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.
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http://dx.doi.org/10.1038/s42003-023-04540-8 | DOI Listing |
Genes (Basel)
January 2025
Department of Physics and Astronomy, College of Science, Clemson University, Clemson, SC 29634, USA.
Background/objectives: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones.
Methods: This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs.
mSystems
January 2025
Biosystems and Bioprocess Engineering, IIM-CSIC, Vigo, Spain.
During batch fermentation, a variety of compounds are synthesized, as microorganisms undergo distinct growth phases: lag, exponential, growth-no-growth transition, stationary, and decay. A detailed understanding of the metabolic pathways involved in these phases is crucial for optimizing the production of target compounds. Dynamic flux balance analysis (dFBA) offers insight into the dynamics of metabolic pathways.
View Article and Find Full Text PDFToxicol Sci
January 2025
Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, 21702, USA.
Because the liver plays a vital role in the clearance of exogenous chemical compounds, it is susceptible to chemical-induced toxicity. Animal-based testing is routinely used to assess the hepatotoxic potential of chemicals. While large-scale high-throughput sequencing data can indicate the genes affected by chemical exposures, we need system-level approaches to interpret these changes.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Marine Science and Technology, Shandong University, Qingdao, China.
Lignin, as the abundant carbon polymer, is essential for carbon cycle and biorefinery. Microorganisms interact to form communities for lignin biodegradation, yet it is a challenge to understand such complex interactions. Here, we develop a coastal lignin-degrading bacterial consortium (LD), through "top-down" enrichment.
View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.
Genome-scale metabolic models (GSMM) are commonly used to identify gene deletion sets that result in growth coupling and pairing product formation with substrate utilization and can improve strain performance beyond levels typically accessible using traditional strain engineering approaches. However, sustainable feedstocks pose a challenge due to incomplete high-resolution metabolic data for non-canonical carbon sources required to curate GSMM and identify implementable designs. Here we address a four-gene deletion design in the Pseudomonas putida KT2440 strain for the lignin-derived non-sugar carbon source, p-coumarate (p-CA), that proved challenging to implement.
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