The development and characterization of synthetic minimal yeast promoters

Controlling gene expression with deep generative design of regulatory DNA

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.

Deep Learning Concepts and Applications for Synthetic Biology

Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.

Engineered Microbial Cell Factories for Sustainable Production of L-Lactic Acid: A Critical Review

With the increasing demand for the biodegradable polymer material polylactic acid and its advantage of being metabolized by the human body, L-lactic acid (L-LA) is becoming increasingly attractive in environmental protection and food industry applications. However, the supply of L-LA is not satisfied, and the price is still high. Compared to enzymatic and chemical synthesis methods, L-LA production by microbial fermentation has the advantages of low cost, large yield, simple operation, and environmental protection. This review summarizes the advances in engineering microbial cell factories to produce L-LA. First, the synthetic pathways and microorganisms for L-LA production are outlined. Then, the metabolic engineering strategies for constructing cell factories to overproduce L-LA are summarized and fermentation modes for L-LA production are also given. Finally, the challenges and prospects of the microbial production of L-LA are discussed. This review provides theoretical guidance for researchers engaged in L-LA production.

Evaluation of Aspergillus niger Six Constitutive Strong Promoters by Fluorescent-Auxotrophic Selection Coupled with Flow Cytometry: A Case for Citric Acid Production

Aspergillus niger is an important industrial workhorse for the biomanufacturing of organic acids, proteins, etc. Well-controlled genetic regulatory elements, including promoters, are vital for strain engineering, but available strong promoters for A. niger are limited. Herein, to efficiently assess promoters, we developed an accurate and intuitive fluorescent-auxotrophic selection workflow based on mCherry, pyrG, CRISPR/Cas9 system, and flow cytometry. With this workflow, we characterized six endogenous constitutive promoters in A. niger. The endogenous glyceraldehyde-3-phosphate dehydrogenase promoter PgpdAg showed a 2.28-fold increase in promoter activity compared with the most frequently used strong promoter PgpdAd from A. nidulans. Six predicted conserved motifs, including the gpdA-box, were verified to be essential for the PgpdAg activity. To demonstrate its application, the promoter PgpdAg was used for enhancing the expression of citrate exporter cexA in a citric acid-producing isolate D353.8. Compared with the cexA controlled by PgpdAd, the transcription level of the cexA gene driven by PgpdAg increased by 2.19-fold, which is consistent with the promoter activity assessment. Moreover, following cexA overexpression, several genes involved in carbohydrate transport and metabolism were synergically upregulated, resulting in up to a 2.48-fold increase in citric acid titer compared with that of the parent strain. This study provides an intuitive workflow to speed up the quantitative evaluation of A. niger promoters and strong constitutive promoters for fungal cell factory construction and strain engineering.

Molecular and experimental tools to design synthetic enhancers

Understanding the grammar of enhancers and how they regulate gene expression is key for both basic research and for the pharma and biotech industries. The design and characterization of synthetic enhancers can expand the known regulatory space. This is achieved by the utilization of DNA Oligo Libraries (OLs), which facilitates screening of as many as millions of synthetic enhancer variants simultaneously. This review includes the latest commercial DNA OL synthesis technology and its capabilities, and a general 'know-how' guide for the design, construction, and analysis of OL-based synthetic enhancer characterization experiments. Specifically, we focus on synthetic-enhancer-based massively parallel reporter assay, Sort-seq methodologies (e.g. flow cytometry, deep sequencing), and a brief description of machine learning-based attempts for OL-analysis and follow-up validation experiments.

Cross-kingdom expression of synthetic genetic elements promotes discovery of metabolites in the human microbiome

Small molecules encoded by biosynthetic pathways mediate cross-species interactions and harbor untapped potential, which has provided valuable compounds for medicine and biotechnology. Since studying biosynthetic gene clusters in their native context is often difficult, alternative efforts rely on heterologous expression, which is limited by host-specific metabolic capacity and regulation. Here, we describe a computational-experimental technology to redesign genes and their regulatory regions with hybrid elements for cross-species expression in Gram-negative and -positive bacteria and eukaryotes, decoupling biosynthetic capacity from host-range constraints to activate silenced pathways. These synthetic genetic elements enabled the discovery of a class of microbiome-derived nucleotide metabolites-tyrocitabines-from Lactobacillus iners. Tyrocitabines feature a remarkable orthoester-phosphate, inhibit translational activity, and invoke unexpected biosynthetic machinery, including a class of "Amadori synthases" and "abortive" tRNA synthetases. Our approach establishes a general strategy for the redesign, expression, mobilization, and characterization of genetic elements in diverse organisms and communities.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

Modulating DNA Repair Pathways to Diversify Genomic Alterations in Saccharomyces cerevisiae

