A quantitative model for mRNA translation in Saccharomyces cerevisiae

Inbuilt Tendency of the eIF2 Regulatory System to Counteract Uncertainties

Eukaryotic initiation factor 2 (eIF2) plays a fundamental role in the regulation of protein synthesis. Investigations have revealed that the regulation of eIF2 is robust against intrinsic uncertainties and is able to efficiently counteract them. The robustness properties of the eIF2 pathway against intrinsic disturbances is also well known. However the reasons for this ability to counteract stresses is less well understood. In this article, the robustness conferring properties of the eIF2 dependent regulatory system is explored with the help of a mathematical model. The novelty of the work presented in this article lies in articulating the possible reason behind the inbuilt robustness of the highly engineered eIF2 system against intrinsic perturbations. Our investigations reveal that the robust nature of the eIF2 pathway may originate from the existence of an attractive natural sliding surface within the system satisfying reaching and sliding conditions that are well established in the domain of control engineering.

Modeling and Dynamic Behavior of eIF2 Dependent Regulatory System With Disturbances

Eukaryotic initiation factor 2 (eIF2) is a central controller of the eukaryotic translational machinery. To sustain the on-going translation activity, eIF2 cycles between its GTP and GDP bound states. However, in response to cellular stresses, the phosphorylation of eIF2 takes place, which acts as an inhibitor of the guanine nucleotide exchange factor eIF2B and switches the translation activity on physiological timescales. The main objective of this paper is to investigate the stability of the regulatory system under nominal conditions, parametric fluctuations, and structural damages. In this paper, a mathematical model of eIF2-dependent regulatory system is used to identify the stability-conferring features within the system with the help of direct and indirect methods of Lyapunov stability theory. To investigate the impact of intrinsic fluctuations and structural damages on the stability of regulatory system, the mathematical model has been linearized around feasible equilibrium point and the variation of system poles has been observed. The investigations have revealed that the regulatory model is stable and able to tolerate the intrinsic stressors but becomes unstable when particular complex is targeted to override the undesirable interaction. Our analyses indicate that, the stability is a collective property and damage in the structure of the system changes the stability of the system.

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.

Multilevel Regulation and Translational Switches in Synthetic Biology

In contrast to the versatility of regulatory mechanisms in natural systems, synthetic genetic circuits have been so far predominantly composed of transcriptionally regulated modules. This is about to change as the repertoire of foundational tools for post-transcriptional regulation is quickly expanding. We provide an overview of the different types of translational regulators: protein, small molecule and ribonucleic acid (RNA) responsive and we describe the new emerging circuit designs utilizing these tools. There are several advantages of achieving multilevel regulation via translational switches and it is likely that such designs will have the greatest and earliest impact in mammalian synthetic biology for regenerative medicine and gene therapy applications.

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.

Specialized yeast ribosomes: a customized tool for selective mRNA translation

Evidence is now accumulating that sub-populations of ribosomes - so-called specialized ribosomes - can favour the translation of subsets of mRNAs. Here we use a large collection of diploid yeast strains, each deficient in one or other copy of the set of ribosomal protein (RP) genes, to generate eukaryotic cells carrying distinct populations of altered ‘specialized’ ribosomes. We show by comparative protein synthesis assays that different heterologous mRNA reporters based on luciferase are preferentially translated by distinct populations of specialized ribosomes. These mRNAs include reporters carrying premature termination codons (PTC) thus allowing us to identify specialized ribosomes that alter the efficiency of translation termination leading to enhanced synthesis of the wild-type protein. This finding suggests that these strains can be used to identify novel therapeutic targets in the ribosome. To explore this further we examined the translation of the mRNA encoding the extracellular matrix protein laminin β3 (LAMB3) since a LAMB3-PTC mutant is implicated in the blistering skin disease Epidermolysis bullosa (EB). This screen identified specialized ribosomes with reduced levels of RP L35B as showing enhanced synthesis of full-length LAMB3 in cells expressing the LAMB3-PTC mutant. Importantly, the RP L35B sub-population of specialized ribosomes leave both translation of a reporter luciferase carrying a different PTC and bulk mRNA translation largely unaltered.

The architecture of eukaryotic translation

Translation in baker’s yeast involves the coordinated interaction of 200 000 ribosomes, 3 000 000 tRNAs and between 15 000 and 60 000 mRNAs. It is currently unknown whether this specific constellation of components has particular relevance for the requirements of the yeast proteome, or whether this is simply a frozen accident. Our study uses a computational simulation model of the genome-wide translational apparatus of yeast to explore quantitatively which combinations of mRNAs, ribosomes and tRNAs can produce viable proteomes. Surprisingly, we find that if we only consider total translational activity over time without regard to composition of the proteome, then there are many and widely differing combinations that can generate equivalent synthesis yields. In contrast, translational activity required for generating specific proteomes can only be achieved within a much more constrained parameter space. Furthermore, we find that strongly ribosome limited regimes are optimal for cells in that they are resource efficient and simplify the dynamics of the system.

