Engineering bacterial translation initiation - Do we have all the tools we need?

Advances in microbial production of geraniol: from metabolic engineering to potential industrial applications

Geraniol, an acyclic monoterpene alcohol, has significant potential applications in various fields, including: food, cosmetics, biofuels, and pharmaceuticals. However, the current sources of geraniol mainly include plant tissue extraction or chemical synthesis, which are unsustainable and suffer severely from high energy consumption and severe environmental problems. The process of microbial production of geraniol has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Escherichia coli (13.2 g/L) and Saccharomyces cerevisiae (5.5 g/L) laid a solid foundation for the microbial production of geraniol. In this review, recent advances in the development of geraniol-producing strains, including: metabolic pathway construction, key enzyme improvement, genetic modification strategies, and cytotoxicity alleviation, are critically summarized. Furthermore, the key challenges in scaling up geraniol production and future perspectives for the development of robust geraniol-producing strains are suggested. This review provides theoretical guidance for the industrial production of geraniol using microbial cell factories.

Role of Small Non-Coding RNA in Gram-Negative Bacteria: New Insights and Comprehensive Review of Mechanisms, Functions, and Potential Applications

Small non-coding RNAs (sRNAs) are a key part of gene expression regulation in bacteria. Many physiologic activities like adaptation to environmental stresses, antibiotic resistance, quorum sensing, and modulation of the host immune response are regulated directly or indirectly by sRNAs in Gram-negative bacteria. Therefore, sRNAs can be considered as potentially useful therapeutic options. They have opened promising perspectives in the field of diagnosis of pathogens and treatment of infections caused by antibiotic-resistant organisms. Identification of sRNAs can be executed by sequence and expression-based methods. Despite the valuable progress in the last two decades, and discovery of new sRNAs, their exact role in biological pathways especially in co-operation with other biomolecules involved in gene expression regulation such as RNA-binding proteins (RBPs), riboswitches, and other sRNAs needs further investigation. Although the numerous RNA databases are available, including 59 databases used by RNAcentral, there remains a significant gap in the absence of a comprehensive and professional database that categorizes experimentally validated sRNAs in Gram-negative pathogens. Here, we review the present knowledge about most recent and important sRNAs and their regulatory mechanism, strengths and weaknesses of current methods of sRNAs identification. Also, we try to demonstrate the potential applications and new insights of sRNAs for future studies.

The prokaryotic activity of the IGR IRESs is mediated by ribosomal protein S1

Internal ribosome entry sites (IRESs) are RNA elements capable of initiating translation on an internal portion of a messenger RNA. The intergenic region (IGR) IRES of the Dicistroviridae virus family folds into a triple pseudoknot tertiary structure, allowing it to recruit the ribosome and initiate translation in a structure dependent manner. This IRES has also been reported to drive translation in Escherichia coli and to date is the only described translation initiation signal that functions across domains of life. Here we show that unlike in the eukaryotic context the tertiary structure of the IGR IRES is not required for prokaryotic ribosome recruitment. In E. coli IGR IRES translation efficiency is dependent on ribosomal protein S1 in conjunction with an AG-rich Shine-Dalgarno-like element, supporting a model where the translational activity of the IGR IRESs is due to S1-mediated canonical prokaryotic translation.

Deciphering the Rules of Ribosome Binding Site Differentiation in Context Dependence

The ribosome binding site (RBS) is a crucial element regulating translation. However, the activity of RBS is poorly predictable, because it is strongly affected by the local possible secondary structure, that is, context dependence. By the Flowseq technique, over 20 000 RBS variants were sorted and sequenced, and the translation of multiple genes under the same RBS was quantitatively characterized to evaluate the context dependence of each RBS variant in E. coli. Two regions, (-7 to -2) and (-17 to -12), of RBS were predicted with a higher possibility to pair with each other to slow down the translation initiation. Associations between phenotypes and the intrinsic factors suspected to affect translation efficiency and context dependence of the RBS, including nucleotide bias at each position, free energy, and conservation, were disentangled. The results showed that translation efficiency was influenced more significantly by conservation of the SD region (-16 to -8), while an AC-rich spacer region (-7 to -1) was associated with low context dependence. We confirmed these characteristics using a series of synthesized RBSs. The average correlation between multiple reporters was significantly higher for RBSs with an AC-rich spacer (0.714) compared with a GU-rich spacer (0.286). Overall, we proposed general design criteria to improve programmability and minimize context dependence of RBS. The characteristics unraveled here can be adapted to other bacteria for fine-tuning target-gene expression.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE's effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.