Nuclease based genome editing systems have emerged as powerful tools to drive genomic alterations and enhance genome evolution via precise engineering in the various human and microbial cells. However, error-prone DNA repair has not been well studied previously to generate diverse genomic alterations and novel phenotypes. Here, we systematically investigated the potential interplay between DNA double strand break (DSB) repair and genome editing tools, and found that modulating the DSB end resection proteins could significantly improve mutational efficiency and diversity without exogenous DNA template in yeast. Deleting SAE2, EXO1, or FUN30, or overexpressing MRE11-H125N (nuclease-dead allele of MRE11), for DSB end resection markedly increased the efficiency of CRISPR/SpCas9 (more than 22-fold) and CRISPR/AsCpf1 (more than 30-fold)-induced mutagenesis. Deleting SAE2 or overexpressing MRE11-H125N substantially diversified CRISPR/SpCas9 or AsCpf1-induced mutation 2–3-fold at URA3 locus, and 3–5-fold at ADE2 locus. Thus, the error-prone DNA repair protein was employed to develop a novel mutagenic genome editing (mGE) strategy, which can increase the mutation numbers and effectively improve the ethanol/glycerol ratio of Saccharomyces cerevisiae through modulating the expression of FPS1 and GPD1. This study highlighted the feasibility of potentially reshaping the capability of genome editing by regulating the different DSB repair proteins and can thus expand the application of genome editing in diversifying gene expression and enhancing genome evolution.

IMPORTANCE Most of the published papers about nuclease-assisted genome editing focused on precision engineering in human cells. However, the topic of inducing mutagenesis via error-prone repair has often been ignored in yeast. In this study, we reported that perturbing DNA repair, especially modifications of the various DSB end resection-related proteins, could greatly improve the mutational efficiency and diversity, and thus functionally reshape the capability of the different genome editing tools without requiring an exogenous DNA template in yeast. Specifically, mutagenic genome editing (mGE) was developed based on CRISPR/AsCpf1 and MRE11-H125N overexpression, and used to generate promoters of different strengths more efficiently. Thus, this work provides a novel method to diversify gene expression and enhance genome evolution.

Programmable Synthetic Upstream Activating Sequence Library for Fine-Tuning Gene Expression Levels in Saccharomyces cerevisiae

A wide dynamic range of promoters is necessary for fine-tuning transcription levels. However, weak intensity and narrow dynamic range limit transcriptional regulation via constitutive promoters. The upstream activation sequence (UAS) located upstream of the core promoter is a crucial region that could obviously enhance promoter strength. Herein, we created a random mutagenesis library consisting of 330 different variants based on the UAS of the TDH3 promoter with an ∼37-fold dynamic range by error-prone polymerase chain reaction (PCR) and obtained strong intensity mutant UAS, which was ∼12-fold greater than the wild-type UAS_TDH3. Analysis of the mutant library revealed 15 strength-enhancing sites and their corresponding bases of the UAS_TDH3 regions, which provided the impetus for a synthetic library. The resulting 32 768 mutant UAS library was constructed by permutation and combination of the bases of the 15 enhancing sites. To characterize the library, a strength prediction model was built by correlating DNA structural features and UAS strength, which provided a model between UAS sequence and intensity. Following characterization, the UAS library was applied to precisely regulate gene expression in the production of β-carotene, proving that the UAS library would be a useful tool for gene tuning in metabolic engineering. In summary, we designed, constructed, and characterized a UAS library that facilitated precise tuning of transcription levels of target proteins.

Transcriptional neighborhoods regulate transcript isoform lengths and expression levels

Sequence features of genes and their flanking regulatory regions are determinants of RNA transcript isoform expression and have been used as context-independent plug-and-play modules in synthetic biology. However, genetic context-including the adjacent transcriptional environment-also influences transcript isoform expression levels and boundaries. We used synthetic yeast strains with stochastically repositioned genes to systematically disentangle the effects of sequence and context. Profiling 120 million full-length transcript molecules across 612 genomic perturbations, we observed sequence-independent alterations to gene expression levels and transcript isoform boundaries that were influenced by neighboring transcription. We identified features of transcriptional context that could predict these alterations and used these features to engineer a synthetic circuit where transcript length was controlled by neighboring transcription. This demonstrates how positional context can be leveraged in synthetic genome engineering.

Advancing Yarrowia lipolytica as a superior biomanufacturing platform by tuning gene expression using promoter engineering

Yarrowia lipolytica is recognized as an excellent non-conventional yeast in the field of biomanufacturing, where it is used as a host to produce oleochemicals, terpenes, organic acids, polyols and recombinant proteins. Consequently, metabolic engineering of this yeast is becoming increasingly popular to advance it as a superior biomanufacturing platform, of which promoters are the most basic elements for tuning gene expression. Endogenous promoters of Yarrowia lipolytica were reviewed, which are the basis for promoter engineering. The engineering strategies, such as hybrid promoter engineering, intron enhancement promoter engineering, and transcription factor-based inducible promoter engineering are described. Additionally, the applications of Yarrowia lipolytica promoter engineering to rationally reconstruct biosynthetic gene clusters and improve the genome-editing efficiency of the CRISPR-Cas systems were reviewed. Finally, research needs and future directions for promoter engineering are also discussed in this review.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Enhancing Homologous Recombination Efficiency in Pichia pastoris for Multiplex Genome Integration Using Short Homology Arms

