Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Metabolic network reconstruction as a resource for analyzing <i>Salmonella</i> Typhimurium SL1344 growth in the mouse intestine
 
research article

Metabolic network reconstruction as a resource for analyzing Salmonella Typhimurium SL1344 growth in the mouse intestine

Vayena, Evangelia  
•
Fuchs, Lea
•
Peyhani, Homa Mohammadi  
Show more
March 1, 2025
Plos Computational Biology

Nontyphoidal Salmonella strains (NTS) are among the most common foodborne enteropathogens and constitute a major cause of global morbidity and mortality, imposing a substantial burden on global health. The increasing antibiotic resistance of NTS bacteria has attracted a lot of research on understanding their modus operandi during infection. Growth in the gut lumen is a critical phase of the NTS infection. This might offer opportunities for intervention. However, the metabolic richness of the gut lumen environment and the inherent complexity and robustness of the metabolism of NTS bacteria call for modeling approaches to guide research efforts. In this study, we reconstructed a thermodynamically constrained and context-specific genome-scale metabolic model (GEM) for S. Typhimurium SL1344, a model strain well-studied in infection research. We combined sequence annotation, optimization methods and in vitro and in vivo experimental data. We used GEM to explore the nutritional requirements, the growth limiting metabolic genes, and the metabolic pathway usage of NTS bacteria in a rich environment simulating the murine gut. This work provides insight and hypotheses on the biochemical capabilities and requirements of SL1344 beyond the knowledge acquired through conventional sequence annotation and can inform future research aimed at better understanding NTS metabolism and identifying potential targets for infection prevention.

  • Details
  • Metrics
Type
research article
DOI
10.1371/journal.pcbi.1012869
Web of Science ID

WOS:001444332800003

PubMed ID

40067815

Author(s)
Vayena, Evangelia  

École Polytechnique Fédérale de Lausanne

Fuchs, Lea

Swiss Federal Institutes of Technology Domain

Peyhani, Homa Mohammadi  

École Polytechnique Fédérale de Lausanne

Lagoda, Konrad  

École Polytechnique Fédérale de Lausanne

Nguyen, Bidong

Swiss Federal Institutes of Technology Domain

Hardt, Wolf-Dietrich

Swiss Federal Institutes of Technology Domain

Hatzimanikatis, Vassily  

École Polytechnique Fédérale de Lausanne

Date Issued

2025-03-01

Publisher

PUBLIC LIBRARY SCIENCE

Published in
Plos Computational Biology
Volume

21

Issue

3

Article Number

e1012869

Subjects

MICROBIOTA

•

IDENTIFICATION

•

INFECTION

•

GENES

•

Science & Technology

•

Life Sciences & Biomedicine

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LCSB  
FunderFunding(s)Grant NumberGrant URL

Swiss National Science Foundation (SNSF)

200021_188623

NCCR Microbiomes, a National Centre of Competence in Research

180575;310030_192567

European Union (EU)

72228

Show more
Available on Infoscience
March 24, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/248174
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés