Antisense expression increases gene expression variability and locus interdependency
2011; Springer Nature; Volume: 7; Issue: 1 Linguagem: Inglês
10.1038/msb.2011.1
ISSN1744-4292
AutoresZhenyu Xu, Wu Wei, Julien Gagneur, Sandra Clauder‐Münster, M. Smolik, Wolfgang Huber, Lars M. Steinmetz,
Tópico(s)Plant and Fungal Interactions Research
ResumoArticle15 February 2011Open Access Antisense expression increases gene expression variability and locus interdependency Zhenyu Xu Zhenyu Xu Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Wu Wei Wu Wei Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Julien Gagneur Julien Gagneur Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Sandra Clauder-Münster Sandra Clauder-Münster Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Miłosz Smolik Miłosz Smolik Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Wolfgang Huber Wolfgang Huber Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Lars M Steinmetz Corresponding Author Lars M Steinmetz Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Zhenyu Xu Zhenyu Xu Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Wu Wei Wu Wei Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Julien Gagneur Julien Gagneur Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Sandra Clauder-Münster Sandra Clauder-Münster Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Miłosz Smolik Miłosz Smolik Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Wolfgang Huber Wolfgang Huber Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Lars M Steinmetz Corresponding Author Lars M Steinmetz Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Author Information Zhenyu Xu1,‡, Wu Wei1,‡, Julien Gagneur1,‡, Sandra Clauder-Münster1, Miłosz Smolik1, Wolfgang Huber1 and Lars M Steinmetz 1 1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ‡These authors contributed equally to this work *Corresponding author. Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg 69117, Germany. Tel.: +49 6221 387 8389; Fax: +49 6221 387 8518; E-mail: [email protected] Molecular Systems Biology (2011)7:468https://doi.org/10.1038/msb.2011.1 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Genome-wide transcription profiling has revealed extensive expression of non-coding RNAs antisense to genes, yet their functions, if any, remain to be understood. In this study, we perform a systematic analysis of sense–antisense expression in response to genetic and environmental changes in yeast. We find that antisense expression is associated with genes of larger expression variability. This is characterized by more 'switching off' at low levels of expression for genes with antisense compared to genes without, yet similar expression at maximal induction. By disrupting antisense transcription, we demonstrate that antisense expression confers an on-off switch on gene regulation for the SUR7 gene. Consistent with this, genes that must respond in a switch-like manner, such as stress–response and environment-specific genes, are enriched for antisense expression. In addition, our data provide evidence that antisense expression initiated from bidirectional promoters enables the spreading of regulatory signals from one locus to neighbouring genes. These results indicate a general regulatory effect of antisense expression on sense genes and emphasize the importance of antisense-initiating regions downstream of genes in models of gene regulation. Synopsis The function of non-coding antisense RNAs in yeast remains to be fully understood. Steinmetz and colleagues provide evidence for a general regulatory effect of antisense expression on sense genes and for a role in spreading regulatory signals between neighboring genes. Antisense expression, the RNA expression on the opposite strand of coding genes, is widespread but its general role has remained elusive. By expression profiling yeast in different environments and genetic backgrounds, the authors observed that genes with antisense are more frequently switched-off and show higher expression variability. This effect is the outcome of repression that specifically acts on low levels of sense expression—a model that is experimentally validated for the SUR7 locus. Furthermore, antisense expression is shown to connect the regulation of neighbouring loci in a setting where the bidirectional promoter of a gene initiates expression antisense to an upstream gene. Together, these findings underline the regulatory potential of the downstream region of genes as promoters of antisense transcripts and indicate antisense expression as a regulatory mechanism to enhance switch-like expression for stress–response and condition-specific genes. Introduction Interleaved organization of transcription (Birney et al, 2007; Kapranov et al, 2007) is widespread in many genomes (David