There is a growing interest in establishing the methylotrophic yeast Pichia pastoris as microbial cell factories for producing fuels, chemicals, and natural products, particularly with methanol as the feedstock. Although CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) based genome editing technology has been established for the integration of multigene biosynthetic pathways, long (500-1000 bp) homology arms are generally required, probably due to low homologous recombination (HR) efficiency in P. pastoris. To achieve efficient genome integration of heterologous genes with short homology arms, we aimed to enhance HR efficiency by introducing the recombination machinery from Saccharomyces cerevisiae. First, we overexpressed HR related genes, including RAD52, RAD59, MRE11, and SAE2, and evaluated their effects on genome integration efficiency. Then, we constructed HR efficiency enhanced P. pastoris, which enabled single-, two-, and three-loci integration of heterologous gene expression cassettes with ∼40 bp homology arms with efficiencies as high as 100%, ∼98%, and ∼81%, respectively. Finally, we demonstrated the construction of β-carotene producing strain and the optimization of betaxanthin producing strain in a single step. The HR efficiency enhanced P. pastoris strains can be used for the construction of robust cell factories, and our machinery engineering strategy can be employed for the modification of other nonconventional yeasts.

A programmable high-expression yeast platform responsive to user-defined signals

Rapidly growing yeasts with appropriate posttranslational modifications are favored hosts for protein production in the biopharmaceutical industry. However, limited production capacity and intricate transcription regulation restrict their application and adaptability. Here, we describe a programmable high-expression yeast platform, SynPic-X, which responds to defined signals and is broadly applicable. We demonstrated that a synthetic improved transcriptional signal amplification device (iTSAD) with a bacterial-yeast transactivator and bacterial-yeast promoter markedly increased expression capacity in Pichia pastoris. CRISPR activation and interference devices were designed to strictly regulate iTSAD in response to defined signals. Engineered switches were then constructed to exemplify the response of SynPic-X to exogenous signals. Expression of α-amylase by SynPic-R, a specific SynPic-X, in a bioreactor proved a methanol-free high-production process of recombinant protein. Our SynPic-X platform provides opportunities for protein production in customizable yeast hosts with high expression and regulatory flexibility.

Peptide-Dependent Growth in Yeast via Fine-Tuned Peptide/GPCR-Activated Essential Gene Expression

Building multicellular microbial consortia that communicate with each other and perform programmed functionalities is the next milestone for synthetic biology. Achieving cell-cell communication within these communities requires programming of the transduction of an extracellular signal into a customized intracellular response. G-protein-coupled receptors (GPCRs) are attractive candidates for engineering signal transduction as they can sense extracellular events with high sensitivity and specificity and transduce them into complex intracellular programs. We recently developed a scalable cell-cell communication language based on fungal mating GPCRs and their secreted peptide ligands. This language allows the assembly of engineered yeast strains into multicellular communication networks and allows them to be made interdependent by peptide signaling. In peptide signaling, one cell secretes a peptide that supports the growth of another cell at nanomolar concentrations, a scalable approach for engineering interdependence. Here we address the challenge of correlating the doubling time of Saccharomyces cerevisiae cells with an increasing external peptide concentration by linking GPCR activation to the expression of an essential gene. The required fine-tuning of downstream signaling is achieved via the transcriptional titration of a set of orthogonal GPCR-activated transcription factors, a series of corresponding promoters with different output dynamics, and the use of chemically recoded peptide ligands with varying activation potentials. As such, our work establishes three control points that allow the tuning of the basal and maximal activation of the GPCR response, fold change activation, and response sensitivity. The presented results enable the implementation of peptide-dependent and peptide-tunable growth but could also facilitate the design and calibration of more complex GPCR-controlled synthetic functionality in the future.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories

Terpenoids form the most diversified class of natural products, which have gained application in the pharmaceutical, food, transportation, and fine and bulk chemical industries. Extraction from naturally occurring sources does not meet industrial demands, whereas chemical synthesis is often associated with poor enantio-selectivity, harsh working conditions, and environmental pollutions. Microbial cell factories come as a suitable replacement. However, designing efficient microbial platforms for isoprenoid synthesis is often a challenging task. This has to do with the cytotoxic effects of pathway intermediates and some end products, instability of expressed pathways, as well as high enzyme promiscuity. Also, the low enzymatic activity of some terpene synthases and prenyltransferases, and the lack of an efficient throughput system to screen improved high-performing strains are bottlenecks in strain development. Metabolic engineering and synthetic biology seek to overcome these issues through the provision of effective synthetic tools. This review sought to provide an in-depth description of novel strategies for improving cell factory performance. We focused on improving transcriptional and translational efficiencies through static and dynamic regulatory elements, enzyme engineering and high-throughput screening strategies, cellular function enhancement through chromosomal integration, metabolite tolerance, and modularization of pathways.

