Maximizing Protein Translation Rate in the Ribosome Flow Model: the Homogeneous Case

Gene translation is the process in which intracellular macro-molecules, called ribosomes, decode genetic information in the mRNA chain into the corresponding proteins. Gene translation includes several steps. During the elongation step, ribosomes move along the mRNA in a sequential manner and link amino-acids together in the corresponding order to produce the proteins. The homogeneous ribosome flow model(HRFM) is a deterministic computational model for translation-elongation under the assumption of constant elongation rates along the mRNA chain. The HRFM is described by a set of n first-order nonlinear ordinary differential equations, where n represents the number of sites along the mRNA chain. The HRFM also includes two positive parameters: ribosomal initiation rate and the (constant) elongation rate. In this paper, we show that the steady-state translation rate in the HRFM is a concave function of its parameters. This means that the problem of determining the parameter values that maximize the translation rate is relatively simple. Our results may contribute to a better understanding of the mechanisms and evolution of translation-elongation. We demonstrate this by using the theoretical results to estimate the initiation rate in M. musculus embryonic stem cell. The underlying assumption is that evolution optimized the translation mechanism. For the infinite-dimensional HRFM, we derive a closed-form solution to the problem of determining the initiation and transition rates that maximize the protein translation rate. We show that these expressions provide good approximations for the optimal values in the n-dimensional HRFM already for relatively small values of n. These results may have applications for synthetic biology where an important problem is to re-engineer genomic systems in order to maximize the protein production rate

A stochastic model for simulating ribosome kinetics in vivo

Computational modelling of in vivo protein synthesis is highly complicated, as it requires the simulation of ribosomal movement over the entire transcriptome, as well as consideration of the concentration effects from 40+ different types of tRNAs and numerous other protein factors. Here I report on the development of a stochastic model for protein translation that is capable of simulating the dynamical process of in vivo protein synthesis in a prokaryotic cell containing several thousand unique mRNA sequences, with explicit nucleotide information for each, and report on a number of biological predictions which are beyond the scope of existing models. In particular, I show that, when the complex network of concentration dependent interactions between elongation factors, tRNAs, ribosomes, and other factors required for protein synthesis are included in full detail, several biological phenomena, such as the increasing peptide elongation rate with bacterial growth rate, are predicted as emergent properties of the model. The stochastic model presented here demonstrates the importance of considering the translational process at this level of detail, and provides a platform to interrogate various aspects of translation that are difficult to study in more coarse-grained models

Novel mRNA-specific effects of ribosome drop-off on translation rate and polysome profile

IS and MCR were supported by the Biotechnology and Biological Sciences Research Council (BBSRC) (http://www.bbsrc.ac.uk) BB/N017161/1. IS was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) (http://www.bbsrc.ac.uk) BB/I020926/1. PB and MCR were supported by the Scottish Universities Life Sciences Alliance (SULSA) (http://www.sulsa.ac.uk). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Development and validation of reagents for understanding tRNA:ribosome interactions one molecule at a time

Translation is responsible for the production of all proteins in a cell making it crucial to the survival of all organisms. Translation involves the decoding of an mRNA by ribosomes and tRNAs. Studying tRNA-ribosome interactions in detail is important for modelling protein synthesis, and this has applications in bioprocessing and in understanding gene regulation in diseases. This project has set out to develop a single molecule technique to image each step of the ribosome:tRNA interaction process. This will enable studying the rate at which translation occurs as well as further define the steps that characterise this process. I have designed and cloned a synthetic DNA sequence which can be used to initiate translation in in vitro reactions. By omitting leucine from the translation reaction, ribosomes can be arrested at a specific leucine codon in a state where a six histidine tag protrudes from the ribosomal exit channel. This should enable immobilisation of translating ribosomes on metal-affinity surfaces. The functionality of the synthetic sequence was demonstrated in a reticulocyte lysate system. A complementary detection system that allows localising individual ribosome:mRNA complexes is currently being developed

