Extensive efforts have been devoted to enhancing the translation efficiency of mRNA delivered to mammalian cells via codon optimization. However, the impact of codon choice on mRNA stability remains underexplored. In this study, we investigated the influence of codon usage on mRNA degradation kinetics in cultured human cell lines using live-cell imaging on single-cell arrays. By measuring mRNA lifetimes at the single-cell level for synthetic mRNA constructs, we confirmed that mRNAs containing slowly translated codon windows have shorter lifetimes. Unexpectedly, these mRNAs did not exhibit decreased stability in the presence of small interfering RNA (siRNA) compared with the unmutated sequence, suggesting an interference of different concurrent degradation mechanisms. We employed stochastic simulations to predict ribosome density along the open reading frame, revealing that the ribosome densities correlated with mRNA stability in a cell-type- and codon-position-specific manner. In summary, our results suggest that the effect of codon choice and its influence on mRNA lifetime is context-dependent with respect to cell type, codon position and RNA interference.

Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts. The proposed technique optimizes an image quality metric by modifying the shape of a computed wavefront using Zernike polynomials and subsequently calculating the corresponding PSFs for input into a deconvolution algorithm. This enables noise-free PSF estimation for the deconvolution of HSI data, leading to significantly improved spatial resolution and spatial co-registration of spectral channels over the entire wavelength range.

Processive enzymes like polymerases or ribosomes are often studied in bulk experiments by monitoring time-dependent signals, such as fluorescence time traces. However, due to biomolecular process stochasticity, ensemble signals may lack the distinct features of single-molecule signals. Here, we demonstrate that, under certain conditions, bulk signals from processive reactions can be decomposed to unveil hidden information about individual reaction steps. Using mRNA translation as a case study, we show that decomposing a noisy ensemble signal generated by the translation of mRNAs with more than a few codons is an ill-posed problem, addressable through Tikhonov regularization. We apply our method to the fluorescence signatures of in-vitro translated LepB mRNA and determine codon-position dependent translation rates and corresponding state-specific fluorescence intensities. We find a significant change in fluorescence intensity after the fourth and the fifth peptide bond formation, and show that both codon position and encoded amino acid have an effect on the elongation rate. This demonstrates that our approach enhances the information content extracted from bulk experiments, thereby expanding the range of these time- and cost-efficient methods.

Ionizing radiation (IR) in high doses is generally lethal to most organisms. Investigating mechanisms of radiation resistance is crucial for gaining insights into the underlying cellular responses and understanding the damaging effects of IR. In this study, we conducted a comprehensive analysis of sequencing data from an evolutionary experiment aimed at understanding the genetic adaptations to ionizing radiation in Escherichia coli. By including previously neglected synonymous mutations, we identified the rpsH c.294T > G variant, which emerged in all 17 examined isolates across four subpopulations. The identified variant is a synonymous mutation affecting the 30S ribosomal protein S8, and consistently exhibited high detection and low allele frequencies in all subpopulations. This variant, along with two additional rpsH variants, potentially influences translational control of the ribosomal spc operon. The early emergence and stability of these variants suggest their role in adapting to environmental stress, possibly contributing to radiation resistance. Our findings shed light on the dynamics of ribosomal variants during the evolutionary process and their potential role in stress adaptation, providing valuable implications for understanding clinical radiation sensitivity and improving radiotherapy.

Most proteins form complexes consisting of two or more subunits, where complex assembly can proceed via two competing pathways: co-translational assembly of a mature and a nascent subunit, and post-translational assembly by two mature protein subunits. Assembly pathway dominance, i.e., which of the two pathways is predominant under which conditions, is poorly understood. Here, we introduce a reaction-diffusion system that describes protein complex formation via post- and co-translational assembly and use it to analyze the dominance of both pathways. Special features of this new system are (i) spatially inhomogeneous sources of reacting species, (ii) a combination of diffusing and immobile species, and (iii) an asymmetric binding competition between the species. We study assembly pathway dominance for the spatially homogeneous system and find that the ratio of production rates of the two protein subunits determines the long-term pathway dominance. This result is independent of the binding rate constants for post- and co-translational assembly and implies that a system with an initial post-translational assembly dominance can eventually exhibit co-translational assembly dominance and vice versa. For exactly balanced production of both subunits, the assembly pathway dominance is determined by the steady state concentration of the subunit that can bind both nascent and mature partners. The introduced system of equations can be applied to describe general dynamics of assembly processes involving both diffusing and immobile components.

