Integration of Rnaseq Data in iFA762 Model Provides Insight into the Ectoines Metabolism of the Halophilic Bacteria Chromohalobacter Salexigens

C. salexigens is a halophilic bacterium which naturally synthesizes and accumulates ectoines in response to osmotic and heat stress. These compounds are compatible solutes which have great biotechnological potential in dermopharmacy, cosmetic, molecular biology and biomedicine. However, the exploitation of this bacterium in industrial biotechnology requires a comprehensive understanding of its physiology and genetics. On the other hand, synthesis of ectoine and hydroxyectoine is closely linked with central metabolism in C. salexigens (1). We have evidence for the existence of a limited catabolism of glucose at low salinity due to a low demand for ectoines synthesis, leading to a higher excretion of by-products or an overflow metabolism (1). Additionally high anaplerotic activity is needed to replenish the TCA cycle with the intermediaries withdrawn for ectoines biosynthesis at high salinity. Consequently, its metabolism has to be adapted to support the ectoines requirements depending on salinity growth conditions.
Because of the complexity of the metabolic pathways for ectoines synthesis and its connection with central metabolism, in the present work, we present an integrative optimization based approach to facilitate the rational design of improved strains. For that porpoise, C. salexigens high quality genome-based metabolic model (iFA762), previously develop by our group, and transcriptomic data were coupled in order to explore the effect of the salinity in the metabolism related with the synthesis of ectoines. Experimental data (RNAseq) were obtained at low and high ectoine production conditions (low and high salinity, respectively) and recently published MADE algorithm (2) was used to integrate transcriptomic data of differential expressed pathways within iFA762 model. Additionally two specific biomass reactions to simulate low and high salinity conditions were formulated based on previous C. salexigens experimental data and incorporated into the model. These provide us both condition-specific models which served as a platform to test metabolic capabilities of low and high salinity phenotypes. This approach will be used to explore the biological implications providing insight into the mechanisms of osmo-adaptation and it will facilitate the rational design of new metabolic engineering strains.

This research was co-financially supported by grants from the Spanish Ministerio de Economía y Competitividad (project BIO2111-22833), European Community Feder grants, and Junta de Andalucía (project P11-CV1-7293 MO).

References:
(1) Pastor et al. Biotechnol. Adv. 28: 782-821 (2010).
(2) Jensen et al. Bioinformatics 27(4):541-547 (2011).