In this study, we have combined single mRNA counting with mathematical modeling to provide fundamental insights into how an individual cell accomplishes what is thought to be one of its most crucial tasks— coordinating gene expression.
Our results revealed that cells transcribe temporally induced genes in a highly coordinated manner. Although there was a large variation in the magnitude of response to galactose between individual cells, the transcript levels of GAL genes within a cell were highly correlated (). These results confirm that measurements at the mRNA level are consistent with studies that used reporter proteins to show that variation in protein levels of induced genes is largely due to cell-to-cell differences in common upstream regulators16,17,39
. Moreover, the correlation between transcripts of GAL genes within individual cells was independent of the galactose concentration used for induction (Supplementary Fig. 10
). A recent assay for quantifying nucleosome occupancy showed that promoter activation upon galactose induction corresponds to the removal of nucleosomes flanking the UAS of GAL genes and coincides with recruitment of the transcriptional machinery to GAL promoters40
. We note that a slightly lower correlation between GAL1
compared to GAL1
, despite common upstream regulation, most likely underscores the importance of chromatin remodeling35
. If two promoters were activated independently, the probability of both promoters being ‘on’ would equal the product of their individual probabilities. However, the probability of a cell transcribing both GAL1
is higher than the product of their individual probabilities (). This result is consistent with the fact that the rate-limiting step of activating the GAL1
promoters through nucleosome removal is mediated by a single UAS common to both promoters. On the other hand, the probability of GAL1
switching ‘on’ together is slightly lower, since their promoters are activated independently through derepression of Gal4p at similar, but distinct UAS. In order to decouple the GAL1
promoters, we introduced independent rate-limiting steps in the activation of these two genes. In wild type cells, histone H2A variant H2A.Z destabilizes the +1 and −1 promoter nucleosomes and is thought to promote gene activation by exposing the transcription start site41
. We found that deletion of HTZ1
, the gene encoding H2A.Z, led to decreased expression of GAL1
and reduced the correlation between these two genes to a value closer to the correlation between GAL1
(Supplementary Fig. 11
). These results suggest that common upstream regulation through transcription factors as well as chromatin structure provides a robust way to maintain equal numbers of transcripts for these genes regardless of induction conditions.
Unlike induced genes, which are activated synchronously during a well-defined time interval by an upstream signal, the transcription of constitutive genes is achieved by independent initiation events with a constant probability over time. Surprisingly, even transcripts from two endogenous alleles of MDN1, with identical promoters, were uncorrelated after accounting for the synchronizing effects of cell division (). Moreover, transcripts of several classes of functionally related and unrelated constitutive genes in individual cells were uncorrelated (, ). These results show that individual cells are unable to coordinate the expression of constitutive genes due to inherently stochastic fluctuations in transcription initiation.
A simple model with only two free parameters is sufficient to describe mRNA variation for constitutive genes in yeast (). We note that our model slightly underestimates the experimentally measured correlation coefficients. More accurate assessment of transcript half-lives would improve these predictions. It is also possible that the discrepancy arises from the fact that our model assumes transcription to be a homogenous Poisson process and does not account for gene duplication prior to cell division. Nevertheless, our model confirms that weak correlations between constitutive genes within a wide range of transcript means and half-lives reflect the lower limit of extrinsic variability due to cell growth and division ()17
How then are cells able to carry out complex functions in a predictable and coordinated manner when the transcriptional output of constitutive genes is essentially random? It has been suggested than in higher eukaryotes, fluctuations in mRNA levels are filtered out at the protein level by long protein half-lives35,42
. However, the average protein half-life is only twice as long as the average mRNA half-life in yeast43,44
. Therefore, protein half-lives only partially explain the low variation observed for functionally related proteins8,18,20,21
. There are several passive and active means to achieve predictable outcomes from a stochastic system. It is, in fact, possible to build a multi-protein complex in a predictable amount of time even if the abundance of each of its subunits varies substantially. Whereas the duration of each binding step might vary due to fluctuations in protein quantities, these fluctuations average out when they are added sequentially to produce the full complex. More generally, any biological process can be passively rectified against stochastic fluctuations, since the central limit theorem predicts that variability in the total duration of a process decreases with increasing number of intermediate steps.
There are also active models that could compensate for the lack of coordination in mRNA abundance. One possibility is that in order to yield predictable outcomes, cells impose checkpoints until all conditions for further progress are satisfied. Assembly of proteasomes, for example, is guided by various chaperones that ensure correct incorporation of each subunit in a specific order45,46
. Chaperones could also act to stabilize the intermediate complexes and ensure that they do not dissociate while ‘waiting’ for the next subunit. In this way, cells can guarantee a predictable outcome, but not the time it takes to achieve it.
Post-transcriptional gene regulation might also play an important role in optimizing the expression of each subunit for efficient assembly of complexes. Efficient regulation requires fast responses to transient variations in protein levels. Therefore, it seems reasonable to control protein abundance by tuning the latest possible step of the production process. Post-transcriptional or even post-translational regulation would provide much quicker responses compared to initiating the much longer process of transcription. RNA binding proteins have been implicated in coordinated regulation of many post-transcriptional steps in the expression of functionally related genes47–49
. Indeed, genes that encode subunits of stoichiometric complexes are thought to have similar transcript and protein decay rates43,44
Our perception of transcription has been influenced over the last half century by bacterial models where gene activity is regulated by its end product. Since the discovery of the lac operon in Escherichia coli
, genes have been viewed as finely tuned thermostats that constantly sense and counter changes in the environment with a precisely coordinated response50
. While there are examples of highly regulated gene networks in various organisms that support this view, it certainly cannot be generalized to constitutive genes. The experimental and modeling results presented here suggest that execution of gene expression programs, particularly at the level of mRNA, is not always precisely coordinated. Many constitutive genes in yeast are essentially clueless entities that produce transcripts with a constant probability over time irrespective of the necessary concentrations of the final gene product. Whether genes can sense and regulate their end product or whether they act autonomously leads to profoundly divergent modes of transcription, and hence assembly of essential complexes. The results presented here suggest a fundamental shift in the way we must think about coordination of biological processes within a cell. Cells have evolved very simple modes of gene expression that require much less coordination than previously thought. Therefore, the regulation of precise stoichiometry must occur post-transcriptionally, and likely post-translationally. Determining the level of post-transcriptional control for many of these genes will show whether active processes further regulate the expression of genes encoding protein complexes or if the downstream processes are just as ‘clueless’ as transcription.