Ribosome profiling (Ribo-seq) is a powerful method for the deep analysis of translation mechanisms and regulatory circuits during gene expression. Extraction and sequencing of ribosome-protected fragments (RPFs) and parallel RNA-seq yields genome-wide insight into translational dynamics and post-transcriptional control of gene expression. Here, we provide details on the Ribo-seq method and the subsequent analysis with the unicellular model alga Chlamydomonas reinhardtii (Chlamydomonas) for generating high-resolution data covering more than 10 000 different transcripts. Detailed analysis of the ribosomal offsets on transcripts uncovers presumable transition states during translocation of elongating ribosomes within the 5' and 3' sections of transcripts and characteristics of eukaryotic translation termination, which are fundamentally distinct for chloroplast translation. In chloroplasts, a heterogeneous RPF size distribution along the coding sequence indicates specific regulatory phases during protein synthesis. For example, local accumulation of small RPFs correlates with local slowdown of psbA translation, possibly uncovering an uncharacterized regulatory step during PsbA/D1 synthesis. Further analyses of RPF distribution along specific cytosolic transcripts revealed characteristic patterns of translation elongation exemplified for the major light-harvesting complex proteins, LHCs. By providing high-quality datasets for all subcellular genomes and attaching our data to the Chlamydomonas reference genome, we aim to make ribosome profiles easily accessible for the broad research community. The data can be browsed without advanced bioinformatic background knowledge for translation output levels of specific genes and their splice variants and for monitoring genome annotation.

Protein complex assembly facilitates the combination of individual protein subunits into functional entities, and thus plays a crucial role in biology and biotechnology. Recently, we developed quasi-twodimensional, silicon-based compartmental biochips that are designed to study and administer the synthesis and assembly of protein complexes. At these biochips, individual protein subunits are synthesized from locally confined high-density DNA brushes and are captured on the chip surface by molecular traps. Here, we investigate single-gene versions of our quasi-twodimensional synthesis systems and introduce the trap-binding efficiency to characterize their performance. We show by mathematical and computational modeling how a finite trap density determines the dynamics of protein-trap binding and identify three distinct regimes of the trap-binding efficiency. We systematically study how protein-trap binding is governed by the system's three key parameters, which are the synthesis rate, the diffusion constant and the trap-binding affinity of the expressed protein. In addition, we describe how spatially differential patterns of traps modulate the protein-trap binding dynamics. In this way, we extend the theoretical knowledge base for synthesis, diffusion, and binding in compartmental systems, which helps to achieve better control of directed molecular self-assembly required for the fabrication of nanomachines for synthetic biology applications or nanotechnological purposes.

For heterologous gene expression, codon optimization is required to enhance the quality and quantity of the protein product. Recently, we introduced the software tool OCTOPOS. This sequence optimizer combines a detailed mechanistic mathematical modeling of in vivo protein synthesis with a state-of-the-art machine learning algorithm to find the sequence that best serves a user's needs. Here, we briefly describe the algorithm and its implementation as well as its application in practice using OCTOPOS.

The assembly of protein machines in cells is precise, rapid, and coupled to protein synthesis with regulation in space and time. The assembly of natural and synthetic nanomachines could be similarly controlled by genetic programming outside the cell. Here, we present quasi-two-dimensional (2D) silicon compartments that enable programming of protein assembly lines by local synthesis from surface-immobilized DNA brushes. Using this platform, we studied the autonomous synthesis and assembly of a structural complex from a bacteriophage and a bacterial RNA-synthesizing machine. Local synthesis and surface capture of complexes provided high assembly yield and sensitive detection of spatially resolved assembly intermediates, with the 3D geometry of the compartment and the 2D pattern of brushes dictating the yield and mode of assembly steps. Localized synthesis of proteins in a single gene brush enhances their interactions, and displacement of their genes in separated brushes leads to step-by-step surface assembly. This methodology enables spatial regulation of protein synthesis, and deciphering, reconstruction and design of biological machine assembly lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Heterologous expression of genes requires their adaptation to the host organism to achieve adequate protein synthesis rates. Typically codons are adjusted to resemble those seen in highly expressed genes of the host organism which lacks a deeper understanding of codon optimality. The codon-specific elongation model (COSEM) identifies optimal codon choices by simulating ribosome dynamics during mRNA translation. COSEM is used in combination with machine learning techniques to predict protein abundance and to optimize codon usage.

