Prokaryotic Promoter Research Articles

Engineering artificial cross-species promoters with different transcriptional strengths

As a fundamental tool in synthetic biology, promoters are pivotal in regulating gene expression, enabling precise genetic control and spurring innovation across diverse biotechnological applications. However, most advances in engineered genetic systems rely on host-specific regulation of the genetic portion. With the burgeoning diversity of synthetic biology chassis cells, there emerges a pressing necessity to broaden the universal promoter toolkit spectrum, ensuring adaptability across various microbial chassis cells for enhanced applicability and customization in the evolving landscape of synthetic biology. In this study, we analyzed and validated the primary structures of natural endogenous promoters from Escherichia coli, Bacillus subtilis, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Pichia pastoris, and through strategic integration and rational modification of promoter motifs, we developed a series of cross-species promoters (Psh) with transcriptional activity in five strains (prokaryotic and eukaryotic). This series of cross species promoters can significantly expand the synthetic biology promoter toolkit while providing a foundation and inspiration for standardized development of universal components The combinatorial use of key elements from prokaryotic and eukaryotic promoters presented in this study represents a novel strategy that may offer new insights and methods for future advancements in promoter engineering.

Predicting Promoters in Multiple Prokaryotes with Prompt.

Promoters are important cis-regulatory elements for the regulation of gene expression, and their accurate predictions are crucial for elucidating the biological functions and potential mechanisms of genes. Many previous prokaryotic promoter prediction methods are encouraging in terms of the prediction performance, but most of them focus on the recognition of promoters in only one or a few bacterial species. Moreover, due to ignoring the promoter sequence motifs, the interpretability of predictions with existing methods is limited. In this work, we present a generalized method Prompt (Promoters in multiple prokaryotes) to predict promoters in 16 prokaryotes and improve the interpretability of prediction results. Prompt integrates three methods including RSK (Regression based on Selected k-mer), CL (Contrastive Learning) and MLP (Multilayer Perception), and employs a voting strategy to divide the datasets into high-confidence and low-confidence categories. Results on the promoter prediction tasks in 16 prokaryotes show that the accuracy (Accuracy, Matthews correlation coefficient) of Prompt is greater than 80% in highly credible datasets of 16 prokaryotes, and is greater than 90% in 12 prokaryotes, and Prompt performs the best compared with other existing methods. Moreover, by identifying promoter sequence motifs, Prompt can improve the interpretability of the predictions. Prompt is freely available at https://github.com/duqimeng/PromptPrompt , and will contribute to the research of promoters in prokaryote.

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model.

Native prokaryotic promoters share common sequence patterns,but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.

Prokaryotic and eukaryotic dual-expression plasmid-mediated delivery of Campylobacter jejuni antigens by live-attenuated Salmonella: A strategy for concurrent Th1 and Th2 immune activation and protection in chickens

Salmonella and Campylobacter are food-borne pathogens that significantly affect poultry production and are transmitted to humans. Long-term protection against these pathogens in chicken relies on a balanced Th1 and Th2 response. C. jejuni antigens were screened and a fusion antigen, including CadF + FlaA adhesin and flagellin antigenic fragments was developed and safely delivered by low-endotoxicity S. Typhimurium through pJHL270, a dual-expression plasmid featuring prokaryotic (Ptrc) and eukaryotic (CMV) promoters. Antigen expression in Salmonella and host cells was confirmed by western blotting and IFA. The vaccine construct JOL2999, triggered significant increases in IgY, IgA antibodies, CD4+ and CD8+ T cells, indicating humoral, mucosal, and cell-mediated responses against both pathogens. Elevations in pro-inflammatory cytokines TNFα, INF-γ, IL-2, and IL-4 and MHC I and II cell populations further suggest simultaneous Th1 and Th2 immune activation. Reduced pathogen load and histopathological inflammatory signs in vital organs upon challenge confirmed the protective efficacy in chickens.

Prediction of prokaryotic and eukaryotic promoters based on information-theoretic features

Promoters are DNA regulatory elements located near the transcription start site and are responsible for regulating the transcription of genes. DNA fragments arranged in a certain order form specific functional regions with different information contents. Information theory is the science that studies the extraction, measurement and transmission of information. The genetic information contained in DNA follows the general laws of information storage. Therefore, method in information theory can be used for the analysis of promoters carrying genetic information.In this study, we introduced the concept of information theory to the study of promoter prediction. We used 107 features extracted based on information theory methods and a backpropagation neural network to build a classifier. Then, the trained classifier was applied to predict the promoters of 6 organisms. The average AUCs of the 6 organisms obtained by using hold-out validation and ten-fold cross-validation were 0.885 and 0.886, respectively. The results verified the effectiveness of information-theoretic features in promoter prediction. Considering the possible redundancy in the feature set, we performed feature selection and obtained key feature subsets related to promoter characteristics. The results indicate the potential utility of information-theoretic features in promoter prediction.

