Rapid evolution of regulatory element libraries for tunable transcriptional and translational control of gene expression

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

Optimization of mammalian expression vector by cis-regulatory element combinations

The regulation of gene expression in mammalian cells by combining various cis-regulatory features has rarely been discussed. In this study, we constructed expression vectors containing various combinations of regulatory elements to examine the regulation of gene expression by different combinations of cis-regulatory elements. The effects of four promoters (CMV promoter, PGK promoter, Polr2a promoter, and EF-1α core promoter), two enhancers (CMV enhancer and SV40 enhancer), two introns (EF-1α intron A and hybrid intron), two terminators (CYC1 terminator and TEF terminator), and their different combinations on downstream gene expression were compared in various mammalian cells using fluorescence microscopy to observe fluorescence, quantitative real-time PCR (qRT-PCR), and western blot. The receptor binding domain (RBD) sequence from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein was used to replace the eGFP sequence in the expression vector and the RBD expression was detected by qRT-PCR and western blot. The results showed that protein expression can be regulated by optimizing the combination of cis-acting elements. The vector with the CMV enhancer, EF-1α core promoter, and TEF terminator was found to express approximately threefold higher eGFP than the unmodified vector in different animal cells, as well as 2.63-fold higher recombinant RBD protein than the original vector in HEK-293T cells. Moreover, we suggest that combinations of multiple regulatory elements capable of regulating gene expression do not necessarily exhibit synergistic effects to enhance expression further. Overall, our findings provide insights into biological applications that require the regulation of gene expression and will help to optimize expression vectors for biosynthesis and other fields. Additionally, we provide valuable insights into the production of RBD proteins, which may aid in developing reagents for diagnosis and treatment during the COVID-19 pandemic.

Modular optimization in metabolic engineering

There is an increasing demand for bioproducts produced by metabolically engineered microbes, such as pharmaceuticals, biofuels, biochemicals and other high value compounds. In order to meet this demand, modular optimization, the optimizing of subsections instead of the whole system, has been adopted to engineer cells to overproduce products. Research into modularity has focused on traditional approaches such as DNA, RNA, and protein-level modularity of intercellular machinery, by optimizing metabolic pathways for enhanced production. While research into these traditional approaches continues, limitations such as scale-up and time cost hold them back from wider use, while at the same time there is a shift to more novel methods, such as moving from episomal expression to chromosomal integration. Recently, nontraditional approaches such as co-culture systems and cell-free metabolic engineering (CFME) are being investigated for modular optimization. Co-culture modularity looks to optimally divide the metabolic burden between different hosts. CFME seeks to modularly optimize metabolic pathways in vitro, both speeding up the design of such systems and eliminating the issues associated with live hosts. In this review we will examine both traditional and nontraditional approaches for modular optimization, examining recent developments and discussing issues and emerging solutions for future research in metabolic engineering.

Strategies for the Biosynthesis of Pharmaceuticals and Nutraceuticals in Microbes from Renewable Feedstock

BACKGROUNDS

Abundant and renewable biomaterials serve as ideal substrates for the sustainable production of various chemicals, including natural products (e.g., pharmaceuticals and nutraceuticals). For decades, researchers have been focusing on how to engineer microorganisms and developing effective fermentation processes to overproduce these molecules from biomaterials. Despite many laboratory achievements, it remains a challenge to transform some of these into successful industrial applications.

RESULTS

Here, we review recent progress in strategies and applications in metabolic engineering for the production of natural products. Modular engineering methods, such as a multidimensional heuristic process markedly improve efficiencies in the optimization of long and complex biosynthetic pathways. Dynamic pathway regulation realizes autonomous adjustment and can redirect metabolic carbon fluxes to avoid the accumulation of toxic intermediate metabolites. Microbial co-cultivation bolsters the identification and overproduction of natural products by introducing competition or cooperation of different species. Efflux engineering is applied to reduce product toxicity or to overcome storage limitation and thus improves product titers and productivities.