et al, 2006; He et al, 2008; Guell et al, 2009), raising the question of whether overlapping transcripts interact. Transcription antisense to coding genes represents ∼55% of the stable uncharacterized transcripts (SUTs) in yeast (Xu et al, 2009) and has been reported for a quarter of the protein coding genes in humans (He et al, 2008). For a handful of cases, regulatory roles of antisense expression on gene expression have been demonstrated. These involve a variety of mechanisms and effects—antisense can inhibit sense expression through transcriptional interference (Hongay et al, 2006) or histone modification (Camblong et al, 2007; Berretta et al, 2008; Houseley et al, 2008; Pinskaya et al, 2009). Such interactions can make gene activation faster (Uhler et al, 2007) or slower (Houseley et al, 2008). How widespread these regulatory effects are across the genome has so far, however, not been determined. We hypothesized that insight into the function of antisense expression could be gained by observing the behaviour of overlapping transcribed regions in response to short-term (environmental) and long-term (genetic) changes. Results We assessed genome-wide transcriptional response to genetic variation in Saccharomyces cerevisiae by profiling transcripts in 48 meiotic products (segregants) of an S288c/YJM789 hybrid strain (Figure 1A, Materials and methods and Supplementary Table S1). These segregants, among which genetic variation is shuffled by recombination, allow analysing transcriptome response to regulatory variation, keeping environment constant. We also analysed environmentally induced gene expression changes (keeping regulatory variation constant) across the main laboratory growth conditions of yeast (ethanol, galactose and glucose media, Figure 1B; Xu et al, 2009). Data were collected on high-resolution tiling arrays that measure strand-specific transcript levels genome-wide with 8-bp resolution (David et al, 2006). Observed transcripts (Materials and methods, Supplementary Tables S2 and S3) were classified as ORF-transcripts (ORF-Ts) when they mainly overlapped coding genes in the same orientation, and as SUTs when they mainly derived from unannotated genomic regions either antisense to genes or from intergenic regions (the term stable indicates that they are detected in wild-type cells as opposed to mutants of the exosome in accordance with our earlier definition; Xu et al (2009), Materials and methods). For legibility, we will use the terms ORF-T and gene interchangeably. In total, 613 (12%) of the ORF-Ts overlapped a SUT on the other strand (antisense transcript) in the segregant data set (Supplementary Table S4), and 474 (9%) in the environmental data set. The data set and expression plots for the whole genome are available in a searchable web database (http://steinmetzlab.embl.de/ASresponse). Figure 1.Genome-wide transcriptional response to genetic and environmental variations. (A) Four examples of sense–antisense transcript pairs (three anti-correlated and one positively correlated). Expression data are displayed along the chromosome (x axis) for the Watson (W, upper half) and the Crick (C, lower half) strands. Normalized signal intensities (higher in dark) are shown for 24 out of 48 segregants, randomly selected and ordered (y axis). Vertical red lines represent inferred transcript boundaries. Genome annotations are shown in the center: annotated ORFs (blue boxes), their mapped UTRs (dashed grey lines), SUTs (orange boxes) and transcript start sites (arrows). (B) Expression data along 15 kb of chromosome VIII across three replicates each for yeast grown in synthetic complete media with glucose (SDC), rich media with galactose (YPGal) and with ethanol (YPE); and three rows (summarizing nine replicates) for yeast grown in rich media with glucose (YPD; Xu et al, 2009). Download figure Download PowerPoint As a control for our quantitation of sense and antisense transcript levels, we verified that the expression levels of transcripts in sense–antisense pairs were not significantly lower when estimated using the tiling array probes of the region of overlap than using the probes outside this region. These data show that any potential competition during hybridization between probes and antisense transcripts did not affect our level measurements (Supplementary Figure S1). Overall, ORF-Ts had much higher expression levels than antisense transcripts (∼5.9-fold between medians, P<2 × 10−16, Wilcoxon rank-sum test). Furthermore, the larger number of genes with antisense transcripts found in the genetic data set is in agreement with our previous observation of more variation in SUT expression observed between the two parental strains than across changes in growth conditions (Xu et al, 2009). Expression characteristics of genes with