Together, these processes give rise to stochastic, often bursting, transcriptional activity.

Transcriptional bursts are an inherent feature of such transcription activation cycles.

Bursting transcription can cause individual cells to remain in synchrony transiently, offering an explanation of transcriptional cycling as observed in cell populations, both on promoter chromatin status and mRNA levels.

Such bursting transcriptional activity has indeed been observed for eukaryotic genes.

As a consequence, cells that are activated at the same moment in time display synchronous transcriptional activity for several transcription cycles.

This provides an explanation for transient transcriptional cycles observed at the level of cell populations.

This hiatus precludes resolving a number of issues: do cyclic transcriptional mechanisms perform better than the reversible ones found in prokaryotes; and are they perhaps even essential?

Is the seconds-timescale of molecular events in agreement with transcriptional cycling of tens of minutes?

Rather, a transcription activation clock model with irreversible ratchets does resolve diffusion limitations, the required multifactorial regulation as well as the experimental observations on transcriptional cycling and bursting.

Precise transcription cycle times, despite inherent molecular noise, can cause transient transcriptional oscillations at the population level

A mechanism in which TFs dissociate irreversibly on the basis of a first-in, first-out principle (Fig 2C) is able to attain much higher transcriptional activity than the equilibrium binding model, whilst transition times between active and inactive gene states are also much shorter.

Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions.

We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%.

One promising strategy is to identify drugs that, at the transcriptional level, reverse the gene expression signature of a disease.

On a set of 21 transcriptional signatures of lung cancer, we identified 247 drugs that significantly reverse these pathological gene expression changes (at FDR < 1%).

In all nine cell lines, drugs that CMapBatch identifies as reversing the transcriptional changes seen with lung cancer are significantly better than other CMap drugs at inhibiting growth (Wilcox test P < 0.01; Fig.

One such resource, the Connectivity Map (CMap), which is the focus of our analyses, catalogues the transcriptional responses to drug treatment in human cell lines for over a thousand small molecules [3].

[8] combined two microarray data sets to create a single transcriptional signature of lung adenocarcinoma and screened it against CMap.

[9] constructed a transcriptional signature of survival in patients with lung adenocarcino-ma; CMap analysis identified several drugs that might improve outcome.

We found that eleven drugs that reverse the transcriptional changes observed in lung cancer were structurally similar to one or more drugs in TOP (Fig.

Transcriptional signatures

This work demonstrates the degree at which ge-nome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

The transcriptional profile of an organism contains clues about the environmental context in Which it has evolved and currently lives, its behavior and cellular state.

(P < 4.3 X 10—7) which is in agreement with previous studies that report the prevalence of phase-dependent transcriptional regulation in a variety of biosynthetic processes [37—39].

The major transcriptional regulator for the entry into stationary phase is RpoS and, as expected, it is present in the set of genes informative for growth phase, along with several genes belonging to its regulon like dnaK, clpx, hemL, dps, rpsK, hfq, rplA, crr, rpsE and gapA [41].

Genome-scale transcriptional profiling has become a standard and relatively inexpensive way to identify the overall cellular state and condition-specific cellular responses to external stimuli.

For instance, different sets of genes are known to be active in each growth phase and medium [1], while strain polymorphisms can result in a remarkably diverse transcriptional repertoire [2,3].

Genome-wide transcriptional profiling can be thought of as a complex representation of all cellular functions and states, with a wealth of multiplexed information that, if decoded efficiently, can provide a fast and quite accurate all-encompassing snapshot of the cell and its environment.

Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs.

We characterise IEGs in a genome-wide sequencing dataset that captures their transcriptional response over time.

Observing that the transcription of the host gene for hsa-mir-155 (an ID-miR) showed clear peaks in the time courses of AoSMC-FGF2, AoSMC-IL1b and MCF7-HRG cells (812 Fig), and that the precursor transcript of hsa-mir-21 showed early or late peaks in expression in three of the CAGE time course datasets we consider, we sought to investigate the relationship between miRNA-mediated repression and transcriptional attenuation, and to test whether or not kinetic signatures can be used to find correspondences between time course datasets.

