Engineering the 5' UTR-Mediated Regulation of Protein Abundance in Yeast Using Nucleotide Sequence Activity Relationships

Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model

BACKGROUND

The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins.

RESULTS

We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels.

CONCLUSION

The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.

An Efficient Prephenate Dehydrogenase Gene for the Biosynthesis of L-tyrosine: Gene Mining, Sequence Analysis, and Expression Optimization

L-tyrosine is a key precursor for synthesis of various functional substances, but the microbial production of L-tyrosine faces huge challenges. The development of new microbial chassis cell and gene resource is especially important for the biosynthesis of L-tyrosine. In this study, the optimal host strain Bacillus amyloliquefaciens HZ-12 was firstly selected by detecting the production capacity of L-tyrosine. Subsequently, the recombinant expression of 15 prephenate dehydrogenase genes led to the discovery of the best gene, Bao-tyrA from B. amyloliquefaciens HZ-12. After the overexpression of Bao-tyrA, the L-tyrosine yield of the recombinant strain HZ/P43-Bao-tyrA reach 411 mg/L, increased by 42% compared with the control strain (HZ/pHY300PLK). Moreover, the nucleic acid sequence and deduced amino acid sequence of the gene Bao-tyrA were analyzed, and their conservative sites and catalytic mechanisms were proposed. Finally, the expression of Bao-tyrA was regulated through a promoter and 5'-UTR sequence to obtain the optimal expression elements. Thereby, the maximum L-tyrosine yield of 475 mg/L was obtained from HZ/P43-UTR3-Bao-tyrA. B. amyloliquefaciens was applied for the first time to produce L-tyrosine, and the optimal prephenate dehydrogenase gene Bao-tyrA and corresponding expression elements were obtained. This study provides new microbial host and gene resource for the construction of efficient L-tyrosine chassis cells, and also lays a solid foundation for the production of various functional tyrosine derivatives.

Translation initiation consistency between in vivo and in vitro bacterial protein expression systems

Strict on-demand control of protein synthesis is a crucial aspect of synthetic biology. The 5'-terminal untranslated region (5'-UTR) is an essential bacterial genetic element that can be designed for the regulation of translation initiation. However, there is insufficient systematical data on the consistency of 5'-UTR function among various bacterial cells and in vitro protein synthesis systems, which is crucial for the standardization and modularization of genetic elements in synthetic biology. Here, more than 400 expression cassettes comprising the GFP gene under the regulation of various 5'-UTRs were systematically characterized to evaluate the protein translation consistency in the two popular Escherichia coli strains of JM109 and BL21, as well as an in vitro protein expression system based on cell lysate. In contrast to the very strong correlation between the two cellular systems, the consistency between in vivo and in vitro protein translation was lost, whereby both in vivo and in vitro translation evidently deviated from the estimation of the standard statistical thermodynamic model. Finally, we found that the absence of nucleotide C and complex secondary structure in the 5'-UTR significantly improve the efficiency of protein translation, both in vitro and in vivo.

Optimization of 5'UTR to evade SARS-CoV-2 Nonstructural protein 1-directed inhibition of protein synthesis in cells

Maximizing the expression level of therapeutic proteins in cells is the general goal for DNA/mRNA therapies. It is particularly challenging to achieve efficient protein expression in the cellular contexts with inhibited translation machineries, such as in the presence of cellular Nonstructural protein 1 (Nsp1) of coronaviruses (CoVs) that has been reported to inhibit overall protein synthesis of host genes and exogenously delivered mRNAs/DNAs. In this study, we thoroughly examined the sequence and structure contexts of viral and non-viral 5'UTRs that determine the protein expression levels of exogenously delivered DNAs and mRNAs in cells expressing SARS-CoV-2 Nsp1. It was found that high 5'-proximal A/U content promotes an escape from Nsp1-directed inhibition of protein synthesis and results in selective protein expression. Furthermore, 5'-proximal Cs were found to significantly enhance the protein expression in an Nsp1-dependent manner, while Gs located at a specific window close to the 5'-end counteract such enhancement. The distinct protein expression levels resulted from different 5'UTRs were found correlated to Nsp1-induced mRNA degradations. These findings ultimately enabled rational designs for optimized 5'UTRs that lead to strong expression of exogenous proteins regardless of the translationally repressive Nsp1. On the other hand, we have also identified several 5'-proximal sequences derived from host genes that are capable of mediating the escapes. These results provided novel perspectives to the optimizations of 5'UTRs for DNA/mRNA therapies and/or vaccinations, as well as shedding light on the potential host escapees from Nsp1-directed translational shutoffs. KEY POINTS: • The 5'-proximal SL1 and 5a/b derived from SARS-CoV-2 genomic RNA promote exogenous protein synthesis in cells expressing Nsp1 comparing with non-specific 5'UTRs. • Specific 5'-proximal sequence contexts are the key determinants of the escapes from Nsp1-directed translational repression and thereby enhance protein expressions. • Systematic mutagenesis identified optimized 5'UTRs that strongly enhance protein expression and promote resistance to Nsp1-induced translational repression and RNA degradation.

