Use of genome-scale models to get new insights into the marine actinomycete genus Salinispora.

BACKGROUND: There is little published regarding metabolism of Salinispora species. In continuation with efforts performed towards this goal, this study is focused on new insights into the metabolism of the three-identified species of Salinispora using constraints-based modeling. At present, only one manually curated genome-scale metabolic model (GSM) for Salinispora tropica strain CNB-440T has been built despite the role of Salinispora strains in drug discovery. RESULTS: Here, we updated, and expanded the scope of the model of Salinispora tropica CNB-440T, and GSMs were constructed for two sequenced type strains covering the three-identified species. We also constructed a Salinispora core model that contains the genes shared by 93 sequenced strains and a few non-conserved genes associated with essential reactions. The models predicted no auxotrophies for essential amino acids, which was corroborated experimentally using a defined minimal medium (DMM). Experimental observations suggest possible sulfur accumulation. The Core metabolic content shows that the biosynthesis of specialised metabolites is the less conserved subsystem. Sets of reactions were analyzed to explore the differences between the reconstructions. Unique reactions associated to each GSM were mainly due to genome sequence data except for the ST-CNB440 reconstruction. In this case, additional reactions were added from experimental evidence. This reveals that by reaction content the ST-CNB440 model is different from the other species models. The differences identified in reaction content between models gave rise to different functional predictions of essential nutrient usage by each species in DMM. Furthermore, models were used to evaluate in silico single gene knockouts under DMM and complex medium. Cluster analysis of these results shows that ST-CNB440, and SP-CNR114 models are more similar when considering predicted essential genes. CONCLUSIONS: Models were built for each of the three currently identified Salinispora species, and a core model representing the conserved metabolic capabilities of Salinispora was constructed. Models will allow in silico metabolism studies of Salinispora strains, and help researchers to guide and increase the production of specialised metabolites. Also, models can be used as templates to build GSMs models of closely related organisms with high biotechnology potential.

Impact of Rearing Conditions on the Ambrosia Beetle's Microbiome.

Ambrosia beetles, along with termites and leafcutter ants, are the only fungus-farming lineages within the tree of life. Bacteria harbored by ambrosia beetles may play an essential role in the nutritional symbiotic interactions with their associated fungi; however, little is known about the impact of rearing conditions on the microbiota of ambrosia beetles. We have used culture-independent methods to explore the effect of rearing conditions on the microbiome associated with Xyleborus affinis, Xyleborus bispinatus, and Xyleborus volvulus, evaluating different media in laboratory-controlled conditions and comparing wild and laboratory conditions. Our results revealed that rearing conditions affected the fungal and bacterial microbiome structure and had a strong influence on bacterial metabolic capacities. We propose that the rearing conditions influence the ambrosia-associated fungal and bacterial communities. Furthermore, bacterial microbiome flexibility may help beetles adapt to different substrates.

Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation.

The first manually curated genome-scale metabolic model for Salinispora tropica strain CNB-440 was constructed. The reconstruction enables characterization of the metabolic capabilities for understanding and modeling the cellular physiology of this actinobacterium. The iCC908 model was based on physiological and biochemical information of primary and specialised metabolism pathways. The reconstructed stoichiometric matrix consists of 1169 biochemical conversions, 204 transport reactions and 1317 metabolites. A total of 908 structural open reading frames (ORFs) were included in the reconstructed network. The number of gene functions included in the reconstructed network corresponds to 20% of all characterized ORFs in the S. tropica genome. The genome-scale metabolic model was used to study strain-specific capabilities in defined minimal media. iCC908 was used to analyze growth capabilities in 41 different minimal growth-supporting environments. These nutrient sources were evaluated experimentally to assess the accuracy of in silico growth simulations. The model predicted no auxotrophies for essential amino acids, which was corroborated experimentally. The strain is able to use 21 different carbon sources, 8 nitrogen sources and 4 sulfur sources from the nutrient sources tested. Experimental observation suggests that the cells may be able to store sulfur. False predictions provided opportunities to gain new insights into the physiology of this species, and to gap fill the missing knowledge. The incorporation of modifications led to increased accuracy in predicting the outcome of growth/no growth experiments from 76 to 93%. iCC908 can thus be used to define the metabolic capabilities of S. tropica and guide and enhance the production of specialised metabolites.