SNP A/S ratio as a measure for selective constraints

To assess whether SNP A/S ratios from the current large-scale SNP data set provide a good measure for selective constraints, we first compared them with Ka/Ks ratios derived from inter-species alignments. We collected 9,759 human proteins with both validated coding-region SNPs and available human-mouse Ka/Ks data from Ensemble [13], binned them by their Ka/Ks values, and measured the SNP A/S ratios for each group. There is a strong positive correlation between these two measure (Figure ​(Figure1a;1a; Kendall's rank correlation [14] τ = 0.50, p-value < 1e-04), which is in agreement with the neutral theory of molecular evolution. Analysis of data from chimpanzee and Old World monkey (Macaca mulatta) led to similar conclusions, although the Ka/Ks values may need to be corrected to subtract the contribution of SNPs due to relatively short evolutionary distance.

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Figure 1

The SNP A/S ratio is a good measure for evolutionary constraints. Error bars represent 95th percentile confidence intervals from bootstrap resampling. (a) SNP A/S ratios correlate with Ka/Ks ratios from human-mouse alignments. Proteins were grouped into bins of equal intervals (interval = 0.05) according to their Ka/Ks ratios, and the SNP A/S ratio was calculated for each bin. (b) SNP A/S ratios correlate negatively with residue conservation scores from protein sequence alignments. All residues were grouped into bins of equal intervals (interval = 0.5) according to their position specific alignment information taken from PSI-BLAST alignment profiles, and the SNP A/S ratio was obtained for each bin.

We next investigated whether the conservation in protein sequences correlates with the SNP A/S ratio under the assumption that both the conservation at the protein sequence level and the SNP A/S ratio at the nucleotide level are indications for selective constraints. Using the position-specific alignment entropy (a measure for conservation) from PSI-BLAST profiles [15], we calculated A/S ratios for residues with different conservation scores. We indeed observed a monotonic decrease of the A/S ratio with an increase in protein sequence conservation (Figure ​(Figure1b).1b). The residues with the conservation range of 0-0.5 have a ratio of 0.33, while those having conservation scores bigger than 3.5 have an A/S ratio of 0.06.

SNP A/S ratios for protein features

Many studies have been published addressing the correlation between evolutionary constraints and other variables, most of which were based on relatively small data sets. Having established the SNP A/S ratio as a good measure for selective constraints, we attempted to use the large-scale human SNP data set to revisit some of the features in the earlier studies, and also to investigate several protein properties that had not been examined before.

Selective constraints and mRNA expression

Until a few years ago, the prevalent theory in molecular evolution was that evolutionary rate is largely dependent on structural and functional constraints. Recently, increasingly more evidence suggests that there is a strong correlation between evolutionary rate and gene expression. It has been observed that highly expressed genes evolve slowly in bacteria [16], yeast [17], and mammals [18]. In yeast, it has been shown by principal component regression that the number of translation events is the dominant determinant of evolutionary rate among several other functional attributes [19], leading to the increasingly popular 'translational robustness' hypothesis [20]. However, a later study suggested that the dominant effect may result from the noise in biological data that confounded the analysis [21]. Studies of human mRNA expression data showed that the breadth of expression (that is, the number of tissues in which a gene is expressed) also correlates with evolutionary rate [22,23]; it is still debatable whether the breadth or the rate of expression is the stronger predictor [18]. We obtained mRNA expression data for 10,885 genes in our data set that are available from a published microarray experiment (Gene Expression Atlas) [24] and investigated the correlation between selective constraints and four gene expression parameters examined previously: peak expression level, mean expression level, expression breadth, and tissue specificity. Overall, this set of genes with available mRNA expression data has an SNP A/S ratio of 0.25, lower than that of our entire data set (0.28). We indeed observed that highly expressed genes tend to have low A/S ratios (Figure 2a,b): both mean and peak expression rate negatively correlate with the SNP A/S ratio (τ = -0.178 and -0.160, respectively; Table S1 in Additional data file 1). Genes with the lowest mean expression levels have an A/S ratio of 0.38, about twice as high as the ratio in the highest expression group (Figure ​(Figure2a).2a). The SNP A/S ratio also correlates well with the breadth of expression (Figure ​(Figure2c;2c; τ = -0.213, p-value < 1e-04), but only marginally with tissue specificity (Figure ​(Figure2d;2d; τ = 0.047, p-value = 0.003). Since these four expression parameters correlate strongly with each other, we carried out partial correlation analysis [14] to identify the stronger predictors for evolutionary rates. The correlation between tissue specificity and the A/S ratio disappeared entirely after controlling for mean expression level (τ = 0.0107, p-value = 0.499; Table S1 in Additional data file 1) or expression breadth (τ = 0.0084, p-value = 0.596; Table S1 in Additional data file 1). Expression breadth and mean expression level both remain significantly correlated with the A/S ratio when controlling one for the other (τ = -0.096 and -0.064, p-values < 1e-04 and 7e-04, respectively; Table S1 in Additional data file 1). Peak expression level is highly correlated with mean expression level and its partial correlation patterns largely resemble those of mean expression level. It has recently been recognized that it is critical to control for expression when studying the statistical relevance of other variables as predictors for evolutionary rates, since many previously reported correlations became insignificant after this control. As expression breadth appeared to have the strongest correlation with the SNP A/S ratio in our data set among the four parameters, we chose to control for it in the following correlation analysis between selective constraints and other variables. The results did not change qualitatively when controlling for mean expression level instead.