TSSUNet-MB – ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning

MotivationComputational promoter prediction (CPP) tools designed to classify prokaryotic promoter regions usually assume that a transcription start site (TSS) is located at a predefined position within each promoter region. Such CPP tools are sensitive to any positional shifting of the TSS in a windowed region, and they are unsuitable for determining the boundaries of prokaryotic promoters. ResultsTSSUNet-MB is a deep learning model developed to identify the TSSs of σ70 promoters. Mononucleotide and bendability were used to encode input sequences. TSSUNet-MB outperforms other CPP tools when assessed using the sequences obtained from the neighborhood of real promoters. TSSUNet-MB achieved a sensitivity of 0.839 and specificity of 0.768 on sliding sequences, while other CPP tool cannot maintain both sensitivities and specificities in a compatible range. Furthermore, TSSUNet-MB can precisely predict the TSS position of σ70 promoter-containing regions with a 10-base accuracy of 77.6%. By leveraging the sliding window scanning approach, we further computed the confidence score of each predicted TSS, which allows for more accurately identifying TSS locations. Our results suggest that TSSUNet-MB is a robust tool for finding σ70 promoters and identifying TSSs.

Optimization of Constitutive Promoters Using a Promoter-Trapping Vector in Burkholderia pyrrocinia JK-SH007.

Selecting suitable promoters to drive gene overexpression can provide significant insight into the development of engineered bacteria. In this study, we analyzed the transcriptome data of Burkholderia pyrrocinia JK-SH007 and identified 54 highly expressed genes. The promoter sequences were located using genome-wide data and scored using the prokaryotic promoter prediction software BPROM to further screen out 18 promoter sequences. We also developed a promoter trap system based on two reporter proteins adapted for promoter optimization in B. pyrrocinia JK-SH007: firefly luciferase encoded by the luciferase gene set (Luc) and trimethoprim (TP)-resistant dihydrofolate reductase (TPr). Ultimately, eight constitutive promoters were successfully inserted into the probe vector and transformed into B. pyrrocinia JK-SH007. The transformants were successfully grown on Tp antibiotic plates, and firefly luciferase expression was determined by measuring the relative light unit (RLU). Five of the promoters (P4, P9, P10, P14, and P19) showed 1.01-2.51-fold higher activity than the control promoter λ phage transcriptional promoter (PRPL). The promoter activity was further validated via qPCR analysis, indicating that promoters P14 and P19 showed stable high transcription levels at all time points. Then, GFP and RFP proteins were overexpressed in JK-SH007. In addition, promoters P14 and P19 were successfully used to drive gene expression in Burkholderia multivorans WS-FJ9 and Escherichia coli S17-1. The two constitutive promoters can be used not only in B. pyrrocinia JK-SH007 itself to gene overexpression but also to expand the scope of application.

An unexpected abundance of bidirectional promoters within Salmonella Typhimurium plasmids.

Transcription of the DNA template, to generate an RNA message, is the first step in gene expression. The process initiates at DNA sequences called promoters. Conventionally, promoters have been considered to drive transcription in a specific direction. However, in recent work, we showed that many prokaryotic promoters can drive divergent transcription. This is a consequence of key DNA sequences for transcription initiation being inherently symmetrical. Here, we used global transcription start site mapping to determine the prevalence of such bidirectional promoters in Salmonella Typhimurium. Surprisingly, bidirectional promoters occur three times more frequently in plasmid components of the genome compared to chromosomal DNA. Implications for the evolution of promoter sequences are discussed.

A CRISPRi-based investigation reveals that multiple promoter elements drive gene expression from the genome of mycobacteriophage D29.

A unique feature found in the genomes of mycobacteriophages such as L5 belonging to the A cluster is the presence of multiple dispersed repeated elements known as stoperators. The phage repressor binds these repeat elements, shutting off transcription globally and thereby promoting lysogeny. Interestingly, the sequence of these stoperators closely matches that of the consensus -35 region of prokaryotic promoters, leading us to propose that they may have a role to play in the initiation of transcription by serving as RNA polymerase binding sites. Mycobacteriophage D29 is closely related to phage L5, and their genome organizations are very similar. As in L5, there are multiple stoperators in the genome of D29. The positions occupied by the stoperators in the two genomes are almost identical. The significant difference between the two phages is that D29 lacks the gene encoding the equivalent of the L5 repressor. Since phage D29 does not produce a repressor, we considered it to be a suitable model for testing our hypothesis that the stoperators function as promoters in the absence of the repressor. To prove our point, we targeted CRISPR guide RNAs against six stoperators. In the case of five out of the six, we found a significant reduction in downstream gene expression and phage growth. Based on this observation and primer extension assays, we conclude that promoting gene expression is likely to be the primary function of stoperators.

IPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory

Background and objectiveThe promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods. MethodsIn this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Secondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was utilized to extract global features. Finally, we adopted the self-attention mechanism to improve the contribution of relatively important features, which further enhances the performance of the model. ResultsOur model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recognition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength prediction. Conclusionsour model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.

A successful hybrid deep learning model aiming at promoter identification

BackgroundThe zone adjacent to a transcription start site (TSS), namely, the promoter, is primarily involved in the process of DNA transcription initiation and regulation. As a result, proper promoter identification is critical for further understanding the mechanism of the networks controlling genomic regulation. A number of methodologies for the identification of promoters have been proposed. Nonetheless, due to the great heterogeneity existing in promoters, the results of these procedures are still unsatisfactory. In order to establish additional discriminative characteristics and properly recognize promoters, we developed the hybrid model for promoter identification (HMPI), a hybrid deep learning model that can characterize both the native sequences of promoters and the morphological outline of promoters at the same time. We developed the HMPI to combine a method called the PSFN (promoter sequence features network), which characterizes native promoter sequences and deduces sequence features, with a technique referred to as the DSPN (deep structural profiles network), which is specially structured to model the promoters in terms of their structural profile and to deduce their structural attributes.ResultsThe HMPI was applied to human, plant and Escherichia coli K-12 strain datasets, and the findings showed that the HMPI was successful at extracting the features of the promoter while greatly enhancing the promoter identification performance. In addition, after the improvements of synthetic sampling, transfer learning and label smoothing regularization, the improved HMPI models achieved good results in identifying subtypes of promoters on prokaryotic promoter datasets.ConclusionsThe results showed that the HMPI was successful at extracting the features of promoters while greatly enhancing the performance of identifying promoters on both eukaryotic and prokaryotic datasets, and the improved HMPI models are good at identifying subtypes of promoters on prokaryotic promoter datasets. The HMPI is additionally adaptable to different biological functional sequences, allowing for the addition of new features or models.

Prokaryotic and eukaryotic promoters identification based on residual network transfer learning.

Promoters contribute to research in the context of many diseases, such as coronary heart disease, diabetes and tumors, and one fundamental task is to identify promoters. Deep learning is widely used in the study of promoter sequence recognition. Although deep models have fast and accurate recognition capabilities, they are also limited by their reliance on large amounts of high-quality data. Therefore, we performed transfer learning on a typical deep network based on residual ideas, called a deep residual network (ResNet), to solve the problem of a deep network's high dependence on large amounts of data in the process of promoter prediction. We used binary one-hot encoding to represent the promoter and took advantage of ResNet to extract feature representations from organisms with a large amount of promoter data. Then, we transferred the learned structural parameters to target organisms with insufficient promoter data to improve the generalization performance of ResNet in target organisms. We evaluated the promoter datasets of four organisms (Bacillus subtilis, Escherichia coli, Saccharomyces cerevisiae and Drosophila melanogaster). The experimental results showed that the AUCs of ResNet's promoter prediction after deep transfer were 0.8537 and 0.8633, which increased by 0.1513 and 0.1376 in prokaryotes and eukaryotes, respectively.

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

Construction of Strong Promoters by Assembling Sigma Factor Binding Motifs.

Development of strong promoters is of growing interest in the field of biotechnology and synthetic biology. Here we present a protocol for the construction of strong prokaryotic promoters that can be recognized by designated multiple sigma factors by interlocking their cognate binding motifs on DNA strands. Strong and stress responsive promoters for Escherichia coli and Bacillus subtilis have been created following the presented protocol. Customized promoters could be easily developed for fine-tuning gene expression or overproducing enzymes with prokaryotic cell factories.

A dual prokaryotic (E. coli) expression system (pdMAX).

In this study, we introduced an efficient subcloning and expression system with two inducible prokaryotic expression promoters, arabinose and lac, in a single plasmid in Escherichia coli. The arabinose promoter unit allows for the expression of a FLAG-tagged protein, while the isopropyl-β-D-thiogalactoside (IPTG)-inducible unit allows for the expression of a Myc-tagged protein. An efficient subcloning (DNA insertion) system (iUnit) follows each promoter. The iUnit, based on a toxin that targets DNA topoisomerase of E. coli, allows for effective selection with arabinose or IPTG induction. With the dual promoter plasmid (pdMAX) system, expressed lacZ (β-galactosidase) activity was significantly decreased compared with the original solo expression system. Despite this disadvantage, we believe that the pdMAX system remains useful. A recombinant plasmid (pdMAX/ara/DsRed/IPTG/EGFP; pdMAX/DsRed/EGFP) with DsRed in the arabinose expression unit and EGFP in the IPTG expression unit showed fluorescent protein expression following additional low-temperature incubation. Thus, the novel pdMAX system allowed efficient subcloning of two different genes and can be used to induce and analyze the expression of two distinct genes. The proposed system can be applied to various types of prokaryotic gene expression analysis.

