Michal Levo

Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited. We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters and applied it to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences. Our analyses reveal a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts which are transcription-factor specific, including a striking ∼10-bp periodic relationship between gene expression and binding-site location. We show how this approach can measure transcription-factor sequence specificities and the sensitivity of transcription-factor sites to the surrounding sequence context, and compare the activity of 75 yeast transcription factors. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control.

Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation

The state of the transcriptome reflects a balance between mRNA production and degradation. Yet how these two regulatory arms interact in shaping the kinetics of the transcriptome in response to environmental changes is not known. We subjected yeast to two stresses, one that induces a fast and transient response, and another that triggers a slow enduring response. We then used microarrays following transcriptional arrest to measure genome‐wide decay profiles under each condition. We found condition‐specific changes in mRNA decay rates and coordination between mRNA production and degradation. In the transient response, most induced genes were surprisingly destabilized, whereas repressed genes were somewhat stabilized, exhibiting counteraction between production and degradation. This strategy can reconcile high steady‐state level with short response time among induced genes. In contrast, the stress that induces the slow response displays the more expected behavior, whereby most induced genes are stabilized, and repressed genes are destabilized. Our results show genome‐wide interplay between mRNA production and degradation, and that alternative modes of such interplay determine the kinetics of the transcriptome in response to stress.

Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast

Understanding how precise control of gene expression is specified within regulatory DNA sequences is a key challenge with far-reaching implications. Many studies have focused on the regulatory role of transcription factor–binding sites. Here, we explore the transcriptional effects of different elements, nucleosome-disfavoring sequences and, specifically, poly(dA:dT) tracts that are highly prevalent in eukaryotic promoters. By measuring promoter activity for a large-scale promoter library, designed with systematic manipulations to the properties and spatial arrangement of poly(dA:dT) tracts, we show that these tracts significantly and causally affect transcription. We show that manipulating these elements offers a general genetic mechanism, applicable to promoters regulated by different transcription factors, for tuning expression in a predictable manner, with resolution that can be even finer than that attained by altering transcription factor sites. Overall, our results advance the understanding of the regulatory code and suggest a potential mechanism by which promoters yielding prespecified expression patterns can be designed.

In pursuit of design principles of regulatory sequences

Instructions for when, where and to what level each gene should be expressed are encoded within regulatory sequences. The importance of motifs recognized by DNA-binding regulators has long been known, but their extensive characterization afforded by recent technologies only partly accounts for how regulatory instructions are encoded in the genome. Here, we review recent advances in our understanding of regulatory sequences that influence transcription and go beyond the description of motifs. We discuss how understanding different aspects of the sequence-encoded regulation can help to unravel the genotype–phenotype relationship, which would lead to a more accurate and mechanistic interpretation of personal genome sequences.

Unraveling determinants of transcription factor binding outside the core binding site

Binding of transcription factors (TFs) to regulatory sequences is a pivotal step in the control of gene expression. Despite many advances in the characterization of sequence motifs recognized by TFs, our ability to quantitatively predict TF binding to different regulatory sequences is still limited. Here, we present a novel experimental assay termed BunDLE-seq that provides quantitative measurements of TF binding to thousands of fully designed sequences of 200 bp in length within a single experiment. Applying this binding assay to two yeast TFs, we demonstrate that sequences outside the core TF binding site profoundly affect TF binding. We show that TF-specific models based on the sequence or DNA shape of the regions flanking the core binding site are highly predictive of the measured differential TF binding. We further characterize the dependence of TF binding, accounting for measurements of single and co-occurring binding events, on the number and location of binding sites and on the TF concentration. Finally, by coupling our in vitro TF binding measurements, and another application of our method probing nucleosome formation, to in vivo expression measurements carried out with the same template sequences serving as promoters, we offer insights into mechanisms that may determine the different expression outcomes observed. Our assay thus paves the way to a more comprehensive understanding of TF binding to regulatory sequences and allows the characterization of TF binding determinants within and outside of core binding sites.

Compensation for differences in gene copy number among yeast ribosomal proteins is encoded within their promoters

Coordinate regulation of ribosomal protein (RP) genes is key for controlling cell growth. In yeast, it is unclear how this regulation achieves the required equimolar amounts of the different RP components, given that some RP genes exist in duplicate copies, while others have only one copy. Here, we tested whether the solution to this challenge is partly encoded within the DNA sequence of the RP promoters, by fusing 110 different RP promoters to a fluorescent gene reporter, allowing us to robustly detect differences in their promoter activities that are as small as ~10%. We found that single-copy RP promoters have significantly higher activities, suggesting that proper RP stoichiometry is indeed partly encoded within the RP promoters. Notably, we also partially uncovered how this regulation is encoded by finding that RP promoters with higher activity have more nucleosome-disfavoring sequences and characteristic spatial organizations of these sequences and of binding sites for key RP regulators. Mutations in these elements result in a significant decrease of RP promoter activity. Thus, our results suggest that intrinsic (DNA-dependent) nucleosome organization may be a key mechanism by which genomes encode biologically meaningful promoter activities. Our approach can readily be applied to uncover how transcriptional programs of other promoters are encoded.

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

Systematic Investigation of Transcription Factor Activity in the Context of Chromatin Using Massively Parallel Binding and Expression Assays

Precise gene expression patterns are established by transcription factor (TFs) binding to regulatory sequences. While these events occur in the context of chromatin, our understanding of how TF-nucleosome interplay affects gene expression is highly limited. Here, we present an assay for high-resolution measurements of both DNA occupancy and gene expression on large-scale libraries of systematically designed regulatory sequences. Our assay reveals occupancy patterns at the single-cell level. It provides an accurate quantification of the fraction of the population bound by a nucleosome and captures distinct, even adjacent, TF binding events. By applying this assay to over 1,500 promoter variants in yeast, we reveal pronounced differences in the dependency of TF activity on chromatin and classify TFs by their differential capacity to alter chromatin and promote expression. We further demonstrate how different regulatory sequences give rise to nucleosome-mediated TF collaborations that quantitatively account for the resulting expression.

Incorporating Nucleosomes into Thermodynamic Models of Transcription Regulation

Transcriptional control is central to many cellular processes and consequently, much effort has been devoted to understanding its underlying mechanisms. Recently, it has become evident that the organization of nucleosomes along promoter regions has an important role in transcriptional control, since most transcription factors cannot bind to sequences bound by nucleosomes, and thus compete with nucleosomes for DNA access. This competition is governed by the relative concentrations of nucleosomes and transcription factors and by their respective sequence binding preferences. Even though competition of nucleosomes and transcription factors may have significant effects on transcription, a mechanistic understanding of its quantitative consequences for gene expression is still missing. Here we employ a thermodynamic framework based on fundamental principles of statistical mechanics to theoretically explore the effect that different nucleosome organizations along promoters have on the activation dynamics of promoters in response to varying concentrations of the regulating transcription factors. We show that even simple landscapes of nucleosome organization reproduce experimental results regarding the effect of nucleosomes as general repressors and as generators of obligate binding cooperativity between transcription factors. Our modeling framework also allows us to characterize the effects that various sequence elements of promoters will have on the induction threshold and on the shape of the promoter activation curves.