Using Metabolic Networks to Study Salmonella Growth in the Intestine

Jenn Hoskins
12th March, 2025

Using Metabolic Networks to Study Salmonella Growth in the Intestine

The iNTS_SL1344 genome-scale metabolic model was reconstructed through a four-step workflow integrating KEGG-based genome annotation, pathway expansion for gut-relevant metabolites, curation against in vitro growth data, and bioinformatic prediction of catalyzing genes, enabling analysis of Salmonella Typhimurium SL1344 metabolism in the murine intestinal environment.

Image adapted from: Vayena et al. / CC BY (Source)

Key Findings

  • Researchers at EPFL, ETH Zurich, and University of Delaware developed a detailed model of Salmonella bacteria in the mouse gut
  • They identified essential nutrients and genes Salmonella needs to grow, pointing to new targets for treatments
  • The study suggests enhancing beneficial gut bacteria could naturally inhibit Salmonella, offering alternative prevention methods
Nontyphoidal Salmonella (NTS) strains are among the most common causes of foodborne illnesses worldwide, leading to significant morbidity and mortality[1]. These bacteria are responsible for acute, self-limiting diarrhea and pose a substantial burden on global health systems. A growing concern is the increasing antibiotic resistance observed in NTS, which complicates treatment and underscores the need for alternative strategies to combat infections. Understanding how NTS bacteria thrive in the gut is crucial for developing effective interventions. The gut lumen, the hollow part of the intestine, provides a rich environment full of nutrients and microbial interactions that support bacterial growth. However, the complexity and metabolic diversity of this environment make it challenging to decipher how NTS bacteria manage to colonize and proliferate[2]. To address this, researchers at the École Polytechnique Fédérale de Lausanne (EPFL), ETH Zurich, and the University of Delaware have undertaken a comprehensive study to model the metabolic processes of Salmonella enterica serovar Typhimurium SL1344, a well-studied strain used in infection research. The research team developed a genome-scale metabolic model (GEM) that is both thermodynamically constrained and context-specific. This model integrates various types of data, including genetic sequences, optimization methods, and experimental findings from both laboratory and living organisms. By doing so, the model can simulate and predict the metabolic behavior of S. Typhimurium under conditions that mimic the murine gut environment. One of the key aspects of this study is the identification of the nutritional requirements and growth-limiting metabolic genes of S. Typhimurium. Previous research has shown that the ability of Salmonella to adapt metabolically is essential for its survival and growth in the gut[3]. For instance, Salmonella can utilize hydrogen (H₂) produced by the gut microbiota as an energy source through anaerobic respiration, using fumarate as an electron acceptor. However, fumarate is limited in the gut, and the bacteria must rely on specific transporters and enzymes to acquire and convert available substrates into usable energy[3][4]. The GEM developed in this study builds on these findings by providing a detailed map of the biochemical pathways that S. Typhimurium employs during infection. By simulating different environmental conditions, the model can predict which metabolic pathways are active and identify potential bottlenecks that limit bacterial growth. This information is invaluable for pinpointing targets that could be disrupted to prevent or reduce infection. For example, the model can highlight essential genes involved in fumarate acquisition and hydrogen consumption, suggesting that inhibiting these processes might hinder the bacteria's ability to thrive in the gut[3][4]. Furthermore, the study leverages insights from previous metabolic reconstructions of S. Typhimurium[5]. Metabolic reconstructions are comprehensive representations of an organism's metabolic network, capturing the relationships between genes, enzymes, and biochemical reactions. The collaborative effort described in prior research emphasized the importance of community-driven approaches to develop accurate and reliable models[5]. By incorporating thermodynamic data and integrating various datasets, the current study enhances the precision of the metabolic model, making it a more powerful tool for understanding bacterial metabolism and identifying intervention points. In addition to mapping metabolic pathways, the GEM allows researchers to explore how S. Typhimurium interacts with the host's microbiota. The gut microbiota plays a significant role in shaping the metabolic landscape, often competing with pathogens for nutrients and space[2]. The model can simulate these interactions, revealing how changes in the microbiota composition might influence Salmonella's growth and virulence. For instance, introducing hydrogen-consuming bacteria into the gut microbiota could potentially starve Salmonella of the hydrogen it needs for energy, thereby inhibiting its growth[4]. The study also highlights the potential of using vaccination and microbiota manipulation as strategies to prevent the evolution and spread of antibiotic-resistant Salmonella strains[2]. By understanding the metabolic dependencies of S. Typhimurium, vaccines could be designed to target specific metabolic pathways, reducing the bacteria's ability to adapt and survive in the gut environment. Similarly, manipulating the microbiota to favor bacteria that compete with Salmonella for essential nutrients could provide a natural means of controlling infections without relying solely on antibiotics. Overall, the genome-scale metabolic model developed by the researchers offers a comprehensive framework for exploring the metabolic capabilities of S. Typhimurium beyond what traditional sequence annotation can reveal[5]. This model not only advances our understanding of how Salmonella adapts and thrives in the complex environment of the gut but also opens up new avenues for developing targeted therapies and preventive measures. By integrating data from various sources and building upon previous studies, this research represents a significant step forward in the fight against antibiotic-resistant NTS infections.

MedicineBiochemAnimal Science

References

Main Study

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

Published 11th March, 2025

https://doi.org/10.1371/journal.pcbi.1012869

Related Studies

2) Salmonella Typhimurium Diarrhea Reveals Basic Principles of Enteropathogen Infection and Disease-Promoted DNA Exchange.

https://doi.org/10.1016/j.chom.2017.03.009

3) Import of Aspartate and Malate by DcuABC Drives H2/Fumarate Respiration to Promote Initial Salmonella Gut-Lumen Colonization in Mice.

https://doi.org/10.1016/j.chom.2020.04.013

4) Microbiota-derived hydrogen fuels Salmonella typhimurium invasion of the gut ecosystem.

https://doi.org/10.1016/j.chom.2013.11.002

5) A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.

https://doi.org/10.1186/1752-0509-5-8


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