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

Efficient expression of recombinant proteins in Bacillus subtilis using a rewired gene circuit of quorum sensing

Bacillus subtilis is a favored chassis for high productivity of several high value-added product in synthetic biology. Efficient production of recombinant proteins is critical but challenging using this chassis because these expression systems in use, such as constitutive and inducible expression systems, demand for coordination of cell growth with production and addition of chemical inducers. These systems compete for intracellular resources with the host, eventually resulting in dysfunction of cell survival. To overcome the problem, in this study, LuxRI quorum sensing (QS) system from Aliivibrio fischeri was functionally reconstituted in B. subtilis for achieving coordinated protein overproduction with cell growth in a cell-density-dependent manner. Furthermore, the output-controlling promoter, PluxI, was engineered through two rounds of evolution, by which we identified four mutants, P22, P47, P56, and P58 that exhibited elevated activity compared to the original PluxI. By incorporating a strong terminator (TB5) downstream of the target gene further enhanced expression level. The expression level of this system surpasses commonly used promoter-based systems in B. subtilis like P43 and PylbP. The LuxRI QS system proves to be a potent platform for recombinant protein overproduction in B. subtilis.

Regulatory significance of terminator: A systematic approach for dissecting terminator-mediated enhancement of upstream mRNA stability

The primary function of terminators is to terminate transcription in gene expression. Although some studies have suggested that terminators also contribute positively to upstream gene expression, the extent and underlying mechanism of this effect remain largely unexplored. Here, the correlation between terminating strength and upstream mRNA stability was investigated by constructing a terminator mutation library through randomizing 5 nucleotides, assisted by FlowSeq technology, terminator variants were categorized based on the downstream fluorescence intensity, followed by high-throughput sequencing. To examine the impact of terminators on mRNA stability, the abundance of downstream gene transcripts for each terminator variant was quantified through cDNA sequencing. The results revealed that the transcript abundance controlled by strong terminators was, on average 2.2 times greater than those controlled by weak terminators on average. Moreover, several distinct features could be ascribed to high relative abundance of upstream gene transcript, including a high GC content at the base region of hairpin, and a high AT content in downstream of the U-tract. Additionally, these terminators showed a free energy between -28 and -22 kcal/mol, and a stem length of 14 nt. Finally, these features ascribed the upstream beneficial terminator were validated across various expression systems. By incorporating the optimal terminator downstream of RSF, GSH and HIS in three different strains, the fermentation productions-NMN SAM and VD13 exhibited a remarkable enhancement of 30 %-70 %. The findings presented here uncovered the terminator characteristics contributed to the upstream mRNA stability, providing guiding principles for gene circuit design.

Optimizing multicopy chromosomal integration for stable high-performing strains

The copy number of genes in chromosomes can be modified by chromosomal integration to construct efficient microbial cell factories but the resulting genetic systems are prone to failure or instability from triggering homologous recombination in repetitive DNA sequences. Finding the optimal copy number of each gene in a pathway is also time and labor intensive. To overcome these challenges, we applied a multiple nonrepetitive coding sequence calculator that generates sets of coding DNA sequence (CDS) variants. A machine learning method was developed to calculate the optimal copy number combination of genes in a pathway. We obtained an engineered Yarrowia lipolytica strain for eicosapentaenoic acid biosynthesis in 6 months, producing the highest titer of 27.5 g l-1 in a 50-liter bioreactor. Moreover, the lycopene production in Escherichia coli was also greatly improved. Importantly, all engineered strains of Y. lipolytica, E. coli and Saccharomyces cerevisiae constructed with nonrepetitive CDSs maintained genetic stability.

Creation of Architecturally Minimal Transcriptionally Activating Riboswitches Responsive to Theophylline Reveals an Unconventional Design Strategy

Riboswitches are noncoding RNA switches that are largely utilized in bacteria and play a significant role in synthetic biology. Nonetheless, their natural counterparts possess lengthy sequences and intricate structures, posing challenges for their modular integration into complex gene circuits. Consequently, it is imperative to develop simplified synthetic riboswitches that can be effortlessly incorporated into gene circuits. The conventional approach to generate synthetic riboswitches entails tedious library construction and extensive screening, which frequently yields suboptimal performance. To overcome this obstacle, alternative methods are urgently needed. In this study, we created a novel approach to designing a diverse set of transcription-activating riboswitches that exhibit high performance and broad compatibility. The strategy involved starting with a synthetic theophylline RNA aptamer and designing an expression platform that forms a transcriptional terminator in its inactive state but switches to an antiterminator when it is activated. Several sequences were designed, constructed, and subjected to virtual screening, resulting in the identification of two transcription-activating riboswitches. These riboswitches were then engineered to reduce the basal leakage and increase the activation level through extending the hairpin region using a screened random sequence. These architecturally minimal synthetic riboswitches were highly adapted to different constitutive promoters in a modular manner, generating a differentially responsive output to theophylline. As a proof-of-principle, the synthetic riboswitches were applied to rewire a synthetic quorum-sensing circuit (QSC). The reprogrammed QSC successfully modulated the temporal responsive profile against the activation. This strategy is expected to expand the variety of high-performance riboswitches that are responsive to different ligands, thereby further facilitating the design of complex genetic circuits.

