X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e. g. , secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X’s gene content, gene expression, and evolution.
X chromosomes are in many regards unusual (reviewed in [1,2]). The peculiarities of the X include an unusual recombination environment, unusual dominance relations and an unusual proportion of time spent in members of the two sexes. The consequences of these peculiarities include, in some taxa, reduced recombination rates and lower mutation rates. In addition, the X chromosome is expected to have an unusual effective population size, this being exaggerated by strong sexual selection [3–8]. Such forces can likely explain many peculiarities of X chromosomes. For example, the human X chromosome, whose genome sequence [9] is much less degraded than that of non-recombining Y chromosome, has an AT-content between that of the Y chromosome and autosomes, consistent with reduced rates of recombination-associated biased gene conversion favoring AT→GC SNPs in the face of a GC→AT mutation bias [10]. That we see an increase in GC-content as we move from the haploid part of the X to pseudoautosomal regions on the X [11,12] strongly supports such a recombination-associated model. The hemizygous nature of the X in males, exposing adaptive mutations, is similarly central to the “faster X” hypothesis—that is, the notion that X-linked genes might be fast evolving [5,6, 13]. Several models also predict that the X will be unusual in its gene content. Two models are pre-eminent. First, Rice noted that because of dominance effects and time spent in the two sexes, an X chromosome might have a tendency to accumulate genes with either male- or female-specific expression [14]. The former is owing to a selective filter enabling the spread of male-advantage/female-disadvantage recessive alleles (the deleterious effects being hidden at the point of invasion of a sexually antagonistic allele). The latter is owing to selection favoring dominant female-advantageous/male-deleterious dominant mutations, given the two thirds of time the X spends in the female germline, thus exposing the advantageous effects more often than the deleterious ones [14]. To account for sex-biased expression, one then evokes modifiers of the sex of expression, reducing expression in the sex in which an allele is deleterious [14]. This we refer to as the sexually antagonistic model. Second, as the X chromosome is inactivated in the male germline, the X has been considered an environment incompatible with the presence of genes needed in spermatogenesis ([15,16], see also [17]). Thus, the traffic of spermatogenesis genes off the X was to be expected, as the ancestral autosome (proto-X) became the X chromosome. The SAXI hypothesis [18] is a fusion hypothesis suggesting that sexual antagonism drove male-biased genes off the X, thereby enabling germline X chromosome inactivation (note that this hypothesis presumes the X chromosome to be incompatible with male-advantage genes although this need not be true). Both the above models can claim some support (for review see [3,4]). There is, for example, evidence for a movement off the mammalian X chromosome via retroposition of genes whose retroposed copies are highly expressed in the male germline [19]. This is potentially consistent with the germline inactivation/SAXI model but not the version of sexual antagonism that predicts accumulation on the X of genes biased towards male-specific expression. However, unexpectedly [19], there is also evidence for a loss on the X of genes whose retrocopies function after the time of germline X inactivation (i. e. , when the X is active again) [19]. Given this potential direct germline effect, tests of the logic of Rice’s hypothesis are best done when considering somatic tissues. While there is evidence that the mouse X is enriched for sex-biased genes not subject to the meiotic sex chromosome inactivation [20], the evidence is somewhat contradictory. For example, one report claims an excess of female-biased gene expression and a dearth of genes biased towards male-specific expression [21]. Another report finds evidence for an enrichment of male-specific genes [22]. In Drosophila, genes expressed in male-specific accessory-gland, but not testis, are excluded from the X [23–25]. Here, following a proposal of Vicoso and Charlesworth [26], we wish to suggest that there is a further, potentially complementary, simple yet powerful, driving force for the evolution of gene content on the X chromosome. This force stems from the fact that in males the X is haploid-expressed. This might limit the maximal expression of any X-linked gene. Put simply, when transcription rates are potentially high, if there are two parallel sites for transcription (diploid expression on autosomes), the net rate of production can be higher than if there are transcriptional traffic jams on the haploid-X. The same limitation is not of great importance if the rate of transcription is not limiting. Thus, we expect the X chromosome to be a non-optimal environment for genes with very high maximal levels of expression. Such high levels of expression cannot be readily achieved, owing to transcriptional traffic jams, with all gene expression running through one promoter, as opposed to two promoters in diploid-expressed autosomal genes. X inactivation in female mammals exacerbates the problem. We call this model the “weak X” or traffic jam model. The precise expectations may depend on the mechanism of dosage compensation. In fruit flies, for example, the X chromosome in males is hyper-transcribed [27,28]. This, it has been argued, will make it hard to increase the expression level even more if there is an upper limit to the rate of transcription, as suggested by Vicoso and Charlesworth [26]. As male-biased expression commonly comes about through increased expression in males, this force alone is enough to explain the absence of male-biased gene expression on the Drosophila X [26]. Similarly, Bachtrog et al. [29] find evidence that Drosophila’s mode of dosage compensation restricts the ability to further up-regulate X-linked genes. More recent work suggests that genes expressed in male-specific accessory-gland, but not testis, are excluded from the X in Drosophila [23–25] because of expression limits on the X. If, however, the fly X is truly hyper-transcribed, it is an environment for the most part compatible with high maximal expression, just not one readily capable of increasing it still further. Indeed, Vicoso and Charlesworth [26] find no evidence that maximal expression on the X is lower than that on autosomes. The hyper-transcription from the fly X contrasts somewhat with the situation in mammals (as best as it is currently understood). In mammals, it was for a long time believed that the X in males is also hyper-transcribed to compensate for the loss of expression on the decaying Y chromosome [30]. However, two recent analyses indicate that, compared with the expression level of the ancestral genes prior to the formation of the X chromosome (as opposed to the consideration of the current X to autosomal expression ratio), the extant X-linked genes have not increased their expression levels [31,32]. A recent proteomics-based analysis supports this finding [33]. Rather, autosomal genes that partake in protein–protein interactions with X-linked counterparts appear instead to have reduced their expression levels [31]. There is also evidence that some X-linked genes associated with protein complexes have increased their expression [32,34]. Assuming no or limited increase in the expression level of X-linked