Identification and characterization of ethanol‐inducible promoters of Zymomonas mobilis based on omics data and dual reporter–gene system. (3rd December 2019) - Yang, Yongfu; Rong, Ziyue; Song, Haoyue; Yang, Xiuxiu; Li, Mian; Yang, Shihui
Abstract: Zymomonas mobilis is a model bacterial ethanologen and has been engineered to produce lignocellulosic biofuels and biochemicals such as 2, 3‐butanediol. We have previously identified promoters of different strengths using systems biology datasets and characterized them using the flow cytometry–based dual reporter–gene system. Here, we further demonstrated the capability of applying the dual reporter–gene system and omics datasets on discovering inducible promoters. Ten candidate ethanol‐inducible promoters were identified through omics datasets mining and clustering. Using the dual reporter–gene system, these promoters were characterized under natural growth, ethanol stress, and ethanol‐induced condition to investigate the transcriptional strength and ethanol inducibility. The results demonstrated that three promoters of P0405, P0435, and P0038 driving the expression of native genes of ZMO0405, ZMO0435, and ZMO0038, correspondingly, are potential ethanol‐responsive promoters and may be growth related. This study not only identified and verified three ethanol‐inducible promoters as biological parts, which can be used to synchronize the expression of heterologous pathway genes with the ethanol production process of Z. mobilis, but also demonstrated the power of combining omics datasets and dual reporter–gene system to identify biological parts for metabolic engineering and synthetic biology applications in Z. mobilis and related microorganisms. Abstract : Candidate ethanol‐inducible promoters were selected by omics data mining. These candidates were then constructed into the dual reporter–gene system and characterized under normal, ethanol stress and ethanol induced conditions using flow cytometry. Finally, three ethanol‐inducible promoters, P0435, P0405 and P0038, were identified and the potential element sequences were analyzed using bioinformatics.