Regulation of release factor expression using a translational negative feedback loop: a systems analysis

Gene Amplification as a Mechanism of Yeast Adaptation to Nonsense Mutations in Release Factor Genes

Protein synthesis (translation) is one of the fundamental processes occurring in the cells of living organisms. Translation can be divided into three key steps: initiation, elongation, and termination. In the yeast Saccharomyces cerevisiae, there are two translation termination factors, eRF1 and eRF3. These factors are encoded by the SUP45 and SUP35 genes, which are essential; deletion of any of them leads to the death of yeast cells. However, viable strains with nonsense mutations in both the SUP35 and SUP45 genes were previously obtained in several groups. The survival of such mutants clearly involves feedback control of premature stop codon readthrough; however, the exact molecular basis of such feedback control remain unclear. To investigate the genetic factors supporting the viability of these SUP35 and SUP45 nonsense mutants, we performed whole-genome sequencing of strains carrying mutant sup35-n and sup45-n alleles; while no common SNPs or indels were found in these genomes, we discovered a systematic increase in the copy number of the plasmids carrying mutant sup35-n and sup45-n alleles. We used the qPCR method which confirmed the differences in the relative number of SUP35 and SUP45 gene copies between strains carrying wild-type or mutant alleles of SUP35 and SUP45 genes. Moreover, we compare the number of copies of the SUP35 and SUP45 genes in strains carrying different nonsense mutant variants of these genes as a single chromosomal copy. qPCR results indicate that the number of mutant gene copies is increased compared to the wild-type control. In case of several sup45-n alleles, this was due to a disomy of the entire chromosome II, while for the sup35-218 mutation we observed a local duplication of a segment of chromosome IV containing the SUP35 gene. Taken together, our results indicate that gene amplification is a common mechanism of adaptation to nonsense mutations in release factor genes in yeast.

Translational autoregulation of RF2 protein in E. coli through programmed frameshifting

Various feedback mechanisms regulate the expression of different genes to ensure the required protein levels inside a cell. In this paper, we develop a kinetic model for one such mechanism that autoregulates RF2 protein synthesis in E. coli through programmed frameshifting. The model finds that the programmed frameshifting autoregulates RF2 protein synthesis by two independent mechanisms. First, it increases the rate of RF2 synthesis from each mRNA transcript at low RF2 concentration. Second, programmed frameshifting can dramatically increase the lifetime of RF2 transcripts when RF2 protein levels are lower than a threshold. This sharp increase in mRNA lifetime is caused by a first-order phase transition from a low to a high ribosome density on an RF2 transcript. The high ribosome density prevents the transcript's degradation by shielding it from nucleases, which increases its average lifetime and hence RF2 protein levels. Our study identifies this quality control mechanism that regulates the cellular protein levels by breaking the hierarchy of processes involved in gene expression.

Expression of the translation termination factor eRF1 is autoregulated by translational readthrough and 3'UTR intron-mediated NMD in Neurospora crassa

Eukaryotic release factor 1 (eRF1) is a translation termination factor that binds to the ribosome at stop codons. The expression of eRF1 is strictly controlled, since its concentration defines termination efficiency and frequency of translational readthrough. Here, we show that eRF1 expression in Neurospora crassa is controlled by an autoregulatory circuit that depends on the specific 3'UTR structure of erf1 mRNA. The stop codon context of erf1 promotes readthrough that protects the mRNA from its 3'UTR-induced nonsense-mediated mRNA decay (NMD). High eRF1 concentration leads to inefficient readthrough, thereby allowing NMD-mediated erf1 degradation. We propose that eRF1 expression is controlled by similar autoregulatory circuits in many fungi and seed plants and discuss the evolution of autoregulatory systems of different translation termination factors.

Nonsense Mutations in the Yeast SUP35 Gene Affect the [PSI+] Prion Propagation

The essential SUP35 gene encodes yeast translation termination factor eRF3. Previously, we isolated nonsense mutations sup35-n and proposed that the viability of such mutants can be explained by readthrough of the premature stop codon. Such mutations, as well as the prion [PSI+], can appear in natural yeast populations, and their combinations may have different effects on the cells. Here, we analyze the effects of the compatibility of sup35-n mutations with the [PSI+] prion in haploid and diploid cells. We demonstrated that sup35-n mutations are incompatible with the [PSI+] prion, leading to lethality of sup35-n [PSI+] haploid cells. In diploid cells the compatibility of [PSI+] with sup35-n depends on how the corresponding diploid was obtained. Nonsense mutations sup35-21, sup35-74, and sup35-218 are compatible with the [PSI+] prion in diploid strains, but affect [PSI+] properties and lead to the formation of new prion variant. The only mutation that could replace the SUP35 wild-type allele in both haploid and diploid [PSI+] strains, sup35-240, led to the prion loss. Possibly, short Sup351-55 protein, produced from the sup35-240 allele, is included in Sup35 aggregates and destabilize them. Alternatively, single molecules of Sup351-55 can stick to aggregate ends, and thus interrupt the fibril growth. Thus, we can conclude that sup35-240 mutation prevents [PSI+] propagation and can be considered as a new pnm mutation.

