Toward Predictable 5'UTRs in Saccharomyces cerevisiae: Development of a yUTR Calculator

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.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

The Influence of the Nucleotide Composition of Genes and Gene Regulatory Elements on the Efficiency of Protein Expression in Escherichia coli

Recombinant proteins expressed in Escherichia coli are widely used in biochemical research and industrial processes. At the same time, achieving higher protein expression levels and correct protein folding still remains the key problem, since optimization of nutrient media, growth conditions, and methods for induction of protein synthesis do not always lead to the desired result. Often, low protein expression is determined by the sequences of the expressed genes and their regulatory regions. The genetic code is degenerated; 18 out of 20 amino acids are encoded by more than one codon. Choosing between synonymous codons in the coding sequence can significantly affect the level of protein expression and protein folding due to the influence of the gene nucleotide composition on the probability of formation of secondary mRNA structures that affect the ribosome binding at the translation initiation phase, as well as the ribosome movement along the mRNA during elongation, which, in turn, influences the mRNA degradation and the folding of the nascent protein. The nucleotide composition of the mRNA untranslated regions, in particular the promoter and Shine-Dalgarno sequences, also affects the efficiency of mRNA transcription, translation, and degradation. In this review, we describe the genetic principles that determine the efficiency of protein production in Escherichia coli.

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina

Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.

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.

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.

Seeding the idea of encapsulating a representative synthetic metagenome in a single yeast cell

Synthetic metagenomics could potentially unravel the complexities of microbial ecosystems by revealing the simplicity of microbial communities captured in a single cell. Conceptionally, a yeast cell carrying a representative synthetic metagenome could uncover the complexity of multi-species interactions, illustrated here with wine ferments.

Reprogramming translation for gene therapy

Translational control plays a fundamental role in the regulation of gene expression in eukaryotes. Modulating translational efficiency allows the cell to fine-tune the expression of genes, spatially control protein localization, and trigger fast responses to environmental stresses. Translational regulation involves mechanisms acting on multiple steps of the protein synthesis pathway: initiation, elongation, and termination. Many cis-acting elements present in the 5' UTR of transcripts can influence translation at the initiation step. Among them, the Kozak sequence impacts translational efficiency by regulating the recognition of the start codon; upstream open reading frames (uORFs) are associated with inhibition of translation of the downstream protein; internal ribosomal entry sites (IRESs) can promote cap-independent translation. CRISPR-Cas technology is a revolutionary gene-editing tool that has also been applied to the regulation of gene expression. In this chapter, we focus on the genome editing approaches developed to modulate the translational efficiency with the aim to find novel therapeutic approaches, in particular acting on the cis-elements, that regulate the initiation of protein synthesis.

Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering

Increasing demands for the supply of biopharmaceuticals have propelled the advancement of metabolic engineering and synthetic biology strategies for biomanufacturing of bioactive natural products. Using metabolically engineered microbes as the bioproduction hosts, a variety of natural products including terpenes, flavonoids, alkaloids, and cannabinoids have been synthesized through the construction and expression of known and newly found biosynthetic genes primarily from model and non-model plants. The employment of omics technology and machine learning (ML) platforms as high throughput analytical tools has been increasingly leveraged in promoting data-guided optimization of targeted biosynthetic pathways and enhancement of the microbial production capacity, thereby representing a critical debottlenecking approach in improving and streamlining natural products biomanufacturing. To this end, this mini review summarizes recent efforts that utilize omics platforms and ML tools in strain optimization and prototyping and discusses the beneficial uses of omics-enabled discovery of plant biosynthetic genes in the production of complex plant-based natural products by bioengineered microbes.

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.

Metabolic Engineering Strategies for Sustainable Terpenoid Flavor and Fragrance Synthesis

Terpenoids derived from plant material are widely applied in the flavor and fragrance industry. Traditional extraction methods are unsustainable, but microbial synthesis offers a promising solution to attain efficient production of natural-identical terpenoids. Overproduction of terpenoids in microbes requires careful balancing of the synthesis pathway constituents within the constraints of host cell metabolism. Advances in metabolic engineering have greatly facilitated overcoming the challenges of achieving high titers, rates, and yields (TRYs). The review summarizes recent development in the molecular biology toolbox to achieve high TRYs for terpenoid biosynthesis, mainly in the two industrial platform microorganisms: Escherichia coli and Saccharomyces cerevisiae. The biosynthetic pathways, including alternative pathway designs, are briefly introduced, followed by recently developed methodologies used for pathway, genome, and strain optimization. Integrated applications of these tools are important to achieve high "TRYs" of terpenoid production and pave the way for translating laboratory research into successful commercial manufacturing.

