Intelligent microbial cell factory with genetic pH shooting (GPS) for cell self-responsive base/acid regulation

Abstract

Background: In industrial fermentation, pH fluctuation resulted from microbial metabolism influences the strain performance and the final production. The common way to control pH is adding acid or alkali after probe detection, which is not a fine-tuned method and often leads to increased costs and complex downstream processing. Here, we constructed an intelligent pH-sensing and controlling genetic circuits called "Genetic pH Shooting (GPS)" to realize microbial self-regulation of pH.

Results: In order to achieve the self-regulation of pH, GPS circuits consisting of pH-sensing promoters and acid-/alkali-producing genes were designed and constructed. Designed pH-sensing promoters in the GPS can respond to high or low pHs and generate acidic or alkaline substances, achieving endogenously self-responsive pH adjustments. Base shooting circuit (BSC) and acid shooting circuit (ASC) were constructed and enabled better cell growth under alkaline or acidic conditions, respectively. Furthermore, the genetic circuits including GPS, BSC and ASC were applied to lycopene production with a higher yield without an artificial pH regulation compared with the control under pH values ranging from 5.0 to 9.0. In scale-up fermentations, the lycopene titer in the engineered strain harboring GPS was increased by 137.3% and ammonia usage decreased by 35.6%.

Conclusions: The pH self-regulation achieved through the GPS circuits is helpful to construct intelligent microbial cell factories and reduce the production costs, which would be much useful in industrial applications.

Keywords: Acid-regulating circuit; Base-regulating circuit; Escherichia coli; Genetic pH regulation; Microbial cell factory.

Fig. 1

The intelligent pH-sensing and controlling genetic circuits. a: ASC, b: BSC, c: GPS

Fig. 2

Relative enzyme activities at different pH values of acid-inducible promoter P-asr (a) and base-inducible promoter P-atp2 (b). The data represent the mean of three biological replicates

Fig. 3

The screening for P-atp2 mutants under different pH values. These mutants were generated through error-prone PCR and selected through promoter activity assay. The data represent the mean of three biological replicates

Fig. 4

Effect of base-regulation circuit (BSC) on sensing and regulating base conditions. a Work principle of BSC. E represents the sigma factor sigE and ASF represents anti-sigma factor. b pH fluctuation curves at different initial pH values during the cultivation of E. coli harboring the BSC and the control check (CK) E. coli without BSC. c Growth curves for E. coli with and without BSC. The data represent the mean of three biological replicates

Fig. 5

Acid regulation ability of glsA and gadA. A comparison of acid regulation ability between glsA and gadA with strains carrying empty plasmids as control. The data represent the mean of three biological replicates

Fig. 6

Effect of acid-shooting circuit (ASC) on sensing and regulating acidic conditions. a Work principle of ASC. b pH fluctuation curves at different initial pH values during the cultivation of E. coli harboring the ASC and the CK. c Growth curves for E. coli with and without ASC. The data represent the mean of three biological replicates

Fig. 7

Application of GPS circuit on the lycopene production strain. a GPS circuit designed in this study. b Effect of ASC on lycopene production in acidic and neutral conditions. c Effect of BSC on lycopene production in alkali and neutral conditions. d Lycopene titers of BW-BIE and BW-pUCATB at different initial pH values. The data represent the mean of three biological replicates

Fig. 8

Lycopene fermentation of the engineered strains in batch fermenters with a stringent pH control manner (pH = 7) and a relaxed pH control manner (pH = 5 ~ 9), respectively. a lycopene yield at 24 h; b Usage of 20% aqueous ammonia after 36 h; c–f pH fluctuations for strain BW-BIE, BW-pUCASC, BW-pUCATB and BW-pUCBSC, respectively