Computational Analyses of mRNA Ribosome Loading in Arabidopsis Thaliana

Translation of mRNA into protein is a critical step in gene expression, but the principles guiding its regulation at the genome level are not completely understood. Translation can be quantified at a genome scale by measuring the ribosome loading of mRNA—the extent to which mRNA is associated with ribosomes. In this dissertation, I present investigations into how genome-wide ribosome loading is controlled in Arabidopsis thaliana. In chapter 1, I give an overview of regulation of ribosome loading and translation. In chapter 2, I present research demonstrating for the first time that genome-wide ribosome loading in plants is partially controlled by the circadian clock. In chapter 3, I present a study of a computational model that describes how various biochemical steps control ribosome loading. And in chapter 4, I conclude by briefly summarizing the dissertation as a whole and discussing future perspectives

A Journey Through the Dark: Exploration of Long Non-Coding RNAs and tRNAs in Chinese Hamster Ovary Cells for Recombinant Protein Production

The importance of biotherapeutic proteins for the treatment of various diseases has grown exponentially over the last few decades, and this growth is predicted to continue in the coming years. Among these, monoclonal antibodies (mAbs) represent the largest class for both revenues and new approvals. Chinese hamster ovary cells (CHO) are the leading platform in industry for the production of these complex molecules requiring human-like post-translational modifications, in particular mAbs. Although CHO cells are capable of producing and secreting mAbs at acceptable yields, numerous attempts to increase the maximum viable cell concentration and productivity have been described in literature exploiting modification of culture conditions, changing the genetic makeup of the vector(s) used to drive expression of the gene(s) of interest, and by engineering host cells. The application of non-coding RNAs, such as miRNAs and siRNAs, to reprogram CHO cells has been explored and allows specific targeting of detrimental genes without loading an additional translational burden on the cell. However, Long non-coding RNAs (LncRNAs), non-coding transcripts >200 nucleotides in length have only recently emerged as key regulators of epigenetics, splicing, microRNAs and translation. Despite the potential for applications in cell engineering, these molecules remain largely unexplored in mammalian expression systems. Further, whilst mRNA translation of coding transcripts is a central regulatory step for cell growth, and thus the yield and quality of recombinant proteins, non-coding tRNAs are an important regulatory molecule in the decoding process. Although recombinant gene sequences are often codon optimized, we do not currently have all the information required around tRNA abundance, modifications and tRNA charging to fully harness codon usage in recombinant sequences. The work reported here presents the first lncRNA and tRNA expression landscape in CHO cells under a variety of conditions and discusses the implications of these on recombinant protein production. To investigate lncRNA and tRNAs in CHO cells, a CHO-S cell linegrown under batch and fed-batch culture was sampled at day 4 and 7 of culture while six IgG1-producing CHO cell lines cultivated in an ambr®15 system with different fed strategies were sampled before inoculation and at day 4, 7 and 12 of culture. The whole transcriptomes were investigated using a mouse microarray providing the surveillance of 24,881 mRNAs and 35,923 lncRNAs for CHO-S samples and RNA-Seq for the IgG-producing cell lines. tRNA abundances were quantified using a previously optimized ARM-Seq protocol. Thousands of differentially expressed lncRNAs were filtered by counting the occurrences of each transcript, assessing sequence conservation, secondary structure and RT-qPCR validation. The behaviour of a group of lncRNAs is described for the first time in CHO cells and the applications for cell engineering discussed. In particular, the CHO cell long non-coding RNA (lncRNA) transcriptome from cells grown in controlled miniature bioreactors is defined under fed-batch conditions using RNA-Seq to identify lncRNAs and how the expression of these changes throughout growth and between IgG producers. lncRNAs associated with productivity and growth characteristics are identified, in particular finding that Adapt15, linked to ER stress,GAS5, linked to mTOR signalling/growth arrest, and PVT1,linked to Myc expression, are differentially regulated during fed-batch culture and whose expression relates to productivity (Adapt15) or growth (GAS5, PVT1). Changes in (non)-coding RNA expression between the seed train and the equivalent day of fed-batch culture are also reported, showing large differences in gene expression between these. The ARM-Seq protocol allowed the identification of 4-5 fold more tRNAs compared to standard sequencing, and was applied to yeast and HEK293 cells to allow comparisons with CHO. Ultimately, tRNA quantifications were used in a translation elongation model to calculate the decoding speed of model recombinant proteins and to generate codon optimized versions based on tRNA abundances