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.

Modulating transcription through development of semi-synthetic yeast core promoters

Altering gene expression regulation by promoter engineering is a very effective way to fine-tune heterologous pathways in eukaryotic hosts. Typically, pathway building approaches in yeast still use a limited set of long, native promoters. With the today’s introduction of longer and more complex pathways, an expansion of this synthetic biology toolbox is necessary. In this study we elucidated the core promoter structure of the well-characterized yeast TEF1 promoter and determined the minimal length needed for sufficient protein expression. Furthermore, this minimal core promoter sequence was used for the creation of a promoter library covering different expression strengths. This resulted in a group of short, 69 bp promoters with an 8.0-fold expression range. One exemplar had a two and four times higher expression compared to the native CYC1 and ADH1 promoter, respectively. Additionally, as it was described that the protein expression range could be broadened by upstream activating sequences (UASs), we integrated earlier described single and multiple short, synthetic UASs in front of the strongest yeast core promoter. This approach resulted to further variation in protein expression and an overall promoter library spanning a 20-fold activity range and covering a length from 69 bp to maximally 129 bp. Furthermore, the robustness of this library was assessed on three alternative carbon sources besides glucose. As such, the suitability of short yeast core promoters for metabolic engineering applications on different media, either in an individual context or combined with UAS elements, was demonstrated.

Modular 5'-UTR hexamers for context-independent tuning of protein expression in eukaryotes

Functional characterization of regulatory DNA elements in broad genetic contexts is a prerequisite for forward engineering of biological systems. Translation initiation site (TIS) sequences are attractive to use for regulating gene activity and metabolic pathway fluxes because the genetic changes are minimal. However, limited knowledge is available on tuning gene outputs by varying TISs in different genetic and environmental contexts. Here, we created TIS hexamer libraries in baker’s yeast Saccharomyces cerevisiae directly 5′ end of a reporter gene in various promoter contexts and measured gene activity distributions for each library. Next, selected TIS sequences, resulted in almost 10-fold changes in reporter outputs, were experimentally characterized in various environmental and genetic contexts in both yeast and mammalian cells. From our analyses, we observed strong linear correlations (R² = 0.75–0.98) between all pairwise combinations of TIS order and gene activity. Finally, our analysis enabled the identification of a TIS with almost 50% stronger output than a commonly used TIS for protein expression in mammalian cells, and selected TISs were also used to tune gene activities in yeast at a metabolic branch point in order to prototype fitness and carotenoid production landscapes. Taken together, the characterized TISs support reliable context-independent forward engineering of translation initiation in eukaryotes.

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

The 5' untranslated region (5'UTR) plays a key role in post-transcriptional regulation, but interaction between nucleotides and directed evolution of 5'UTRs as synthetic regulatory elements remain unclear. By constructing a library of synthesized random 5'UTRs of 24 nucleotides in Saccharomyces cerevisiae, we observed strong epistatic interactions among bases from different positions in the 5'UTR. Taking into account these base interactions, we constructed a mathematical model to predict protein abundance with a precision of R² = 0.60. On the basis of this model, we developed an approach to engineer 5'UTRs according to nucleotide sequence activity relationships (NuSAR), in which 5'UTRs were engineered stepwise through repeated cycles of backbone design, directed screening, and model reconstruction. After three rounds of NuSAR, the predictive accuracy of our model was improved to R² = 0.71, and a strong 5'UTR was obtained with 5-fold higher protein abundance than the starting 5'UTR. Our findings provide new insights into the mechanism of 5'UTR regulation and  contribute to a new translational elements engineering approach in synthetic biology.

Design of RNA hairpin modules that predictably tune translation in yeast

Modular parts for tuning translation are prevalent in prokaryotic synthetic biology but lacking for eukaryotic synthetic biology. Working in Saccharomyces cerevisiae yeast, we here describe how hairpin RNA structures inserted into the 5′ untranslated region (5′UTR) of mRNAs can be used to tune expression levels by 100-fold by inhibiting translation. We determine the relationship between the calculated free energy of folding in the 5′UTR and in vivo protein abundance, and show that this enables rational design of hairpin libraries that give predicted expression outputs. Our approach is modular, working with different promoters and protein coding sequences, and outperforms promoter mutation as a way to predictably generate a library where a protein is induced to express at a range of different levels. With this new tool, computational RNA sequence design can be used to predictably fine-tune protein production for genes expressed in yeast.