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Figure 2

Correlation between SNP A/S ratios and expression parameters. Genes were grouped into bins of roughly nine equal intervals according to several expression measurements from a microarray experiment, and the SNP A/S ratio was obtained for each bin. Error bars represent 95th percentile confidence intervals from bootstrap resampling. (a) Negative correlation between SNP A/S ratios and mean mRNA expression levels. (b) Negative correlation between SNP A/S ratios and peak mRNA expression levels. (c) Negative correlation between SNP A/S ratios and expression breadth. (d) No correlation between SNP A/S ratios and expression tissue specificity.

SNP A/S ratio and evolutionary variables

Consistent with the hypothesis that gene duplications are an important source of new protein function, it has been observed that duplicated genes evolve under weaker purifying selection than unduplicated ones [25,26]. We collected 12,460 human genes without paralogs and 167 genes with paralogs according to the HomoloGene database [27,28], and found that the A/S ratio is markedly higher for genes with paralogs (0.46 versus 0.27, p-value < 1e-04; Figure ​Figure3a,3a, dark gray bars). To control for expression breadth, we analyzed the subset of genes with mRNA expression data from the Gene Expression Atlas [24]. The two groups of genes do not differ in their distribution of expression breadth (Kolmogorov-Smirnov test, p-value = 0.507). The difference in the A/S ratio did not change significantly when the expression breadth was controlled by Monte Carlo sampling (Figure ​(Figure3a,3a, light gray bars and white bars). We then examined whether the higher rate could be solely explained by additional copies of paralogs while keeping one copy stable. When we selected the fastest evolving genes from each homology group, they have an A/S ratio of 0.55 compared with 0.36 for the batch of the slowest-evolving genes from each homology group. Both numbers are higher than the A/S ratio for genes without paralogs (0.27), suggesting that both duplicated copies are evolving faster than unduplicated genes. The much bigger variation in the with-paralog group (95th percentile confidence interval = [0.38, 0.58]) reflects the small number of genes in that particular group.

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Figure 3

SNP A/S ratios and evolutionary variables. (a) Proteins with paralogs (167 proteins) are under weaker selective pressure than proteins without paralogs (12,460 proteins). The 95th percentile confidence intervals of the A/S ratio are [0.38, 0.58] for proteins with paralogs, and [0.26, 0.27] for proteins without paralogs (dark gray bars). To control for expression breadth, the subset of proteins with mRNA expression data were analyzed (65 proteins with paralogs and 10,612 without, light gray bars) and Monte Carlo samplings were performed so that the two groups had the same distribution of expression breadth. The differences in A/S ratios are significant both before (light gray bars) and after (white bars) controlling for expression. (b) Proteins that arose early in evolution are subject to stronger evolutionary constraints.