A Brief Review for Identifying Prokaryotic Promoters Based on Computational Biology

As a key region of deoxyribonucleic acid (DNA), prokaryotic promoter contains the conserved sequence required for specific binding of ribonucleic acid (RNA) polymerase and transcription initiation, and plays an important role in transcription regulation. However, due to the limitations of experimental methods that are long experimental period and high cost, the identification of prokaryotic promoter sequences remains a major challenge. With the development of computer technology, dozens of prokaryotic promoter identification methods based on computational biology have emerged, which show a high degree of diversity in terms of data quality, dataset size, extracted features, feature selection techniques, classification algorithms and evaluation strategies. Thus, there is an urgent need to systematically compare and summarize these methods so as to improve and further develop prokaryotic promoter recognition techniques.

A plant CitPITP1 protein-coding exon sequence serves as a promoter in bacteria

Genetic manipulation of plant genes in prokaryotes has been widely used in molecular biology, but the function of a DNA sequence is far from being fully known. Here, we discovered that a plant protein-coding gene containing the CRAL_TRIO domain serves as a promoter in bacteria. We firstly characterized CitPITP1 from Citrus, which contains the CRAL_TRIO domain, and identified a 64-bp sequence (key64) that is critical for prokaryotic promoter activity. In vitro experiments indicated that the bacterial RNA polymerase subunit RpoD specifically binds to key64. We then expanded our research to fungi, plant and animal species to identify key64-like sequences. Five such prokaryotic promoters were isolated from Amborella, Rice, Arabidopsis and Citrus. Two conserved motifs were identified, and mutation analysis indicated that the nucleotides at positions 7, 29 and 30 are crucial for key64-like transcription activity. We detected full-length recombinant CitPITP1 from E. coli, and visualized a CitPITP1-GFP fusion protein in plant cells, supporting the idea that CitPITP1 encodes a protein. However, although exon 4 of CitPITP1 contained key64, it did not demonstrate promoter activity in plants. Our study describes a new basal promoter, provides evidence for neofunction of gene elements across different kingdoms, and provides new knowledge for the modular design of promoters.

COMPUTATIONAL IDENTIFICATION OF PROMOTER REGIONS IN PROKARYOTES AND EUKARYOTES

Promoters are modular DNA structures that contain complex regulatory elements required for the initiation of gene transcription. Therefore, the use of machine learning methods to identify promoters is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods for predicting eukaryotic and prokaryotic promoters have been proposed. However, the performance of these methods is still far from satisfactory. In this article, we have developed a hybrid method (called IPMD) that combines a position correlation score function and diversity increment with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. The precise calculation and identification of promoters remains a challenge because these key DNA regulatory regions have variable structures composed of functional motifs that can provide gene-specific transcription initiation. The promoter is a regulatory DNA region, which is very important for gene transcription regulation. It is located near the transcription start site (TSS) upstream of the corresponding gene. In the post-genomics era, the availability of data makes it possible to build computational models to detect promoters robustly, because these models are expected to be helpful to academia and drug discovery. Until recently, the developed model only focused on distinguishing sequences into promoters and non-promoters. However, by considering the classification of weak and strong promoters, promoter predictors can be further improved. INDEX TERMS—: deep learning, DNA sequence analysis, Promoter prediction, Promoters, Promoter elements

PPD: A Manually Curated Database for Experimentally Verified Prokaryotic Promoters

As a key region, promoter plays a key role in transcription regulation. A eukaryotic promoter database called EPD has been constructed to store eukaryotic POL II promoters. Although there are some promoter databases for specific prokaryotic species or specific promoter type, such as RegulonDB for Escherichia coli K-12, DBTBS for Bacillus subtilis and Pro54DB for sigma 54 promoter, because of the diversity of prokaryotes and the development of sequencing technology, huge amounts of prokaryotic promoters are scattered in numerous published articles, which is inconvenient for researchers to explore the process of gene regulation in prokaryotes. In this study, we constructed a Prokaryotic Promoter Database (PPD), which records the experimentally validated promoters in prokaryotes, from published articles. Up to now, PPD has stored 129,148 promoters across 63 prokaryotic species manually extracted from published papers. We provided a friendly interface for users to browse, search, blast, visualize, submit and download data. The PPD will provide relatively comprehensive resources of prokaryotic promoter for the study of prokaryotic gene transcription. The PPD is freely available and easy accessed at http://lin-group.cn/database/ppd/.