Search

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. Electronic supplementary material The online version of this article (10.1186/s12918-019-0683-1) contains supplementary material, which is available to authorized users.

Soybean is an important crop as both human food and animal feed. However, the yield of soybean is heavily impacted by biotic stresses including insect attack and pathogen infection. Insect bites usually make the plants vulnerable to pathogen infection, which causes diseases. Fungi, oomycetes, bacteria, viruses, and nematodes are major soybean pathogens. The infection by pathogens and the defenses mounted by soybean are an interactive and dynamic process. Using fungi, oomycetes, and bacteria as examples, we will discuss the recognition of pathogens by soybean at the molecular level. In this review, we will discuss both the secretory peptides for soybean plant infection and those for pathogen inhibition. Pathogenic secretory peptides and peptides secreted by soybean and its associated microbes will be included. We will also explore the possible use of externally applied antimicrobial peptides identical to those secreted by soybean and its associated microbes as biopesticides.

Legumes are unique among plants as they can obtain nitrogen through symbiosis with nitrogen-fixing rhizobia that form root nodules in the host plants. Therefore they are valuable crops for sustainable agriculture. Increasing nitrogen fixation efficiency is not only important for achieving better plant growth and yield, but it is also crucial for reducing the use of nitrogen fertilizer. Arbuscular mycorrhizal fungi (AMF) are another group of important beneficial microorganisms that form symbiotic relationships with legumes. AMF can promote host plant growth by providing mineral nutrients and improving the soil ecosystem. The trilateral legume-rhizobia-AMF symbiotic relationships also enhance plant development and tolerance against biotic and abiotic stresses. It is known that domestication and agricultural activities have led to the reduced genetic diversity of cultivated germplasms and higher sensitivity to nutrient deficiencies in crop plants, but how domestication has impacted the capability of legumes to establish beneficial associations with rhizospheric microbes (including rhizobia and fungi) is not well-studied. In this review, we will discuss the impacts of domestication and agricultural practices on the interactions between legumes and soil microbes, focusing on the effects on AMF and rhizobial symbioses and hence nutrient acquisition by host legumes. In addition, we will summarize the genes involved in legume-microbe interactions and studies that have contributed to a better understanding of legume symbiotic associations using metabolic modeling.

Legumes are rich in secondary metabolites, such as polyphenols, alkaloids, and saponins, which are important defense compounds to protect the plant against herbivores and pathogens, and act as signaling molecules between the plant and its biotic environment. Legume-sourced secondary metabolites are well known for their potential benefits to human health as pharmaceuticals and nutraceuticals. During domestication, the color, smell, and taste of crop plants have been the focus of artificial selection by breeders. Since these agronomic traits are regulated by secondary metabolites, the basis behind the genomic evolution was the selection of the secondary metabolite composition. In this review, we will discuss the classification, occurrence, and health benefits of secondary metabolites in legumes. The differences in their profiles between wild legumes and their cultivated counterparts will be investigated to trace the possible effects of domestication on secondary metabolite compositions, and the advantages and drawbacks of such modifications. The changes in secondary metabolite contents will also be discussed at the genetic level to examine the genes responsible for determining the secondary metabolite composition that might have been lost due to domestication. Understanding these genes would enable breeding programs and metabolic engineering to produce legume varieties with favorable secondary metabolite profiles for facilitating adaptations to a changing climate, promoting beneficial interactions with biotic factors, and enhancing health-beneficial secondary metabolite contents for human consumption.

Nitrogen is the most limiting macronutrient for plant growth, and rhizobia are important bacteria for agriculture because they can fix atmospheric nitrogen and make it available to legumes through the establishment of a symbiotic relationship with their host plants. In this work, we studied the nitrogen fixation process in the microsymbiont Sinorhizobium fredii at the genome level. A metabolic model was built using genome annotation and literature to reconstruct the symbiotic form of S. fredii. Genes controlling the nitrogen fixation process were identified by simulating gene knockouts. Additionally, the nitrogen-fixing capacities of S. fredii CCBAU45436 in symbiosis with cultivated and wild soybeans were evaluated. The predictions suggested an outperformance of S. fredii with cultivated soybean, consistent with published experimental evidence. The reconstruction presented here will help to understand and improve nitrogen fixation capabilities of S. fredii and will be beneficial for agriculture by reducing the reliance on fertilizer applications., Rhizobia are soil bacteria able to establish symbiosis with diverse host plants. Specifically, Sinorhizobium fredii is a soil bacterium that forms nitrogen-fixing root nodules in diverse legumes, including soybean. The strain S. fredii CCBAU45436 is a dominant sublineage of S. fredii that nodulates soybeans in alkaline-saline soils in the Huang-Huai-Hai Plain region of China. Here, we present a manually curated metabolic model of the symbiotic form of Sinorhizobium fredii CCBAU45436. A symbiosis reaction was defined to describe the specific soybean-microsymbiont association. The performance and quality of the reconstruction had a 70% score when assessed using a standardized genome-scale metabolic model test suite. The model was used to evaluate in silico single-gene knockouts to determine the genes controlling the nitrogen fixation process. One hundred forty-one of 541 genes (26%) were found to influence the symbiotic process, wherein 121 genes were predicted as essential and 20 others as having a partial effect. Transcriptomic profiles of CCBAU45436 were used to evaluate the nitrogen fixation capacity in cultivated versus in wild soybean inoculated with the microsymbiont. The model quantified the nitrogen fixation activities of the strain in these two hosts and predicted a higher nitrogen fixation capacity in cultivated soybean. Our results are consistent with published data demonstrating larger amounts of ureides and total nitrogen in cultivated soybean than in wild soybean. This work presents the first metabolic network reconstruction of S. fredii as an example of a useful tool for exploring the potential benefits of microsymbionts to sustainable agriculture and the ecosystem. IMPORTANCE Nitrogen is the most limiting macronutrient for plant growth, and rhizobia are important bacteria for agriculture because they can fix atmospheric nitrogen and make it available to legumes through the establishment of a symbiotic relationship with their host plants. In this work, we studied the nitrogen fixation process in the microsymbiont Sinorhizobium fredii at the genome level. A metabolic model was built using genome annotation and literature to reconstruct the symbiotic form of S. fredii. Genes controlling the nitrogen fixation process were identified by simulating gene knockouts. Additionally, the nitrogen-fixing capacities of S. fredii CCBAU45436 in symbiosis with cultivated and wild soybeans were evaluated. The predictions suggested an outperformance of S. fredii with cultivated soybean, consistent with published experimental evidence. The reconstruction presented here will help to understand and improve nitrogen fixation capabilities of S. fredii and will be beneficial for agriculture by reducing the reliance on fertilizer applications.

University of Chile FONDEF D08I1051 CONICYT Millennium Scientific Initiative 'Institute for Cell Dynamics and Biotechnology' Millenium Institute CONICYT FB0001 CeBiB

Macroalgae have high potential to be an efficient, and sustainable feedstock for the production of biofuels and other more valuable chemicals. Attempts have been made to enable the co-fermentation of alginate and mannitol by Saccharomyces cerevisiae to unlock the full potential of this marine biomass. However, the efficient use of the sugars derived from macroalgae depends on the equilibrium of cofactors derived from the alginate and mannitol catabolic pathways. There are a number of strong metabolic limitations that have to be tackled before this bioconversion can be carried out efficiently by engineered yeast cells. An analysis of the redox balance during ethanol fermentation from alginate and mannitol by Saccharomyces cerevisiae using metabolic engineering tools was carried out. To represent the strain designed for conversion of macroalgae carbohydrates to ethanol, a context-specific model was derived from the available yeast genome-scale metabolic reconstructions. Flux balance analysis and dynamic simulations were used to determine the flux distributions. The model indicates that ethanol production is determined by the activity of 4-deoxy-l-erythro-5-hexoseulose uronate (DEHU) reductase (DehR) and its preferences for NADH or NADPH which influences strongly the flow of cellular resources. Different scenarios were explored to determine the equilibrium between NAD(H) and NADP(H) that will lead to increased ethanol yields on mannitol and DEHU under anaerobic conditions. When rates of mannitol dehydrogenase and DehRNADH tend to be close to a ratio in the range 1–1.6, high growth rates and ethanol yields were predicted. The analysis shows a number of metabolic limitations that are not easily identified through experimental procedures such as quantifying the impact of the cofactor preference by DEHU reductase in the system, the low flux into the alginate catabolic pathway, and a detailed analysis of the redox balance. These results show that production of ethanol and other chemicals can be optimized if a redox balance is achieved. A possible methodology to achieve this balance is presented. This paper shows how metabolic engineering tools are essential to comprehend and overcome this limitation., Graphical Abstract, Highlights • We studied a strain designed for bioconversion of macroalgae sugars to ethanol. • A genome-scale model was used to simulate biomass and by-product formation. • The characterization of the current metabolic state of the strain was achieved. • Biofuel production depends on the redox balance derived from alginate and mannitol. • Flux split into DehR determines the redox balance, by-products and ethanol level.