To determine whether the SNP A/S ratio correlates with the age of proteins, we classified each protein into one of seven age groups according to their most ancient homologs. It appears that young proteins (for example, those found in human or primates only) have the highest A/S ratios (0.76 for human and 0.66 for primates), whereas proteins traceable to all animals or other eukaryotes have much lower ratios of about 0.25 (Figure ​(Figure3b).3b). This is consistent with a previous finding that proteins that arose earlier in evolution tend to have a larger proportion of sites subjected to negative selection [29], although there was some debate about whether the observation was an artifact resulting from the inability of BLAST to detect homology for the fastest-evolving genes [30,31]. We examined the functions of proteins in each group by their Gene Ontology (GO) [32] annotation of biological process. The human-specific group is the least well annotated, with only 6% having GO annotation compared with 62% overall and 84% for proteins conserved in both eukaryotes and prokaryotes (the 'universal' group). Among the proteins with GO annotation of biological process, we observed the enrichment of 'epidermis development', 'defense response to bacterium', and 'spermatogenesis' in the human and primate groups, whereas 'amino acid metabolic process', 'glycolysis', and 'fatty acid metabolic process' are overrepresented in the 'universal' group.

SNP A/S ratios and sequence/structure variables

As an example of the many conflicting reports in the literature about correlations with evolutionary rates, for a variable as simple as protein length, it was shown that there was positive correlation [33], negative correlation [34,35], or no correlation [36]. In addition, there was a study based on protein sequence alignments that showed that less conserved proteins are shorter than more conserved ones on average [37]. In our data set, we observed a negative correlation between protein length and SNP A/S ratio (Kendall's τ = -0.137, p-value < 1e-04). The correlation did not change upon controlling for expression breadth. Our analysis also showed that this correlation is only prominent for proteins shorter than 500 residues, and disappears for longer proteins (Figure ​(Figure4a4a).

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Figure 4

Evolutionary constraints on protein sequence and structure features. Error bars represent 95th percentile confidence intervals from bootstrap resampling. (a) For proteins shorter than 500 residues, short proteins have high A/S ratios. (b) Buried residues are under stronger selection. The 95th percentile confidence intervals of the A/S ratio are [0.23, 0.25] for buried residues, and [0.30, 0.32] for exposed residues. (c) Loop residues have relaxed evolutionary constraints. The 95th percentile confidence intervals of the A/S ratio are [0.25, 0.26] for residues in alpha-helices, [0.24, 0.27] for residues in beta-strands, and [0.30, 0.32] for residues in loops. (d) Proteins with disordered regions are more conserved, while disordered residues are under lower selective pressure. (e) Residues in low complexity regions evolve faster.

Solvent accessibility measures the degree of an amino acid residue's exposure to the surrounding solvent. There have been a number of studies about the effect of mutations on solvent accessibility and its implication in human diseases; most of them were based on relatively small collections of SNPs in known protein structures. The general consensus was that buried residues are less likely to vary and their mutations are more likely to cause disease [38,39]. We obtained solvent accessibility predictions for all proteins in our dataset using PROFacc [40], and compared the SNP A/S ratios. Exposed residues have an A/S ratio of 0.31, significantly higher than that of 0.24 for the buried residues (Figure ​(Figure4b).4b). The p-value for this difference is smaller than 1e-04 according to bootstrap analysis. Similar results were obtained when using three-state prediction (buried, intermediate, and exposed) or numeric relative accessibility values. This underscores higher selective constraints on buried residues, possibly due to their importance in maintaining protein stability.

We also investigated selective constraints upon different protein structure conformations. We first grouped all residues into different secondary structure conformations (alpha-helix, beta-strand, or loop) according to predictions by PSIPRED [41]. Significantly higher A/S ratios were observed for residues in the loop conformation (Figure ​(Figure4c),4c), suggesting relaxed selective pressure on these residues. There is no difference between residues in alpha-helices and beta-strands. We next examined natively disordered proteins, a class of structurally flexible proteins that have recently gained traction because of their potential important roles in dynamic molecular recognition of macromolecules [42]. It has been estimated that one-third of eukaryotic proteins contains disordered regions [43], and that they are more likely to be involved in regulatory functions and protein-protein interactions [44,45]. We obtained disorder predictions using DISOPRED2 [43] and retained only the disordered regions longer than 30 residues. Interestingly, while proteins with disordered regions have a lower A/S ratio (Figure ​(Figure4d;4d; Figure S2b in Additional data file 1), the residues in disordered regions have a much higher A/S ratio than other residues (0.38 versus 0.22; Figure ​Figure4d).4d). This seems to suggest that disordered proteins as a class are under stronger selective pressure, but the disordered residues are allowed to evolve much faster to explore different ways to interact with other molecules. Since disordered regions are often characterized by low sequence complexity [42,44], we also examined the selective constraints on low complexity regions as defined by SEG [46]. Not surprisingly, low complexity regions have a higher A/S ratio, but the profile is different from that of the disordered regions (Figure ​(Figure4e),4e), confirming that disorder and low complexity are related but different sequence features.