genes, this suggests a simple explanation for the fortune of genes as the diploid proto-therian X evolved into the haploid-X. Unlike in fly (with hyper-transcription), any gene that had high maximal expression on the proto-mammalian X could not sustain this. If a reduction in dosage was cost-free, then no further adaptation was needed. If a reduction in a gene’s dosage was not cost-free, then an adaptation of some variety might have been required. This might mean divestment of some of a gene’s function to autosomal genes, possibly mediated by changed expression of compensating paralogs or the creation of such paralogs, if not already present. We thus expect a net movement away from (or avoidance of) the X chromosome for genes with high maximal expression. This could, in principle, explain why highly expressed germline genes are moved away from the X, even if there is no germline X inactivation during their time of activity, an observation previously posed as unexpected [19]. If broad expression tends to be coupled with high maximal expression, it might also possibly explain why genes tend to be more tissue-specific on the X chromosome (Table 1) [30,35]. Here, we seek to test these models. To this end, we employ an exceptional expression resource, Functional Annotation of the Mammalian Genome (FANTOM5), and in addition, a merge of FANTOM5 with The Encyclopedia of DNA Elements (ENCODE). FANTOM5 [36] is an extensive atlas of mammalian expression patterns at a single-nucleotide resolution level [37], including libraries from 179 human tissues, 513 isolates of primary cells, and 260 cancer cell-lines, generated using Cap Analysis of Gene Expression (CAGE) technology [38]. Unlike microarrays, CAGE is not limited to preselected features, and it samples the entire genome space in an unbiased fashion. ENCODE [39] is a detailed atlas of regulatory elements. Although ENCODE experiments were performed on separate cell-lines, standardized laboratory protocols and a unified analytical pipeline [40] allow one to merge ENCODE data into a single meta-dataset [41,42]. This work is part of the FANTOM5 (Functional Annotation of the Mammalian Genome 5) Project. Data downloads, genomic tools, and copublished manuscripts are summarized at http: //fantom. gsc. riken. jp/5/.
A prediction of the “weak X” model is that the upper limit for the highest attainable level of gene expression for X-linked genes should be lower than the maximal attainable for autosomal genes. Consistent with this idea, the average maximal expression on the human X chromosome is three times lower than on autosomes, that is to say 87 versus 261 tags per million (TPM) (Fig 1, Table 2). The difference is highly significant (a Wilcoxon test, p-value < 2. 2e-16; randomization test’s p-value = 0 based on 1 million random subsamples in a test designed to measure the probability of obtaining similarly skewed, i. e. , deviant from null, the mean maximal on the X by chance; see Methods). Median maximal expression on the X is also lower than on autosomes: 23 versus 39 TPM. In human tissues, the absolute maximal expression on the X chromosome (highest for any gene in any tissue) is 5604 TPM (brain expressed, X-linked 1 [BEX1], NM_018476, expressed in the medial temporal gyrus library). In contrast, the maximal expression on autosomes is over 62 higher at 3. 48e+05 (hemoglobin, beta; NM_000518 expressed in blood; Table 3). Note that maximal expression is the greatest numerical value attained (in TPM) for a transcript across all relevant CAGE libraries and thus, unlike the mean (or the median) expression, maximal expression does not greatly suffer from method problems relating to cutoffs to call a gene expressed or not. As then expected, the effect is robust to the exclusion of “non-expressed” genes, with the average maximal expression on the X and autosomes at 122 and 321 TPM respectively (a Wilcoxon test, p-value = 2. 96e-12) for genes with a minimum maximum expression level of 10 TPM. The result is also robust when considering maximal expression per gene (where multiple transcripts from one gene are amalgamated by averaging or summing their expression levels), instead of maximal expression per transcript (S1 Table). An analysis of the behavior of the large pseudoautosomal region (PAR1), with its 24 genes, is broadly compatible with the view that the maximal expression constraint is specific to the haploid part of the X chromosome (S2 Table). Definitive statements should, however, not be made, owing to the limited sample size. Indeed, if we randomly select 24 autosomal genes and ask how often these are significantly different in their mean maximal expression to genes on the haploid part of the X, then in less than 50% of randomizations do we detect any effect (S2 Table), while the comparison of all autosomal genes to all X-linked genes is unambiguous. This caveat aside, we note two things. First, the mean maximal expression for PAR1 genes is higher than that of haploid-X genes, this being on the edge of significance (94 versus 86 TPM, p = 0. 059, a Wilcoxon test). However, additionally, we note one peculiarity, this being that the average breadth of expression (BoE) of PAR1 genes is rather low (S2 Table). As the low breadth of expression is likely to correlate with low maximal expression (for sampling reasons alone, see below), we also ask about the maximal expression of PAR1 genes controlling for the breadth of expression. This we do by performing a loess regression of maximal expression predicted by the breadth of expression for all genes on the X chromosome (PAR1 included) and then calculating the residuals. A positive residual implies a maximal level of expression that is high given the underlying breadth of expression. We find that PAR1 genes have on average positive residuals (the mean of 54) whilst the haploid-X-linked genes have weakly negative residuals (the mean of -3. 27). The two sets of residuals are significantly different (p = 0. 007 in a Wilcoxon test). Thus, controlling for the breadth of expression, PAR1 genes have higher maximal expression than genes on the haploid-X. Why PAR1 genes have a reduced breadth of expression is unclear, but with just 24 data points, and a tendency for tissue-specific genes to cluster [35], this may be little more than sampling artifact. If high expression of X-linked genes is difficult, then we might expect that lowly expressed X-linked genes might be able to increase their expression more readily than highly expressed ones, the latter having a problem with transcriptional traffic jams. To address this we consider Brawand’s RNAseq dataset [44], presenting expression of orthologous genes across five somatic tissues in males in several primates. Because it includes primate data, the Brawand et al. dataset is better suited to address this aspect of our analysis than FANTOM5. Note that we do not wish to determine whether immediately after the formation of the X, the X was up-regulated; rather we wish to know whether during a more normal phase of expression evolution, genes on the X are constrained in their ability to increase expression. Here, we used a Bayesian approach to infer the ancestral expression state in the human/Chimpanzee common ancestor. We then used the ancestral state to define the change in expression from the ancestor to the current human expression level, expressing this as a Z-score. The Z-score factors in noise in both the estimation of current levels and the ancestral state. Positive Z-scores imply increases in expression since the common ancestor. We excluded from the analysis genes with no expression in the ancestor in any given tissue, as these are most likely unexpressed genes (although this exclusion makes no qualitative difference). We calculated a tissue-specific p-value comparing the Z-scores for X-linked genes and Z-scores for autosomal genes via a Wilcoxon test for each tissue (Fig 2). We then combined these scores using Fisher’s method to generate a single p-value for each test. Note that our metric of expression change is not in terms of fold-change as this would almost certainly bias towards finding a larger effect for lowly expressed genes (it is easier to double expression of a lowly expressed gene than it is of a highly expressed gene). Instead, we take a more conservative measure, asking about absolute change in standard deviation units, not least because the thesis we are testing concerns the difficulties in increasing the absolute expression level. The median expression level for X-linked genes in the Brawand et al. dataset is much lower than that for genes on autosomes (the mean ratio of median-X to median-autosome equals 0. 