Reasoning that miRNA-mediated repression will be reflected in the CAGE signals, either by direct action on mRNA or indirectly through transcriptional inactivation processes, we sought to establish a connection between the targets of mature miRNA that are assigned to the dip signature, and protein-coding genes with CAGE clusters assigned to the early peak signature in MCF7 cells stimulated with HRG.

The absence of a correlation between DNaseI counts and CAGE eXpression suggests that IEG promoters need not be located in the most accessible chromatin, rather a minimum level of accessibility is required and is not otherwise predictive of transcriptional activity.

This class of transcripts is currently understudied, but lncRNAs are differentially expressed during differentiation, are preferentially localised in chromatin and have been proposed to ‘f1ne-tune’ cell fate via their roles in transcriptional regulation [14—16].

It is also worth noting that significant downregulation did not occur until the second hour, and this may require both early induction of transcriptional repressors and the RNA degradation proteins BTG2 and ZFP36 (tristetraprolin) [29].

The transcriptional repressor NAB2 peaked relatively late in both MCF7 time courses.

The time at which short IEGs reach their transcriptional peak was up to three hours after the stimulus suggesting their activation rates coordinate their eXpression with diverse processes and pathways: late-acting IEGs are not delayed due to gene length.

Transcriptional activation and repression of miRNA precursors in the immediate-early response is readily apparent in the small intersection of the datasets for MCF7-HRG.

The notion that enriched tandem repeats in S. cerevisiae could guide transcriptional modulation has been established for genes carrying very variable tracts of repeats in promoter; the involved genes have the general feature of interacting with the cell environment and so requiring rapid response changes [59, 60].

This issue assumes relevant significance for gene evolution and tandem repeats have been considered able to drive transcriptional divergence and to confer evolvability to gene expression [61].

[38] coming from mRNA decay profiles measured by microarrays following transcriptional shutoff.

The authors of [23] read weak and isolated signals as indicative of a low transcriptional activity; this occurs only in nine peaks, so it is nearly negligble.

These findings clearly indicate the presence of termination signals in absence of annotated transcriptional units; therefore, peaks which are positioned at 3’ UTR may also mark non coding RNA genes, that frequently may be antisense transcripts.

They have been described in yeast, where they may depend on the dense arrangement of genes and possibly to cause transcriptional interference [27].

Further IMEI, encoding a master regulator of meiosis and its convergent gene UME6, the key transcriptional regulator of early meiotic genes; moreover MFA], encoding the essential mating pheromone a-factor, STESO the major protein involved in mating response.

A manual inspection was then performed on nucleosome occupancy of all peaks localized in 3’UTR of convergent genes, to be sure to consider only transcriptional terminators.

There have been many descriptions of binding partners for TIF2 under conditions where TIF2 reverses a transcriptional response [46—50].

Cofactors and comodulators assist or impede the transcriptional activity of DNA-associated steroid receptors [2,3].

Beyond this, it is currently not possible to predict the transcriptional outcome for any specific combination of gene, receptor, and cofactor/comodulator.

An unresolved question is Whether the underlying mechanism of each transcriptional component is the same or changes With the direction of gene eXpression output (i.e., increase in induction vs. decrease in repression).

This suggests that many transcriptional cofactors/comodulators are mono-functional and similarly modulate basic steps in gene eXpression irrespective of the directional change in gene product levels.

The commitment is called START in S. cerevisiae and constitutes the transcriptional activation of more than 200 genes by the transcription factor complexes SBF and MBF [2].

SBF/MBF activity is controlled by the G1 network, which involves the cyclin dependent kinase (CDK) Cdc28, its activating subunits the G1 cyclins Cln1/2/3 and the transcriptional repressor Whi5 (reviewed in [3]).

The core network architecture with the competition between the active CDK and the transcriptional repressor is analogous to the Restriction Point, which is the equivalent of START in mammalian cells [7].