Recent years have seen intensive progress in measuring protein translation. However, the contributions of coding sequences to the efficiency of the process remain unclear. Here, we identify a universally conserved profile of translation efficiency along mRNAs computed based on adaptation between coding sequences and the tRNA pool. In this profile, the first approximately 30-50 codons are, on average, translated with a low efficiency. Additionally, in eukaryotes, the last approximately 50 codons show the highest efficiency over the full coding sequence. The profile accurately predicts position-dependent ribosomal density along yeast genes. These data suggest that translation speed and, as a consequence, ribosomal density are encoded by coding sequences and the tRNA pool. We suggest that the slow "ramp" at the beginning of mRNAs serves as a late stage of translation initiation, forming an optimal and robust means to reduce ribosomal traffic jams, thus minimizing the cost of protein expression.
Noise in gene expression is generated at multiple levels, such as transcription and translation, chromatin remodeling and pathway-specific regulation. Studies of individual promoters have suggested different dominating noise sources, raising the question of whether a general trend exists across a large number of genes and conditions. We examined the variation in the expression levels of 43 Saccharomyces cerevisiae proteins, in cells grown under 11 experimental conditions. For all classes of genes and under all conditions, the expression variance was approximately proportional to the mean; the same scaling was observed at steady state and during the transient responses to the perturbations. Theoretical analysis suggests that this scaling behavior reflects variability in mRNA copy number, resulting from random 'birth and death' of mRNA molecules or from promoter fluctuations. Deviation of coexpressed genes from this general trend, including high noise in stress-related genes and low noise in proteasomal genes, may indicate fluctuations in pathway-specific regulators or a differential activation pattern of the underlying gene promoters.
A dichotomous choice for metazoan cells is between proliferation and differentiation. Measuring tRNA pools in various cell types, we found two distinct subsets, one that is induced in proliferating cells, and repressed otherwise, and another with the opposite signature. Correspondingly, we found that genes serving cell-autonomous functions and genes involved in multicellularity obey distinct codon usage. Proliferation-induced and differentiation-induced tRNAs often carry anticodons that correspond to the codons enriched among the cell-autonomous and the multicellularity genes, respectively. Because mRNAs of cell-autonomous genes are induced in proliferation and cancer in particular, the concomitant induction of their codon-enriched tRNAs suggests coordination between transcription and translation. Histone modifications indeed change similarly in the vicinity of cell-autonomous genes and their corresponding tRNAs, and in multicellularity genes and their tRNAs, suggesting the existence of transcriptional programs coordinating tRNA supply and demand. Hence, we describe the existence of two distinct translation programs that operate during proliferation and differentiation.
Several computational methods based on microarray data are currently used to study genome-wide transcriptional regulation. Few studies, however, address the combinatorial nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a new approach using microarray data to uncover novel functional motif combinations in the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif combinations that affect expression patterns during the cell cycle, sporulation and various stress responses, we observed regulatory cross-talk among several of these processes. We have also generated motif-association maps that provide a global view of transcription networks. The maps are highly connected, suggesting that a small number of transcription factors are responsible for a complex set of expression patterns in diverse conditions. This approach may be useful for modeling transcriptional regulatory networks in more complex eukaryotes.
microRNAs (miRs) are small RNAs that regulate gene expression at the posttranscriptional level. It is anticipated that, in combination with transcription factors (TFs), they span a regulatory network that controls thousands of mammalian genes. Here we set out to uncover local and global architectural features of the mammalian miR regulatory network. Using evolutionarily conserved potential binding sites of miRs in human targets, and conserved binding sites of TFs in promoters, we uncovered two regulation networks. The first depicts combinatorial interactions between pairs of miRs with many shared targets. The network reveals several levels of hierarchy, whereby a few miRs interact with many other lowly connected miR partners. We revealed hundreds of “target hubs” genes, each potentially subject to massive regulation by dozens of miRs. Interestingly, many of these target hub genes are transcription regulators and they are often related to various developmental processes. The second network consists of miR–TF pairs that coregulate large sets of common targets. We discovered that the network consists of several recurring motifs. Most notably, in a significant fraction of the miR–TF coregulators the TF appears to regulate the miR, or to be regulated by the miR, forming a diversity of feed-forward loops. Together these findings provide new insights on the architecture of the combined transcriptional–post transcriptional regulatory network.
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