Identification and characterization of ethanol-inducible promoters of Zymomonas mobilis based on omics data and dual reporter-gene system.

Zymomonas mobilis is a model bacterial ethanologen and has been engineered to produce lignocellulosic biofuels and biochemicals such as 2,3-butanediol. We have previously identified promoters of different strengths using systems biology datasets and characterized them using the flow cytometry-based dual reporter-gene system. Here, we further demonstrated the capability of applying the dual reporter-gene system and omics datasets on discovering inducible promoters. Ten candidate ethanol-inducible promoters were identified through omics datasets mining and clustering. Using the dual reporter-gene system, these promoters were characterized under natural growth, ethanol stress, and ethanol-induced condition to investigate the transcriptional strength and ethanol inducibility. The results demonstrated that three promoters of P0405, P0435, and P0038 driving the expression of native genes of ZMO0405, ZMO0435, and ZMO0038, correspondingly, are potential ethanol-responsive promoters and may be growth related. This study not only identified and verified three ethanol-inducible promoters as biological parts, which can be used to synchronize the expression of heterologous pathway genes with the ethanol production process of Z. mobilis, but also demonstrated the power of combining omics datasets and dual reporter-gene system to identify biological parts for metabolic engineering and synthetic biology applications in Z. mobilis and related microorganisms.

iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition.

The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.

Influence of cis Element Arrangement on Promoter Strength in Trichoderma reesei.

Trichoderma reesei can produce up to 100 g/liter of extracellular proteins. The major and industrially relevant products are cellobiohydrolase I (CBHI) and the hemicellulase XYNI. The genes encoding both enzymes are transcriptionally activated by the regulatory protein Xyr1. The first 850 nucleotides of the cbh1 promoter contain 14 Xyr1-binding sites (XBS), and 8 XBS are present in the xyn1 promoter. Some of these XBS are arranged in tandem and others as inverted repeats. One such cis element, an inverted repeat, plays a crucial role in the inducibility of the xyn1 promoter. We investigated the impact of the properties of such cis elements by shuffling them by insertion, exchange, deletion, and rearrangement of cis elements in both the cbh1 and xyn1 promoter. A promoter-reporter assay using the Aspergillus nigergoxA gene allowed us to measure changes in the promoter strength and inducibility. Most strikingly, we found that an inverted repeat of XBS causes an important increase in cbh1 promoter strength and allows induction by xylan or wheat straw. Furthermore, evidence is provided that the distances of cis elements to the transcription start site have important influence on promoter activity. Our results suggest that the arrangement and distances of cis elements have large impacts on the strength of the cbh1 promoter, whereas the sheer number of XBS has only secondary importance. Ultimately, the biotechnologically important cbh1 promoter can be improved by cis element rearrangement.IMPORTANCE In the present study, we demonstrate that the arrangement of cis elements has a major impact on promoter strength and inducibility. We discovered an influence on promoter activity by the distances of cis elements to the transcription start site. Furthermore, we found that the configuration of cis elements has a greater effect on promoter strength than does the sheer number of transactivator binding sites present in the promoter. Altogether, the arrangement of cis elements is an important factor that should not be overlooked when enhancement of gene expression is desired.

Condition-specific promoter activities in Saccharomyces cerevisiae.

BACKGROUND: Saccharomyces cerevisiae is widely studied for production of biofuels and biochemicals. To improve production efficiency under industrially relevant conditions, coordinated expression of multiple genes by manipulating promoter strengths is an efficient approach. It is known that gene expression is highly dependent on the practically used environmental conditions and is subject to dynamic changes. Therefore, investigating promoter activities of S. cerevisiae under different culture conditions in different time points, especially under stressful conditions is of great importance. RESULTS: In this study, the activities of various promoters in S. cerevisiae under stressful conditions and in the presence of xylose were characterized using yeast enhanced green fluorescent protein (yEGFP) as a reporter. The stresses include toxic levels of acetic acid and furfural, and high temperature, which are related to fermentation of lignocellulosic hydrolysates. In addition to investigating eight native promoters, the synthetic hybrid promoter P3xC-TEF1 was also evaluated. The results revealed that P TDH3 and the synthetic promoter P3xC-TEF1 showed the highest strengths under almost all the conditions. Importantly, these two promoters also exhibited high stabilities throughout the cultivation. However, the strengths of P ADH1 and P PGK1 , which are generally regarded as 'constitutive' promoters, decreased significantly under certain conditions, suggesting that cautions should be taken to use such constitutive promoters to drive gene expression under stressful conditions. Interestingly, P HSP12 and P HSP26 were able to response to both high temperature and acetic acid stress. Moreover, P HSP12 also led to moderate yEGFP expression when xylose was used as the sole carbon source, indicating that this promoter could be used for inducing proper gene expression for xylose utilization. CONCLUSION: The results here revealed dynamic changes of promoter activities in S. cerevisiae throughout batch fermentation in the presence of inhibitors as well as using xylose. These results provide insights in selection of promoters to construct S. cerevisiae strains for efficient bioproduction under practical conditions. Our results also encouraged applications of synthetic promoters with high stability for yeast strain development.

