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 (). 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 (more ...)
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 whole range (Supplementary Figure S8
, green curve), which agrees with antisense-mediated inhibition, but with a weaker effect on the high range of sense expression. Consistent with this, higher variability was observed for all classes, but is more pronounced for the anti-correlated pairs. Overall, these observations support that the ultrasensitive regulation of gene expression induced by antisense is strengthened in the presence of, although not dependent on, anti-correlated sense–antisense expression behaviour. Furthermore, we note that the correlation coefficients are usually small; that is, a change of antisense expression is not always accompanied by a change of sense expression or vice versa suggesting that the main driving force of sense expression change is not antisense expression. Instead, the effect of antisense is more likely to be fine-tuning with a stronger effect on the low range than the high range of gene expression.
Across the segregants, 110 antisense-containing genes appeared switched off in at least one of the segregants (~2% of all genes). Assuming that antisense transcripts overlapping sense TSS exert a regulatory role, the total number of genes that could be affected by antisense expression is 282 (~ 5.5% of all genes). This covers about half of all antisense transcripts we detected. Nevertheless, due to the limited number of segregants and conditions that we profiled, the number of genes that are regulated by antisense could be larger.
It is not clear from the genomic data alone how, mechanistically, antisense expression exerts its role on sense expression. Our data cannot discriminate between a role of the antisense transcript or of the act of antisense transcription itself. Our analysis of sense–antisense overlap configuration supports an effect at the promoter level, but this could involve a variety of mechanisms. Silencing of the sense promoter through histone modifications induced by antisense transcript elongation has previously been suggested in the case of the GAL10
gene (Houseley et al, 2008
; Pinskaya et al, 2009
). Also, pausing of RNA polymerases (transcribing a sense gene) on the promoter of an antisense transcript has been shown in Escherichia coli
and suggested as a mediator of sense–antisense inhibition (Palmer et al, 2009
Which gene categories benefit from antisense-mediated regulation? Condition-specific genes are more subject to transcriptional variability than housekeeping genes, as cells tune gene expression to activate cellular processes that respond to genetic and environmental changes. In our data, genes with antisense are depleted in essential genes (Materials and methods, P
=1 × 10−11
) yet enriched for environmental stress–response (Gasch et al, 2000
; Materials and methods, P
=1 × 10−6
) and plasma membrane genes (enrichment screen for Gene Ontology categories, Materials and methods, P
=8 × 10−5
), which function in sensing and responding to external environmental signals. In addition, we showed increased expression variability between cells in a clonal population for genes with antisense expression. This variability could be advantageous within a population where cell-specific expression patterns enable some cells to be in an ‘anticipatory' state for a sudden environmental change (Wykoff et al, 2007
). Also along evolutionary time, a species may benefit from amplifying the regulatory impact of mutations for condition-specific genes, as opposed to growth-related genes. This would allow exploring transcriptional states beneficial to unforeseen changes (Lopez-Maury et al, 2008
). Thus, antisense-mediated threshold regulation could provide a simple mechanism for short-term and long-term adaptation.
Notably, genes with antisense were more frequently switched off. Guaranteeing a gene to be off might be most important for genes whose qualitative presence (as opposed to quantitative abundance) can commit a cell into cell fate-altering transcriptional programmes. This is the case for IME4
, whose expression has been shown to be determined by an antisense transcript that controls the entry into meiosis by repressing IME4
in haploid cells (Hongay et al, 2006
). Notably, transcription factors were enriched for genes with antisense expression (19% compared with 13% for other genes, P
=0.02, Materials and methods). This supports the hypothesis that antisense-mediated switching off is important for controlling cell fate decisions.
If the observed enrichment of antisense expression among condition-specific genes is the result of natural selection, one can ask whether this is exerted by a positive selection for the presence of antisense in condition-specific genes, or by stronger negative selection against antisense expression for essential genes. A recent deep sequencing study (Yassour et al, 2010
) identified about 1100 genes with antisense expression in rich media. In agreement with our observations, these are enriched for stress–response and condition-specific genes and tend to show opposite patterns of regulation than their antisense counterparts when profiled in the relevant conditions. Following a handful of cases across 5 yeast species, this study showed conservation of antisense expression and of anti-correlation. This provides further support to a conserved functional role of antisense expression and regulation.
As antisense expression is a universal feature of eukaryotic genomes (Kapranov et al, 2007
), our results in yeast may generalize to higher eukaryotes. The non-coding RNA transcriptome is more complex in humans, nevertheless we observed that genes with an antisense show larger variance across five human cell lines (He et al, 2008
; Materials and methods, Supplementary Figure S9
=8 × 10−6
). Thus, antisense-mediated threshold regulation of genes could be an ancient mechanism to enhance gene expression response to genetic and environmental variation.