Buffering of transcription rate by mRNA half-life is a conserved feature of Rett syndrome models

Models of MECP2 dysfunction in Rett syndrome (RTT) assume that transcription rate changes directly correlate with altered steady-state mRNA levels. However, limited evidence suggests that transcription rate changes are buffered by poorly understood compensatory post-transcriptional mechanisms. Here we measure transcription rate and mRNA half-life changes in RTT patient neurons using RATE-seq, and reinterpret nuclear and whole-cell RNAseq from Mecp2 mice. Genes are dysregulated by changing transcription rate only or half-life only and are buffered when both are changed. We utilized classifier models to understand the direction of transcription rate changes based on gene-body DNA sequence, and combined frequencies of three dinucleotides were better predictors than contributions by CA and CG. MicroRNA and RNA-Binding Protein (RBP) motifs were enriched in 3ʹUTRs of genes with half-life changes. Motifs for nuclear localized RBPs were enriched on buffered genes with increased transcription rate. Our findings identify post-transcriptional mechanisms in humans and mice that alter half-life only or buffer transcription rate changes when a transcriptional modulator gene is mutated in a neurodevelopmental disorder.

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

To what extent do cell-to-cell differences in transcription rate affect RNA copy number distributions, and what can this variation tell us about biological processes underlying transcription? We argue that successfully answering such questions requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis); in particular, such models enable the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a stochastic transcription rate (governed by a stochastic differential equation) coupled to a discrete stochastic RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about observed transcription rate variation. One hypothesis assumes transcription rate variation is due to DNA experiencing mechanical strain and relaxation, while the other assumes that variation is due to fluctuations in the number of an abundant regulator. Through a thorough mathematical analysis, we show that these two models are challenging to distinguish: properties like first- and second-order moments, autocorrelations, and several limiting distributions are shared. However, our analysis also points to the experiments which best discriminate between them. Our work illustrates the importance of theory-guided data collection in general, and multimodal single-molecule data in particular for distinguishing between competing hypotheses. We use this theoretical case study to introduce and motivate a general framework for constructing and solving such nontrivial continuous-discrete models.

Trade-offs among transcription elongation rate, number, and duration of ubiquitous pauses on highly transcribed bacterial genes

In this paper, we study the limitations imposed on the transcription process by the presence of short ubiquitous pauses and crowding. These effects are especially pronounced in highly transcribed genes such as ribosomal genes (rrn) in fast growing bacteria. Our model indicates that the quantity and duration of pauses reported for protein-coding genes is incompatible with the average elongation rate observed in rrn genes. When maximal elongation rate is high, pause-induced traffic jams occur, increasing promoter occlusion, thereby lowering the initiation rate. This lowers average transcription rate and increases average transcription time. Increasing maximal elongation rate in the model is insufficient to match the experimentally observed average elongation rate in rrn genes. This suggests that there may be rrn-specific modifications to RNAP, which then experience fewer pauses, or pauses of shorter duration than those in protein-coding genes. We identify model parameter triples (maximal elongation rate, mean pause duration time, number of pauses) which are compatible with experimentally observed elongation rates. Average transcription time and average transcription rate are the model outputs investigated as proxies for cell fitness. These fitness functions are optimized for different parameter choices, opening up a possibility of differential control of these aspects of the elongation process, with potential evolutionary consequences. As an example, a gene’s average transcription time may be crucial to fitness when the surrounding medium is prone to abrupt changes. This paper demonstrates that a functional relationship among the model parameters can be estimated using a standard statistical analysis, and this functional relationship describes the various trade-offs that must be made in order for the gene to control the elongation process and achieve a desired average transcription time. It also demonstrates the robustness of the system when a range of maximal elongation rates can be balanced with transcriptional pause data in order to maintain a desired fitness.