Induced gene expression in industrial Saccharomyces pastorianus var. carlsbergensis TUM 34/70: evaluation of temperature and ethanol inducible native promoters.

Induced gene expression is an important trait in yeast metabolic engineering, but current regulations prevent the use of conventional expression systems, such as galactose and copper, in food and beverage fermentations. This article examines the suitability of temperature-inducible native promoters for use in the industrial yeast strain Saccharomyces pastorianus var. carlsbergensis TUM 34/70 under brewing conditions. Ten different promoters were cloned and characterized under varying temperature shifts and ethanol concentrations using a green fluorescent protein reporter. The activities of these promoters varied depending upon the stress conditions applied. A temperature shift to 4°C led to the highest fold changes of PSSA3, PUBI4 and PHSP104 by 5.4, 4.5 and 5.0, respectively. Ethanol shock at 24°C showed marked, concentration-dependent induction of the promoters. Here, PHSP104 showed its highest induction at ethanol concentrations between 4% (v/v) and 6% (v/v). The highest fold changes of PSSA3 and PUBI4 were found at 10% (v/v) ethanol. In comparison, the ethanol shock at a typical fermentation temperature (12°C) leads to lower induction patterns of these promoters. Taken together, the data show that three promoters (PHSP104, PUBI4 and PSSA3) have high potential for targeted gene expression in self-cloning brewing yeast using temperature shifts.

Structural and functional analysis of an asymmetric bidirectional promoter in Arabidopsis thaliana.

Bidirectional promoters are relatively abundant in eukaryotic genomes, suggesting that they have an important biological significance. As yet, few of these promoters have been characterized in detail. Here, using a promoter::GUS transgene approach has revealed that the intergenic region of Arabidopsis thaliana divergent genes At1g71850 and At1g71860 is an asymmetric bidirectional promoter, which exhibits an orientation-dependent expression profile. The strength of the forward promoter was greater than that of the reverse promoter, and their tissue specificities were not identical. Deletion analyses revealed that this bidirectional promoter could be divided into three functional regions. The basal level and tissue specificity of the promoter in the reverse orientation were regulated positively by region II and negatively by region III, whereas promoter activity in the forward orientation was regulated negatively by region II and positively by region I. Thus the 52-bp stretch of region II had a dual function, enhancing expression in the reverse orientation and suppressing it in the forward orientation. These results demonstrated that the activity of the At1g71850-At1g71860 bidirectional promoter was modulated by complex interactions between both positive and negative cis-acting elements. These findings will enhance our understanding of the regulatory mechanisms of plant bidirectional promoters.

Bioluminescence-based measurement of viability of Pseudomonas aeruginosa ATCC 9027 harbouring plasmid-based lux genes under the control of constitutive promoters.

