Mechanism for Utilization of the Populus-Derived Metabolite Salicin by a Pseudomonas-Rahnella Co-Culture.

Pseudomonas fluorescens GM16 associates with Populus, a model plant in biofuel production. Populus releases abundant phenolic glycosides such as salicin, but P. fluorescens GM16 cannot utilize salicin, whereas Pseudomonas strains are known to utilize compounds similar to the aglycone moiety of salicin-salicyl alcohol. We propose that the association of Pseudomonas to Populus is mediated by another organism (such as Rahnella aquatilis OV744) that degrades the glucosyl group of salicin. In this study, we demonstrate that in the Rahnella-Pseudomonas salicin co-culture model, Rahnella grows by degrading salicin to glucose 6-phosphate and salicyl alcohol which is secreted out and is subsequently utilized by P. fluorescens GM16 for its growth. Using various quantitative approaches, we elucidate the individual pathways for salicin and salicyl alcohol metabolism present in Rahnella and Pseudomonas, respectively. Furthermore, we were able to establish that the salicyl alcohol cross-feeding interaction between the two strains on salicin medium is carried out through the combination of their respective individual pathways. The research presents one of the potential advantages of salicyl alcohol release by strains such as Rahnella, and how phenolic glycosides could be involved in attracting multiple types of bacteria into the Populus microbiome.

Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation.

Pseudomonas aeruginosa is one of the leading causes of hospital-acquired infections. To decipher the metabolic mechanisms associated with virulence and antibiotic resistance, we have developed an updated genome-scale model (GEM) of P. aeruginosa. The model (iSD1509) is an extensively curated, three-compartment, and mass-and-charge balanced BiGG model containing 1509 genes, the largest gene content for any P. aeruginosa GEM to date. It is the most accurate with prediction accuracies as high as 92.4% (gene essentiality) and 93.5% (substrate utilization). In iSD1509, we newly added a recently discovered pathway for ubiquinone-9 biosynthesis which is required for anaerobic growth. We used a modified iSD1509 to demonstrate the role of virulence factor (phenazines) in the pathogen survival within biofilm/oxygen-limited condition. Further, the model can mechanistically explain the overproduction of a drug susceptibility biomarker in the P. aeruginosa mutants. Finally, we use iSD1509 to demonstrate the drug potentiation by metabolite supplementation, and elucidate the mechanisms behind the phenotype, which agree with experimental results.

Novel context-specific genome-scale modelling explores the potential of triacylglycerol production by Chlamydomonas reinhardtii.

Gene expression data of cell cultures is commonly measured in biological and medical studies to understand cellular decision-making in various conditions. Metabolism, affected but not solely determined by the expression, is much more difficult to measure experimentally. Finding a reliable method to predict cell metabolism for expression data will greatly benefit metabolic engineering. We have developed a novel pipeline, OVERLAY, that can explore cellular fluxomics from expression data using only a high-quality genome-scale metabolic model. This is done through two main steps: first, construct a protein-constrained metabolic model (PC-model) by integrating protein and enzyme information into the metabolic model (M-model). Secondly, overlay the expression data onto the PC-model using a novel two-step nonconvex and convex optimization formulation, resulting in a context-specific PC-model with optionally calibrated rate constants. The resulting model computes proteomes and intracellular flux states that are consistent with the measured transcriptomes. Therefore, it provides detailed cellular insights that are difficult to glean individually from the omic data or M-model alone. We apply the OVERLAY to interpret triacylglycerol (TAG) overproduction by Chlamydomonas reinhardtii, using time-course RNA-Seq data. We show that OVERLAY can compute C. reinhardtii metabolism under nitrogen deprivation and metabolic shifts after an acetate boost. OVERLAY can also suggest possible 'bottleneck' proteins that need to be overexpressed to increase the TAG accumulation rate, as well as discuss other TAG-overproduction strategies.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models.

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.

Genome-Scale Modeling of Thermophilic Microorganisms.

Thermophilic microorganisms are of increasing interest for many industries as their enzymes and metabolisms are highly efficient at elevated temperatures. However, their metabolic processes are often largely different from their mesophilic counterparts. These differences can lead to metabolic engineering strategies that are doomed to fail. Genome-scale metabolic modeling is an effective and highly utilized way to investigate cellular phenotypes and to test metabolic engineering strategies. In this review we chronicle a number of thermophilic organisms that have recently been studied with genome-scale models. The microorganisms spread across archaea and bacteria domains, and their study gives insights that can be applied in a broader context than just the species they describe. We end with a perspective on the future development and applications of genome-scale models of thermophilic organisms.

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome.

BACKGROUND: Clostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering. RESULTS: We constructed a genome-scale metabolic model iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles (i.e., the ethanol to acetate ratio) between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production. CONCLUSIONS: Our developed genome-scale model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.