Cell volume homeostatically controls the rDNA repeat copy number and rRNA synthesis rate in yeast

The adjustment of transcription and translation rates to the changing needs of cells is of utmost importance for their fitness and survival. We have previously shown that the global transcription rate for RNA polymerase II in budding yeast Saccharomyces cerevisiae is regulated in relation to cell volume. Total mRNA concentration is constant with cell volume since global RNApol II-dependent nascent transcription rate (nTR) also keeps constant but mRNA stability increases with cell size. In this paper, we focus on the case of rRNA and RNA polymerase I. Contrarily to that found for RNA pol II, we detected that RNA polymerase I nTR increases proportionally to genome copies and cell size in polyploid cells. In haploid mutant cells with larger cell sizes, the rDNA repeat copy number rises. By combining mathematical modeling and experimental work with the large-size cln3 strain, we observed that the increasing repeat copy number is based on a feedback mechanism in which Sir2 histone deacetylase homeostatically controls the amplification of rDNA repeats in a volume-dependent manner. This amplification is paralleled with an increase in rRNA nTR, which indicates a control of the RNA pol I synthesis rate by cell volume.

High levels of Dorsal transcription factor downregulate, not promote, snail expression by regulating enhancer action

In Drosophila embryos, genes expressed along the dorsal-ventral axis are responsive to concentration of the Dorsal (Dl) transcription factor, which varies in space; however, levels of this morphogen also build over time. Since expression of high-threshold Dl target genes such as snail (sna) is supported before Dl levels peak, it is unclear what role increasing levels have if any. Here we investigated action of two enhancers that control sna expression in embryos, demonstrating using genome editing that Dl binding sites within one enhancer located promoter proximally, sna.prox, can limit the ability of the other distally-located enhancer, sna.dis, to increase sna levels. In addition, MS2-MCP live imaging was used to study sna transcription rate in wildtype, dl heterozygote, and a background in which a photo-sensitive degron is fused to Dl (dl-BLID). The results demonstrate that, when Dl levels are high, Dl acts through sna.prox to limit the activity of sna.dis and thereby influence sna transcription rate. In contrast, when Dl levels are kept low using dl-BLID, sna.prox positively influences sna transcription rate. Collectively, our data support the view that Dl’s effect on gene expression changes over time, switching from promoting sna expression at low concentration to dampening sna expression at high concentration by regulating enhancer interactions. We propose this differential action of the Dl morphogen is likely supported by occupancy of this factor first to high and then low affinity binding sites over time as Dl levels rise to coordinate action of these two co-acting enhancers.Significance statementA gradient of the maternal transcription factor Dorsal is important for establishing spatial expression of target genes along the dorsal-ventral axis of Drosophila embryos. Dorsal levels are also dynamic as nuclear concentration builds in time. Surprisingly, expression of high-threshold target genes such as snail is supported before levels peak, raising the question why levels continue to build. Our data support the view that peak Dorsal levels act to preferentially support activity of one enhancer over another to effectively decrease snail expression. In addition, while the morphogen Dorsal acts early to support gene expression, later it effectively acts as a damper to limit gene expression. Our results suggest other morphogens also have effects on gene expression that change over time.

Triblock copolymer micelle model of spherical paraspeckles

ABSTRACTParaspeckles are nuclear bodies composed of architectural RNA (arcRNA) and RNA-binding proteins. In the wild type, the blocks at the two terminal regions of arcRNAs compose the shell of paraspeckles and the middle region between the two terminal blocks composes the core, analogous to micelles of ABC triblock copolymers. We here use an extension of the theory of polymer micelles to predict the structure and size of paraspeckles as one decreases the length of one of the terminal blocks of arcRNA by CRISPR/Cas9, assuming that paraspeckles are spherical. Our theory predicts that when the length of the edited terminal blocks is larger than a critical value, paraspeckles show discontinuous transitions between the structure in which all the edited terminal blocks are localized in the shell and the structure in which all the edited terminal blocks are localized in the core at a threshold value of the transcription rate of arcRNA. In contrast, when the length of the edited terminal blocks is smaller than the critical value, the population of edited terminal blocks in the shell decreases continuously as one increases the transcription rate of arcRNA. The size of paraspeckles increases as one decreases the length of the edited terminal blocks. These predictions are consistent with our experiments.

Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics

The responses of plants to their environment often hinge on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, lack of implementation of these technologies in plants has limited concomitant quantitative studies. Here, we applied the PP7 and MS2 RNA-labeling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we count nascent RNA transcripts in real-time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana (Arabidopsis). Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the number of cells engaged in transcription rather than the transcription rate of active cells. This switch-like behavior, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from the stochasticity intrinsic to individual alleles. Taken together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.