58). This confirms a lower expression on the X previously claimed for this data [31]). More importantly, Z-score is lower for the X in each of the five tissues, the net difference being significant (chi-squared = 30. 9, d. f. = 10, p < 0. 001). We then split the autosomal and X-linked genes into two groups: a highly expressed half (according to the expression level in the ancestor, for each chromosome class) and a lowly expressed half (Fig 2). For each of the five somatic tissues, the highly expressed autosomal genes have a greater median Z-score than the highly expressed X-linked genes (Fisher’s method for combination of p-values, chi-squared = 26. 9, d. f. = 10, p < 0. 005). By contrast, for the lowly expressed half of the genes, the X has a higher median Z-score than autosomes have in two cases and a lower median Z-score in three. In two tissues, the effect is significant via a Wilcoxon test, one where the X has a higher Z-score (brain) and one where autosomes have the higher Z-score (heart). In sum, the data support the notion that highly expressed X-linked genes typically do not increase their expression as much as highly expressed autosomal genes, but the same is not true for lowly expressed genes. These results are as predicted by the traffic jam hypothesis. If intolerance of genes with high maximal expression shapes the X chromosome, it should be also the case that tissues with highly expressed tissue-specific transcripts should be avoided on the X. To test this, we calculated the average expression for a selected set of the top 1% or 0. 1% of most tissue-specific genes for each CAGE library (these metrics are called tissue-specific maximal expression or TSME-1% and TSME-0. 1%). These metrics we assume to reflect the maximal expression level needed in any given tissue to carry out its tissue-specific physiological functions. We chose two cutoffs to make the analysis more robust. Our expectation is that tissues requiring high expression of their tissue-specific genes (that is high TSME), such as glands or specialized epithelia, should be also those whose specific genes are underrepresented on the X chromosome. To estimate over- and under-representation on the X, we define the metric of binary enrichment for a set of genes as the fold enrichment on the X against the random expectation based on the X-to-autosomal ratio of the total human gene set (see the Methods section Defining enrichment metrics, for the detailed definition of binary enrichment). A tissue with high binary enrichment would have most of its tissue-specific genes on the X chromosome. Next, we correlate the TSME measures with the metrics of tissue’s binary enrichment on the X chromosome for the matching sets of top 1% or 0. 1% tissue-specific genes (Fig 3). Both TSME and binary enrichment are compatible metrics focusing on a tissue’s uniquely expressed and preferentially expressed genes (which are the ones we are interested in in this test). If tissues with high-level maximal expression of tissue-specific genes are tissues for which tissue-specific expression is avoided on the X chromosome, then we expect a negative correlation between X-enrichment and TSME. As expected, we indeed see such a correlation (although it is only statistically significant for the top 0. 1% of each tissue’s preferentially expressed genes with rho = -0. 1328 and p = 0. 0788 for TSME-1%; and rho = -0. 2376 and p = 0. 001499 for TSME-0. 1%). The above correlations (i. e. , between TSME and binary enrichment metrics) are stronger when sex-specific tissues (both male and female) are removed from all stages of calculations (rho = -0. 1874 and p = 0. 01628 for TSME-1%; rho = -0. 2843 and p = 0. 0002253 for TSME-0. 1%). This suggests that this result is not explained by sexual antagonism or the germline inactivation hypothesis. In asking about the enrichment of tissue-specific genes on the X chromosome after controlling for a tissue’s expression level, our model provides an explanation for the patterns of tissue enrichment not obviously accounted for by other models. That is to say, tissues whose specific genes are under-represented on the X tend to be secretory or rich in structural proteins. These are, for example, the exocrine glands of the gastrointestinal tract (i. e. , the submaxillary gland, the parotid gland, the pancreas, and the liver) or highly keratinized tissues such as the tongue, throat, or esophagus (analog enrichment in Fig 4, S3 Table; binary enrichment in S4–S6 Tables). These are also likely to be highly transcriptionally active tissues. An analysis of the most highly expressed genes on autosomes and the X reinforces the same conclusion regarding the avoidance of tissue associated with active secretory processes on the X. There are 401 autosomal transcripts/expression sites with higher maximal expression than the maximum of all values on the X chromosome, which is 5,604 TPM (these transcripts and expression sites are listed in S7 Table). These 401 transcript-in-tissue data points are derived from 159 distinct transcripts. It should be noted that not only this tail of transcripts with high-maximal expression is absent from the X, but also X’s mean maximal expression is 3-times lower—see the first Results section). The observation of the biased autosomal/X distribution of these highly maximally expressed transcripts is statistically significant (Fisher' s exact test for count data: p = 0. 00143). The 159 transcripts were also strongly biased functionally, with many secreted proteins, protease and peptidase inhibitors, muscle proteins, liver enzymes, coagulation factors, lipid transporters, digestion enzymes, hormones, and proteins involved in reproduction (for details and p-values see S8 Table). In contrast, 159 top maximally expressed X-linked transcripts appear to not be associated with secretory processes (S9 Table). Instead they are linked to functional terms for melanoma antigen E (MAGE) tumor-specific antigens, actin binding, association with the mitochondrial membrane, endoplasmic reticulum and microsome, erythrocyte differentiation, hemopoiesis, nucleosome assembly, DNA packaging, neuron development, neurogenesis, ribosome and cell death (for details and p-values see S9 Table). It is interesting to ask a complementary question: one about libraries under-represented in the expression domain of X-linked genes in comparison to autosomal genes. To this end, we define the metric of binary exclusion, which asks about the autosomal-to-X ratio for all genes expressed (i. e. ," on" ) in a given tissue (for details see the Methods section Defining enrichment metrics). Binary exclusion is complementary, but not exactly the opposite of binary enrichment, as the former focuses on all genes expressed in a given tissue, while the latter asks only about tissue-specific genes. When we analyze histograms of binary exclusion (S1A Fig, see also S10–S12 Tables), only 7 tissues are more than 1. 