Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

Metabolic engineering consistently demands to produce the maximum carbon and energy flux to target chemicals. To balance metabolic flux, gene expression levels of artificially synthesized pathways usually fine-tuned using multimodular optimization strategy. However, forward construction is an engineering conundrum because a vast number of possible pathway combinations need to be constructed and analyzed.Here, an iterative high-throughput balancing (IHTB) strategy was established to thoroughly fine-tune the (2S)-naringenin biosynthetic pathway. A series of gradient constitutive promoters from Escherichia coli were randomly cloned upstream of pathway genes, and the resulting library was screened using an ultraviolet spectrophotometry-fluorescence spectrophotometry high-throughput method, which was established based on the interactions between AlCl 3 and (2S)-naringenin. The metabolic flux of the screened high-titer strains was analyzed and iterative rounds of screening were performed based on the analysis results. After several rounds, the metabolic flux of the (2S)-naringenin synthetic pathway was balanced, reaching a final titer of 191.9 mg/L with 29.2 mg/L p-coumaric acid accumulation. Chalcone synthase was speculated to be the rate-limiting enzyme because its expression level was closely related to the production of both (2S)-naringenin and p-coumaric acid. The established IHTB strategy can be used to efficiently balance multigene pathways, which will accelerate the development of efficient recombinant strains. K E Y W O R D S flavonoids, metabolic engineering, modular optimization, promoter Biotechnology and Bioengineering. 2019;116:1392-1404. wileyonlinelibrary.com/journal/bit 1392 |

A promoter is one of the most important and basic tools used to achieve diverse synthetic biology goals. Escherichia coli is one of the most commonly used model organisms in synthetic biology to produce useful target products and establish complicated regulation networks. During the fine-tuning of metabolic or regulation networks, the limited number of well-characterized inducible promoters has made implementing complicated strategies difficult. In this study, 104 native promoter-5'-UTR complexes (PUTR) from E. coli were screened and characterized based on a series of RNA-seq data. The strength of the 104 PUTRs varied from 0.007% to 4630% of that of the P promoter in the transcriptional level and from 0.1% to 137% in the translational level. To further upregulate gene expression, a series of combinatorial PUTRs and cascade PUTRs were constructed by integrating strong transcriptional promoters with strong translational 5'-UTRs. Finally, two combinatorial PUTRs (P-UTR and P-UTR) and two cascade PUTRs (PUTR-PUTR and PUTR-PUTR) were identified as having the highest activity, with expression outputs of 170%, 137%, 409%, and 203% of that of the P promoter, respectively. These engineered PUTRs are stable for the expression of different genes, such as the red fluorescence protein gene and the β-galactosidase gene. These results show that the PUTRs characterized and constructed in this study may be useful as a plug-and-play synthetic biology toolbox to achieve complicated metabolic engineering goals in fine-tuning metabolic networks to produce target products.

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

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Background Production of l -tyrosine is gaining grounds as the market size of 3,4-dihydroxyphenyl- l -alanine ( l -DOPA) is expected to increase due to increasing cases of Parkinson’s disease a neurodegenerative disease. Attempts to overproduce l -tyrosine for conversion to l -DOPA has stemmed on the overexpressing of critical pathway enzymes, an introduction of feedback-resistant enzymes, and deregulation of transcriptional regulators. Results An E. coli BL21 (DE3) was engineered by deleting tyrR , ptsG , crr , pheA and pykF while directing carbon flow through the overexpressing of galP and glk . TktA and PpsA were also overexpressed to enhance the accumulation of E4P and PEP. Directed evolution was then applied on HpaB to optimize its activity. Three mutants, G883R, G883A, L1231M, were identified to have improved activity as compared to the wild-type hpaB showing a 3.03-, 2.9- and 2.56-fold increase in l -DOPA production respectively. The use of strain LP-8 resulted in the production of 691.24 mg/L and 25.53 g/L of l -DOPA in shake flask and 5 L bioreactor, respectively. Conclusion Deletion of key enzymes to channel flux towards the shikimate pathway coupled with the overexpression of pathway enzymes enhanced the availability of l -tyrosine for L-DOPA production. Enhancing the activity of HpaB increased l -DOPA production from glucose and glycerol. This work demonstrates that increasing the availability of l -tyrosine and enhancing enzyme activity ensures maximum l -DOPA productivity. Electronic supplementary material The online version of this article (10.1186/s12934-019-1122-0) contains supplementary material, which is available to authorized users.

Promoters are one of the most critical regulatory elements controlling metabolic pathways. However, the fast and accurate prediction of promoter strength remains challenging, leading to time- and labor-consuming promoter construction and characterization processes. This dilemma is caused by the lack of a big promoter library that has gradient strengths, broad dynamic ranges, and clear sequence profiles that can be used to train an artificial intelligence model of promoter strength prediction. To overcome this challenge, we constructed and characterized a mutant library of Trc promoters (P trc) using 83 rounds of mutation-construction-screening-characterization engineering cycles. After excluding invalid mutation sites, we established a synthetic promoter library that consisted of 3665 different variants, displaying an intensity range of more than two orders of magnitude. The strongest variant was ∼69-fold stronger than the original P trc and 1.52-fold stronger than a 1 mM isopropyl-β-d-thiogalactoside-driven P T7 promoter, with an ∼454-fold difference between the strongest and weakest expression levels. Using this synthetic promoter library, different machine learning models were built and optimized to explore the relationships between promoter sequences and transcriptional strength. Finally, our XgBoost model exhibited optimal performance, and we utilized this approach to precisely predict the strength of artificially designed promoter sequences (R 2 = 0.88, mean absolute error = 0.15, and Pearson correlation coefficient = 0.94). Our work provides a powerful platform that enables the predictable tuning of promoters to achieve optimal transcriptional strength.

Flavonoids have diverse biological functions in human health. All flavonoids contain a common 2phenyl chromone structure (C6-C3-C6) as a scaffold. Hence, in using such a scaffold, plenty of highvalue-added flavonoids can be synthesized by chemical or biological catalyzation approaches. (2S)-Naringenin is one of the most commonly used flavonoid scaffolds. However, biosynthesizing (2S)naringenin has been restricted not only by low production but also by the expensive precursors and inducers that are used. Herein, we established an induction-free system to de novo biosynthesize (2S)-naringenin in Escherichia coli. The tyrosine synthesis pathway was enhanced by overexpressing feedback inhibition-resistant genes (aroG fbr and tyrA fbr ) and knocking out a repressor gene (tyrR). After optimizing the fermentation medium and conditions, we found that glycerol, glucose, fatty acids, potassium acetate, temperature, and initial pH are important for producing (2S)-naringenin. Using the optimum fermentation medium and conditions, our best strain, Nar-17LM1, could produce 588 mg/l (2S)-naringenin from glucose in a 5-L bioreactor, the highest titer reported to date in E. coli.