Deciphering Translation - a Genome-Wide Analysis of Translation Limitations and Competitions in Saccharomyces cerevisiae

Protein synthesis is one of the central elements in every living cell. For this process, the mRNAs coding for genes are simultaneously competing for the same translation machinery, i.e. for the same ribosomes and the same protein building blocks, the amino acids. But the ultimate determinants of cellular functions are the proteins, it is therefore of crucial importance to understand well this translation process. Nowadays, it is possible to measure the changes in the levels of all mRNAs and proteins in the cell, under different environmental and genetic perturbations. The large amount of data from transcriptomic and proteomic studies requires development of efficient systems-wide mathematical and computational frameworks to analyze and interpret the data. For this purpose, we developed a genome-wide, mechanistic, mathematical model for translation in S. cerevisiae. This model includes information on the translation state of each mRNA (i.e. polysome size), and mRNA length, and can be used to estimate genome-scale kinetics of the protein synthesis machinery. It is then used to quantify the rate limiting steps of the translation process and gain insights into the mechanism of regulation of expression between different proteins. This framework allows us therefore to quantify the effect of a change in the total number of ribosomes, or also the effect of varying the level of a single mRNA on the various proteins. This information is then used to determine functional couplings in the yeast genome. We find that while 95% of the mRNA species are initiation and elongation limited, approximately 5% are elongation limited and have higher specific protein synthesis rates. We also observed that the mRNAs with ribosomal coverage greater than 0.8 display a decrease in the specific protein production rate with increasing coverage, and can be acting as sinks for system resources like ribosomes. It was also possible to determine which mRNA species have the most / least influence on the other, and we found that the most influential species are linked to proteins which form structural constituents of ribosomes, proteins related to translation elongation factor activity or key enzymes of the central carbon metabolism. Thus a perturbation to these key mRNA species has the maximum impact on protein synthesis from other mRNA species. Our results thus have implications in the understanding of the translation machinery response to perturbations and of the global systems response.

Deciphering translation - a genome-wide analysis of translation in Escherichia coli

Vassily Hatzimanikatis, Julien Racle

Protein synthesis is one of the central elements in every living cell. For this process, mRNAs coding for genes are simultaneously competing for the same translation machinery (ribosomes and amino acids). But the ultimate determinants of cellular functions are the proteins, a good understanding of this process is therefore of crucial importance. For this purpose, we developed a genome-wide, mechanistic, mathematical model of translation, taking into account individual codon kinetics. It is used to quantify the rate limiting steps of translation and gain insight into the mechanism of regulation of expression between different proteins. We observe for each gene in a first low-occupancy phase a nearly linear increase in protein synthesis rate with amount of bound ribosomes to the mRNA, followed by a plateau of maximum synthesis rate. A further increase in the ribosomal coverage of the mRNA leads to a decrease in protein production rate, and might act as sinks for system resources like ribosomes. Native E. coli gene sequences are also compared to randomly obtained synonymous sequences, coding the same protein, and it is found some genes of higher significance for the cell have been better optimized for protein synthesis rate than other.

2010

A Novel Pulse-Chase SILAC Strategy Measures Changes in Protein Decay and Synthesis Rates Induced by Perturbation of Proteostasis with an Hsp90 Inhibitor

Vassily Hatzimanikatis, Céline Hernández, Julien Racle

Standard proteomics methods allow the relative quantitation of levels of thousands of proteins in two or more samples. While such methods are invaluable for defining the variations in protein concentrations which follow the perturbation of a biological system, they do not offer information on the mechanisms underlying such changes. Expanding on previous work [1], we developed a pulse-chase (pc) variant of SILAC (stable isotope labeling by amino acids in cell culture). pcSILAC can quantitate in one experiment and for two conditions the relative levels of proteins newly synthesized in a given time as well as the relative levels of remaining preexisting proteins. We validated the method studying the drug-mediated inhibition of the Hsp90 molecular chaperone, which is known to lead to increased synthesis of stress response proteins as well as the increased decay of Hsp90 “clients”. We showed that pcSILAC can give information on changes in global cellular proteostasis induced by treatment with the inhibitor, which are normally not captured by standard relative quantitation techniques. Furthermore, we have developed a mathematical model and computational framework that uses pcSILAC data to determine degradation constants kd and synthesis rates Vs for proteins in both control and drug-treated cells. The results show that Hsp90 inhibition induced a generalized slowdown of protein synthesis and an increase in protein decay. Treatment with the inhibitor also resulted in widespread protein-specific changes in relative synthesis rates, together with variations in protein decay rates. The latter were more restricted to individual proteins or protein families than the variations in synthesis. Our results establish pcSILAC as a viable workflow for the mechanistic dissection of changes in the proteome which follow perturbations. Data are available via ProteomeXchange with identifier PXD000538.

Public Library of Science2013

Computational methods and modeling of cellular function for the optimization of protein production and the study of cellular disease states

Joana Raquel Pinto Vieira

Systems biology is a multidisciplinary field that weaves together all the basic sciences through the use of computational and bioinformatics tools, to provide a more integrative view of the complex molecular interactions taking place within and among cells. The successes in the development and improvement of techniques for high throughput âomics has rapidly increased the amount of available data. The complexity of the underlying biological system it describes requires the development of tools to properly process it and analyze it. Computational models mathematically describe the systems interactions, allowing intrinsic properties of the data to emerge that would otherwise be overlooked. These models provide context to the data and are used to make predictions about the behavior of the system and to simulate a broader landscape of hypothesis, saving the time and cost of performing numerous experiments. However, the number of required parameters to mathematically formulate the system increases with the model complexity to integrate the available data. Thus, the development of estimation procedures and workflows to retrieve these values from literature and databases become crucial. In this work, we developed a set of computational models, analysis tools, and pipelines to support the study of two biological systems crucial to the cell survival: metabolism and protein synthesis. Metabolism is responsible for the production of most cell biomass, including proteins. Together, these two systems balance the renewal of protein in the cell, where metabolism provides the amino acids obtained from protein breakdown to the mRNA translation machinery. Deregulation of these systems is known to cause multiple disorders, such as neurodegenerative diseases and cancer. In the study of protein synthesis, we employ a combination of deterministic and stochastic modeling approaches to understand its intrinsic mechanistic properties and its rate-limiting steps. A better understanding of the system properties can have a profound impact on the development of drug targets and, in particular, in the optimization of heterologous protein production. Our studies revealed that more than one factor plays a role in the speed of translation: competition for tRNA resources and the type of cognate binding interaction between tRNA and the mRNA-ribosome complex. We also derived an equation that, given the knowledge about certain intracellular parameters pertaining to the host organism of interest, can assist in the design of transcripts for optimizing heterologous protein production. For the study of human metabolism, we established a pipeline to generate tissue-specific reduced metabolic models that can be used to study the metabolic reprogramming of different cancers and compare it with the metabolic phenotype of a healthy cell type. Despite being presented herein for human models, this pipeline is general and can be applied to the models of any organism. Starting from a human genome scale metabolic model, the pipeline improves compound annotation, identification, thermodynamics parameter retrieval, and facilitates data integration through the connection of several compound databases in a semi-automatized fashion. This work sets a standard for metabolic model assessment and curation and improves on existing tools to generate the first thermodynamically feasible reduced model of human metabolism, which is specifically tailored to the physiology and conditions under study.