Investigating Plant Biosynthetic Pathways Using Heterologous Gene Expression: Yeast as a Heterologous Host

Plant natural products (PNPs) are valuable resources for the development of pharmaceuticals and agrochemicals, yet the biosynthesis and metabolism of PNPs are largely unknown. Heterologous pathway reconstitution is a heavily adopted strategy in secondary metabolism characterization. Yeast systems have been broadly utilized in the heterologous production of PNPs and have been considered as a promising platform to investigate plant biosynthetic pathways. Here, we describe the reconstitution and verification of the upstream part of brassinolide biosynthesis in S. cerevisiae using this method.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Engineering of cis-Element in Saccharomyces cerevisiae for Efficient Accumulation of Value-Added Compound Squalene via Downregulation of the Downstream Metabolic Flux

Transcriptional downregulation is widely used for metabolic flux control. Here, marO, a cis-element of Escherichia coli mar operator, was explored to engineer promoters of Saccharomyces cerevisiae for downregulation. First, the ADH1 promoter (P_ADH1) and its enhanced variant P_UADH1 were engineered by insertion of marO into different sites, which resulted in decrease in both gfp5 transcription and GFP fluorescence intensity to various degrees. Then, marO was applied to engineer the native ERG1 and ERG11 promoters due to their importance for accumulation of value-added intermediates squalene and lanosterol. Elevated squalene content (4.9-fold) or lanosterol content (4.8-fold) and 91 or 28% decrease in ergosterol content resulted from the marO-engineered promoter P_ERG1(M5) or P_ERG11(M3), respectively, indicating the validity of the marO-engineered promoters in metabolic flux control. Furthermore, squalene production of 3.53 g/L from cane molasses, a cheap and bulk substrate, suggested the cost-effective and promising potential for squalene production.

Synthetic biology applications of the yeast mating signal pathway

Cell fusion is a fundamental biological process that is involved in the development of most eukaryotic organisms. During the fusion process in Saccharomyces cerevisiae, cells respond to pheromones to trigger the MAPK (mitogen-activated protein kinase) cascade to initiate mating, followed by polarization, cell-wall remodeling, membrane fusion, and karyogamy. We highlight the applications of the yeast mating signal pathway in promoter engineering for tuning the expression of output genes, as well as in metabolic engineering for decoupling growth and metabolism, biosensors for sensitive detection and signal amplification, genetic circuits for programmable biological functionalities, and artificial consortia for cell-cell communication. Strategies such as exploiting rational engineering of modular circuits and optimizing the reproductive pathway to precisely maneuver physiological events have implications for scientific research and industrial development.

Profiling of chromatin accessibility identifies transcription factor binding sites across the genome of Aspergillus species

Background

The identification of open chromatin regions and transcription factor binding sites (TFBs) is an important step in understanding the regulation of gene expression in diverse species. ATAC-seq is a technique used for such purpose by providing high-resolution measurements of chromatin accessibility revealed through integration of Tn5 transposase. However, the existence of cell walls in filamentous fungi and associated difficulty in purifying nuclei have precluded the routine application of this technique, leading to a lack of experimentally determined and computationally inferred data on the identity of genome-wide cis-regulatory elements (CREs) and TFBs. In this study, we constructed an ATAC-seq platform suitable for filamentous fungi and generated ATAC-seq libraries of Aspergillus niger and Aspergillus oryzae grown under a variety of conditions.

Results

We applied the ATAC-seq assay for filamentous fungi to delineate the syntenic orthologue and differentially changed chromatin accessibility regions among different Aspergillus species, during different culture conditions, and among specific TF-deleted strains. The syntenic orthologues of accessible regions were responsible for the conservative functions across Aspergillus species, while regions differentially changed between culture conditions and TFs mutants drove differential gene expression programs. Importantly, we suggest criteria to determine TFBs through the analysis of unbalanced cleavage of distinct TF-bound DNA strands by Tn5 transposase. Based on this criterion, we constructed data libraries of the in vivo genomic footprint of A. niger under distinct conditions, and generated a database of novel transcription factor binding motifs through comparison of footprints in TF-deleted strains. Furthermore, we validated the novel TFBs in vivo through an artificial synthetic minimal promoter system.

Conclusions

We characterized the chromatin accessibility regions of filamentous fungi species, and identified a complete TFBs map by ATAC-seq, which provides valuable data for future analyses of transcriptional regulation in filamentous fungi.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01114-0.