96 standard deviations over the mean degree of exclusion (the mean = 1. 48, SD = 0. 24). These tissues are the fingernail, cruciate ligaments, the adult pancreas, skin of the palm, the Achilles tendon, the inferior rectus of the eye, and tongue epidermis—suggesting preferential exclusion only for secretory tissues, or tissues extremely rich in highly expressed structural proteins. Moreover, it is striking that all tissues except the substantia nigra, a tiny brain structure located in the midbrain, are excluded to some extent. This suggests that the lowering of the breadth of expression on the X is a universal phenomenon affecting all tissues, rather than having its origin in exclusion from any particular type of tissues such as sex-specific or mammalian-specific tissues. The analysis of retroposed genes provides further tests of the traffic jam hypothesis. Just as genes ancestrally highly expressed on the proto-X cannot, we suggest, sustain themselves on the new haploid-X, so, too, retrocopies from highly expressed autosomal genes on to the X chromosome should be weakly and narrowly expressed. As reported previously [50], a high proportion of paralogs on the X were retroposed from autosomes (in our data this proportion was 3. 9%, please see Table 5 and Table 6). We find that a retroposition to the X was accompanied by a greater reduction in the breadth of expression (in comparison to the autosomal parental gene), maximal expression, and TfbsNo than that observed for non-retroposed autosomal-X paralog pairs (ΔTfbsNo = -10. 42 for retroposed versus -3. 91 for non-retroposed, consistent with the promoter-less mechanism). High maximal expression of autosomal paralogs of X-linked retrogenes (Table 5, X→autosomal pairs) suggested a scenario where highly expressed autosomal genes, many of which were transcription factors, seeded retro-copies on the X that are much more tissue-specific and weakly expressed than their parental genes (with on average a 22-times lower maximal expression level, see Table 5). Perhaps most striking is the finding that, as predicted, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X (the mean is 32±61, where “±” signifies standard deviation), than seen for X-to-autosome retrogenes (the mean of 129±293, p-value = 6. 347e-12 in a Wilcoxon test). This test controls for the mode of duplication and so is perhaps the clearest indication of the importance of being on the X chromosome as regards to a low maximal expression level. Both retroposed and non-retroposed X chromosome paralogs diverged asymmetrically after gene duplications, with the X-linked paralog being more tissue-specific and having fewer transcription factor binding sites (Table 5 and Table 6). After the X was formed, two waves of gene duplications facilitated remodeling of its content towards tissue-specific expression: Eutherian (with approximately 50% retropositions) and Therian (with only 9% retropositions). However, older pre-existing genes, derived from two rounds of whole genome duplication (2R-WGD), chordate and bilaterian duplications also experienced pressure to exclude housekeeping genes and genes with high maximal expression from the X. 2R-ohnologs (2ROs) represented the most numerous wave of duplications in the human genome [51] and the X chromosome was no exception (with 572 out of 1,078 X-linked duplication nodes mapping to 2R-WGD, see Table 6). Assuming the maximal expression level to be the key issue, one might also expect the more highly expressed X-linked genes to have evolved some other adaptations to enable higher transcription or translation rates in the face of haploid expression. X-linked genes could have evolved longer half-lives of their mRNAs or proteins. Alternatively, mRNAs of X-linked genes could be more capable of ribosome binding than equally expressed autosomal genes, enabling more protein production per transcriptional event. Some of these issues have recently been analyzed and indeed, X-linked genes have longer mRNA half-lives and a higher density of ribosomes [52]. This is consistent with X-linked genes being adapted to making the most of their relatively low expression levels. Two further possible adaptations that we can examine are alternative transcriptional start sites (TSSes) and tandem duplications. Both of these provide possible mimics to the diploid situation by doubling the number of promoters available for transcription factor binding. We find no evidence to support the former possibility. The average number of TSSes per gene on autosomes is, if anything, higher than on the X chromosome (4. 41 versus 3. 95, Wilcox one-sided test p = 0. 0263). The formation of tandem duplicates would result in larger gene families, but genes on the X tend to belong to smaller families than genes on autosomes (an average X-linked gene belongs to a family of 2. 72 genes, while an average autosomal gene belongs to a family of 5. 76 genes, Wilcox p < 2. 2e-16). This, however, does not address the core issue, namely whether X-linked genes duplicate more post the formation of the X chromosome. Consistent with the adaptation to traffic jam model, duplicability since the formation of the X (see Methods for the definition of duplicability) is twice as high on the X in comparison to autosomes (1. 11 versus 0. 55, p-value < 2. 2e-16 in a Wilcoxon test). Moreover, the X has only 22% (166) singleton genes versus 39% (7,411) on autosomes. However, we might also expect the selection for duplicate retention to be the strongest on the more highly expressed genes, but we see no evidence for higher duplicability for high maximally expressed genes on the X. In fact, there is an overall negative correlation between duplicability and maximal expression (rho = -0. 22; p-value < 2. 2e-16 on autosomes; and rho = -0. 34; p-value < 2. 2e-16 on the X chromosome), most simply explained as a duplication bias towards non-essential/lowly expressed genes [53], seen in other taxa. Similarly, when we divided X’s non-singleton genes into high and low maximally expressed using the median maximal expression (that is 31 TPM) as the cutoff and then calculated average duplicability, the result for low-maximal genes was 1. 67 and for high-maximal only 0. 57 (p = 3. 028e-10 in a Wilcoxon test). One might however argue that if a highly expressed X-linked gene had duplicated in order to increase its total net dosage, then those that had not duplicated should have the highest expression per gene. The duplicates could have lower expression per gene, but a higher net expression when adding together the contribution from each duplicate. Arguing against this, however, is the finding that for many gene families (e. g. , MAGE) duplicates tend to be specialists for expression in different tissues, so the net expression in any one tissue is approximately the expression of the most highly expressed paralog in that tissue. This suggests the net expression level is not the driver of duplications and the trends are better explained as a bias towards the retention of genes that are less likely to be deleterious immediately after the duplication.