Advances in promoter engineering: novel applications and predefined transcriptional control

Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.

Microbial Production of Bioactive Retinoic Acid Using Metabolically Engineered Escherichia coli

Microbial production of bioactive retinoids, including retinol and retinyl esters, has been successfully reported. Previously, there are no reports on the microbial biosynthesis of retinoic acid. Two genes (blh_SR and raldh_HS) encoding retinoic acid biosynthesis enzymes [β-carotene 15,15′-oxygenase (Blh) and retinaldehyde dehydrogenase2 (RALDH2)] were synthetically redesigned for modular expression. Co-expression of the blh_SR and raldh_HS genes on the plasmid system in an engineered β-carotene-producing Escherichia coli strain produced 0.59 ± 0.06 mg/L of retinoic acid after flask cultivation. Deletion of the ybbO gene encoding a promiscuous aldehyde reductase induced a 2.4-fold increase in retinoic acid production to 1.43 ± 0.06 mg/L. Engineering of the 5’-UTR sequence of the blh_SR and raldh_HS genes enhanced retinoic acid production to 3.46 ± 0.16 mg/L. A batch culture operated at 37 °C, pH 7.0, and 50% DO produced up to 8.20 ± 0.05 mg/L retinoic acid in a bioreactor. As the construction and culture of retinoic acid–producing bacterial strains are still at an early stage in the development, further optimization of the expression level of the retinoic acid pathway genes, protein engineering of Blh and RALDH2, and culture optimization should synergistically increase the current titer of retinoic acid in E. coli.

High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies

Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major technical challenge. Here, we develop a high-throughput strategy to design, screen, and optimize 5' UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identify naturally occurring 5' UTRs with high translation efficiencies and use this information with in silico genetic algorithms to generate synthetic 5' UTRs. A total of ~12,000 5' UTRs are then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identify three synthetic 5' UTRs that outperform commonly used non-viral gene therapy plasmids in expressing protein payloads. In summary, we demonstrate that high-throughput screening of 5' UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

The Ongoing Quest to Crack the Genetic Code for Protein Production

Understanding the genetic design principles that determine protein production remains a major challenge. Although the key principles of gene expression were discovered 50 years ago, additional factors are still being uncovered. Both protein-coding and non-coding sequences harbor elements that collectively influence the efficiency of protein production by modulating transcription, mRNA decay, and translation. The influences of many contributing elements are intertwined, which complicates a full understanding of the individual factors. In natural genes, a functional balance between these factors has been obtained in the course of evolution, whereas for genetic-engineering projects, our incomplete understanding still limits optimal design of synthetic genes. However, notable advances have recently been made, supported by high-throughput analysis of synthetic gene libraries as well as by state-of-the-art biomolecular techniques. We discuss here how these advances further strengthen understanding of the gene expression process and how they can be harnessed to optimize protein production.

5' untranslated regions: the next regulatory sequence in yeast synthetic biology

When developing industrial biotechnology processes, Saccharomyces cerevisiae (baker's yeast or brewer's yeast) is a popular choice as a microbial host. Many tools have been developed in the fields of synthetic biology and metabolic engineering to introduce heterologous pathways and tune their expression in yeast. Such tools mainly focus on controlling transcription, whereas post-transcriptional regulation is often overlooked. Herein we discuss regulatory elements found in the 5' untranslated region (UTR) and their influence on protein synthesis. We provide not only an overall picture, but also a set of design rules on how to engineer a 5' UTR. The reader is also referred to currently available models that allow gene expression to be tuned predictably using different 5' UTRs.