Gene expression depends on the frequency of transcription events (burst frequency) and on the number of mRNA molecules made per event (burst size). Both processes are encoded in promoter sequence, yet their dependence on mutations is poorly understood. Theory suggests that burst size and frequency can be distinguished by monitoring the stochastic variation (noise) in gene expression: Increasing burst size will increase mean expression without changing noise, while increasing burst frequency will increase mean expression and decrease noise. To reveal principles by which promoter sequence regulates burst size and frequency, we randomly mutated 22 yeast promoters chosen to span a range of expression and noise levels, generating libraries of hundreds of sequence variants. In each library, mean expression (m) and noise (coefficient of variation, h) varied together, defining a scaling curve: h 2 = b/m + h ext 2 . This relation is expected if sequence mutations modulate burst frequency primarily. The estimated burst size (b) differed between promoters, being higher in promoter containing a TATA box and lacking a nucleosome-free region. The rare variants that significantly decreased b were explained by mutations in TATA, or by an insertion of an out-of-frame translation start site. The decrease in burst size due to mutations in TATA was promoter-dependent, but independent of other mutations. These TATA box mutations also modulated the responsiveness of gene expression to changing conditions. Our results suggest that burst size is a promoter-specific property that is relatively robust to sequence mutations but is strongly dependent on the interaction between the TATA box and promoter nucleosomes.

Interactions between genes and proteins are crucial for efficient processing of internal or external signals, but this connectivity also amplifies stochastic fluctuations by propagating noise between components. Linear (unbranched) cascades were shown to exhibit an interplay between the sensitivity to changes in input signals and the ability to buffer noise. We searched for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. Negative feedback can buffer this type of noise, but this buffering comes at the expense of an even greater reduction in signaling sensitivity. By systematically analyzing three-component circuits, we identify positive feedback as a central motif allowing for the buffering of propagated noise while maintaining sensitivity to long-term changes in input signals. We show analytically that noise reduction in the presence of positive feedback results from improved averaging of rapid fluctuations over time, and discuss in detail a particular implementation in the control of nutrient homeostasis in yeast. As the design of biological networks optimizes for multiple constraints, positive feedback can be used to improve sensitivity without a compromise in the ability to buffer propagated noise.

Although the genetic code is redundant, synonymous codons for the same amino acid are not used with equal frequencies in genomes, a phenomenon termed "codon usage bias." Previous studies have demonstrated that synonymous changes in a coding sequence can exert significant cis effects on the gene's expression level. However, whether the codon composition of a gene can also affect the translation efficiency of other genes has not been thoroughly explored. To study how codon usage bias influences the cellular economy of translation, we massively converted abundant codons to their rare synonymous counterpart in several highly expressed genes in Escherichia coli. This perturbation reduces both the cellular fitness and the translation efficiency of genes that have high initiation rates and are naturally enriched with the manipulated codon, in agreement with theoretical predictions. Interestingly, we could alleviate the observed phenotypes by increasing the supply of the tRNA for the highly demanded codon, thus demonstrating that the codon usage of highly expressed genes was selected in evolution to maintain the efficiency of global protein translation. codon usage evolution | tRNA | codon-to-tRNA balance | translation efficiency | genome engineering S ince there are 61 sense codons but only 20 amino acids, most amino acids are encoded by more than a single codon. However, synonymous codons for the same amino acid are not utilized to the same extent across different genes or genomes. This phenomenon, termed "codon usage bias," has been the subject of intense research and was shown to affect gene expression and cellular function through varied processes in bacteria, yeast, and mammals (1-4).Although differential codon usage can result from neutral processes of mutational biases and drift (5-7), certain codon choices could be specifically favored as they increase the efficiency (8-12) or accuracy (13-17) of protein synthesis. These forces would typically lead to codon biases in a gene because they locally exert their effect on the gene in which the codons reside. Indeed, there is a positive correlation between a gene's expression level and the degree of its codon bias (1). Various systems have demonstrated how altering the codon usage synonymously can alter the expression levels of the manipulated genes (18-21), an effect that could reach more than 1,000-fold (22).In addition to such cis effects, it is possible that codon usage also acts in trans, namely, that the codon choice of some genes would affect the translation of others due to a "shared economy" of the entire translation apparatus (23-25). Previous theoretical works have suggested that an increase in the elongation rate may reduce the number of ribosomes on mRNAs and therefore may indirectly increase the rate of initiation of other transcripts due to an increase in the pool of free ribosomes (6,26). In addition, a recent computational study in yeast has also examined the indirect effects of synonymous codon changes on the translation of the entire transcriptome (2...

Gene expression shows a significant variation (noise) between genetically identical cells. Noise depends on the gene expression process regulated by the chromatin environment. We screened for chromatin factors that modulate noise in S. cerevisiae and analyzed the results using a theoretical model that infers regulatory mechanisms from the noise vs. mean relationship. Distinct activities of the Rpd3(L) and Set3 histone deacetylase complexes were predicted. Both HDACs repressed expression. Yet, Rpd3(L)C decreased the frequency of transcriptional bursts, while Set3C decreased the burst size, as did H2B mono-ubiquitination (ubH2B). We mapped the acetylation of H3 Lysine 9 (H3K9ac) upon deletion of multiple subunits of Set3C and Rpd3(L)C, and of ubH2B effectors. ubH2B and Set3C appear to function in the same pathway to reduce the probability that an elongating PolII produces a functional transcript (PolII processivity), while Rpd3(L)C likely represses gene expression at a step preceding elongation.

BackgroundRNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such as Illumina Next-Seq have yielded easy-to-operate machines, with high throughput, at a lower price per base. However, processing this data requires bioinformatics expertise to tailor and execute specific solutions for each type of library preparation.ResultsIn order to enable fast and user-friendly data analysis, we developed an intuitive and scalable transcriptome pipeline that executes the full process, starting from cDNA sequences derived by RNA-Seq [Nat Rev Genet 10:57-63, 2009] and bulk MARS-Seq [Science 343:776-779, 2014] and ending with sets of differentially expressed genes. Output files are placed in structured folders, and results summaries are provided in rich and comprehensive reports, containing dozens of plots, tables and links.ConclusionOur User-friendly Transcriptome Analysis Pipeline (UTAP) is an open source, web-based intuitive platform available to the biomedical research community, enabling researchers to efficiently and accurately analyse transcriptome sequence data.