antisense transcripts Notably, expression variation in response to our genetic and environmental changes was larger for genes with antisense transcripts than for genes without (Figure 2A and B, P<2 × 10−16 and P=6 × 10−12, respectively, Wilcoxon rank-sum test). Higher variability was also observed at evolutionary scales. Genes with antisense showed higher expression divergence across 5 yeast species (Tirosh et al, 2006; Figure 2C, P=4 × 10−12, one-tailed Wilcoxon rank-sum test here and in the following unless specified). Furthermore, larger variability between cells in a single population (i.e., cell-to-cell variability; Newman et al, 2006) was observed for protein abundance of genes with antisense (Figure 2D, P=2 × 10−4). All these observations on gene expression variability are reminiscent of properties of the TATA-box (Lopez-Maury et al, 2008), but remained significant when controlling for the presence of a TATA-box in gene promoters (Supplementary Figure S2, Materials and methods). These results indicate that, at different scales, antisense expression associates with a larger dynamic range of gene expression, and this association is independent of the increased expression variability known for TATA-containing genes (Lopez-Maury et al, 2008). Figure 2.Antisense expression associates with larger gene expression variability. (A–D) Expression variability of ORF-Ts with or without antisense. (A) Mean of the log2 expression standard deviation across all segregants and (B) across environmental conditions, (C) mean of the expression divergence across five yeast species as provided by Tirosh et al (2006) and (D) mean of cell-to-cell protein expression variability (DM as provided by Newman et al (2006)) for ORF-Ts without antisense (blue) and ORF-Ts with antisense (red). Error bars indicate standard error of the mean. The number of ORF-Ts in each category is shown below the bar. (E) Q-Q plots of expression levels of ORF-Ts with and without antisense. The Q-Q plot compares two distributions by plotting for every quantile the two corresponding expression values against each other (expression level for ORF-Ts without antisense (x axis) against those for ORF-Ts with antisense (y axis)). Two data sets with the same distribution would align on the diagonal (grey line). ORF-Ts with antisense have lower expression levels (below the diagonal, bottom left) for the small quantiles, and similar expression levels for the large quantiles (top right). The shade around the curve (black line) represents bootstrap standard deviation (Materials and methods). (F, G) Smoothed histograms (distribution density estimates) of minimum (F) and maximum (G) expression values across the 48 segregants for ORF-Ts with antisense (red) and without (blue). Vertical line at x=0 indicates our threshold for calling a transcript expressed. Download figure Download PowerPoint A larger dynamic range could be the result of lower minimal levels or higher maximal levels. Across the segregants, genes with antisense showed a notable depression at the lower end of their expression range, but almost no difference in the high range, compared with genes without antisense (Figure 2E). Similar observations on an independent strand-specific RNA-sequencing data set (Yassour et al, 2010) confirmed that these results are not an artefact due to saturation of the microarray signals (Supplementary Figure S3 and Supplementary information). Specifically, genes with an antisense transcript had minimal levels significantly lower than genes without antisense (Figure 2F, P<2 × 10−16). A large fraction of these per-gene minimum levels were consistent with no expression, that is, with microarray signal in the background range (18% for genes with antisense versus 5% for genes without, P<2 × 10−16, one-sided Fisher test, see Materials and methods). In contrast, maximal expression levels were similar for both classes of genes (Figure 2G). Analogous behaviour was observed for the growth condition data (Supplementary information). One interpretation of these observations is that antisense inhibits sense expression particularly at low levels of sense expression and that such inhibition is relaxed when sense expression is high. Another interpretation, although not in contradiction with the former, is that sense represses antisense expression and thus antisense is more easily expressed when sense expression is low—an interpretation that is perhaps in favour of a non-functional role of non-coding RNAs (Struhl, 2007). To find further support for a role (or lack thereof) of antisense expression in sense regulation, we examined the position of sense–antisense overlap. The distributions of the 3′ end positions of either sense or antisense transcripts peaked slightly beyond the transcription start sites (TSS) of each other (98±45 and 77±19 bp, respectively, Figure 3A and Materials and methods). Thus, the typical arrangement of sense–antisense pairs involves an overlap of both promoter regions. In addition, variability of sense gene expression depended on the presence of this TSS overlap. Among genes with an antisense transcript, genes with an overlapped TSS showed larger expression variance across segregants and environmental conditions (Figure 3B, P<2 × 10−16 and P=4 × 10−5, respectively), larger expression divergence across species (P=4 × 10−5) and larger cell-to-cell variability (P=0.09; Supplementary Figure S4). Also, among the 282 genes of which the TSS was overlapped by an antisense transcript, 26% were switched off in at least one of the segregants, compared with only 11% of the 331 that were not overlapped at the TSS (P=1 × 10−6, Fisher test). Hence, the effects on sense gene expression depended strongly on the overlap of the antisense transcript at the position of sense transcript initiation, favouring a model in which antisense expression affects sense expression. Figure 3.Sense expression variability depends on TSS overlap. (A) Overlap of sense–antisense pairs. d1 (x axis) is the distance (base pairs) of the 3′ end of the antisense SUT to the TSS of the sense ORF-T and is positive if the 3′ end of the antisense extends beyond the sense TSS. d2 (y axis) is the distance (base pairs) of the 3′ end of the sense ORF-T to the TSS of the antisense SUT. The panel shows the scatterplot of d1 and d2 and the histograms their marginal distributions, with dashed grey lines marking the modes of the distribution. (B) Variability across segregants for genes grouped by type of sense–antisense overlap. Mean of the expression standard deviation across the 48 segregants for ORF-Ts without antisense SUT (blue), with antisense SUT not overlapping the TSS (pink) and with antisense SUT overlapping the TSS (red). Error bars indicate standard error of the mean. The number of ORF-Ts in each category is shown below the bar. Download figure Download PowerPoint Taken together, the genomic data support a model in which antisense expression induces a threshold-dependent or ultrasensitive (Koshland et al, 1982) on-off switch on sense gene regulation. This model proposes that in the absence of activation of the sense promoter, antisense expression switches off low, basal sense expression. In response to a sufficiently activating stimulus on the sense promoter, sense expression turns on and antisense inhibition is relaxed. Validating the model Elements of this model are supported by mechanistic studies. For example, experiments that block antisense expression have demonstrated an increase of sense expression for PHO84 (Camblong et al, 2007), IME4 (Hongay et al, 2006), KCS1 (Nishizawa et al, 2008) and GAL10 (Houseley et al, 2008; Pinskaya et al, 2009) showing that antisense expression represses sense expression. Analysis of data that we have published previously (Xu et al, 2009) reveals that in a mutant of RRP6, a component of the exosome machinery, in which the degradation of non-coding RNAs is impaired, 76 of 174 (44%) genes were repressed upon increased RNA levels of an antisense transcript that proceeded through their TSS (Materials and methods). This is significantly larger than the 25% of downregulated genes among those that lacked an antisense transcript (Fisher exact test, P=3 × 10−8), bolstering an argument for the inhibitory role of antisense in the regulation of multiple genes. At high levels of gene expression, the effect of antisense appears reduced. The strength of a highly active gene promoter may override inhibitory effects exerted by antisense expression. In addition, reciprocal inhibition could explain the relaxation of inhibition at higher levels, where high sense expression inhibits antisense expression. Consistent with this, our sense–antisense overlap analysis showed an enrichment of sense transcripts overlapping the antisense promoter region. We also observed a significant enrichment for anti-correlation within sense–antisense pairs across conditions (Xu et al, 2009) and segregants, compared with random pairs of sense and antisense transcripts (Materials and methods, P<2 × 10−16, Supplementary Figure S5 and Figure 1A for particular instances). Moreover, anti-correlation is stronger not only for pairs with overlap of the sense–TSS but also for those where only the antisense TSS is overlapped (compared with pairs with neither TSS overlapped, P=2 × 10−7 and 6 × 10−7, respectively). Finally, an inhibitory function of sense on antisense expression has been demonstrated for IME4, where overexpression of the sense was shown to reduce antisense expression (Hongay et al, 2006). These data suggest that sense expression could display an inhibitory function on antisense