Front. Cell. Infect. Microbiol.
Sec. Bacteria and Host
doi: 10.3389/fcimb.2022.925215

Towards the human nasal microbiome: simulating D. pigrum and S. aureus

  • 1Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
  • 2Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen, Germany
  • 3German Centre for Infection Research (DZIF), Partner Site Tuebingen, Germany
  • 4Cluster of Excellence ‘Controlling Microbes to Fight Infections,’ University of Tübingen, Germany
Provisionally accepted:
The final, formatted version of the article will be published soon.

The human nose harbors various microbes that decisively influence the well-being and health
of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus.
Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium
based on its positive association with health, including negative associations with S. aureus.
Carefully curated genome-scale metabolic models (GEMs) are available for both bacterial species
that reliably simulate their growth behavior in isolation. To unravel the mutual effects amongst
bacteria, building community models for simulating co-culture growth is necessary. However,
modeling microbial communities remains challenging.
This article illustrates how applying the nasal community modeling workflow (NCMW) fosters
our understanding of two microbes’ joint growth conditions in the nasal habitat and their intricate
interplay from a metabolic modeling perspective. The resulting community model combines the
latest available curated GEMs of D. pigrum and S. aureus. This use-case illustrates how to
incorporate genuine GEMs of participating microorganisms and creates a basic community model
mimicking the human nasal environment.
Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum
examined experimentally in the lab. By this, we identify and characterize metabolic exchange
factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate
factor for a deep insight into the associated species. This method may serve as a blueprint for
developing more complex microbial interaction models. Its direct application suggests new ways
to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus
through microbe-microbe interactions.

Keywords: microbial communities, Staphylococcus aureus, Dolosigranulum pigrum, nasal microbiome, Computational Biology, Genome-scale metabolic model

Received: 21 Apr 2022; Accepted: 15 Aug 2022.

Copyright: © 2022 Mostolizadeh, Glöckler and Dräger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Reihaneh Mostolizadeh, Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Baden-Württemberg, Germany