Dynamic models of metabolism are instrumental for gaining insight and predicting possible outcomes of perturbations. Current approaches start from the selection of lumped enzyme kinetics and determine the parameters within a large parametric space. However, kinetic parameters are often unknown and obtaining these parameters requires detailed characterization of enzyme kinetics. In many cases, only steady-state fluxes are measured or estimated, but these data have not been utilized to construct dynamic models. Here, we extend the previously developed Ensemble Modeling methodology by allowing various kinetic rate expressions and employing a more efficient solution method for steady states. We show that anchoring the dynamic models to the same flux reduces the allowable parameter space significantly such that sampling of high dimensional kinetic parameters becomes meaningful. The methodology enables examination of the properties of the model's structure, including multiple steady states. Screening of models based on limited steady-state fluxes or metabolite profiles reduces the parameter space further and the remaining models become increasingly predictive. We use both succinate overproduction and central carbon metabolism in Escherichia coli as examples to demonstrate these results.

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.

For years, micro-organisms have been used for the industrial production of heterologous proteins. However, the production of foreign proteins causes undesirable problems for the host organism. The analysis of this phenomenon is desirable to improve the production of relevant proteins and so far no analysis of the effects of recombinant protein production on genetic regulation has been reported. In this work, network component analysis (NCA) was used to deduce transcription factor activities (TFAs) in a recombinant strain of Saccharomyces cerevisiae that produces a foreign protein, human superoxide dismutase (SOD) and compared the behavior of this yeast strain to the wild-type host to see the effect of the expression of the protein at the regulatory level. NCA identified a network of 817 genes regulated by 87 transcription factors (TFs). From the identified TFs, 45 show significant change in their activities at least in one of the three phases of diauxic growth (glucose, ethanol, and early stationary phase). The major effect of the expression of SOD on the activity of the TFs was observed in the early stationary phase with 34 of them perturbed in comparison with 12 on glucose and 20 on ethanol. These TFs cover the main functions of the cell, such as developmental processes, cell cycle, metabolism, and environmental response. Five of them are present in all growth phases: Hal9, Mac1, Oaf3, Stp1, and Urc2. In addition, TFA analysis corroborates the hypothesis that copper plays a key role as a stress factor and suggests that YJL206C, a TF with unknown function, could be related to Ace2, a cell cycle regulator.

One of the main strategies to improve the production of relevant metabolites has been the manipulation of single or multiple key genes in the metabolic pathways. This kind of strategy requires several rounds of experiments to identify enzymes that impact either yield or productivity. The use of mathematical tools to facilitate this process is desirable. In this work, we apply the Ensemble Modeling (EM) framework, which uses phenotypic data (effects of enzyme overexpression or genetic knockouts on the steady-state production rate) to screen for potential models capable of describe existing data and thus gaining insight to improve strains for l-lysine production. Described herein is a strategy to generate a set of kinetic models that describe a set of enzyme overexpression phenotypes previously determined in an Escherichia coli strain that produces increased levels of l-lysine in an industrial laboratory. This final ensemble of models captures the kinetic characteristics of the cell through screening of phenotypes after sequential overexpression of enzymes. Furthermore, these models demonstrate some predictive capability, as starting from the reference producing strain (overexpressing desensitized dihydrodipicolinate synthetase (dapA*)) this set of models is able to predict that the desensitization of aspartate kinase (lysC*) is the next rate-controlling step in the l-lysine pathway. Moreover, this set of models allows for the generation of further targets for testing, for example, phosphoenolpyruvate (Ppc), aspartate aminotransferase (AspC), and glutamate dehydrogenase (GdhA). This work demonstrates the usefulness, applicability, and scope that the Ensemble Modeling framework offers to build production strains.