Enhanced Prodigiosin Production in Serratia marcescens JNB5-1 by Introduction of a Polynucleotide Fragment into the pigN 3' Untranslated Region and Disulfide Bonds into O-Methyl Transferase (PigF)

In Serratia marcescens JNB5-1, prodigiosin was highly produced at 30°C, but it was noticeably repressed at ≥37°C. Our initial results demonstrated that both the production and the stability of the O-methyl transferase (PigF) and oxidoreductase (PigN) involved in the prodigiosin pathway in S. marcescens JNB5-1 sharply decreased at ≥37°C. Therefore, in this study, we improved mRNA stability and protein production using de novo polynucleotide fragments (PNFs) and the introduction of disulfide bonds, respectively, and observed their effects on prodigiosin production. Our results demonstrate that adding PNFs at the 3' untranslated regions of pigF and pigN significantly improved the mRNA half-lives of these genes, leading to an increase in the transcript and expression levels. Subsequently, the introduction of disulfide bonds in pigF improved the thermal stability, pH stability, and copper ion resistance of PigF. Finally, shake flask fermentation showed that the prodigiosin titer with the engineered S. marcescens was increased by 61.38% from 5.36 to 8.65 g/liter compared to the JNB5-1 strain at 30°C and, significantly, the prodigiosin yield increased 2.05-fold from 0.38 to 0.78 g/liter at 37°C. In this study, we revealed that the introduction of PNFs and disulfide bonds greatly improved the expression and stability of pigF and pigN, hence efficiently enhancing prodigiosin production with S. marcescens at 30 and 37°C. IMPORTANCE This study highlights a promising strategy to improve mRNA/enzyme stability and to increase production using de novo PNF libraries and the introduction of disulfide bonds into the protein. PNFs could increase the half-life of target gene mRNA and effectively prevent its degradation. Moreover, PNFs could increase the relative intensity of target genes without affecting the expression of other genes; as a result, it could alleviate the cellular burden compared to other regulatory elements such as promoters. In addition, we obtained a PigF variant with improved activity and stability by the introduction of disulfide bonds into PigF. Collectively, we demonstrate here a novel approach for improving mRNA/enzyme stability using PNFs, which results in enhanced prodigiosin production in S. marcescens at 30°C.

Saccharomyces cerevisiae as host for the recombinant production of polyketides and nonribosomal peptides

As a robust, fast growing and genetically tractable organism, the budding yeast Saccharomyces cerevisiae is one of the most widely used hosts in biotechnology. Its applications range from the manufacturing of vaccines and hormones to bulk chemicals and biofuels. In recent years, major efforts have been undertaken to expand this portfolio to include structurally complex natural products, such as polyketides and nonribosomally synthesized peptides. These compounds often have useful pharmacological properties, which make them valuable drugs for the treatment of infectious diseases, cancer, or autoimmune disorders. In nature, polyketides and nonribosomal peptides are generated by consecutive condensation reactions of short chain acyl-CoAs or amino acids, respectively, with the substrates and reaction intermediates being bound to large, multidomain enzymes. For the reconstitution of these multistep catalytic processes, the enzymatic assembly lines need to be functionally expressed and the required substrates must be supplied in reasonable quantities. Furthermore, the production hosts need to be protected from the toxicity of the biosynthetic products. In this review, we will summarize and evaluate the status quo regarding the heterologous production of polyketides and nonribosomal peptides in S. cerevisiae. Based on a comprehensive literature analysis, prerequisites for a successful pathway reconstitution could be deduced, as well as recurring bottlenecks in this microbial host.

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.

Fine-tuning the expression of pathway gene in yeast using a regulatory library formed by fusing a synthetic minimal promoter with different Kozak variants

BACKGROUND

Tailoring gene expression to balance metabolic fluxes is critical for the overproduction of metabolites in yeast hosts, and its implementation requires coordinated regulation at both transcriptional and translational levels. Although synthetic minimal yeast promoters have shown many advantages compared to natural promoters, their transcriptional strength is still limited, which restricts their applications in pathway engineering.

RESULTS

In this work, we sought to expand the application scope of synthetic minimal yeast promoters by enhancing the corresponding translation levels using specific Kozak sequence variants. Firstly, we chose the reported UAS_F-E-C-Core1 minimal promoter as a library template and determined its Kozak motif (K₀). Next, we randomly mutated the K₀ to generate a chimeric promoter library, which was able to drive green fluorescent protein (GFP) expression with translational strengths spanning a 500-fold range. A total of 14 chimeric promoters showed at least two-fold differences in GFP expression strength compared to the K₀ control. The best one named K₅₂₈ even showed 8.5- and 3.3-fold increases in fluorescence intensity compared with UAS_F-E-C-Core1 and the strong native constitutive promoter P_TDH3, respectively. Subsequently, we chose three representative strong chimeric promoters (K₅₄₀, K₅₃₆, and K₅₂₈) from this library to regulate pathway gene expression. In conjunction with the tHMG1 gene for squalene production, the K₅₂₈ variant produced the best squalene titer of 32.1 mg/L in shake flasks, which represents a more than 10-fold increase compared to the parental K₀ control (3.1 mg/L).

