Background: The halophilic bacterium Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached.
Results: We generated and validated a high quality genome-based C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments (periplasmic, cytoplasmic and external medium), and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems.
Conclusions: Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5759318 | PMC |
http://dx.doi.org/10.1186/s12934-017-0852-0 | DOI Listing |
PLoS One
January 2025
Key Laboratory for Prevention and Control of Common Animal Diseases in General Higher Education Institutions of Heilongjiang Province, College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.
This study aims to provide a theoretical foundation for the future management of diabetes at various stages induced by a high-fat diet. Specifically, it seeks to determine the appropriate pharmacological interventions for each phase of diabetes development and the targeted therapeutic directions at different stages of diabetes progression. This investigation employed C57BL6 mice as experimental subjects, successfully establishing an insulin resistance model through a 12-week high-fat diet.
View Article and Find Full Text PDFPLoS One
January 2025
Australian National Phenome Center and Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia.
Understanding the distribution and variation in inflammatory markers is crucial for advancing our knowledge of inflammatory processes and evaluating their clinical utility in diagnosing and monitoring acute and chronic disease. 1H NMR spectroscopy of blood plasma and serum was applied to measure a composite panel of inflammatory markers based on acute phase glycoprotein signals (GlycA and GlycB) and sub-regions of the lipoprotein derived Supramolecular Phospholipid Composite signals (SPC1, SPC2 and SPC3) to establish normal ranges in two healthy, predominantly white cohorts from Australia (n = 398) and Spain (n = 80; ages 20-70 years). GlycA, GlycB, SPC1 and SPC3 were not significantly impacted by age or sex, but SPC2 (an HDL-related biomarker) was significantly higher in women across all age ranges by an average of 33.
View Article and Find Full Text PDFPLoS Genet
January 2025
Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada.
Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed.
View Article and Find Full Text PDFJ Proteome Res
January 2025
Department of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210000, Jiangsu, China.
This study used untargeted lipidomics to analyze the characteristics of lipid metabolism in the serum of women with antiphospholipid syndrome. Twenty female patients with APS and 20 healthy controls were recruited to this study. Untargeted lipidomics with liquid chromatography-tandem mass spectrometry was used to profile serum lipids.
View Article and Find Full Text PDFDiabetes Technol Ther
January 2025
Children's Mercy Kansas City, Endocrinology, Kansas City, Missouri, USA.
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!