Theory of transcription bursting: Stochasticity in the transcription rates

ABSTRACTTranscription bursting creates variation among the individuals of a given population. Bursting emerges as the consequence of turning on and off the transcription process randomly. There are at least three sub-processes involved in the bursting phenomenon with different timescale regimes viz. flipping across the on-off state channels, microscopic transcription elongation events and the mesoscopic transcription dynamics along with the mRNA recycling. We demonstrate that when the flipping dynamics is coupled with the microscopic elongation events, then the distribution of the resultant transcription rates will be over-dispersed. This in turn reflects as the transcription bursting with over-dispersed non-Poisson type distribution of mRNA numbers. We further show that there exist optimum flipping rates (αC, βC) at which the stationary state Fano factor and variance associated with the mRNA numbers attain maxima. These optimum points are connected via . Here α is the rate of flipping from the on-state to the off-state, β is the rate of flipping from the off-state to the on-state and γr is the decay rate of mRNA. When α = β = χ with zero rate in the off-state channel, then there exist optimum flipping rates at which the non-stationary Fano factor and variance attain maxima. Here (here is the rate of transcription purely through the on-state elongation channel) is the optimum flipping rate at which the variance of mRNA attains a maximum and χC, κ ≃ 1.72/t is the optimum flipping rate at which the Fano factor attains a maximum. Close observation of the transcription mechanism reveals that the RNA polymerase performs several rounds of stall-continue type dynamics before generating a complete mRNA. Based on this observation, we model the transcription event as a stochastic trajectory of the transcription machinery across these on-off state elongation channels. Each mRNA transcript follows different trajectory. The total time taken by a given trajectory is the first passage time (FPT). Inverse of this FPT is the resultant transcription rate associated with the particular mRNA. Therefore, the time required to generate a given mRNA transcript will be a random variable. For a stall-continue type dynamics of RNA polymerase, we show that the overall average transcription rate can be expressed as where is the microscopic transcription elongation rate in the on-state channel and L is the length of a complete mRNA transcript and is the stationary state probability of finding the transcription machinery in the on-state.

The impact of leadered and leaderless gene structures on translation efficiency, transcript stability, and predicted transcription rates in Mycobacterium smegmatis

ABSTRACTRegulation of gene expression is critical for the pathogen Mycobacterium tuberculosis to tolerate stressors encountered during infection, and for non-pathogenic mycobacteria such as Mycobacterium smegmatis to survive stressors encountered in the environment. Unlike better studied models, mycobacteria express ∼14% of their genes as leaderless transcripts. However, the impacts of leaderless transcript structures on mRNA half-life and translation efficiency in mycobacteria have not been directly tested. For leadered transcripts, the contributions of 5’ UTRs to mRNA half-life and translation efficiency are similarly unknown. In both M. tuberculosis and M. smegmatis, the essential sigma factor, SigA, is encoded by an unstable transcript with a relatively short half-life. We hypothesized that sigA’s long 5’ UTR caused this instability. To test this, we constructed fluorescence reporters and then measured protein abundance, mRNA abundance, and mRNA half-life. From these data we also calculated relative transcription rates. We found that the sigA 5’ UTR confers an increased transcription rate, a shorter mRNA half-life, and a decreased translation rate compared to a synthetic 5’ UTR commonly used in mycobacterial expression plasmids. Leaderless transcripts produced less protein compared to any of the leadered transcripts. However, translation rates were similar to those of transcripts with the sigA 5’ UTR, and the protein levels were instead explained by lower transcript abundance. A global comparison of M. tuberculosis mRNA and protein abundances failed to reveal systematic differences in protein:mRNA ratios for natural leadered and leaderless transcripts, consistent with the idea that variability in translation efficiency among mycobacterial genes is largely driven by factors other than leader status. The variability in mRNA half-life and predicted transcription rate among our constructs could not be explained by their different translation efficiencies, indicating that other factors are responsible for these properties and highlighting the myriad and complex roles played by 5’ UTRs and other sequences downstream of transcription start sites.