Faculty Bibliography

Effective translation of the viral genome during the infection cycle most likely enhances its fitness. In this study, we reveal two different strategies employed by cyanophages, viruses infecting cyanobacteria, to enhance their translation efficiency. Cyanophages of the T7-like Podoviridae family adjust their GC content and codon usage to those of their hosts. In contrast, cyanophages of the T4-like Myoviridae family maintain genomes with low GC content, thus sometimes differing from that of their hosts. By introducing their own specific set of tRNAs, they appear to modulate the tRNA pools of hosts with tRNAs that fit the viral low GC preferred codons. We assessed the possible effects of those viral tRNAs on cyanophages and cyanobacterial genomes using the tRNA adaptation index, which measures the extent to which a given pool of tRNAs translates efficiently particular genes. We found a strong selective pressure to gain and maintain tRNAs that will boost translation of myoviral genes when infecting a high GC host, contrasted by a negligible effect on the host genes. Thus, myoviral tRNAs may represent an adaptive strategy to enhance fitness when infecting high GC hosts, thereby potentially broadening the spectrum of hosts while alleviating the need to adjust global parameters such as GC content for each specific host.

MicroRNAs (miRs) are considered major contributors to the evolution of animal morphological complexity. Multiple bursts of novel miR families were documented throughout animal evolution, yet, their evolutionary origins are not understood. Here, we discuss two alternative genomic sources for novel miR families, namely, transposable elements, which were previously described, and a newly proposed origin: CpG islands. We show that these two origins are evolutionarily distinct and that they correspond to marked differences in several functional and genomic characteristics. Together, our results shed light on the intriguing origin of one of the major constituents of regulatory networks in animals, miRs.

Survival in natural habitats selects for microorganisms that are well-adapted to a wide range of conditions. Recent studies revealed that cells evolved innovative response strategies that extend beyond merely sensing a given stimulus and responding to it on encounter. A diversity of microorganisms, including Escherichia coli, Vibrio cholerae, and several yeast species, were shown to use a predictive regulation strategy that uses the appearance of one stimulus as a cue for the likely arrival of a subsequent one. A better understanding of such a predictive strategy requires elucidating the interplay between key biological and environmental forces. Here, we describe a mathematical framework to address this challenge. We base this framework on experimental systems featuring early preparation to either a stress or an exposure to improvement in the growth medium. Our model calculates the fitness advantage originating under each regulation strategy in a given habitat. We conclude that, although a predictive response strategy might by advantageous under some ecologies, its costs might exceed the benefit in others. The combined theoretical-experimental treatment presented here helps assess the potential of natural ecologies to support a predictive behavior.

Proper functioning of biological cells requires that the process of protein expression be carried out with high efficiency and fidelity. Given an amino-acid sequence of a protein, multiple degrees of freedom still remain that may allow evolution to tune efficiency and fidelity for each gene under various conditions and cell types. Particularly, the redundancy of the genetic code allows the choice between alternative codons for the same amino acid, which, although 'synonymous,' may exert dramatic effects on the process of translation. Here we review modern developments in genomics and systems biology that have revolutionized our understanding of the multiple means by which translation is regulated. We suggest new means to model the process of translation in a richer framework that will incorporate information about gene sequences, the tRNA pool of the organism and the thermodynamic stability of the mRNA transcripts. A practical demonstration of a better understanding of the process would be a more accurate prediction of the proteome, given the transcriptome at a diversity of biological conditions.

Genetic regulatory circuits are often regarded as precise machines that accurately determine the level of expression of each protein. Most experimental technologies used to measure gene expression levels are incapable of testing and challenging this notion, as they often measure levels averaged over entire populations of cells. Yet, when expression levels are measured at the single cell level of even genetically identical cells, substantial cell-to-cell variation (or "noise") may be observed. Sometimes different genes in a given genome may display different levels of noise; even the same gene, expressed under different environmental conditions, may display greater cell-to-cell variability in specific conditions and more tight control in other situations. While at first glance noise may seem to be an undesired property of biological networks, it might be beneficial in some cases. For instance, noise will increase functional heterogeneity in a population of microorganisms facing variable, often unpredictable, environmental changes, increasing the probability that some cells may survive the stress. In that respect, we can speculate that the population is implementing a risk distribution strategy, long before genetic heterogeneity could be acquired. Organisms may have evolved to regulate not only the averaged gene expression levels but also the extent of allowed deviations from such an average, setting it at the desired level for every gene under each specific condition. Here we review the evolving understanding of noise, its molecular underpinnings, and its effect on phenotype and fitness--when it can be detrimental, beneficial, or neutral and which regulatory tools eukaryotic cells may use to optimally control it.