AIMS: Detection of microbial contamination in pharmaceuticals, food and cosmetics has been problematic for several decades. Numerous investigations highlight the urgency for novel methods; development of bioluminescent constructs allows real-time monitoring, rapid analysis and high-throughput screening of products. Microbial growth can be studied by measuring constitutive gene expression. The aim is to develop whole-cell microbial biosensors with Pseudomonas aeruginosa and quantify their growth rate by measuring constitutive expression of lux. METHODS AND RESULTS: Pseudomonas aeruginosa cells were genetically modified to produce bioluminescence constitutively. Strains were characterized by assessing their growth kinetics, plasmid stability and gene expression with bacterial replication. Furthermore, cell viability was measured by fluorescence quantification. Promoter strengths were evaluated by comparing bioluminescence (RLU) per colony-forming units (CFU) at various growth stages and related to promoter sequences. Promoter strength decreased in the order of P(lpp) > P(tat) > P(lysS) > P(ldcC) > P(spc) during exponential phase whilst P(tat) was stronger than P(lpp) during stationary phase. Good correlations between RLU and CFU at 24 h indicated a strong relationship for all bioluminescent strains; however, weaker correlations between RLU and CFU and between fluorescence (RFU) and CFU beyond 24 h indicated that a proportion of cells had lost the ability to culture. CONCLUSIONS: Equivalence analysis showed no significant difference between bioluminescence and plate counting for all five bioluminescent strains. Pseudomonas aeruginosa-containing P(tat) had steady bioluminescence when correlated to CFU (R > 0·9), and together with fluorescence data, it can be concluded that Ps. aeruginosa ATCC 9027 tat-pMElux is preferred for testing microbial viability. SIGNIFICANCE AND IMPACT OF THE STUDY: These whole-cell bioluminescent strains provide a platform for utilization in monitoring toxicity and contamination of compounds in environmental biology and microbial ecology, preservative efficacy testing (PET) in the pharmaceutical cosmetics and food industries; the use of such biosensors provides an alternative, fast and efficient method to traditional methods.

iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.

The promoter is a key DNA sequence whose primary function is to control the initiation time and the degree of expression of gene transcription. Accurate identification of promoters is essential for understanding gene expression studies. Traditional sequencing techniques for identifying promoters are costly and time-consuming. Therefore, the development of computational methods to identify promoters has become critical. Since deep learning methods show great potential in identifying promoters, this study proposes a new promoter prediction model, called iPro2L-DG. The iPro2L-DG predictor, based on an improved Densely Connected Convolutional Network (DenseNet) and a Global Attention Mechanism (GAM), is constructed to achieve the prediction of promoters. The promoter sequences are combined feature encoding using C2 encoding and nucleotide chemical property (NCP) encoding. An improved DenseNet extracts advanced feature information from the combined feature encoding. GAM evaluates the importance of advanced feature information in terms of channel and spatial dimensions, and finally uses a Full Connect Neural Network (FNN) to derive prediction probabilities. The experimental results showed that the accuracy of iPro2L-DG in the first layer (promoter identification) was 94.10% with Matthews correlation coefficient value of 0.8833. In the second layer (promoter strength prediction), the accuracy was 89.42% with Matthews correlation coefficient value of 0.7915. The iPro2L-DG predictor significantly outperforms other existing predictors in promoter identification and promoter strength prediction. Therefore, our proposed model iPro2L-DG is the most advanced promoter prediction tool. The source code of the iPro2L-DG model can be found in https://github.com/leirufeng/iPro2L-DG.

EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework.

Introduction: In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters. Methods: In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter. Results: In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R2). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R2) compared with non-EVMP SOTA record. Discussion: In further study, extended vision (or k-mer) is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP.

A modular cloning (MoClo) toolkit for reliable intracellular protein targeting in the yeast Saccharomyces cerevisiae.

Modular Cloning (MoClo) allows the combinatorial assembly of plasmids from standardized genetic parts without the need of error-prone PCR reactions. It is a very powerful strategy which enables highly flexible expression patterns without the need of repetitive cloning procedures. In this study, we describe an advanced MoClo toolkit that is designed for the baker's yeast Saccharomyces cerevisiae and optimized for the targeting of proteins of interest to specific cellular compartments. Comparing different targeting sequences, we developed signals to direct proteins with high specificity to the different mitochondrial subcompartments, such as the matrix and the intermembrane space (IMS). Furthermore, we optimized the subcellular targeting by controlling expression levels using a collection of different promoter cassettes; the MoClo strategy allows it to generate arrays of expression plasmids in parallel to optimize gene expression levels and reliable targeting for each given protein and cellular compartment. Thus, the MoClo strategy enables the generation of protein-expressing yeast plasmids that accurately target proteins of interest to various cellular compartments.

Deep Learning-Assisted Design of Novel Promoters in Escherichia coli.

Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.

RBS and Promoter Strengths Determine the Cell-Growth-Dependent Protein Mass Fractions and Their Optimal Synthesis Rates.