While by no means being a full explanation, the possible centrality of the upper limit on the expression level on the X chromosome calls into question evidence in support of other models. For example, it was noted in support of the X inactivation model that many X-linked genes spawned autosomal retrogenes with high expression levels in male germline [19]. However, as previously noted [19], the model makes only a partial explanation of the facts, as many of those germline expressed genes are highly expressed at the times when the X chromosome is not inactivated. Our model places emphasis on the highly expressed issue more than it does on the germline expression issue. The selection we suggest may be to enable high expression from a chromosomal environment incompatible with high-level expression in any tissue (not just the germline). Indeed, we suggest that the logical order of events likely requires a gene movement prior to the evolution of X inactivation (as noted by the SAXI model [18]). If one considers the process in the other direction then the explanation appears unparsimonious; if the X has shut down and essential male germline genes have not moved away, then the shutdown of the X might well lead to sterility. Thus, only if the essential genes already have moved from the X could X’s inactivation have been favored. But if the movement off the X was prior to X inactivation, then X inactivation could not have caused the movement. If this logic, as previously voiced [18], is correct, this suggests that germline X inactivation is a follow-on consequence enabled by, and not the cause of, the movement. Why then might male germline be especially involved? The SAXI [18] hypothesis suggests sexual antagonism as being at the heart of the issue. The emphasis on retrogenes here possibly provides an alternative answer. By definition, for retrogenes to be created and transmitted requires germline expression. If male germline expression is higher than female germline expression, then we expect many highly expressed male germline genes to be favored to spawn autosomal copies (for no better reason than because it enables higher expression in the germline following the formation of the haploid-expressed X chromosome). In addition, the chance of retroposition will be the highest for those genes that are most highly expressed on the X (more RNA transcripts), thus rendering the movement away from the X not only advantageous (for dosage reasons) but also more likely for highly expressed germline expressed X-linked genes. In principle, then, the loss of testes-expressed genes to autosomes via retroposition may be selectively advantageous without having to evoke sexual antagonism. The loss of the expression of an X-linked paralog may happen before or after X inactivation. That we see an equally reduced breadth of expression for X-linked genes when we remove sex-specific tissues from the analysis suggests that the maximal expression argument has a much broader explanatory power than the sexually antagonistic model, although it is hard to discount sex-specific selection on alternative alleles in non sex-specific tissues. But might the facts that appear to support the sexually antagonistic model actually be alternatively understood in the context of a limit on X-linked gene expression levels? Such a reinterpretation has been defended for flies. Vicoso and Charlesworth [26] consider in the case of the fly that a direct coupling between sex-biased expression and expression possibilities on the X might be limited. The X is hyper-expressed in the fly so, they suggest, increasing expression further on the X is likely to be improbable. As male-biased expression predominantly occurs via up-regulation, a cap on expression level they suggest might thus explain the dearth of genes biased towards male-specific expression on the X. Incidentally, it is not transparent as to why the fly X evolved hyper-expression while the mammalian X apparently did not. The proportion of all genes on the fly X is much higher than in mammals and birds and so the net selective effect of not hyperactivating may be lesser on the mammalian X, but this is just a speculation. It is also not clear why testes-expressed genes can increase the expression level on the human X [54], when most other genes cannot. While the X appears not to be hyperactivated in mammals, a difficulty in up-regulating genes on the X may be an issue (see evidence above). Our data, however, suggest that expression in testes is especially common on the X chromosome, in contrast to what is seen in flies. Is then our data broadly consistent with the sexual antagonism model as an additional force, beyond the maximal expression limit? Evidence looks supportive at first glance. We find that genes with specific expression in many sex-specific tissues show enrichment on the X (the epididymis, the testis, the uterus, see Fig 3). Although maximal expression levels of these tissues’ tissue-specific genes is low (in part accounting for over-abundance), this enrichment appears to be greater than expected even allowing for decreased maximal expression levels of genes in these tissues (Fig 3). While at first glance this is broadly consistent with the sexually antagonistic model, for reasons unknown, amongst the set of tissues enriched in X-linked expression is the placenta (also enriched after controlling for its relatively low TSME). The placenta is especially noteworthy as it is not sex-specific but reproduction-specific. This questions whether prior trends ascribed to sexual antagonism might not have an alternative explanation, possibly related to the commonly reported faster evolution of reproduction-related genes [55–57] and hemizygosity of the X. Against such a hypothesis is the recent finding that in mammals any faster sequence evolution of X-linked genes is not owing to hemizygosity, but can be accounted for in terms of GC-bias and low expression [58]. No matter what the explanation, that the trends are seen after controlling for expression maxima suggests that the enrichment of reproduction-related genes is not simply explained by our maximal expression hypothesis, but neither is it necessarily owing to sex specificity per se. It is commonly the case that patterns of enrichment of certain functional classes of genes on the X prompt adaptive speculation. Above we caution that fixation on the problem of sex-specificity may have distracted us from the general issue of reproduction-specificity. A more general caveat concerns whether an adaptive explanation is needed at all. The other tissue whose preferentially expressed genes are over-represented on the X, allowing for maximal expression, is the brain (see analog enrichment data in Fig 4 and S3 Table and binary enrichment data in S4–S6 Tables). Brain tissues are also among the least depleted in expression on the X using the metric of binary exclusion (S1A Fig, S10–S12 Tables, Wilcoxon one-sided test p = 0. 