sec24 related domains are present in Sec23p, Sec24p and Sfb3p; all proteins are associated with the formation of the COPII vesicle coat, essential for retrograde vesicular transport between ER and Golgi.33 Proteins containing the SNARE domain, also strongly overrepresented in deleterious proteins, also function in the same retrograde vesicle transport pathway. In a further example, the deleterious proteins Snl1p, Skn7p and Ssk1p all contain the response regulator receiver domain and act in the branched two-component osmosensing pathway.34 Figure 7. The average hydropathy of proteins (GRAVY) shows a strong negative correlation with the percent of protein disorder. Highly hydrophilic proteins have a tendency to be structurally disordered and highly hydrophobic proteins have a tendency to be structurally ordered. This trend is similar for proteins which are deleterious (a) or neutral (b) on overexpression, and is common to all proteins (c).
of whether proteins that self-associate, to form homodimers or larger homomultimers, are less likely to be deleterious on overexpression. A lack of data for the propensity of proteins to self-multimerise makes it difficult to currently answer this question. In considering the likelihood that protein overexpression will be deleterious on a complex, we must also consider if temporal patterns of expression will be altered. Recently, it was proposed that many protein complexes contain static proteins which are expressed in a constitutive manner along with some dynami1224
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Conclusions This study has shown that S. cerevisiae proteins which are deleterious on overexpression have specific characteristics, as compared to proteins of neutral phenotype. However, there appear to be different reasons why certain proteins are deleterious, reflecting their roles in different pathways and cellular functions. Some proteins are deleterious on overexpression in that they carry specific domains that by themselves, or through their interactions, are toxic. Other proteins show high intrinsic disorder, which is likely to be associated with their deleterious effect. Yet others are tightly regulated, and are likely to perturb cellular homeostasis when the dynamics of their expression is disrupted. It is suggested that these particular features, where known, might serve to prioritize ‘proteins of interest’ in proteomic studies when linking these to a deleterious phenotype. However, it must equally be kept in mind that combinations of many protein characteristics,
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Proteins Deleterious on Overexpression reflecting the diversity of protein functions in the cell, might need to be co-considered to make strong predictions. Artificial intelligence approaches, such as hidden Markov models or neural networks, could be of use in future studies, although any predictions will need careful interpretation to ensure they are biologically relevant.
Acknowledgment. C.N.I.P. is the recipient of an Australian Postgraduate Award. M.R.W. and the New South Wales Systems Biology Initiative acknowledge financial support from the New South Wales State Government Office for Science and Medical Research and the University of New South Wales. Supporting Information Available: A full list of domains enriched in deleterious proteins and figure of pairwise comparisons. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Sopko, R.; Huang, D.; Preston, N.; Chua, G.; Papp, B.; Kafadar, K.; Snyder, M.; Oliver, S. G.; Cyert, M.; Hughes, T. R.; Boone, C.; Andrews, B. Mapping pathways and phenotypes by systematic gene overexpression. Mol. Cell 2006, 21 (3), 319–30. (2) Hong, E. L.; Balakrishnan, R.; Dong, Q.; Christie, K. R.; Park, J.; Binkley, G.; Costanzo, M. C.; Dwight, S. S.; Engel, S. R.; Fisk, D. G.; Hirschman, J. E.; Hitz, B. C.; Krieger, C. J.; Livstone, M. S.; Miyasato, S. R.; Nash, R. S.; Oughtred, R.; Skrzypek, M. S.; Weng, S.; Wong, E. D.; Zhu, K. K.; Dolinski, K.; Botstein, D.; Cherry, J. M. Gene Ontology annotations at SGD: new data sources and annotation methods. Nucleic Acids Res. 2008, 36 (Database issue), D577–81. (3) UniProt Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Res 2007, 35 (Database issue), D193–7. (4) Bjellqvist, B.; Hughes, G. J.; Pasquali, C.; Paquet, N.; Ravier, F.; Sanchez, J. C.; Frutiger, S.; Hochstrasser, D. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis 1993, 14 (10), 1023–31. (5) Kyte, J.; Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 1982, 157 (1), 105– 32. (6) Finn, R. D.; Marshall, M.; Bateman, A. iPfam: visualization of protein-protein interactions in PDB at domain and amino acid resolutions. Bioinformatics 2005, 21 (3), 410–2. (7) Pang, C. N.; Krycer, J. R.; Lek, A.; Wilkins, M. R. Are protein complexes made of cores, modules and attachments. Proteomics 2008, 8 (3), 425–34. (8) Ghaemmaghami, S.; Huh, W. K.; Bower, K.; Howson, R. W.; Belle, A.; Dephoure, N.; O’Shea, E. K.; Weissman, J. S. Global analysis of protein expression in yeast. Nature 2003, 425 (6959), 737–41. (9) Belle, A.; Tanay, A.; Bitincka, L.; Shamir, R.; O’Shea, E. K. Quantification of protein half-lives in the budding yeast proteome. Proc. Natl. Acad. Sci. U.S.A. 2006, 103 (35), 13004–9. (10) Gavin, A. C.; Aloy, P.; Grandi, P.; Krause, R.; Boesche, M.; Marzioch, M.; Rau, C.; Jensen, L. J.; Bastuck, S.; Dumpelfeld, B.; Edelmann, A.; Heurtier, M. A.; Hoffman, V.; Hoefert, C.; Klein, K.; Hudak, M.; Michon, A. M.; Schelder, M.; Schirle, M.; Remor, M.; Rudi, T.; Hooper, S.; Bauer, A.; Bouwmeester, T.; Casari, G.; Drewes, G.; Neubauer, G.; Rick, J. M.; Kuster, B.; Bork, P.; Russell, R. B.; SupertiFurga, G. Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440 (7084), 631–6. (11) Kim, P. M.; Sboner, A.; Xia, Y.; Gerstein, M. The role of disorder in interaction networks: a structural analysis. Mol. Syst. Biol. 2008, 4, 179. (12) Quinn, G.; Keough, M. Experimental Design and Data Analysis for Biologists; Cambridge University Press: Cambridge, 2002.
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