SNP A/S ratios and protein subcellular localization

Subcellular localization is an important aspect of protein function. There have been conflicting reports about the correlation between protein subcellular localization and evolutionary rate. While a previous survey of human SNPs in 2002 did not find a significant correlation of selective pressure against deleterious non-synonymous SNPs with localization [47], a more recent study of mammalian sequences found that secreted proteins evolve much faster than cytoplasmic proteins (Ka/Ks 0.27 versus 0.12), and that membrane segments are under higher selective pressure than non-membrane segments (0.07 versus 0.15) [48]. We attempted to address this issue by examining A/S ratios from several subcellular localization assignment methods. When we divide our data set into 3,064 secreted proteins and 10,622 non-secreted proteins according to SignalP [49] predictions, there is a small and insignificant difference between these two classes, but the residues within the signal peptides appear under much less selective pressure (A/S ratios of 0.42 versus 0.29; Figure ​Figure5a).5a). Interestingly, when only the subset of genes that have mRNA expression data was examined (both before and after controlling for expression), secreted proteins had significantly higher A/S ratios than non-secreted proteins (p-value < 1e-04; Figure S3a in Additional data file 1). There is no difference between membrane proteins and non-membrane proteins, membrane segments and non-membrane segments according to TMHMM [50] predictions (Figure ​(Figure5b;5b; Figure S3b in Additional data file 1). We also obtained predictions of subcellular localizations for non-membrane proteins by LOCtree [51], a hierarchical prediction system mimicking cellular sorting mechanisms. Predicted extracellular proteins have an A/S ratio of 0.34 on average, significantly higher than nuclear and cytoplasmic proteins (Figure ​(Figure5c).5c). Lastly, we examined A/S ratios of 6,228 proteins that have unambiguous GO cellular component assignments. We observed the same trend as for the LOCtree predictions, although the absolute numbers are slightly lower (Figure ​(Figure5d).5d). This may be explained by the fact that more conserved proteins are more likely to get GO annotation through sequence homology. The selective constraints acted upon membrane proteins seem to fall between the extracellular and cytoplasmic proteins according to the GO annotations (Figure ​(Figure5d).5d). The results from both LOCtree predictions and GO annotation did not change qualitatively when controlling for expression breadth (Figure S3c,d in Additional data file 1). Overall, our analysis suggests that extracellular proteins are indeed under more relaxed selection than cytoplasmic and nuclear proteins, but the difference is not as dramatic as previously reported. The absence of difference between membrane and non-membrane proteins according to TMHMM predictions may result from the lack of distinction between the extracellular and cytoplasmic/nuclear proteins.

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Figure 5

Selective pressures on protein subcellular localization. Error bars represent 95th percentile confidence intervals from bootstrap resampling. (a) Analysis of SignalP predictions suggests that while there is no significant difference in selective pressure between secreted and non-secreted proteins, residues within signal peptides are evolving faster. (b) TMHMM predictions show no difference in A/S ratios between membrane proteins and non-membrane proteins, transmembrane segments and non-transmembrane segments. (c) LOCtree predictions of protein subcellular localization indicate extracellular proteins (1,587 proteins) are under more relaxed selective pressure than cytoplasmic proteins (2,105) and nuclear proteins (5,431). (d) GO cellular component annotations suggest extracellular proteins (522 proteins) are under more relaxed selective pressure than cytoplasmic proteins (1,030) and nuclear proteins (1,961), while membrane proteins (2,715) fall in between. The 95th percentile confidence intervals of the A/S ratio are [0.27, 0.33] for extracellular proteins, [0.21, 0.24] for nuclear proteins, [0.22, 0.26] for cytoplasmic proteins, and [0.26, 0.29] for membrane proteins.

