Hadas Zur

Multiple roles of the coding sequence 5′ end in gene expression regulation

The codon composition of the coding sequences (ORF) 5′ end first few dozen codons is known to be distinct to that of the rest of the ORF. Various explanations for the unusual codon distribution in this region have been proposed in recent years, and include, among others, novel regulatory mechanisms of translation initiation and elongation. However, due to the fact that many overlapping regulatory signals are suggested to be associated with this relatively short region, its research is challenging. Here, we review the currently known signals that appear in this region, the theories related to the way they regulate translation and affect the organismal fitness, and the debates they provoke.

Strong association between mRNA folding strength and protein abundance in S. cerevisiae

One of the open questions in regulatory genomics is how the efficiency of gene translation is encoded in the coding sequence. Here we analyse recently generated measurements of folding energy in Saccharomyces cerevisiae, showing that genes with high protein abundance tend to have strong mRNA folding (mF; R=0.68). mF strength also strongly correlates with ribosomal density and mRNA levels, suggesting that this relation at least partially pertains to the efficiency of translation elongation, presumably by preventing aggregation of mRNA molecules.

New Universal Rules of Eukaryotic Translation Initiation Fidelity

The accepted model of eukaryotic translation initiation begins with the scanning of the transcript by the pre-initiation complex from the 5′end until an ATG codon with a specific nucleotide (nt) context surrounding it is recognized (Kozak rule). According to this model, ATG codons upstream to the beginning of the ORF should affect translation. We perform for the first time, a genome-wide statistical analysis, uncovering a new, more comprehensive and quantitative, set of initiation rules for improving the cost of translation and its efficiency. Analyzing dozens of eukaryotic genomes, we find that in all frames there is a universal trend of selection for low numbers of ATG codons; specifically, 16–27 codons upstream, but also 5–11 codons downstream of the START ATG, include less ATG codons than expected. We further suggest that there is selection for anti optimal ATG contexts in the vicinity of the START ATG. Thus, the efficiency and fidelity of translation initiation is encoded in the 5′UTR as required by the scanning model, but also at the beginning of the ORF. The observed nt patterns suggest that in all the analyzed organisms the pre-initiation complex often misses the START ATG of the ORF, and may start translation from an alternative initiation start-site. Thus, to prevent the translation of undesired proteins, there is selection for nucleotide sequences with low affinity to the pre-initiation complex near the beginning of the ORF. With the new suggested rules we were able to obtain a twice higher correlation with ribosomal density and protein levels in comparison to the Kozak rule alone (e.g. for protein levels r = 0.7 vs. r = 0.31; p<10−12).

ANAT: A Tool for Constructing and Analyzing Functional Protein Networks

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins. It encompasses a number of network inference algorithms and provides access to networks of physical associations in several organisms. In contrast to existing software tools, ANAT can be used to infer subnetworks that connect hundreds of proteins to each other or to a given set of “anchor” proteins, a fundamental step in reconstructing cellular subnetworks. The interactive component of ANAT provides an array of tools for evaluating and exploring the resulting subnetwork models and for iteratively refining them. We demonstrate the utility of ANAT by studying the crosstalk between the autophagic and apoptotic cell death modules in humans, using a network of physical interactions. Relative to published software tools, ANAT is more accurate and provides more features for comprehensive network analysis. The latest version of the software is available at http://www.cs.tau.ac.il/~bnet/ANAT_SI.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants

Deducing generic causal relations between RNA transcript features and protein expression profiles from endogenous gene expression data remains a major unsolved problem in biology. The analysis of gene expression from heterologous genes contributes significantly to solving this problem, but has been heavily biased toward the study of the effect of 5′ transcript regions and to prokaryotes. Here, we employ a synthetic biology driven approach that systematically differentiates the effect of different regions of the transcript on gene expression up to 240 nucleotides into the ORF. This enabled us to discover new causal effects between features in previously unexplored regions of transcripts, and gene expression in natural regimes. We rationally designed, constructed, and analyzed 383 gene variants of the viral HRSVgp04 gene ORF, with multiple synonymous mutations at key positions along the transcript in the eukaryote S. cerevisiae. Our results show that a few silent mutations at the 5′UTR can have a dramatic effect of up to 15 fold change on protein levels, and that even synonymous mutations in positions more than 120 nucleotides downstream from the ORF 5′end can modulate protein levels up to 160%–300%. We demonstrate that the correlation between protein levels and folding energy increases with the significance of the level of selection of the latter in endogenous genes, reinforcing the notion that selection for folding strength in different parts of the ORF is related to translation regulation. Our measured protein abundance correlates notably(correlation up to r = 0.62 (p=0.0013)) with mean relative codon decoding times, based on ribosomal densities (Ribo-Seq) in endogenous genes, supporting the conjecture that translation elongation and adaptation to the tRNA pool can modify protein levels in a causal/direct manner. This report provides an improved understanding of transcript evolution, design principles of gene expression regulation, and suggests simple rules for engineering synthetic gene expression in eukaryotes.

