Abstract

The hyperthermophilic archaeon Thermococcus onnurineus NA1 can grow and produce H2 on carbon monoxide (CO) and its H2 production rates have been improved through metabolic engineering. In this study, we applied adaptive evolution to enhance H2 productivity. After over 150 serial transfers onto CO medium, cell density, CO consumption rate and H2 production rate increased. The underlying mechanism for those physiological changes could be explained by using multi-omics approaches including genomic, transcriptomic and epigenomic analyses. A putative transcriptional regulator was newly identified to regulate the expression levels of genes related to CO oxidation. Transcriptome analysis revealed significant changes in the transcript levels of genes belonging to the categories of transcription, translation and energy metabolism. Our study presents the first genome-scale methylation pattern of hyperthermophilic archaea. Adaptive evolution led to highly enhanced H2 productivity at high CO flow rates using synthesis gas produced from coal gasification.

Highlights

  • Microorganisms have been metabolically engineered, employing integrated strategies of systems biology, synthetic biology and evolutionary engineering, to enhance their output of valuable products[1]

  • We applied an evolutionary approach to enable T. onnurineus NA1 to adapt to 100% pure carbon monoxide (CO)

  • T. onnurineus NA1 was grown to stationary phase to trigger spontaneous mutation[23,24,25,26] and transferred to fresh medium containing CO as an energy source; the strain was transferred over 150 times

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Summary

Introduction

Microorganisms have been metabolically engineered, employing integrated strategies of systems biology, synthetic biology and evolutionary engineering, to enhance their output of valuable products[1]. Metabolic engineering via knowledge-based rational design has been used to enhance the output of target products; this approach requires a priori genetic or biochemical information, and the complexity of cellular physiological responses must be considered[2]. In this regard, an evolutionary engineering approach may serve as an alternative for obtaining suitable phenotypes in cases where there is a lack of prior knowledge[3,4,5,6].

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