Selective constraints on functional classes and protein families

We next studied the variation in SNP distribution of functional categories based on GO annotations. A/S ratios were calculated for 176 GO biological process categories and 152 molecular function categories that have at least 20 genes in our data set. As expected, there are dramatic differences in selective constraints among different categories: A/S ratios range from 0.72 for 'sensory perception of smell' to 0.07 for 'protein kinase C activation' (Table ​(Table1).1). We compared our results with a comparative genomic study of human and chimpanzee [12]. Seven of the top ten categories with highest divergence rates between human and chimpanzee are not present in our entire set of 176 categories due to differences in gene sets and the availability of SNP data. Among the three that are present, all show elevated A/S ratios, and two of them are also in our top ten list (GO:0007608 sensory perception of smell and GO:0007565 female pregnancy). When GO terms were mapped to a small set of high level terms according to Gene Ontology Annotation [52] (GOA slim), the biological process category with the most relaxed selective constraint was 'response to stimulus', which has a significantly higher A/S ratio of 0.33 compared with 'multicellular organismal development', 'transport', 'macromolecule metabolic process', and 'cell differentiation' (Figure ​(Figure6a).6a). In terms of molecular function, the least variable groups are 'protein transporter activity' and 'motor activity', and the opposite groups are 'receptor activity' and 'isomerase activity' (Figure ​(Figure6b6b).

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Figure 6

Evolutionary constraints on protein functional categories. Error bars represent 95th percentile confidence intervals from bootstrap resampling. GO annotations were extracted for each protein, and the GO terms were mapped to high level GOA slim terms for (a) biological process and (b) molecular function. SNP A/S ratios were then calculated for each group.

We also sought to quantify the selective pressure on protein families. Of the 13,686 proteins in our data set, 10,629 can be assigned to at least one Pfam [53] family using the HMMER program. Among the 190 Pfam families that have at least 20 members, the families with the lowest A/S ratios include protein kinase C-terminal domain family (PF00433) and core histones (PF00125); on the high end there are mammalian taste receptors (PF05296), the rhodopsin family (PF00001), and glutathione S-transferases (PF02798 and PF00043) (Table ​(Table2).2). We took a closer look at the G protein-coupled receptor (GPCR) family. GPCRs comprise a large protein family of seven transmembrane receptors that play important roles in sensing environmental signals. They are the targets of more than 40% of all modern drugs. There are five Pfam GPCR families that have more than 20 proteins in our data set. Mammalian taste receptor proteins (PF05296) and rhodopsin family (PF00001) are among the most variable protein families, with an A/S ratio of 0.49. The other three (PF00002 secretin family, PF00003 metabotropic glutamate family, and PF01461 7TM chemoreceptor) have A/S ratios of around 0.25, similar to the overall A/S ratio of 0.28 in our entire dataset. There are 558 proteins that belong to the rhodopsin family, including 286 olfactory receptors. The elevated A/S ratio in the family can be largely attributed to olfactory receptors (A/S = 0.73): the non-olfactory receptors in this family have an A/S ratio of 0.30. Therefore, it appears that among GPCRs, only olfactory and taste receptors have extraordinarily high variations, while other proteins behave like average human proteins.

Selective pressure on disease-related proteins

Knowledge about the degree of selection for disease-related genes can help us understand the etiology of human diseases. An early study found that human disease genes evolve faster at both synonymous and non-synonymous sites than non-disease genes, and Ka/Ks ratios of disease genes are 24% higher [54]. Although the elevated Ks has subsequently been confirmed by others, later studies reported no difference in Ka/Ks between disease genes and non-disease genes [55] or lower Ka for disease genes [56]. It has also been shown that significant differences exist between the Ka/Ks ratio for different pathophysiological classes: genes related to neurological diseases evolve much slower than those associated with immune, hematological and pulmonary diseases [55]. We investigated the SNP distribution of human disease genes using two cancer-related gene collections (243 genes from Cancer Gene Census (CGC) [57], and 3,103 genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) [58]) and the catalog of heritable human disease genes from Online Mendelian Inheritance in Man (OMIM; 2,334 genes) [27]. These three data sets represent 4,649 unique human genes, and 139 genes are common to all three sets. Our analysis of the SNP data shows that disease related genes indeed have a higher synonymous SNP density (OMIM, 5.14; COSMIC, 4.41; CGC, 4.73; non-disease, 4.19, per 1,000 synonymous sites). However, the numbers of non-synonymous SNPs per site for disease genes are lower than that for non-disease genes, resulting in significantly lower A/S ratios in disease genes (p-value < 1e-04; Figure ​Figure7).7). The difference between our analysis and some previous studies could be explained by two factors. First, our data sets are substantially bigger than what were used in previous studies. For example, the Smith and Eyre-Walker study [54] analyzed only 392 genes in the disease set and 2,038 genes in the non-disease set, and the Huang et al. study [55] included 1,178 human disease genes. The other possibility is that the evolution of disease-related genes has different patterns in the human lineage, leading to the difference in SNP A/S ratios and Ka/Ks ratios from human-rodent alignments. It has also been suggested that when non-disease genes are partitioned into housekeeping genes and others, the evolutionary rates of disease genes lie between them [59]. This is consistent with our data: the SNP A/S ratio for OMIM is 0.24, indeed higher than housekeeping genes (genes with the broadest expression patterns, A/S = 0.19; Figure ​Figure2c).2c). Moreover, when controlling for expression breadth (so that different groups have the same distribution of expression breadth, and thus the same proportion of housekeeping genes), non-disease genes still showed significantly higher A/S ratios than genes in the OMIM and COSMIC sets, while the confidence interval of A/S ratios for genes in the CGC set slightly overlapped with that for non-disease genes (Figure S2c in Additional data file 1), mostly due to large variance in the CGC set resulting from a smaller number of genes.