Hidden coding potential of eukaryotic genomes: nonAUG started ORFs

It is widely considered that the vast majority of eukaryotic mRNAs contain only one open reading frame (ORF) and encode single protein. However, eukaryotic ribosomes can initiate translation at alternative start codons due to leaky scanning or reinitiation mechanisms that provides an opportunity to synthesize several protein products. Recent investigations also demonstrated that alternative translation from nonAUG start codons and AUG codons in a weak nucleotide context could make an important contribution to eukaryotic proteomes. However, accurate prediction of alternative start codons demands detailed investigation of mRNA features influencing their recognition by eukaryotic ribosomes. In this work, we present the results of computational analysis of characteristics of yeast and mammalian mRNAs potentially involved in the recognition of nonAUG start codons. It was found that sequence features of nonAUG started Saccharomyces cerevisiae upstream ORFs (uORFs) were adjusted for efficient translation and these uORFs could frequently encode functional polypeptides. In particular, our initial studies revealed that predicted tertiary structures downstream of nonAUG start sites in mammalian mRNAs were energetically more stable than those predicted for AUG start sites with strong Kozak context. We hypothesize that presence of such stable tertiary structure downstream of nonAUG start sites could be an important factor for the ribosome to recognize noncanonical start codons.

A mechanistic overview of translation initiation in eukaryotes

Translation initiation in eukaryotes is a complex and highly regulated process requiring the action of at least 12 protein factors. The pathway is distinguished by the formation of a pre-initiation complex that recruits the 5' end of the mRNA and scans along it to locate the start codon. During the past decade, a combination of genetics, biochemistry and structural studies has begun to illuminate key molecular events in this critical phase of gene expression. Here, we outline our current understanding of eukaryotic translation initiation and discuss important outstanding challenges.

Mathematical and Computational Modelling of Ribosomal Movement and Protein Synthesis: an overview

Translation or protein synthesis consists of a complex system of chemical reactions, which ultimately result in decoding of the mRNA and the production of a protein. The complexity of this reaction system makes it difficult to quantitatively connect its input parameters (such as translation factor or ribosome concentrations, codon composition of the mRNA, or energy availability) to output parameters (such as protein synthesis rates or ribosome densities on mRNAs). Mathematical and computational models of translation have now been used for nearly five decades to investigate translation, and to shed light on the relationship between the different reactions in the system. This review gives an overview over the principal approaches used in the modelling efforts, and summarises some of the major findings that were made.

A systems view of the protein expression process

Many biological processes are regulated by changing the concentration and activity of proteins. The presence of a protein at a given subcellular location at a given time with a certain conformation is the result of an apparently sequential process. The rate of protein formation is influenced by chromatin state, and the rates of transcription, translation, and degradation. There is an exquisite control system where each stage of the process is controlled both by seemingly unregulated proteins as well as through feedbacks mediated by RNA and protein products. Here we review the biological facts and mathematical models for each stage of the protein production process. We conclude that advances in experimental techniques leading to a detailed description of the process have not been matched by mathematical models that represent the details of the process and facilitate analysis. Such an exercise is the first step towards development of a framework for a systems biology analysis of the protein production process.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-011-9088-1) contains supplementary material, which is available to authorized users.

Analysing GCN4 translational control in yeast by stochastic chemical kinetics modelling and simulation

Background

The yeast Saccharomyces cerevisiae responds to amino acid starvation by inducing the transcription factor Gcn4. This is mainly mediated via a translational control mechanism dependent upon the translation initiation eIF2·GTP·Met-tRNA_i^Met ternary complex, and the four short upstream open reading frames (uORFs) in its 5' mRNA leader. These uORFs act to attenuate GCN4 mRNA translation under normal conditions. During amino acid starvation, levels of ternary complex are reduced. This overcomes the GCN4 translation attenuation effect via a scanning/reinitiation control mechanism dependent upon uORF spacing.

Results

Using published experimental data, we have developed and validated a probabilistic formulation of GCN4 translation using the Chemical Master Equation (Model 1). Model 1 explains GCN4 translation's nonlinear dependency upon uORF placements, and predicts that an as yet unidentified factor, which was proposed to regulate GCN4 translation under some conditions, only has pronounced effects upon GCN4 translation when intercistronic distances are unnaturally short. A simpler Model 2 that does not include this unidentified factor could well represent the regulation of a natural GCN4 mRNA. Using parameter values optimised for this algebraic Model 2, we performed stochastic simulations by Gillespie algorithm to investigate the distribution of ribosomes in different sections of GCN4 mRNA under distinct conditions. Our simulations demonstrated that ribosomal loading in the 5'-untranslated region is mainly determined by the ratio between the rates of 5'-initiation and ribosome scanning, but was not significantly affected by rate of ternary complex binding. Importantly, the translation rate for codons starved of cognate tRNAs is predicted to be the most significant contributor to the changes in ribosomal loading in the coding region under repressing and derepressing conditions.

Conclusions

Our integrated probabilistic Models 1 and 2 explained GCN4 translation and helped to elucidate the role of a yet unidentified factor. The ensuing stochastic simulations evaluated different factors that may impact on the translation of GCN4 mRNA, and integrated translation status with ribosomal density.