Alternative Transcription Initiation and the AUG Context Configuration Control Dual-Organellar Targeting and Functional Competence of Arabidopsis Lon1 Protease

Cellular homeostasis relies on components of protein quality control including chaperones and proteases. In bacteria and eukaryotic organelles, Lon proteases play a critical role in removing irreparably damaged proteins and thereby preventing the accumulation of deleterious degradation-resistant aggregates. Gene expression, live-cell imaging, immunobiochemical, and functional complementation approaches provide conclusive evidence for Lon1 dual-targeting to chloroplasts and mitochondria. Dual-organellar deposition of Lon1 isoforms depends on both transcriptional regulation and alternative translation initiation via leaky ribosome scanning from the first AUG sequence context that deviates extensively from the optimum Kozak consensus. Organelle-specific Lon1 targeting results in partial complementation of Arabidopsis lon1-1 mutants, whereas full complementation is solely accomplished by dual-organellar targeting. Both the optimal and non-optimal AUG sequence contexts are functional in yeast and facilitate leaky ribosome scanning complementing the pim1 phenotype when the mitochondrial presequence is used. Bioinformatic search identified a limited number of Arabidopsis genes with Lon1-type dual-targeting sequence organization. Lon4, the paralog of Lon1, has an ambiguous presequence likely evolved from the twin presequences of an ancestral Lon1-like gene, generating a single dual-targeted protein isoform. We postulate that Lon1 and its subfunctional paralog Lon4 evolved complementary subsets of transcriptional and posttranscriptional regulatory components responsive to environmental cues for dual-organellar targeting.

Complementary Post Transcriptional Regulatory Information is Detected by PUNCH-P and Ribosome Profiling

Two novel approaches were recently suggested for genome-wide identification of protein aspects synthesized at a given time. Ribo-Seq is based on sequencing all the ribosome protected mRNA fragments in a cell, while PUNCH-P is based on mass-spectrometric analysis of only newly synthesized proteins. Here we describe the first Ribo-Seq/PUNCH-P comparison via the analysis of mammalian cells during the cell-cycle for detecting relevant differentially expressed genes between G1 and M phase. Our analyses suggest that the two approaches significantly overlap with each other. However, we demonstrate that there are biologically meaningful proteins/genes that can be detected to be post-transcriptionally regulated during the mammalian cell cycle only by each of the approaches, or their consolidation. Such gene sets are enriched with proteins known to be related to intra-cellular signalling pathways such as central cell cycle processes, central gene expression regulation processes, processes related to chromosome segregation, DNA damage, and replication, that are post-transcriptionally regulated during the mammalian cell cycle. Moreover, we show that combining the approaches better predicts steady state changes in protein abundance. The results reported here support the conjecture that for gaining a full post-transcriptional regulation picture one should integrate the two approaches.

Exploiting Hidden Information Interleaved in the Redundancy of the Genetic Code without Prior Knowledge

MOTIVATION Dozens of studies in recent years have demonstrated that codon usage encodes various aspects related to all stages of gene expression regulation. When relevant high-quality large-scale gene expression data are available, it is possible to statistically infer and model these signals, enabling analysing and engineering gene expression. However, when these data are not available, it is impossible to infer and validate such models. RESULTS In this current study, we suggest Chimera-an unsupervised computationally efficient approach for exploiting hidden high-dimensional information related to the way gene expression is encoded in the open reading frame (ORF), based solely on the genome of the analysed organism. One version of the approach, named Chimera Average Repetitive Substring (ChimeraARS), estimates the adaptability of an ORF to the intracellular gene expression machinery of a genome (host), by computing its tendency to include long substrings that appear in its coding sequences; the second version, named ChimeraMap, engineers the codons of a protein such that it will include long substrings of codons that appear in the host coding sequences, improving its adaptation to a new hosts gene expression machinery. We demonstrate the applicability of the new approach for analysing and engineering heterologous genes and for analysing endogenous genes. Specifically, focusing on Escherichia coli, we show that it can exploit information that cannot be detected by conventional approaches (e.g. the CAI-Codon Adaptation Index), which only consider single codon distributions; for example, we report correlations of up to 0.67 for the ChimeraARS measure with heterologous gene expression, when the CAI yielded no correlation. AVAILABILITY AND IMPLEMENTATION For non-commercial purposes, the code of the Chimera approach can be downloaded from http://www.cs.tau.ac.il/∼tamirtul/Chimera/download.htm. CONTACT tamirtul@post.tau.ac.il SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Mapping the translation initiation landscape of an S. cerevisiae gene using fluorescent proteins.

Accurate and efficient gene expression requires that protein translation initiates from mRNA transcripts with high fidelity. At the same time, indiscriminate initiation of translation from multiple ATG start-sites per transcript has been demonstrated, raising fundamental questions regarding the rate and rationale governing alternative translation initiation. We devised a sensitive fluorescent reporter assay for monitoring alternative translation initiation. To demonstrate it, we map the translation initiation landscape of a Saccharomyces cerevisiae gene (RMD1) with a typical ATG sequence context profile. We found that up to 3%-5% of translation initiation events occur from alternative out-of-frame start codons downstream of the main ATG. Initiation from these codons follows the ribosome scanning model: initiation rates from different start sites are determined by ATG order, rather than their context strength. Genomic analysis of S. cerevisiae further supports the scanning model: ATG codons downstream rather than upstream of the main ATG tend to have higher context scores.