05994 when brain tissues are compared against non-brain tissues). The last effect holds even after controlling for brain’s relatively low TSME (Fig 3). Although the enrichment of brain-specific genes and links with mental-retardation disorders on the X were previously known [59,60], FANTOM’s broad collection of brain-related libraries allows a new appraisal of the extent of this trend, with most X-linked brain-specific genes being expressed widely in the hindbrain, the midbrain, the brain stem, as well as in libraries from adult, newborn, and embryonic brain samples. Using the binary enrichment metric (see Methods), almost all brain tissues (the brain stem, the midbrain, the higher brain, as well as brain glands such as the pituitary) were relatively highly expressed on the X (S3 Table). On the cellular level, we found that brain X-linked genes were enriched in astrocytes, perineural cells, neural stem cells, and neurons (S5 Table) underlining the broad functionality of these molecules across the nervous system. How to interpret an excess of brain-expressed genes on the X, which exists before and persists after controlling for maximal expression (Fig 3), is uncertain. We suggest that caution is advisable in ascribing a selectionist explanation, as the association with brain appears to predate the formation of the proto-X. For example, X-linked brain-specific expression is associated with duplications predating the formation of the X (taxa Amniota and older, S16 Table). Similarly, there are numerous examples of X-linked brain-specific genes that exist on autosomal syntenic regions in chicken (see S9 Fig for the map of human-chicken orthology) and are brain-expressed in chicken (see S17 Table for four examples). For the most part, then, it is unresolved whether the brain enrichment on the X chromosome reflects an accident of history or whether the retention and later evolution require a special explanation. We note, in favor of the latter, that the MAGE family of tumor-testis-specific antigens has undergone much duplication and tends to be expressed across the brain (and frequently also in reproductive tissues, either male, or female, see S18 Table). Recent evidence suggests that, unlike the fly X chromosome, the mammalian X is not hyper-expressed. Rather, it is expressed at levels approximately equivalent to those of the ancestral autosome (per promoter). As such, we might expect the X not to be a tolerable environment for genes with very high maximal levels of expression (as all the expression, in males at least, must run through just a single promoter). Moreover, we might expect that genes relatively highly expressed on the X would be constrained in their ability to increase their expression levels. The analysis of expression data supports all of these predictions. The traffic jam model also explains the known fact of the lower breadth of expression for genes on the X (and the bird Z), as genes with broad expression are, on average, those with high maximal expression. The traffic jam model also predicts that tissues in which tissue-specific genes are very highly expressed (e. g. , secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X. These trends cannot be fully accounted for in terms of alternative models of biased expression. These results suggest that a force no more subtle than limited rates of expression on haploid chromosomes is a fundamental driver of the biology of the mammalian X chromosome.
There were 44,218 human RefSeq transcripts. This total consisted of 36,382 mature messenger RNAs (NM-accessions), and 7,836 non-coding transcripts (NR-accessions). The non-coding transcripts included structural RNAs and transcribed pseudogenes (no filtering was performed on the dataset). At the beginning of the analysis, RefSeq transcripts (mapped onto February 2009 human genome assembly—hg19) were downloaded from the UCSC Genome Browser (http: //genome. ucsc. edu/) in the Browser Extensible Data (BED) file format, using the table browser tool (http: //genome. ucsc. edu/cgi-bin/hgTables). The beginnings of RefSeq transcripts (that is the start positions of mapped transcripts) defined the location of TSSes. ENCODE ChIP-seq data define the location of transcription factor binding sites and were processed as previously described [46]. To be specific, we used multi-cell-line clustered data, from the January 2011, data-freeze [19]. These data included 2,750,490 peaks for 148 transcription factors, derived from 71 cell-types with 24 additional experimental cell culture conditions [9]. The data were generated by eight different labs, adhering to a common and standardized set of protocols and controls [12]. The labs involved were as follows: the Myers Lab at the HudsonAlpha Institute for Biotechnology; the labs of Michael Snyder, Mark Gerstein and Sherman Weismann at Yale University; the lab of Peggy Farnham at UC Davis; the labs of Kevin Struhl at Harvard, Kevin White at The University of Chicago, and Vishy Iyer at The University of Texas Austin. For analyses of proximal promoters, we used only the highest quality transcription factor binding sites, with the reliability score above five hundred points. However, as shown in S3A–S3D Fig, the trends discussed here were independent of, and robust to, the variation in the quality score. We also verified that results analogous to those for the January 2011, data-freeze (see S3 Fig) were obtained using a broader September 2012, data-freeze (ENCODE-TfbsV3 2012). The 2012 data-freeze consisted of 161 transcription factors and 91 human cell types under various treatment conditions [20]. To measure TfbsNo, ENCODE transcription factor binding sites were simply counted in a symmetrical window around the TSS, as previously described [46]. Unless stated otherwise, the size of the window was one kb (±500 base pairs) and only the most reliable ENCODE transcription factor binding sites (i. e. , those with ENCODE quality scores above 500) were taken into account, which proved representative in previous analyses [46]). In previous work, we also explored various ways of measuring TfbsNo (that is simple counting, counting unique sites, and counting TfbsNo excluding PolII) and showed them to be essentially equivalent and all a good predictor of the breadth of expression [46]. S13 Table shows that TfbsNo is much lower on the X for all promoter window sizes and regardless of whether all transcription factor binding sites or only those with the ENCODE quality score above 500 were considered. As described previously [46], the distribution of transcription factor binding sites per gene was highly non-normal and followed a power law (S13 Table). CAGE tags were mapped to RefSeq transcripts +/-500 base pairs (bps) from their TSSes and normalized to tags per million (TPM), as previously described [37,46]. The signal of ten TPM was chosen as the cutoff for a gene to be classified as “on” (this cutoff was accepted as the standard for human data throughout the consortium). FANTOM5 is the most comprehensive expression dataset ever generated, including 952 human and 396 mouse tissues, primary cells and cancer cell-lines. FANTOM5 is based on cap analysis of gene expression (CAGE) a unique technology that characterizes TSSes across the entire genome in an unbiased fashion and at a single-base resolution level [21]. CAGE automatically sums expression levels of all transcripts beginning at a given transcription start site. TreeFam release eight [61,62] was used for data on gene families and to infer paralogy, as previously described [46,63]. For expression analysis, multiple transcripts derived from the same gene were assumed to share the same evolutionary history (i. e. , the same pattern of past duplications). When expressly stated in Results, the paralogy dataset was pre-processed to report only the most recent duplication event for each gene (that is the taxon of the youngest duplication node from which a gene descends). We detected duplication events using an algorithm aiming at the reconciliation of the gene tree with the species tree [64]. The taxon of duplication was assigned using the principles of phylogenetic timing, that is to say dating duplication events on the basis of the phylogenetic distribution in extant species (assuming a known species tree). This data was used as previously described and as previously duplications with species intersection support equaled zero were not taken into account [51]. From the total set of human duplications, extracted from TreeFam as described previously [46,51,63], we isolated only those which were dated to taxa younger than Amniota (that is