Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities

Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.IMPORTANCECystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium's genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.

Reconstruction of a Genome-Scale Metabolic Model of Scenedesmus obliquus and Its Application for Lipid Production under Three Trophic Modes

Green microalgae have emerged as beneficial feedstocks for biofuel production. A systems-level understanding of the biochemical network is needed to harness the microalgal metabolic capacity for bioproduction. Genome-scale metabolic modeling (GEM) showed immense potential in rational metabolic engineering, utilizing biochemical flux distribution analysis. Here, we report the first GEM for the green microalga, Scenedesmus obliquus (iAR632), a promising biodiesel feedstock with high lipid-storing capability. iAR632 comprises 1467 reactions, 734 metabolites, and 632 genes distributed among 7 compartments. The model was optimized under three different trophic modes of microalgal cultivation, i.e., autotrophy, mixotrophy, and heterotrophy. The robustness of the reconstructed network was confirmed by analyzing its sensitivity to the biomass components. Pathway-level flux profiles were analyzed, and significant flux space expansion was noticed majorly in reactions associated with lipid biosynthesis. In agreement with the experimental observation, iAR632 predicted about 3.8-fold increased biomass and almost 4-fold higher lipid under mixotrophy than the other trophic modes. Thus, the assessment of the condition-specific metabolic flux distribution of iAR632 suggested that mixotrophy is the preferred cultivation condition for improved microalgal growth and lipid production. Overall, the reconstructed GEM and subsequent analyses will provide a systematic framework for developing model-driven strategies to improve microalgal bioproduction.

IMFLer: A Web Application for Interactive Metabolic Flux Analysis and Visualization

Increasing genome-wide data in biological sciences and medicine has contributed to the development of a variety of visualization tools. Several automatic, semiautomatic, and manual visualization tools have already been developed. Some even have integrated flux balance analysis (FBA), but in most cases, it depends on separately installed third party software that is proprietary and does not allow customization of its functionality and has many restrictions for easy data distribution and analysis. In this study, we present an interactive metabolic flux analyzer and visualizer (IMFLer)-a static single-page web application that enables the reading and management of metabolic model layout maps, as well as immediate visualization of results from both FBA and flux variability analysis (FVA). IMFLer uses the Escher Builder tool to load, show, edit, and save metabolic pathway maps. This makes IMFLer an attractive and easily applicable tool with a user-friendly interface. Moreover, it allows to faster interpret results from FBA and FVA and improves data interoperability by using a standardized file format for the genome-scale metabolic model. IMFLer is a fully open-source tool that enables the rapid visualization and interpretation of the results of FBA and FVA with no time setup and no programming skills required, available at https://lv-csbg.github.io/IMFLer/.

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

Metabolic impact assessment for heterologous protein production in Streptomyces lividans based on genome-scale metabolic network modeling

The metabolic impact exerted on a microorganism due to heterologous protein production is still poorly understood in Streptomyces lividans. In this present paper, based on exometabolomic data, a proposed genome-scale metabolic network model is used to assess this metabolic impact in S. lividans. Constraint-based modeling results obtained in this work revealed that the metabolic impact due to heterologous protein production is widely distributed in the genome of S. lividans, causing both slow substrate assimilation and a shift in active pathways. Exchange fluxes that are critical for model performance have been identified for metabolites of mouse tumor necrosis factor, histidine, valine and lysine, as well as biomass. Our results unravel the interaction of heterologous protein production with intracellular metabolism of S. lividans, thus, a possible basis for further studies in relieving the metabolic burden via metabolic or bioprocess engineering.

Exploration and comparison of inborn capacity of aerobic and anaerobic metabolisms of Saccharomyces cerevisiae for microbial electrical current production

Saccharomyces cerevisiae possesses numerous advantageous biological features, such as being robust, easily handled, mostly non-pathogenic and having high catabolic rates, etc., which can be considered as merits for being used as a promising biocatalyst in microbial fuel cells (MFCs) for electricity generation. Previous studies have developed efficient MFC configurations to convert metabolic electron shuttles, such as cytoplasmic NADH, into usable electric current. However, no studies have elucidated the maximum potential of S. cerevisiae for current output and the underlying metabolic pathways, resulting from the interaction of thousands of reactions inside the cell during MFC operation. To address these two key issues, this study used in silico metabolic engineering techniques, flux balance analysis (FBA), and flux variability analysis with target flux minimization (FATMIN), to model the metabolic perturbation of S. cerevisiae under the MFC-energy extraction. The FBA results showed that, in the cytoplasmic NADH-dependent mediated electron transfer (MET) mode, S. cerevisiae had a potential to produce currents at up to 5.781 A/gDW for the anaerobic and 6.193 A/gDW for the aerobic environments. The FATMIN results showed that the aerobic and anaerobic metabolisms are resilient, relying on six and five contributing reactions respectively for high current production. Two reactions, catalyzed by glutamate dehydrogenase (NAD) (EC 1.4.1.3) and methylene tetrahydrofolate dehydrogenase (NAD) (EC 1.5.1.5), were shared in both current-production modes and contributed to over 80% of the identified maximum current outputs. It is also shown that the NADH regeneration was much less energy costly than biomass production rate. Taken together, our finding suggests that S. cerevisiae should receive more research effort for MFC electricity production.