expression. So far, the threshold mediated on-off switch on gene regulation has not been directly tested. We tested this hypothesis on SUR7, a gene that has not been investigated for its antisense-mediated regulation before. SUR7 exhibits both high and low levels of expression in two distinct conditions, and its antisense transcript (SUT719) can be disrupted without altering the sequence of the sense transcript. In galactose media, SUT719 is expressed antisense to SUR7 and extends beyond the SUR7 TSS (Figure 4A). SUR7 is a gene of uncharacterized function and has been reported to be strongly downregulated in response to stimulation by α-factor pheromone (Roberts et al, 2000). We observed that SUR7 is highly expressed in standard galactose media and is below detectable levels upon α-factor stimulation, whereas the antisense remains highly expressed in both conditions (Figure 4A). SUT719 expression was disrupted without affecting the sequence of the SUR7 RNA by deleting the Gal4 binding site of the SUT719 promoter (Materials and methods). In agreement with our model, when disrupting antisense expression, expression of SUR7 could be detected upon α-factor stimulation with a large increase compared with wild type (4.5-fold above background), whereas a moderate increase of expression was observed in the absence of α-factor (1.2-fold, Figure 4B). The possibility of a GAL80-mediated feedback responsible for the upregulation of SUR7 was ruled out by an experiment in which a drug-selectable cassette was inserted between the end of the SUR7 transcript and the Gal4 binding site. Both experiments yielded the same conclusion on SUR7 regulation, whereas the latter had no effect on GAL80 expression (Supplementary Figure 6, Materials and methods). These experiments demonstrate that antisense expression leads to threshold-dependent regulation on SUR7 sense expression by specifically inhibiting sense expression when it is induced at low levels. Figure 4.Antisense-mediated regulation of SUR7. (A) Expression maps around the SUR7–GAL80 locus. Expression data is displayed along the chromosome (x axis) for the Watson (W, upper half) and the Crick (C, lower half) strands. Normalized signal intensities (higher in dark) are shown for the profiled samples (y axis, bottom-up): wild-type strain grown in YPD (WT_YPD1-3), wild-type strain grown in YPGal (WT_YPGal1-3), mutant strain with Gal4 binding site knocked out grown in YPD (dAS_YPD1-3), mutant strain with Gal4 binding site knocked out grown in YPGal (dAS_YPGal1-3), all for three biological replicates and without α-factor stimulation. The next 12 samples (bottom up) follow the same order, but with α-factor stimulation. Vertical red lines represent inferred transcript boundaries. Nucleosome positions (green tracks, darker for more significant scores; Mavrich et al (2008)) and genome annotations are shown in the center: annotated ORFs (blue boxes), their mapped UTRs (dashed grey lines), SUTs (orange boxes) and transcript start sites (arrows). (B) Mean of the log2 expression levels of SUR7 across three biological replicates for strains grown in YPGal, with (left) or without (right) α-factor stimulation for the wild-type strain (orange) or the antisense-disrupted strain (green). Values of individual samples are shown by open circles. (C) Mean of the log2 expression levels of SUR7 across three biological replicates for strains under α-factor stimulation grown in YPD (purple) or YPGal (turquoise), for the wild-type (left) or the antisense-disrupted strain (right). Values of individual samples are shown by open circles. (D) Model for spreading of regulatory signals on the SUR7–GAL80 locus. SUR7 and GAL80 are two ORFs (blue boxes) in tandem configuration. The promoter of GAL80 is bidirectional and initiates SUT719, a transcript antisense to SUR7. Regulatory signals in response to galactose media (Gal4 transcription factor, green oval) upregulate expression of GAL80 as well as the antisense SUT719, thereby repressing SUR7. Download figure Download PowerPoint Spreading of regulatory signals To obtain further support for antisense-mediated regulation, we examined neighbouring genes linked by non-coding RNAs. Specifically, we addressed the effect of bidirectional promoters on the regulation of tandem genes. Co-expression and functional correlation between adjacent genes have been observed (Cohen et al, 2000). Interleaved transcription (Kapranov et al, 2007) is a natural mechanism for building connections between adjacent genes. We have previously shown that antisense transcripts typically originate from bidirectional promoters shared with divergent genes (Xu et al, 2009). Combined with our current findings on antisense function, bidirectional transcription provides a possible mechanism for how the expression regulation