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Figure 7

Disease-related genes are under stronger selective pressure. Disease related genes were obtained from CGC (243 genes), COSMIC (3,103 genes), and OMIM (2,334 genes) databases. The SNP A/S ratio was calculated for each group. The 95th percentile confidence intervals from bootstrap resampling (shown as error bars) are [0.19, 0.27] for CGC, [0.20, 0.22] for COSMIC, [0.23, 0.26] for OMIM, and [0.31, 0.33] for others.

Selective constraints and protein-protein interaction

It is still debatable whether there is any correlation between protein-protein interaction and selective pressure. Most studies so far have been based on data from the budding yeast Saccharomyces cerevisiae. After an initial report that yeast proteins with more interaction partners evolve slowly [60], several studies suggested that the correlation is dependent on interaction data sets [61], or that it may be a secondary effect due to protein abundance [62]. The latest and most conclusive study in yeast suggested that there is no correlation between connectivity and evolutionary rate in a higher quality literature curated interaction data set, while negative correlations observed in some high-throughput data sets even after controlling for expression could be artifacts of the data sets [63]. We obtained human protein-protein interaction data from the IntAct database [64] and examined how SNP A/S ratios are correlated with the connectivity of proteins in the protein-protein interaction network. When all types of interactions were included, proteins with more than five interaction partners appear to have significantly lower A/S ratios than proteins with no more than one partner (Figure ​(Figure8a,8a, gray bars). We also noticed that proteins with more interaction partners tend to have higher mRNA expression (Figure ​(Figure8b,8b, gray bars). The Kendall's rank correlation between connectivity and the SNP A/S ratio was -0.131 (p-value < 1e-04), and it dropped to -0.106 (p-value < 1e-04) after controlling for both mean expression level and expression breadth. The correlation between protein abundance and high connectivity in the interaction network could be either a real biological phenomenon or experimental bias; for example, mass spectrometry-based protein complex pulldown experiments are more likely to identify interaction partners for abundant proteins. When we included only yeast two-hybrid interactions in our analysis, which are supposedly less biased with respect to intrinsic expression levels, the correlation between connectivity and abundance largely disappeared, except for the proteins with no interaction partners in the database (Figure ​(Figure8b,8b, white bars); at the same time, the difference in A/S ratios between proteins with only one partner and those with more than one became smaller and lost statistical significance in some cases according to bootstrap analysis (Figure ​(Figure8a,8a, white bars). For yeast two-hybrid interactions only, the correlation between connectivity and the SNP A/S ratio was -0.100, and it dropped only slightly to -0.090 (p-value = 0.007) after controlling for expression. Nevertheless, the partial correlations were still statistically significant in both the yeast two-hybrid interaction set and the all interaction set. Our analysis supports the idea that the correlation between evolutionary rate and connectivity in the interaction network can, in part, be explained by protein abundance and that some of the correlation may result from experimental bias. Similar to all the conflicting studies in yeast, it is likely that this result is inconclusive and may vary from data set to data set.