CONCLUSIONS

All these results demonstrate that this chimeric promoter library developed in this study is an effective tool for pathway engineering in yeast.

Engineering Saccharomyces cerevisiae to produce plant benzylisoquinoline alkaloids

Benzylisoquinoline alkaloids (BIAs) are a diverse family of plant natural products with extensive pharmacological properties, but the yield of BIAs from plant is limited. The understanding of BIA biosynthetic mechanism in plant and the development of synthetic biology enable the possibility to produce BIAs through microbial fermentation, as an alternative to agriculture-based supply chains. In this review, we discussed the engineering strategies to synthesize BIAs in Saccharomyces cerevisiae (yeast) and improve BIA production level, including heterologous pathway reconstruction, enzyme engineering, expression regulation, host engineering and fermentation engineering. We also highlight recent metabolic engineering advances in the production of BIAs in yeast.

Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae

This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.

Yeast Synthetic Minimal Biosensors for Evaluating Protein Production

The unfolded protein response (UPR) is a highly conserved cellular response in eukaryotic cells to counteract endoplasmic reticulum (ER) stress, typically triggered by unfolded protein accumulation. In addition to its relevance to human diseases like cancer, the induction of the UPR has a significant impact on the recombinant protein production in eukaryotic cell factories, including the industrial workhorseSaccharomyces cerevisiae. Being able to accurately detect and measure this ER stress response in single cells, enables the rapid optimization of protein production conditions and high-throughput strain selection strategies. Current methodologies to monitor the UPR in S. cerevisiae are often temporally and spatially removed from the cultivation stage or lack updated systematic evaluation. To this end, we constructed and systematically evaluated a series of high-throughput UPR sensors by different designs, incorporating either yeast native UPR promoters or novel synthetic minimal UPR promoters. The native promoters of DER1 and ERO1 were identified to have suitable UPR biosensor properties and served as an expression level guide for orthogonal sensor benchmarking. Our best synthetic minimal sensor is only 98 bp in length, has minimal homology to other native yeast sequences and displayed superior sensor characteristics. The synthetic minimal UPR sensor was able to accurately distinguish between cells expressing different heterologous proteins and between the different secretion levels of the same protein. This work demonstrated the potential of synthetic UPR biosensors as high-throughput tools to predict the protein production capacity of strains, interrogate protein properties hampering their secretion, and guide rational engineering strategies for optimal heterologous protein production.

An Internal Promoter Drives the Expression of a Truncated Form of CCC1 Capable of Protecting Yeast from Iron Toxicity

In yeast, iron storage and detoxification depend on the Ccc1 transporter that mediates iron accumulation in vacuoles. While deletion of the CCC1 gene renders cells unable to survive under iron overload conditions, the deletion of its previously identified regulators only partially affects survival, indicating that the mechanisms controlling iron storage and detoxification in yeast are still far from well understood. This work reveals that CCC1 is equipped with a complex transcriptional structure comprising several regulatory regions. One of these is located inside the coding sequence of the gene and drives the expression of a short transcript encoding an N-terminally truncated protein, designated as s-Ccc1. s-Ccc1, though less efficiently than Ccc1, is able to promote metal accumulation in the vacuole, protecting cells against iron toxicity. While the expression of the s-Ccc1 appears to be repressed in the normal genomic context, our current data clearly demonstrates that it is functional and has the capacity to play a role under iron overload conditions.

Data-Driven and in Silico-Assisted Design of Broad Host-Range Minimal Intrinsic Terminators Adapted for Bacteria

Efficient transcription termination relying on intrinsic terminators is critical to maintain cell fitness by avoiding unwanted read-through in bacteria. Natural intrinsic terminator (NIT) typically appears in mRNA as a hairpin followed by approximately eight conserved uridines (U-tract) at the 3' terminus. Owing to their simple structure, small size, and protein independence, assorted NITs have been redesigned as robust tools to construct gene circuits. However, most NITs exert functions to adapt to their physiological requirements rather than the demand for building synthetic gene circuits, rendering uncertain working performance when they are constructed intact in synthetic gene circuits. Here, rather than modifying NITs, we established a data-driven and in silico-assisted (DISA) design framework to forward engineer minimal intrinsic terminators (MITs). By comprehensively analyzing 75 natural intrinsic terminators from Bacillus subtilis, we revealed that two pivotal features, the length of the U-tract and the thermodynamics of the terminator hairpin, were involved in the sequence-activity relationship (SAR) of termination efficiency (TE). As per the SAR, we leveraged DISA to fabricate an array of MITs composed of in silico-assisted designed minimal hairpins and fixed U-tracts. Most of these MITs exhibited high TE in diverse Gram-positive and Gram-negative bacteria. In contrast, the TEs of the NITs were highly varied in different hosts. Moreover, TEs of MITs were flexibly tuned over a wide range by modulating the length of the U-tract. Overall, these results demonstrate an efficient framework to forward design functional and broad host-range terminators independent of tedious and iterative screening of mutagenesis libraries of natural terminators.