Regulation and maintenance of protein synthesis are vital to all organisms and are thus key targets of attack and defense at the cellular level. Here, we mathematically analyze protein synthesis for its sensitivity to the inhibition of elongation factor EF-Tu and/or ribosomes in dependence of the system's tRNA and codon compositions. We find that protein synthesis reacts ultrasensitively to a decrease in the elongation factor's concentration for systems with an imbalance between codon usages and tRNA concentrations. For well-balanced tRNA/codon compositions, protein synthesis is impeded more effectively by the inhibition of ribosomes instead of EF-Tu. Our predictions are supported by re-evaluated experimental data as well as by independent computer simulations. Not only does the described ultrasensitivity render EF-Tu a distinguished target of protein synthesis inhibiting antibiotics. It may also enable persister cell formation mediated by toxin-antitoxin systems. The strong impact of the tRNA/codon composition provides a basis for tissue-specificities of disorders caused by mutations of human mitochondrial EF-Tu as well as for the potential use of EF-Tu targeting drugs for tissue-specific treatments.

Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.

During protein synthesis, the nascent peptide chain traverses the peptide exit tunnel of the ribosome. We monitor the co-translational movement of the nascent peptide using a fluorescent probe attached to the N-terminus of the nascent chain. Due to fluorophore quenching, the time-dependent fluorescence signal emitted by an individual peptide is determined by co-translational events, such as secondary structure formation and peptide-tunnel interactions. To obtain information on these individual events, the measured ensemble fluorescence signal has to be decomposed into position-dependent intensities. Here, we describe mRNA translation as a Markov process with specific fluorescence intensities assigned to the different states of the process. Combining the computed stochastic time evolution of the translation process with a sequence of observed ensemble fluorescence time courses, we compute the unknown position-specific intensities and obtain detailed information on the kinetics of the translation process. In particular, we find that translation of poly(U) mRNAs dramatically slows down at the fourth UUU codon. The method presented here detects subtle differences in the position-specific fluorescence intensities and thus provides a novel approach to study translation kinetics in ensemble experiments.

During protein synthesis, the nascent peptide chain traverses the ribosomal exit tunnel. We monitor this co-translational movement via fluorescent probes attached to the N-termini of the nascent chains. Due to fluorophore quenching, the time-dependent fluorescence signal emitted by an individual peptide is determined by co-translational events, such as secondary structure formation and peptide-tunnel interactions. To obtain information on these local events, the measured total fluorescence signal has to be decomposed into position-dependent intensities. To this end, we describe mRNA translation as a Markov process and assign a specific fluorescence intensity to each position of the ribosome. Combining the stochastic time evolution of the ribosomal position with the observed time course of the total fluorescence, we obtain a system of linear equations. We solve this system for the unknown position-specific intensities. Our results indicate that the N-terminus of poly-phenylalanine experiences major environmental changes during elongation of the first five amino acids.

To synthesize a protein, a ribosome moves along a messenger RNA (mRNA), reads it codon by codon, and takes up the corresponding ternary complexes which consist of aminoacylated transfer RNAs (aa-tRNAs), elongation factor Tu (EF-Tu), and GTP. During this process of translation elongation, the ribosome proceeds with a codon-specific rate. Here, we present a general theoretical framework to calculate codon-specific elongation rates and error frequencies based on tRNA concentrations and codon usages. Our theory takes three important aspects of in-vivo translation elongation into account. First, non-cognate, near-cognate and cognate ternary complexes compete for the binding sites on the ribosomes. Second, the corresponding binding rates are determined by the concentrations of free ternary complexes, which must be distinguished from the total tRNA concentrations as measured in vivo. Third, for each tRNA species, the difference between total tRNA and ternary complex concentration depends on the codon usages of the corresponding cognate and near-cognate codons. Furthermore, we apply our theory to two alternative pathways for tRNA release from the ribosomal E site and show how the mechanism of tRNA release influences the concentrations of free ternary complexes and thus the codon-specific elongation rates. Using a recently introduced method to determine kinetic rates of in-vivo translation from in-vitro data, we compute elongation rates for all codons in Escherichia coli. We show that for some tRNA species only a few tRNA molecules are part of ternary complexes and, thus, available for the translating ribosomes. In addition, we find that codon-specific elongation rates strongly depend on the overall codon usage in the cell, which could be altered experimentally by overexpression of individual genes.

The molecular machinery of life relies on complex multistep processes that involve numerous individual transitions, such as molecular association and dissociation steps, chemical reactions, and mechanical movements. The corresponding transition rates can be typically measured in vitro but not in vivo. Here, we develop a general method to deduce the in-vivo rates from their in-vitro values. The method has two basic components. First, we introduce the kinetic distance, a new concept by which we can quantitatively compare the kinetics of a multistep process in different environments. The kinetic distance depends logarithmically on the transition rates and can be interpreted in terms of the underlying free energy barriers. Second, we minimize the kinetic distance between the in-vitro and the in-vivo process, imposing the constraint that the deduced rates reproduce a known global property such as the overall in-vivo speed. In order to demonstrate the predictive power of our method, we apply it to protein synthesis by ribosomes, a key process of gene expression. We describe the latter process by a codon-specific Markov model with three reaction pathways, corresponding to the initial binding of cognate, near-cognate, and non-cognate tRNA, for which we determine all individual transition rates in vitro. We then predict the in-vivo rates by the constrained minimization procedure and validate these rates by three independent sets of in-vivo data, obtained for codon-dependent translation speeds, codon-specific translation dynamics, and missense error frequencies. In all cases, we find good agreement between theory and experiment without adjusting any fit parameter. The deduced in-vivo rates lead to smaller error frequencies than the known in-vitro rates, primarily by an improved initial selection of tRNA. The method introduced here is relatively simple from a computational point of view and can be applied to any biomolecular process, for which we have detailed information about the in-vitro kinetics.

All living cells rely on ribosomes, powerful nanomachines that synthesize proteins by translating the information encoded in mRNA molecules. Over the past two decades, the various substeps of the translation process have been studied in much detail using in vitro systems, but it is often questioned to what extend these results can be applied to real living cells. Although in vivo translation should proceed via essentially the same steps as in vitro, the average protein synthesis rate turns out to be much faster. This difference in synthesis rate has led to a long and controversial debate because - in contrast to in vitro systems - it has not been possible to study individual conformational transitions of the translating ribosome in vivo. Here, we address this long-lasting and unresolved puzzle and introduce a general computational scheme, which enables us to map in vitro onto in vivo rates. Using new experimental data on in vitro translation at 20°C and 37°C, we first derive a complete set of in vitro rates for the individual substeps of translation. We then use our scheme, together with available in vivo data, to determine the corresponding in vivo rates. As a result, we obtain a comprehensive description of in vitro and in vivo translation for various experimental and growth conditions. This description allows us to predict codon- and mRNA-specific translation rates, which can be used, e.g., to investigate translational pauses and ribosome traffic.

During protein synthesis, a ribosome moves along a messenger RNA (mRNA) and translates it codon by codon by binding and recognizing the appropriate aminoacylated transfer RNAs (aa-tRNAs). We developed a stochastic theory for this process of translation elongation and determined the dependence of elongation speed on codon sequence as well as on the concentrations of ribosomes and and aa-tRNAs. We find that the variation of a single aa-tRNA concentration leads to three distinct regimes of protein synthesis. In the depletion regime characterized by low aa-tRNA concentrations, the ribosomal peptide synthesis rate follows a general Michaelis-Menten law. Furthermore, we find a remarkably broad range of intermediate aa-tRNA concentrations, for which translation is very stable and the ribosomal peptide synthesis rate is essentially constant. For large concentrations, crowding of aa-tRNAs and the competition between non-cognate and cognate aa-tRNAs strongly impede protein synthesis. Our results provide new insights into the process of protein synthesis and the role of aa-tRNA concentrations therein. Knowing the influence of aa-tRNA concentrations on translation is pivotal to further progress in understanding and controlling protein synthesis, e.g., in tRNA over-expressing cancer cells as well as in cell-free expression systems and organisms bioengineered for high yield protein production.