of adjacent genes could be linked. In such an arrangement, exemplified by the SUR7–GAL80 pair, a gene is under the control of its upstream promoter as well as a downstream promoter shared by an antisense and a downstream tandem gene (Figure 4A). The antisense of SUR7, SUT719, initiates from the same nucleosome-depleted region as GAL80. SUT719 responds to changes in sugar source, being expressed in galactose, but not in glucose, media. Its response is co-regulated with that of GAL80. In support of this, the deletion of the Gal4 binding site in the shared bidirectional promoter reduces the expression of both GAL80 and SUT719 (Figure 4A). In addition, we observed a complex pattern of expression of SUR7 responding both to sugar source changes and to stimulation by α-factor pheromone. SUR7 reaches high expression without α-factor in glucose and slightly lower levels in galactose. In the presence of α-factor, SUR7 shows low levels of expression in glucose and is below array-detection levels in galactose (Figure 4A, wild type). Strikingly, when SUT719 expression is disrupted, SUR7 is no longer repressed after shifting from glucose to galactose media (Figure 4C), showing that the response of SUR7 to galactose is mainly mediated by SUT719 expression. Together, these results indicate that regulatory signals impinging on the GAL80 promoter also affect the expression of the upstream gene, SUR7, by the regulated expression of an antisense transcript from the bidirectional promoter (Figure 4D). The possibility that regulatory signals can spread across neighbouring loci by ncRNA expression, as shown here, stresses the importance of gene order and genomic organization (Kapranov et al, 2007). Because antisense expression can actually repress expression of sense genes, the relation is likely to be more complex than simple positive co-expression patterns within chromosomal domains as previously reported (Cohen et al, 2000; Ebisuya et al, 2008). Consistent with this, correlations between tandem gene pairs in the segregant data set are significantly smaller if the promoter of the downstream gene initiates a transcript antisense to the upstream gene, as in the SUR7–GAL80 configuration (Supplementary Figure S7, median correlation 0.17 and 0.22, respectively, P=6 × 10−5, Wilcoxon rank test). These data support the hypothesis of antisense-mediated gene regulation between neighbouring loci. Discussion We have shown that antisense expression can induce threshold dependent gene regulation, by repressing sense expression particularly in the low range, whereas this inhibition is relaxed when sense expression is high. This enables an on-off switch on gene expression for antisense-containing genes, which leads to greater expression variability for antisense-containing genes. One simple possible mechanism for reduced inhibition at high levels is that reciprocal inhibition of sense on antisense relaxes the inhibition of antisense on sense expression (Figure 5). We have also shown that antisense expression initiated from bidirectional promoters can spread regulatory signals between neighbouring genes. Figure 5.Model of antisense-mediated regulation. The sense gene (red dashed line, coding sequence as blue box) and the antisense SUT (green dashed line) typically extend beyond the TSS of each other. In the absence of sufficiently strong activating signals on the sense promoter, the antisense is expressed and inhibits sense expression (left T-shaped arrow). Upon activation of the sense promoter, the inhibition from the antisense SUT is relaxed, possibly through reciprocal inhibition of sense on antisense expression (right T-shaped arrow). This leads to threshold behaviour of gene regulation, where the gene is switched-off unless activation reaches a certain threshold. Download figure Download PowerPoint Our results underline the regulatory potential of the downstream region of a gene as a possible promoter of an antisense transcript. Hence, cloning the canonical region of a gene, defined by the promoter, the ORF and its UTRs, might not capture the whole local regulation if the cloned region does not include the possible antisense and its promoter. Similarly, computational predictions of cis-regulatory elements should include the 3′ region of genes. Although sense–antisense pairs were enriched in anti-correlated expression patterns, we also observed a large proportion of positively or non-correlated expression pairs. Interestingly, all groups showed evidence of threshold-dependent ultrasensitive regulation (Supplementary Figure S8 and Material and methods). For example, for the 61 antisense transcripts with (approximately) constant levels of expression, the levels of their sense partners were reduced throughout the who
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