Human, Homo/Pan/Gorilla, Catarrhini, Eutheria, or Theria) and where both children genes were located on the X chromosome. This dataset comprised 112 duplication events grouped in 36 families. This approach aimed at the identification of gene families specifically expanded on the X after its formation (rather than families that already had multiple members on the proto-X at the time of its formation). In S18 Table, we show the families with at least three X-linked gene members. To estimate the expression pattern of each of these families, we averaged expression levels of their transcripts and reported the top five tissues of expression. The GC-content of the proximal promoter was calculated for the nucleotide sequence in one kilobase pair (kbp) around the TSS (±500 bps) on the hg19 human genome assembly. The R function alphabetFrequency from the package Biostrings was used. To calculate the GC-content of the isochore, a 20 kilo base pairs (kbps) window around the TSS was used (±10,000 bps). Unless stated otherwise, a masked genome sequence from the R package BSgenome. Hsapiens. UCSC. hg19 was used with standard BioC-Biostrings functionality (masked for assembly gaps using RepeatMasker and Tandem Repeats Finder). Data points overlapping entirely with masked regions were omitted. To calculate GC3, coding regions were isolated from RefSeq sequences (according to annotations in the RefSeq GenBank file). Every third codon position was isolated into an R character vector and the R function table was used to calculate the frequency of GC nucleotides. Maximal expression is the greatest numerical value attained (in TPM) for each transcript in any single library. Each library was processed separately: in a few cases where multiple donors were available for the same tissues, these were not averaged, to preserve information on the sex and age of the donor. Thus, the maximum does not arithmetically depend on the breadth of expression or average expression. Unless expressly stated otherwise, maximal expression was calculated for the human tissue set in FANTOM5. However, it should be noted that maximal expression correlated very highly between the three classes of human FANTOM5 samples. Maximal expression calculated for human tissues correlated with maximal expression for primary cells and cancer cell-lines with the values of rho of 0. 71 (p < 2. 2e-16) and 0. 698 (p < 2. 2e-16). The breadth of expression was calculated per transcript, as the fraction of samples in which the transcript was detectable (using the cutoff of 10 TPM, as described previously [46]). Unless stated otherwise, the breadth of expression was calculated for the human tissue subset of FANTOM5. However, it should be noted that the breadth of expression correlated very highly between the three classes of human FANTOM5 samples. The breadth of expression calculated for human tissues correlated with those for primary cells and cancer cell-lines with rhos of 0. 85 (p < 2. 2e-16) and 0. 84 (p < 2. 2e-16), respectively. Preferential expression measure (PEM) is calculated by taking the ratio of a normalized signal for a transcript in a library (in TPM) to the average signal of this transcript in all libraries of a given type. PEM was purposefully calculated for each CAGE library in relation to other libraries in its respective sample category only (that is either in tissues, or primary cells, or cancer cell-lines). In the case of brain, the presence of 75 libraries from different brain subsets (see S19 Table) could lead to the under-estimation of the brain PEM. We, therefore, verified that results analogous to Fig 3 could be obtained when PEM for each brain library is calculated only in relation to non-brain libraries (S10 Fig). We used a randomization procedure to estimate the probability that the skewed distribution of maximal expression on the X could be derived by chance. The total set of 31,095 transcripts was sampled one million times to pick a random subset of transcripts identical in size to the number of transcripts on the X (that is 1,433). After each sampling, the average maximal expression value was calculated for the sampling subset. p-value was estimated by counting the number of times the mean maximum of the sampling subset was lower than that observed for the entire data set (which was never, leading to p = 0). FANTOM5 chicken libraries consisted of 25 CAGE libraries including: chicken aortic smooth muscles, hepatocytes, mesenchymal stem cells, leg buds, wing buds, embryo extra-embryonic tissue (day 7 and day 15), and whole body developmental time course (from 5 h 30 min to 20 d). The number of available data points to which TPM was normalized was limited by the number of annotated chicken RefSeq transcripts (which was approximately six times smaller than human, n = 4,426 on autosomes, and n = 241 on chromosome Z). Consequently, the cutoff for a gene to be classified as “on” was adjusted six times higher to 60 TPM. In the analysis of 2ROs, X-linked duplication nodes were defined as those with one autosomal and one X-linked paralog. Given their high frequency on the X chromosome, 2ROs can be estimated to account for −120. 12−208. 3=0. 58 of the overall reduction in the breadth of expression. This is calculated from data in Table 6 as the number of X-linked 2R-WGD duplication nodes times their shift in expression breadth, that is N * ΔBoE = −120. 12, divided by the sum for all nodes and taxa, that is, ∑i=humanbilateriaNi*ΔBoEi=−208. 3. Single-exon genes were identified as putative retroposed genes. If the closest paralog to a putative retroposed gene had more than two exons, the pair was classified as a retroposition event. Further sub-classification was into either X→auto, auto→X, auto-auto, X-X, X-Y, or auto-Y retroposition events, depending on the location of the multi-exon parental gene. If both members of the paralog pair were single-exon, the retroposition was classified as non-directional. DAVID version 6. 7 (http: //david. abcc. ncifcrf. gov/) was used for the enrichment analysis and clustering of gene annotations. DAVID works with the" DAVID gene concept," a method linking gene and protein identifiers across a number of databases such as National Centre for Biotechnology Information (NCBI), PIR and Uniprot/SwissProt [65,66]. For several analyses, specific subsets of FANTOM5 tissues were defined as follows: First, we selected the top set of 1% (that is 311 transcripts) or 0. 1% (that is 31 transcripts) of the most tissue-specific transcripts (that is transcripts with the highest PEM) for each library. Then we calculated their average expression in a given tissue (by averaging signals obtained for each selected transcript, in TPM, in the tissue under consideration). Duplicability was established as follows: for each non-singleton gene in the genome, the number of duplication events from which the gene was descending since the formation of X (taxa of duplication Human, Homo/Pan/Gorilla, Catarrhini, Eutheria, and Theria) was calculated. For example, if gene’s most recent duplication was at the base of vertebrates, duplicability equaled zero. But if gene’s most recent duplication is dated to taxon Theria, duplicability will equal one. Three different ways of defining tissue-specific expression enrichment or exclusion on the X were explored, adding to the robustness of the analysis presented here. Firstly, for each tissue we calculated the average expression (in TPM) of the X and autosomal genes (Fig 4), either for all or only tissue-specific transcripts (the breadth of expression <0. 