BACKGROUND:Translation efficiency is affected by a diversity of parameters, including secondary structure of the transcript and its codon usage. Here we examine the effects of codon usage on translation efficiency by re-analysis of previously constructed synthetic expression libraries in Escherichia coli. RESULTS:We define the region in a gene that takes the longest time to translate as the bottleneck. We found that localization of the bottleneck at the beginning of a transcript promoted a high level of expression, especially if the computed dwell time of the ribosome within this region was sufficiently long. The location and translation time of the bottleneck were not correlated with the cost of expression, approximated by the fitness of the host cell, yet utilization of specific codons was. Particularly, enhanced usage of the codons UCA and CAU was correlated with increased cost of production, potentially due to sequestration of their corresponding rare tRNAs. CONCLUSIONS:The distribution of codons along the genes appears to affect translation efficiency, consistent with analysis of natural genes. This study demonstrates how synthetic biology complements bioinformatics by providing a set-up for well controlled experiments in biology.

Retrograde signaling from axon to soma activates intrinsic regeneration mechanisms in lesioned peripheral sensory neurons; however, the links between axonal injury signaling and the cell body response are not well understood. Here, we used phosphoproteomics and microarrays to implicate approximately 900 phosphoproteins in retrograde injury signaling in rat sciatic nerve axons in vivo and approximately 4500 transcripts in the in vivo response to injury in the dorsal root ganglia. Computational analyses of these data sets identified approximately 400 redundant axonal signaling networks connected to 39 transcription factors implicated in the sensory neuron response to axonal injury. Experimental perturbation of individual overrepresented signaling hub proteins, including Abl, AKT, p38, and protein kinase C, affected neurite outgrowth in sensory neurons. Paradoxically, however, combined perturbation of Abl together with other hub proteins had a reduced effect relative to perturbation of individual proteins. Our data indicate that nerve injury responses are controlled by multiple regulatory components, and suggest that network redundancies provide robustness to the injury response.

Epidermal growth factor (EGF) stimulates cells by launching gene expression programs that are frequently deregulated in cancer. MicroRNAs, which attenuate gene expression by binding complementary regions in messenger RNAs, are broadly implicated in cancer. Using genome-wide approaches, we showed that EGF stimulation initiates a coordinated transcriptional program of microRNAs and transcription factors. The earliest event involved a decrease in the abundance of a subset of 23 microRNAs. This step permitted rapid induction of oncogenic transcription factors, such as c-FOS, encoded by immediate early genes. In line with roles as suppressors of EGF receptor (EGFR) signaling, we report that the abundance of this early subset of microRNAs is decreased in breast and in brain tumors driven by the EGFR or the closely related HER2. These findings identify specific microRNAs as attenuators of growth factor signaling and oncogenesis.

MicroRNAs (miRNAs) appear to be key players in the maintenance of genomic integrity. Recent evidence implies that cancers often avoid miRNA-mediated regulation, and global repression of miRNAs is associated with increased tumorigenicity. Here we suggest that miRNAs are directly involved in the maintenance of genomic integrity through global repression of transposable elements (TEs), whose expression and transposition are well-documented causes of genomic instability in mammalian somatic tissues. Hence, one outcome of the tumor's ability to avoid miRNA-mediated regulation might be the enhancement of genomic instability and mutability due to derepression of TEs. We outline possible mechanisms underlying TE repression by miRNAs, including post-transcriptional silencing and transcriptional silencing through DNA and histone methylation. This hypothesis calls into consideration the need to study the role of miRNAs and the RNAi machinery in the nucleus, and specifically their impact on the maintenance of genomic integrity in the context of cancer.

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.