CONCLUSION

Without dispute, many of the innovative methods and strategies developed are gradually catalyzing this transformation from the laboratory into the industry in the biosynthesis of natural products. Sometimes, it is necessary to combine two or more strategies to acquire additive or synergistic benefits. As such, we foresee a bright future of the biosynthesis of pharmaceuticals and nutraceuticals in microbes from renewable biomaterials.

Combinatorial modulation of initial codons for improved zeaxanthin synthetic pathway efficiency in Escherichia coli

A balanced and optimized metabolic pathway is the basis for efficient production of a target metabolite. Traditional strategies mostly involve the manipulation of promoters or ribosome-binding sites, which can encompass long sequences and can be complex to operate. In this work, we found that by changing only the three nucleotides of the initiation codons, expression libraries of reporter proteins RFP, GFP, and lacZ with a large dynamic range and evenly distributed expression levels could be established in Escherichia coli (E. coli). Thus, a novel strategy that uses combinatorial modulation of initial codons (CMIC) was developed for metabolic pathway optimization and applied to the three genes crtZ, crtY, and crtI of the zeaxanthin synthesis pathway in E. coli. The initial codons of these genes were changed to random nucleotides NNN, and the gene cassettes were assembled into vectors via an optimized strategy based on type II restriction enzymes. With minimal labor time, a combinatorial library was obtained containing strains with various zeaxanthin production levels, including a strain with a titer of 6.33 mg/L and specific production value of 1.24 mg/g DCW-a striking 10-fold improvement over the starting strain. The results demonstrated that CMIC was a feasible technique for conveniently optimizing metabolic pathways. To our best knowledge, this is the first metabolic engineering strategy that relies on manipulating the initiation codons for pathway optimization in E. coli.

Overproduction of gentamicin B in industrial strain Micromonospora echinospora CCTCC M 2018898 by cloning of the missing genes genR and genS

In pharmaceutical industry, isepamicin is mainly manufactured from gentamicin B, which is produced by Micromonospora echinospora as a minor component of the gentamicin complex. Improvement of gentamicin B production through metabolic engineering is therefore important to satisfy the increasing demand for isepamicin. We hypothesized that gentamicin B was generated from gentamicin JI-20A via deamination of the C2’ amino group. Using kanJ and kanK as the gene probes, we identified the putative deamination-related genes, genR and genS, through genome mining of the gentamicin B producing strain M. echinospora CCTCC M 2018898. Interestingly, genR and genS constitute a gene cassette located approximately 28.7 kb away from the gentamicin gene cluster. Gene knockout of genR and genS almost abolished the production of gentamicin B in the mutant strain, suggesting that these two genes, which are responsible for the last steps in gentamicin B biosynthesis, constitute the missing part of the known gentamicin biosynthetic pathway. Based on these finding, we successfully constructed a gentamicin B high-yielding strain (798 mg/L), in which an overexpression cassette of genR and genS was introduced. Our work fills the missing piece to solve the puzzle of gentamicin B biosynthesis and may inspire future metabolic engineering efforts to generate gentamycin B high-yielding strains that could eventually satisfy the need for industrial manufacturing of isepamicin.

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Two missing genes in the biosynthetic pathway of gentamicin B were found.

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CRISPR/Cas9 was applied successfully to delete genes in Micromonospora echinospora.

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Overexpression of genR/S cassette improved gentamicin B titer by 64% in current industrial strain.

Techniques for chromosomal integration and expression optimization in Escherichia coli

Due to the inherent expression stability and low metabolic burden to the host cell, the expression of heterologous proteins in the bacterial chromosome in a precise and efficient manner is highly desirable for metabolic engineering and live bacterial applications. However, obtaining suitable chromosome expression levels is particularly challenging. In this minireview, we briefly present the technologies available for the integration of heterologous genes into Escherichia coli chromosomes and strategies to optimize the expression levels of heterologous proteins.