A Computational Framework for Identifying Promoter Sequences in Nonmodel Organisms Using RNA-seq Data Sets

Engineering microorganisms into biological factories that convert renewable feedstocks into valuable materials is a major goal of synthetic biology; however, for many nonmodel organisms, we do not yet have the genetic tools, such as suites of strong promoters, necessary to effectively engineer them. In this work, we developed a computational framework that can leverage standard RNA-seq data sets to identify sets of constitutive, strongly expressed genes and predict strong promoter signals within their upstream regions. The framework was applied to a diverse collection of RNA-seq data measured for the methanotroph Methylotuvimicrobium buryatense 5GB1 and identified 25 genes that were constitutively, strongly expressed across 12 experimental conditions. For each gene, the framework predicted short (27-30 nucleotide) sequences as candidate promoters and derived -35 and -10 consensus promoter motifs (TTGACA and TATAAT, respectively) for strong expression in M. buryatense. This consensus closely matches the canonical E. coli sigma-70 motif and was found to be enriched in promoter regions of the genome. A subset of promoter predictions was experimentally validated in a XylE reporter assay, including the consensus promoter, which showed high expression. The pmoC, pqqA, and ssrA promoter predictions were additionally screened in an experiment that scrambled the -35 and -10 signal sequences, confirming that transcription initiation was disrupted when these specific regions of the predicted sequence were altered. These results indicate that the computational framework can make biologically meaningful promoter predictions and identify key pieces of regulatory systems that can serve as foundational tools for engineering diverse microorganisms for biomolecule production.

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.

Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era

Simple Summary

Promoters are DNA sequences where the process of transcription starts. They can work constitutively or be controlled by environmental signals of different types. The quantity of proteins and RNA present in yeast genetic circuits highly depends on promoter strength. Hence, they have been deeply studied and modified over, at least, the last forty years, especially since the year 2000 when Synthetic Biology was born. Here, we present how promoter engineering changed over these four decades and discuss its possible future directions due to novel computational methods and technology.

Abstract

Synthetic gene circuits are made of DNA sequences, referred to as transcription units, that communicate by exchanging proteins or RNA molecules. Proteins are, mostly, transcription factors that bind promoter sequences to modulate the expression of other molecules. Promoters are, therefore, key components in genetic circuits. In this review, we focus our attention on the construction of artificial promoters for the yeast S. cerevisiae, a popular chassis for gene circuits. We describe the initial techniques and achievements in promoter engineering that predated the start of the Synthetic Biology epoch of about 20 years. We present the main applications of synthetic promoters built via different methods and discuss the latest innovations in the wet-lab engineering of novel promoter sequences.

Novel S. cerevisiae Hybrid Synthetic Promoters Based on Foreign Core Promoter Sequences

Promoters are fundamental components of synthetic gene circuits. They are DNA segments where transcription initiation takes place. New constitutive and regulated promoters are constantly engineered in order to meet the requirements for protein and RNA expression into different genetic networks. In this work, we constructed and optimized new synthetic constitutive promoters for the yeast Saccharomyces cerevisiae. We started from foreign (e.g., viral) core promoters as templates. They are, usually, unfunctional in yeast but can be activated by extending them with a short sequence, from the CYC1 promoter, containing various transcription start sites (TSSs). Transcription was modulated by mutating the TATA box composition and varying its distance from the TSS. We found that gene expression is maximized when the TATA box has the form TATAAAA or TATATAA and lies between 30 and 70 nucleotides upstream of the TSS. Core promoters were turned into stronger promoters via the addition of a short UAS. In particular, the 40 nt bipartite UAS from the GPD promoter can enhance protein synthesis considerably when placed 150 nt upstream of the TATA box. Overall, we extended the pool of S. cerevisiae promoters with 59 new samples, the strongest overcoming the native TEF2 promoter.

Improving the design of an oxidative stress sensing biosensor in yeast

Transcription factor (TF)-based biosensors have proven useful for increasing biomanufacturing yields, large-scale functional screening, and in environmental monitoring. Most yeast TF-based biosensors are built from natural promoters, resulting in large DNA parts retaining considerable homology to the host genome, which can complicate biological engineering efforts. There is a need to explore smaller, synthetic biosensors to expand the options for regulating gene expression in yeast. Here, we present a systematic approach to improving the design of an existing oxidative stress sensing biosensor in Saccharomyces cerevisiae based on the Yap1 transcription factor. Starting from a synthetic core promoter, we optimized the activity of a Yap1-dependent promoter through rational modification of a minimalist Yap1 upstream activating sequence. Our novel promoter achieves dynamic ranges of activation surpassing those of the previously engineered Yap1-dependent promoter, while reducing it to only 171 base pairs. We demonstrate that coupling the promoter to a positive-feedback-regulated TF further improves the biosensor by increasing its dynamic range of activation and reducing its limit of detection. We have illustrated the robustness and transferability of the biosensor by reproducing its activity in an unconventional probiotic yeast strain, Saccharomyces boulardii. Our findings can provide guidance in the general process of TF-based biosensor design.