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Figure 8

Selective pressures on connectivity in protein-protein interaction networks. Error bars represent 95th percentile confidence intervals from bootstrap resampling. (a) Proteins with more interaction partners appear to have lower A/S ratios (gray bars); however, for yeast two-hybrid interactions, the differences are less significant for proteins with at least one interaction partner (white bars). (b) Proteins with more interaction partners tend to have higher mRNA expression levels (gray bars). This could result from experimental bias: for yeast two-hybrid interactions, the differences are not significant for proteins with at least one interaction partner (white bars).

mRNA expression data

mRNA expression data were obtained from Gene Expression Atlas [24,67]. Only normal adult samples were included in the analysis. Samples were sorted into 54 non-redundant tissue types. The expression level of each probe set in a given tissue was calculated as the mean of log (base 10) MAS5 signal intensities of all samples in that tissue. The 'mean expression level' of a probe set was defined as the mean across all tissues, while 'peak expression level' was defined as the maximum among all tissues. The tissue specificity of a probe set was defined as the heterogeneity of its expression level across all tissues. It was calculated according to [68] as:

where n = 54 is the number of human tissues examined here, S_j is the expression level in each tissue, and S_max is the highest expression level of the probe set across all tissues. When a gene has multiple probe sets, its expression levels and tissue specificity were represented by the probe set with the highest mean expression level.

Affymetrix present/absent calls were used to calculate the breadth of the expression. A probe set was considered 'present' in a tissue if it had 'present' calls in no less than half of the samples in that tissue, and the expression breadth of a probe set was defined as the number of tissues in which the probe set was 'present'. When a gene has multiple probe sets, its expression breadth was represented by the probe set with the highest value of breadth.

Structural and functional features

Ka/Ks ratios from human-mouse alignments were downloaded from Ensembl [13]. The information about human paralogs was extracted from HomoloGene database [27,28] release 57; the existence of paralogs is indicated by the presence of other human proteins in the same homology group. To investigate the degree of conservation throughout the evolutionary history, that is, the age of a protein, we performed BLAST searches [15] of each protein sequence against NCBI's RefSeq database, and collected all hits with an e-value < 1e-10 and a protein length difference smaller than 30% as potential homologs. The query protein was then classified into one of the seven age groups (human, primate, mammal, vertebrate, animal, eukaryote, or universal) according to its most ancient homolog. The use of different e-values and length difference cutoffs did not change the results qualitatively. We also obtained the conservation score for each residue in all proteins in our data set by running PSI-BLAST [15] against NCBI's nr database (parameters: '-j 3 -h 5e-3 -F F') and taking the values in the 'information per position' (a measure of alignment entropy) column from the ASCII format of PSI-BLAST profiles.

We obtained protein structure features by the following computational methods using their default parameters: two-state (exposed or buried) solvent accessibility by PROFacc [40], signal peptides by SignalP version 3.0 [49], transmembrane helices by TMHMM 2.0 [50], secondary structures by PSIPRED [41], sequence complexity by SEG [46], and natively disordered proteins by Disopred2 [43]. Disopred2 predictions were subsequently filtered to retain only the disordered regions longer than 30 residues.

GO annotations [32] were extracted from the GenBank record of each gene, and the GO terms were subsequently mapped to a selection of high-level terms according to GOA slim [52,69]. For cellular component annotations, the GOA slim terms were further collapsed into four categories (extracellular region, nucleus, cytoplasm, and membrane) where appropriate. Proteins assigned to more than one of these four categories were excluded from the analysis of subcellular localization by GO annotation. Subcellular localization prediction for non-membrane proteins (that is, no transmembrane helix predictions from TMHMM) were also obtained by using LOCtree [51]. To assign proteins to protein families, we ran hmmpfam from the HMMER package (version 2.3) against Pfam_ls [53] models (release 21.0) and obtained Pfam family hits with an e-value < 0.01. Numbers of protein-protein interaction partners were obtained from the IntAct database [64,70] (28 September 2007 release). We also obtained disease related genes from CGC (243 genes) [57,71], COSMIC [58,72] (3,103 genes), and OMIM databases [27,73] on 28 September 2007. For OMIM, only those genes with the 'confirmed' status (2,334 genes) were included in the analysis.

Sequences within 70 bp of an exon-intron junction were collected in the same way as described in [65]. Briefly, transcripts with less than three exons were excluded from the analysis. All internal exons were trimmed so that the first base was the first base of the first complete codon, and the last base the last of the final complete codon. The first and last codons were then removed from each exon, and remaining codons within 70 bp of the intron-exon boundary were defined as sequences close to the boundary.

The features we considered can also be divided into two classes: protein-level features and residue-level features. A protein-level feature describes the property of an entire protein, for example, whether a protein has a transmembrane helix or not; in contrast, a residue-level feature describes the property of a subset of residues within a protein, for example, whether a residue resides in the transmembrane helix or not.