Origins of robustness in translational control via eukaryotic translation initiation factor (eIF) 2

Phosphorylation of eukaryotic translation initiation factor 2 (eIF2) is one of the best studied and most widely used means for regulating protein synthesis activity in eukaryotic cells. This pathway regulates protein synthesis in response to stresses, viral infections, and nutrient depletion, among others. We present analyses of an ordinary differential equation-based model of this pathway, which aim to identify its principal robustness-conferring features. Our analyses indicate that robustness is a distributed property, rather than arising from the properties of any one individual pathway species. However, robustness-conferring properties are unevenly distributed between the different species, and we identify a guanine nucleotide dissociation inhibitor (GDI) complex as a species that likely contributes strongly to the robustness of the pathway. Our analyses make further predictions on the dynamic response to different types of kinases that impinge on eIF2.

Probabilistic Boolean Network Modelling and Analysis Framework for mRNA Translation

mRNA translation is a complex process involving the progression of ribosomes on the mRNA, resulting in the synthesis of proteins, and is subject to multiple layers of regulation. This process has been modelled using different formalisms, both stochastic and deterministic. Recently, we introduced a Probabilistic Boolean modelling framework for mRNA translation, which possesses the advantage of tools for numerically exact computation of steady state probability distribution, without requiring simulation. Here, we extend this model to incorporate both random sequential and parallel update rules, and demonstrate its effectiveness in various settings, including its flexibility in accommodating additional static and dynamic biological complexities and its role in parameter sensitivity analysis. In these applications, the results from the model analysis match those of TASEP model simulations. Importantly, the proposed modelling framework maintains the stochastic aspects of mRNA translation and provides a way to exactly calculate probability distributions, providing additional tools of analysis in this context. Finally, the proposed modelling methodology provides an alternative approach to the understanding of the mRNA translation process, by bridging the gap between existing approaches, providing new analysis tools, and contributing to a more robust platform for modelling and understanding translation.

mRNA translation and protein synthesis: an analysis of different modelling methodologies and a new PBN based approach

Background

mRNA translation involves simultaneous movement of multiple ribosomes on the mRNA and is also subject to regulatory mechanisms at different stages. Translation can be described by various codon-based models, including ODE, TASEP, and Petri net models. Although such models have been extensively used, the overlap and differences between these models and the implications of the assumptions of each model has not been systematically elucidated. The selection of the most appropriate modelling framework, and the most appropriate way to develop coarse-grained/fine-grained models in different contexts is not clear.

Results

We systematically analyze and compare how different modelling methodologies can be used to describe translation. We define various statistically equivalent codon-based simulation algorithms and analyze the importance of the update rule in determining the steady state, an aspect often neglected. Then a novel probabilistic Boolean network (PBN) model is proposed for modelling translation, which enjoys an exact numerical solution. This solution matches those of numerical simulation from other methods and acts as a complementary tool to analytical approximations and simulations. The advantages and limitations of various codon-based models are compared, and illustrated by examples with real biological complexities such as slow codons, premature termination and feedback regulation. Our studies reveal that while different models gives broadly similiar trends in many cases, important differences also arise and can be clearly seen, in the dependence of the translation rate on different parameters. Furthermore, the update rule affects the steady state solution.

Conclusions

The codon-based models are based on different levels of abstraction. Our analysis suggests that a multiple model approach to understanding translation allows one to ascertain which aspects of the conclusions are robust with respect to the choice of modelling methodology, and when (and why) important differences may arise. This approach also allows for an optimal use of analysis tools, which is especially important when additional complexities or regulatory mechanisms are included. This approach can provide a robust platform for dissecting translation, and results in an improved predictive framework for applications in systems and synthetic biology.

The translational machinery is an optimized molecular network that affects cellular homoeostasis and disease

Translation involves interactions between mRNAs, ribosomes, tRNAs and a host of translation factors. Emerging evidence on the eukaryotic translational machinery indicates that these factors are organized in a highly optimized network, in which the levels of the different factors are finely matched to each other. This optimal factor network is essential for producing proteomes that result in optimal fitness, and perturbations to the optimal network that significantly affect translational activity therefore result in non-optimal proteomes, fitness losses and disease. On the other hand, experimental evidence indicates that translation and cell growth are relatively robust to perturbations, and viability can be maintained even upon significant damage to individual translation factors. How the eukaryotic translational machinery is optimized, and how it can maintain optimization in the face of changing internal parameters, are open questions relevant to the interaction between translation and cellular disease states.