Robust and flexible platform for directed evolution of yeast genetic switches

A wide repertoire of genetic switches has accelerated prokaryotic synthetic biology, while eukaryotic synthetic biology has lagged in the model organism Saccharomyces cerevisiae. Eukaryotic genetic switches are larger and more complex than prokaryotic ones, complicating the rational design and evolution of them. Here, we present a robust workflow for the creation and evolution of yeast genetic switches. The selector system was designed so that both ON- and OFF-state selection of genetic switches is completed solely by liquid handling, and it enabled parallel screen/selection of different motifs with different selection conditions. Because selection threshold of both ON- and OFF-state selection can be flexibly tuned, the desired selection conditions can be rapidly pinned down for individual directed evolution experiments without a prior knowledge either on the library population. The system's utility was demonstrated using 20 independent directed evolution experiments, yielding genetic switches with elevated inducer sensitivities, inverted switching behaviours, sensory functions, and improved signal-to-noise ratio (>100-fold induction). The resulting yeast genetic switches were readily integrated, in a plug-and-play manner, into an AND-gated carotenoid biosynthesis pathway.

Synthetic biology-driven microbial production of folates: Advances and perspectives

With the development and application of synthetic biology, significant progress has been made in the production of folate by microbial fermentation using cell factories, especially for using generally regarded as safe (GRAS) microorganism as production host. In this review, the physiological functions and applications of folates were firstly discussed. Second, the current advances of folate-producing GRAS strains development were summarized. Third, the applications of synthetic biology-based metabolic regulatory tools in GRAS strains were introduced, and the progress in the application of these tools for folate production were summarized. Finally, the challenges to folates efficient production and corresponding emerging strategies to overcome them by synthetic biology were discussed, including the construction of biosensors using tetrahydrofolate riboswitches to regulate metabolic pathways, adaptive evolution to overcome the flux limitations of the folate pathway. The combination of new strategies and tools of synthetic biology is expected to further improve the efficiency of microbial folate synthesis.

Quorum sensing-mediated protein degradation for dynamic metabolic pathway control in Saccharomyces cerevisiae

Dynamic regulation has been widely applied to optimize metabolic flux distribution. However, compared with prokaryotes, quorum sensing-mediated pathway control is still very limited in Saccharomyces cerevisiae. In this study, we designed quorum sensing-regulated protein degradation circuits for dynamic metabolic pathway control in S. cerevisiae. The synthetic quorum sensing circuits were developed by integration of a plant hormone cytokinin system with the endogenous yeast Ypd1-Skn7 signal transduction pathway and the positive feedback circuits were optimized by promoter engineering. We then constructed an auxin-inducible protein degradation system and used quorum sensing circuits to regulate auxin synthesis to achieve dynamic control of protein degradation. As a demonstration, the circuits were applied to control Erg9 degradation to produce α-farnesene and the titer of α-farnesene increased by 80%. The population-regulated protein degradation system developed here extends dynamic regulation to the protein level in S. cerevisiae and is a promising approach for metabolic pathway control.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

Rational design of minimal synthetic promoters for plants

Promoters serve a critical role in establishing baseline transcriptional capacity through the recruitment of proteins, including transcription factors. Previously, a paucity of data for cis-regulatory elements in plants meant that it was challenging to determine which sequence elements in plant promoter sequences contributed to transcriptional function. In this study, we have identified functional elements in the promoters of plant genes and plant pathogens that utilize plant transcriptional machinery for gene expression. We have established a quantitative experimental system to investigate transcriptional function, investigating how identity, density and position contribute to regulatory function. We then identified permissive architectures for minimal synthetic plant promoters enabling the computational design of a suite of synthetic promoters of different strengths. These have been used to regulate the relative expression of output genes in simple genetic devices.

Rational design of minimal synthetic promoters for plants

Promoters serve a critical role in establishing baseline transcriptional capacity through the recruitment of proteins, including transcription factors. Previously, a paucity of data for cis-regulatory elements in plants meant that it was challenging to determine which sequence elements in plant promoter sequences contributed to transcriptional function. In this study, we have identified functional elements in the promoters of plant genes and plant pathogens that utilize plant transcriptional machinery for gene expression. We have established a quantitative experimental system to investigate transcriptional function, investigating how identity, density and position contribute to regulatory function. We then identified permissive architectures for minimal synthetic plant promoters enabling the computational design of a suite of synthetic promoters of different strengths. These have been used to regulate the relative expression of output genes in simple genetic devices.

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.