Transcriptomic and proteomic analyses on ethanol-tolerance and protein turnover in microbial systems.

摘要

Biofuels have emerged as potentially major alternatives to gasoline and diesel fuels derived from petroleum. The current generation of biofuel focuses on converting directly or indirectly non-food biomass, especially lignocellulosic plant biomass, to biofuels including bioethanol. Clostridium thermocellum is capable of degrading cellulosic materials directly to produce ethanol as the main product, but the application of using C. thermocellum for ethanol production has been hindered due to its low ethanol tolerance and production yield.;In this study, we developed an ethanol-tolerant strain using serial transfer method. The growth of wild type strain was typically inhibited by 1% (w/v) ethanol, while the tolerant strain we obtained showed sustainable growth in 6% (w/v) ethanol-containing medium. Fractional factorial design was applied for screening essential nutrient components for ethanol production in fed-batch culture. Both wild type and the tolerant strain had a significantly higher ethanol yield in fed-batch than batch culture. More interestingly, the tolerant strain had higher specific ethanol production rate and prolonged stationary phase than wild type. Samples were taken from both exponential and stationary phases for transcriptome and proteome analysis. Our results indicated that the enhanced ethanol tolerance in tolerant strain during long-term adaptation was accompanied with strengthened electron transport mechanism and general stress response mechanism.;In the comparative transcriptome and proteome study, the dynamics of some genes were observed only in one dataset, but not in the other. One of the prominent factors affecting the mRNA and protein correlation is the divergent protein turnover rate. A novel multi-tagging strategy combining SILAC and iTRAQ labeling was used to study the protein turnover rate in a highly dynamic bacterial system, Streptomyces coelicolor. We believe that this "proof-of-concept" demonstration of the SILAC-iTRAQ multi-tagging strategy to estimate protein turnover rates will find applications in other organisms as well and significantly aid in our comprehension of biological systems.