Genome-scale metabolic models are widely used to enhance our understanding of metabolic features of organisms, host-pathogen interactions and to identify therapeutics for diseases. Here we present iTMU798, the genome-scale metabolic model of the mouse whipworm Trichuris muris. The model demonstrates the metabolic features of T. muris and allows the prediction of metabolic steps essential for its survival. Specifically, that Thioredoxin Reductase (TrxR) enzyme is essential, a prediction we validate in vitro with the drug auranofin. Furthermore, our observation that the T. muris genome lacks gsr-1 encoding Glutathione Reductase (GR) but has GR activity that can be inhibited by auranofin indicates a mechanism for the reduction of glutathione by the TrxR enzyme in T. muris. In addition, iTMU798 predicts seven essential amino acids that cannot be synthesised by T. muris, a prediction we validate for the amino acid tryptophan. Overall, iTMU798 is as a powerful tool to study not only the T. muris metabolism but also other Trichuris spp. in understanding host parasite interactions and the rationale design of new intervention strategies.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10618284 | PMC |
| http://dx.doi.org/10.1038/s41467-023-42552-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Interspecies interactions mediated by arginine metabolism enhance the stress tolerance of against .
Microbiol Spectr
January 2025
School of Pharmacy, Lanzhou University, Lanzhou, China.
Colorectal cancer (CRC) is a common cancer accompanied by microbiome dysbiosis. Exploration of probiotics against oncogenic microorganisms is promising for CRC treatment. Here, differential microorganisms between CRC and healthy control were analyzed.
View Article and Find Full Text PDFFurther Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.
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.
An integrated multiphase dynamic genome-scale model explains batch fermentations led by species of the genus.
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 PDFQuantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model.
Toxicol 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 PDFModeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium.
Nat 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 PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!