Host-Dependent Improvement of GFP Expression in Pseudomonas putida KT2440 Using Terminators of Metagenomic Origin

Transcriptional terminators are key players in the flow of genetic information, but are often overlooked in circuit design. In this work, we used the Standard European Vector Architecture (SEVA) as a scaffold to investigate the effects of different terminators in the output of a reporter construct expressed in two bacterial species, and found that replacing the conventional T1 and T0 transcriptional terminators of the SEVA vector format with a set of broad-host metagenomic terminators resulted in a significant improvement in the signal of a fluorescent device in Pseudomonas putida KT2440 but not in Escherichia coli DH10B. Our results suggest that replacing the default set of terminators present in the SEVA specification may be an useful strategy for fine-tuning circuit expression in P. putida, which can be leveraged for the development of new devices with improved performance in this microbial host.

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B. subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.

Autoinduction Expression Modules for Regulating Gene Expression in Bacillus subtilis

Although quorum sensing (QS) promoters that can autonomously activate gene expression have been identified and engineered in Bacillus subtilis, researchers focus on quantifying individual promoters while ignoring the interaction between other genetic regulatory elements. Here, we constructed the autoinduction expression modules consisting of promoters responsive to QS ComQXPA, ribosome binding sites (RBSs), and terminators. Using superfolder green fluorescent protein (sfGFP) as a reporter gene, three individual element libraries were generated from 945 promoters, 12,000 RBSs, and 425 terminators by random mutation, de novo design, and database mining strategies, respectively. Then, the efficiency of three libraries in regulating gene expression was further enhanced by engineering the core region of each optimal element. After hybridizing the element libraries, the generated expression modules exhibited a 627-fold range in regulating gene expression without significantly affecting the autoinduction initiation. Here, the hybrid modules with broad expression strength may benefit the application of QS-based autoinduction systems in B. subtilis.

Sequence and thermodynamic characteristics of terminators revealed by FlowSeq and the discrimination of terminators strength

The intrinsic terminator in prokaryotic forms secondary RNA structure and terminates the transcription. However, leaking transcription is common due to varied terminator strength. Besides of the representative hairpin and U-tract structure, detailed sequence and thermodynamic features of terminators were not completely clear, and the effect of terminator on the upstream gene expression was unclearly. Thus, it is still challenging to use terminator to control expression with higher precision. Here, in E. Coli, we firstly determined the effect of the 3′-end sequences including spacer sequences and terminator sequences on the expression of upstream and downstream genes. Secondly, terminator mutation library was constructed, and the thermodynamic and sequence features differing in the termination efficiency were analyzed using the FlowSeq technique. The result showed that under the regulation of terminators, a negative correlation was presented between the expression of upstream and downstream genes (r=−0.60), and the terminators with lower free energy corelated with higher upstream gene expression. Meanwhile, the terminator with longer stem length, more compact loop and perfect U-tract structure was benefit to the transcription termination. Finally, a terminator strength classification model was established, and the verification experiment based on 20 synthetic terminators indicated that the model can distinguish strong and weak terminators to certain extent. The results help to elucidate the role of terminators in gene expression, and the key factors identified are crucial for rational design of terminators, and the model provided a method for terminator strength prediction.

Minimization and optimization of α-amylase terminator for heterologous protein production in Bacillus licheniformis

Terminators serve as the regulatory role in gene transcription termination; however, few researches about terminator optimization have been conducted, which leads to the lack of available and universal terminator for gene expression regulation in Bacillus. To solve this problem and expand synthetic biology toolbox of Bacillus licheniformis, the terminator T1 of endogenous α-amylase gene (amyL) was characterized in this research, with a termination efficiency of 87.81%. Then, we explored and optimized the termination strength of terminator T1 from four aspects: the distance between stop codon and terminator, GC content at the bottom of stem structure, loop size, and U-tract length, and the best terminator T24 was attained by combination optimization strategy, which termination efficiency was increased to 97.97%, better than the commonly used terminator T7 (T7P) from Escherichia coli. Finally, terminator T24 was applied to protein expression, which, respectively, led to 33.00%, 25.93%, and 11.78% increases of green fluorescence intensity, red fluorescence intensity, and keratinase activity, indicating its universality in protein expression. Taken together, this research not only expands a plug-and-play synthetic biology toolbox in B. licheniformis but also provides a reference for the artificial design of versatile intrinsic terminator.

Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators

It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often insufficient for learning features of specific ncRNA families or structural motifs. Aiming to reliably identify intrinsic transcription terminators in bacteria, we introduce a novel pre-training approach that uses inverse folding to generate training data for predicting or identifying a specific family or structural motif of ncRNA. We assess the ability of neural networks to identify secondary structure by systematic in silico mutagenesis experiments. In a study to identify intrinsic transcription terminators as functionally well-understood RNA structural motifs, our inverse folding based pre-training approach significantly boosts the performance of neural network topologies, which outperform previous approaches to identify intrinsic transcription terminators. Inverse-folding based pre-training provides a simple, yet highly effective way to integrate the well-established thermodynamic energy model into deep neural networks for identifying ncRNA families or motifs. The pre-training technique is broadly applicable to a range of network topologies as well as different types of ncRNA families and motifs.

Intrinsic transcriptional terminators are essential regulators in determining the 3’ end of transcripts in bacteria. The underlying mechanism involves RNA secondary structure, where nucleotides fold into a specific hairpin motif. Identifying terminator sequences in bacterial genomes has conventionally been approached with well-established energy models for structural motifs. However, the folding mechanism of transcription terminators is understood only partially, limiting the success of energy-model based identification.

Neural networks have been proposed to overcome these limitations. However, their adoption for predicting and identifying RNA secondary structure has been a double edged sword: Neural networks promise to learn features that are not represented by the energy models, while they are black boxes that lack explicit modeling assumptions and may fail to account for features that are well understandable based on decades-old energy models.

Here, we introduce a pre-training approach for neural networks that uses energy-model based inverse folding of structural motifs. As we demonstrate, this approach “brings back the energy model” to identify transcriptional terminators and overcomes the limitations of previous energy-model based predictions. Our approach works for diverse types of neural networks, and is suitable for the identification of structural motifs of many other RNA molecules beyond transcriptional terminators.

Development and Application of Transcription Terminators for Polyhydroxylkanoates Production in Halophilic Halomonas bluephagenesis TD01

Halomonas bluephagenesis TD01 is one of the ideal chassis for low-cost industrial production based on “Next Generation Industrial Biotechnology,” yet the limited genetically regulatory parts such as transcriptional terminators, which are crucial for tuned regulations on gene expression, have hampered the engineering and applications of the strain. In this study, a series of intrinsic Rho-independent terminators were developed by either genome mining or rational design, and seven of them proved to exhibit higher efficiencies than the canonical strong T7 terminator, among which three terminators displayed high efficiencies over 90%. A preliminary modeling on the sequence-efficiency relationship of the terminators suggested that the poly U sequence regularity, the length and GC content of the stem, and the number and the size of hairpin loops remarkably affected the termination efficiency (TE). The rational and de novo designs of novel synthetic terminators based on the sequence-efficiency relationship and the “main contributor” engineering strategy proved to be effective, and fine-tuned polyhydroxylkanoates production was also achieved by the regulation of these native or synthetic terminators with different efficiencies. Furthermore, a perfectly positive correlation between the promoter activity and the TE was revealed in our study. The study enriches our knowledge of transcriptional termination via its sequence–strength relationship and enables the precise regulation of gene expression and PHA synthesis by intrinsic terminators, contributing to the extensive applications of H. bluephagenesis TD01 in the low-cost production of various chemicals.

Internal Transcription Terminators Control Stoichiometry of ABC Transporters in Cellulolytic Clostridia

The extracellular substrate-binding proteins (SBPs) of ATP-binding cassette (ABC) importers tend to be expressed in excess relative to their cognate translocators, but how the stoichiometry of ABC transporters is controlled remains unclear. Here, we elucidated a mechanism contributing to differential gene expression in operons encoding ABC importers by employing cellulolytic Clostridia species, specifically Ruminiclostridium cellulolyticum. We found that there were usually stem-loop structures downstream of SBP genes, which could prematurely terminate the transcription of ABC importers and were putative internal intrinsic terminators, resulting in high transcript levels of upstream SBP genes and low transcript levels of downstream cognate translocator genes. This was determined by their termination efficiencies. Internal terminators had a lower U content in their 3′ U-rich tracts and longer GC-rich stems, which distinguishes them from canonical terminators and potentially endows them with special termination efficiencies. The pairing of U-rich tracts and the formation of unpaired regions in these internal terminators contributed to their folding energies, affecting the stability of their upstream SBP transcripts. Our findings revealed a strategy of internal transcriptional terminators controlling in vivo stoichiometry of their flanking transcripts.

IMPORTANCE Operons encoding protein complexes or metabolic pathways usually require fine-tuned gene expression ratios to create and maintain the appropriate stoichiometry for biological functions. In this study, a strategy for controlling differential expression of genes in an operon was proposed by utilizing ABC importers from Ruminiclostridium cellulolyticum. We found that a stem-loop structure is introduced into the intergenic regions of operons encoding ABC importers as the putative internal terminator, which results in the premature termination of transcription. Consequently, the stoichiometric ratio of genes flanking terminators is precisely determined by their termination efficiencies and folding energies at the transcriptional level. Thus, it can be utilized as a promising synthetic biology tool to control the differential expression of genes in an operon.

Intelligent host engineering for metabolic flux optimisation in biotechnology

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