Biosensor-based Multi-gene Pathway Optimization For Enhancing The Production Of Glycolate

Glycolic Acid（Glycolate）is an important compound with a variety of industrial uses.It is widely used in cosmetics and medical materials.The biosynthesis of glycolate has the advantages of green and simple production process,which is expected to solve the problems of severe environmental pollution and complicated preparation conditions in the traditional chemical synthesis method.However,an expensive inducer was used in previous studies,which cannot be used in large-scale industrial fermentation and may seriously affect the growth of the organism.To constitutively biosynthesize glycolate,the expression level of each gene of the glycolate synthetic pathway needs to be systemically optimized.In contrast,the precise multi-gene pathway optimization for glycolate synthesis faces two major challenges.On the one hand,the gradient optimization of each gene generates a large number of combinations,and it is difficult to construct all combinations.On the other hand,how to select or screen the optimum strain from the randomly assembled library by an efficient high-throughput method within a short period.In response to the above challenges,we used 22 promoter-5’-UTR complexes（PUTR）of different strengths to coordinate the glycolate synthesis pathway and construct a random assembled pathway library.Subsequently,a glycolate-responsive biosensor was designed to establish a high-throughput screening method for glycolate.Finally,a high-producing strain of glycolate was obtained from the library through a series of screening processes,with an glycolate titer of 40.9±3.7 g·L^-1.It was also found that glyoxylate reductase Ycd W and citrate synthase Glt A were the key rate-limiting steps of glycolate synthesis.The details of the study were as follows.（1）Based on GlcC,a transcription factor regulating glycolate metabolism in the genome of Escherichia coli,an glycolate-responsive biosensor was established and its performance was optimized in this study.It was found that the expression level of GlcC had the greatest influence on the detection limit and dynamic range of the sensor.By optimizing the expression level of GlcC,the detection range and dynamic range of the biosensor p GBS-P_{ff S}-sfgfp were significantly improved.The detection range was 0.1 m M～200 m M,and the dynamic range could reach 79-fold.In addition,the constructed biosensor did not respond to any of the glycolate structural analogues,indicating a high specificity of the biosensor.Based on the above experimental results,the glycolate biosensor with superior performance was established.（2）Constuction of high-throughput screening method based on glycolate biosensor.Using the constructed glycolate biosensor,a high-throughput screening method at agar plate and 48-well plate scale was developed for rapid screening of hyperproducing strains from a large library.In the agar plate screening stage,a positive correlation was found between colony diameter size and glycolate titer by comparing the two;subsequently,the fluorescence values of the sensor cells induced by fermentation broth in the 48-well plates were compared with glycolate titer,and a linear fit R² of 0.9 was found.Finally,the sensor was combined with the temperature-sensitive replicon Ori101 to construct a sensor that could be repeatedly eliminated.Based on the above results,an efficient and iterative high-throughput screening platform was established.（3）Twenty-two gradient strength promoter-5’-UTR complexes were randomly cloned upstream of the genes of the glycolate synthetic pathway,generating a large random assembled library.Using the above high-throughput screening platform,the best strain Magly6-H1 was screened from 6×10⁵transformants in a week,and it achieved a titer of 40.9±3.7 g·L^-1glycolate in a 5-L bioreactor,with a yield of 0.66 g·（g glucose）^-1.Furthermore,high expression levels of the enzymes Ycd W and Glt A were found to promote glycolate production,whereas Ace A has no obvious impact on glycolate production.Overall,the glycolate biosensor-based pathway optimization strategy presented in this work provides a paradigm for other multi-gene pathway optimizations.