33), and calculated the ratio of average autosomal over the average X chromosome expression in a given tissue. We refer to this measure of differential expression as the metric of “analog enrichment” as it preserves the information on the level of expression of individual genes. A downside of this definition of expression enrichment is that the value of the metric could be dominated by a few very strongly expressed genes. Secondly, we isolated the set of the top 1% most tissue-specific transcripts in each library and calculated the fold enrichment on the X for these transcripts against the random expectation based on the X-to-autosomal ratio of the total human gene set. p-values were calculated by Fisher’s exact test (S4 Table—human tissue, S5 Table—human primary cells, S6 Table—human cancer cell-lines; samples under-represented in expression on the X have the fold-enrichment values lower than one). We refer to this measure as the metric of “binary enrichment” in expression, as it is not affected by the strength of expression of individual tissue-specific genes. Finally, we calculated the degree of “binary exclusion” from the X for each tissue. First, we classified all genes in a given tissue as either “on” or “off” based on the cut-off of 10 TPM (the standard definition used within the FANTOM5 consortium). Next, we compared the observed X-to-autosomal distribution of the “on” genes against the random expectation. The p-value was calculated by Fisher’s exact test. We refer to this measure as the metric of “binary exclusion” from the X. This metric is rather different from “analog enrichment” and “binary enrichment” outlined above, as instead of focusing on tissue-specific genes it defines the degree to which each tissue or cell-type contributed to the lower breadth of expression on the X (S10 Table—human tissues, S11 Table—human primary cells, and S12 Table—human cancer cell-lines). Brawand et al. [67] RNAseq gene expression data from six tissues in five primates was employed to estimate the extent to which any given human gene changed expression since the human-Chimpanzee common ancestor. Data in the file NormalizedRPKM_ConstitutiveAlignedExons_Primate1to1Orthologues. txt was employed with strand information from Human_Ensembl57_TopHat_UniqueReads. txt. Both files are in the supplementary materials of Brawand et al. [67]. This provides reads per kilobase per million mapped reads (RPKM) figures for 13,027 genes. We employed BayesTraits [68] to estimate the change in gene expression between current levels in humans and that seen in the human-Chimpanzee common ancestor. We employed the same phylogeny and branch lengths as those in Brawand et al. [67]. Brawand et al. [67] normalized RPKM values were passed to BayesTraits. For each gene, the mean of the normalized RPKM values in any given tissue in human was calculated separately for male and female samples. Similarly, under the circumstance that more than one male or female sample was available in any of the tissues in non-human primates, their mean was computed. If only one sample was described, this value was employed and pasted as an input to BayesTraits. To estimate the expression level for any given gene in the common ancestor of human and Chimpanzee, BayesTraits program was run to build the estimated gene expression tree for expression in males. This was done for each gene in each tissue. From the inspection of convergence trends, we concluded that the terminal 10% of BayesTraits estimates were robust. Given that this is not a point estimate but a series of estimates, we determined both the mean (Ea) and variance (Va) of the estimated human-Chimpanzee ancestral state. We also examined the consequence of the relaxation the 10% cut-off and concluded that results were qualitatively unchanged. The estimation procedure was implemented independently for each gene in each tissue. If Ecurrent is the mean expression of a given gene, in a given tissue, in a given sex (or Ec in an abbreviated form), and the variance is Vc, (assuming it to be estimable), while that for the ancestral condition is Ea and Va, then the degree of expression divergence we define as a Z-score: Z=Ec−EaVc+Va Note that Z normalizes the extent of difference between the mean current expression level and ancestral level, by the variation both in current estimates (this could be expression noise or measurement error) and the magnitude of uncertainty in the human-Chimpanzee ancestral state. A Z-score greater than zero is indicative of an increase in gene expression since this ancestor. If we suppose that there is neither an increase nor a decrease in net transcriptional output in any given tissue, it might be reasonable to assume that for each tissue the median expression change must be zero. A minor adjustment of the Z-scores for all genes in all tissues is required to achieve this. If we designate the median Z-score (in any given tissue in a given sex) as M, then we can define modified Z as Zmod = Z–M. After such a modification all tissues have a median Z of zero. All analyses were performed on Zmod. We refer to Z, for convenience, where Zmod is what we are employing. Our method, note, has the advantage that it should be relatively insensitive to any RNAseq amplification biases (e. g. , owing to GC-content): nucleotide content is almost identical between human and Chimpanzee and hence any amplification bias should affect human and Chimpanzee in equal measure. The degree of change from the ancestor, as assayed by Z, should then largely exclude amplification biases. As then expected, the mean correlation, across all tissues, between the change in GC (between human and Chimpanzee) and change in expression (i. e. , Z-score) is indistinguishable from zero.
Genes located on the human X chromosome are not a random mix of genes: they tend to be expressed in relatively few tissues or are specific for a particular set of tissues, e. g. , brain regions. Prior attempts to explain this skewed gene content have hypothesized that the X chromosome might be peculiar because it has to balance mutations that are advantageous to one sex but deleterious to the other, or because it has to shut down during the process of sperm manufacture in males. Here we suggest and test a third possible explanation: that genes on the X chromosome are limited in their transcription levels and thus tend to be genes that are lowly or specifically expressed. We consider the suggestion that since these genes can only be expressed from one chromosome, as males only have one X, the ability to express a gene at very high rates is limited owing to potential transcriptional traffic jams. As predicted, we find that human X-located genes have maximal expression rates far below that of genes residing on autosomes. When we look at genes that have moved onto or off the X chromosome during recent evolution, we find the maximal expression is higher when not on the X chromosome. We also find that X-located genes that are relatively highly expressed are not able to increase their expression level further. Our model explains both the enrichment for tissue specificity and the paucity of certain tissues with X-located genes. Genes underrepresented on the X are either expressed in many tissues—such genes tend to have high maximal expression—or are from tissues that require a lot of transcription (e. g. , fast secreting tissues like the liver). Just as many of the findings cannot be explained by the two earlier models, neither can the traffic jam model explain all the peculiar features of the genes found on the X chromosome. Indeed, we find evidence of a reproduction-related bias in X-located genes, even after allowing for the traffic jam problem.
The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome