Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript

Are Bacterial Processes Dependent on Global Ribosome Pausing Affected by tRNA Modification Defects?

By integrating a literature review with transcriptomic, proteomic, and phenotypic data from two model bacteria, Escherichia coli and Vibrio cholerae, we put forward the hypothesis that defects in tRNA modification broadly impact processes that are evolutionarily tuned to be sensitive to translation speed. These include the translation of regulatory proteins associated with motility, iron homeostasis, and leader peptide-driven attenuation mechanisms. Some of these translation speed-dependent processes are influenced by the absence of a single modification, while others are affected by the absence of multiple modifications. Although further experiments are needed to clarify the mechanisms involved in each case, this work provides a foundational framework to guide future research.

prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics

Mass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, interactive differential expression analysis tools become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to nonprogrammers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements or multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software, respectively. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs.

A new look at the fluorescent protein-based approach for identifying optimal coding sequence for recombinant protein expression in E. coli

Due to the degeneracy of the genetic code, most amino acids are encoded by several codons. The choice among synonymous codons at the N-terminus of genes has a profound effect on protein expression in Escherichia coli. This is often explained by the different contributions of synonymous codons to mRNA secondary structure formation. Strong secondary structures at the 5'-end of mRNA interfere with ribosome binding and affect the process of translation initiation. In silico optimization of the gene 5'-end can significantly increase the level of protein expression; however, this method is not always effective due to the uncertainty of the exact mechanism by which synonymous substitutions affect expression; thus, it may produce nonoptimal variants as well as miss some of the best producers. In this paper, an alternative approach is proposed based on screening a partially randomized library of expression constructs comprising hundreds of selected synonymous variants. The effect of such substitutions was evaluated using the gene of interest fused to the reporter gene of the fluorescent protein with subsequent screening for the most promising candidates according to the reporter's signal intensity. The power of the approach is demonstrated by a significant increase in the prokaryotic expression of three proteins: canine cystatin C, human BCL2-associated athanogene 3 and human cardiac troponin I. This simple approach was suggested which may provide an efficient, easy, and inexpensive optimization method for poorly expressed proteins in bacteria.

Codon-optimization in gene therapy: promises, prospects and challenges

Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.

Advancing reproducibility can ease the 'hard truths' of synthetic biology

Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.

Genes for highly abundant proteins in Escherichia coli avoid 5' codons that promote ribosomal initiation

In many species highly expressed genes (HEGs) over-employ the synonymous codons that match the more abundant iso-acceptor tRNAs. Bacterial transgene codon randomization experiments report, however, that enrichment with such "translationally optimal" codons has little to no effect on the resultant protein level. By contrast, consistent with the view that ribosomal initiation is rate limiting, synonymous codon usage following the 5' ATG greatly influences protein levels, at least in part by modifying RNA stability. For the design of bacterial transgenes, for simple codon based in silico inference of protein levels and for understanding selection on synonymous mutations, it would be valuable to computationally determine initiation optimality (IO) scores for codons for any given species. One attractive approach is to characterize the 5' codon enrichment of HEGs compared with the most lowly expressed genes, just as translational optimality scores of codons have been similarly defined employing the full gene body. Here we determine the viability of this approach employing a unique opportunity: for Escherichia coli there is both the most extensive protein abundance data for native genes and a unique large-scale transgene codon randomization experiment enabling objective definition of the 5' codons that cause, rather than just correlate with, high protein abundance (that we equate with initiation optimality, broadly defined). Surprisingly, the 5' ends of native genes that specify highly abundant proteins avoid such initiation optimal codons. We find that this is probably owing to conflicting selection pressures particular to native HEGs, including selection favouring low initiation rates, this potentially enabling high efficiency of ribosomal usage and low noise. While the classical HEG enrichment approach does not work, rendering simple prediction of native protein abundance from 5' codon content futile, we report evidence that initiation optimality scores derived from the transgene experiment may hold relevance for in silico transgene design for a broad spectrum of bacteria.

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning

It has been known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data were used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model that only relies on the sequence information of the first eight codons. In this region, close to the translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI) is the key determinant of protein production. This study clearly demonstrates the key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.