Models of gene expression considering host-circuit interactions are relevant for understanding both the strategies and associated trade-offs that cell endogenous genes have evolved and for the efficient design of heterologous protein expression systems and synthetic genetic circuits. Here, we consider a small-size model of gene expression dynamics in bacterial cells accounting for host-circuit interactions due to limited cellular resources. We define the cellular resources recruitment strength as a key functional coefficient that explains the distribution of resources among the host and the genes of interest and the relationship between the usage of resources and cell growth. This functional coefficient explicitly takes into account lab-accessible gene expression characteristics, such as promoter and ribosome binding site (RBS) strengths, capturing their interplay with the growth-dependent flux of available free cell resources. Despite its simplicity, the model captures the differential role of promoter and RBS strengths in the distribution of protein mass fractions as a function of growth rate and the optimal protein synthesis rate with remarkable fit to the experimental data from the literature for Escherichia coli. This allows us to explain why endogenous genes have evolved different strategies in the expression space and also makes the model suitable for model-based design of exogenous synthetic gene expression systems with desired characteristics.

Fine-Tuning Native Promoters of Synechococcus elongatus PCC 7942 To Develop a Synthetic Toolbox for Heterologous Protein Expression.

The cyanobacterium Synechococcus elongatus PCC 7942 is a potential photosynthetic cell-factory. In this study, two native promoters from S. elongatus PCC 7942 driving the expression of abundant cyanobacterial proteins phycocyanin (P cpcB7942) and RuBisCO (P rbc7942) were characterized in relation to their sequence features, expression levels, diurnal behavior, and regulation by light and CO2, major abiotic factors important for cyanobacterial growth. P cpcB7942 was repressed under high light intensity, but cultivation at higher CO2 concentration was able to recover promoter activity. On the other hand, P rbc7942 was repressed by elevated CO2 with a negative regulatory region between 300 and 225 bp. Removal of this region flipped the effect of CO2 with Rbc225 being activated only at high CO2 concentration, besides leading to the loss of circadian rhythm. The results from this study on promoter features and regulation will help expand the repertoire of tools for pathway engineering in cyanobacteria.

PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence.

We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the σ70 factor of RNA polymerase. σ70 promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. We parsed a comprehensive database of bacterial promoters for the -35 and -10 hexamer regions of σ70-binding promoters and used these sequences to construct the respective position weight matrices (PWM). Next we used a well-characterized set of promoters to train a multivariate linear regression model and learn the mapping between PWM scores of the -35 and -10 hexamers and the promoter strength. We found that the log of the promoter strength is significantly linearly associated with a weighted sum of the -10 and -35 sequence profile scores. We applied our model to 100 sets of 100 randomly generated promoter sequences to generate a sampling distribution of mean strengths of random promoter sequences and obtained a mean of 6E-4 ± 1E-7. Our model was further validated by cross-validation and on independent datasets of characterized promoters. PromoterPredict accepts -10 and -35 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more robust estimates of promoter strength. PromoterPredict is available as both a web service (https://promoterpredict.com) and standalone tool (https://github.com/PromoterPredict). Our work presents an intuitive generalization applicable to modelling the strength of other promoter classes.

Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli.

BACKGROUND: UP elements (upstream element) are DNA sequences upstream of a promoter that interact with the α-subunit of RNA polymerase (RNAP) and can affect transcription by altering the binding RNAP to DNA. However, details of UP element and binding affinity effects on transcriptional strength are unclear. RESULTS: Here, we investigated the effects of UP element sequences on gene transcription, binding affinity, and gene expression noise. Addition of UP elements resulted in increased gene expression (maximum 95.7-fold increase) and reduced gene expression noise (8.51-fold reduction). Half UP element sequences at the proximal subsite has little effect on transcriptional strength despite increasing binding affinity by 2.28-fold. In vitro binding assays were used to determine dissociation constants (Kd) and in the in vitro system, the full range of gene expression occurs in a small range of dissociation constants (25 nM < Kd < 45 nM) indicating that transcriptional strength is highly sensitive to small changes in binding affinity. CONCLUSIONS: These results demonstrate the utility of UP elements and provide mechanistic insight into the functional relationship between binding affinity and transcription. Given the centrality of gene expression via transcription to biology, additional insight into transcriptional mechanisms can foster both fundamental and applied research. In particular, knowledge of the DNA sequence-specific effects on expression strength can aid in promoter engineering for different organisms and for metabolic engineering to balance pathway fluxes.