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

Interventions targeting the nasal microbiome to eradicate MRSA

BACKGROUND

Staphylococcus aureus is an important pathogen in many sites, including bloodstream, skin and soft tissue, bone and joints. When infection is caused by methicillin resistant S. aureus (MRSA) therapy is more difficult and outcomes are less favorable. Nasal colonization is associated with increased risk for MRSA infections. The nasal microbiome may play a role in risk for nasal colonization and infection.

OBJECTIVES

To review the role of the microbiome in MRSA nasal colonization and infection.

SOURCES

Peer reviewed literature identified in a Medline search using MRSA, S. aureus, prebiotic and microbiota as search terms.

CONTENT

Reduction of S. aureus nasal colonization has been shown to reduce risk of S. aureus infections, but decolonization methods are imperfect. The role of the nasal microbiome in host defense against S. aureus colonization and infection is explored. Numerous organisms have been shown to be negatively associated with S. aureus colonization. Antimicrobial molecules produced by these organisms are an active area of research.

IMPLICATIONS

Future research should focus on development of safe and effective molecules that can inhibit S. aureus in the nasal vestibule. Damage to the diverse nasal microbiota by unnecessary antibiotics should be avoided.

Insights into the role of the respiratory tract microbiome in defense against bacterial pneumonia

The respiratory tract microbiome (RTM) is a microbial ecosystem inhabiting different niches throughout the airway. A critical role for the RTM in dictating lung infection outcomes is underlined by recent efforts to identify community members benefiting respiratory tract health. Obligate anaerobes common in the oropharynx and lung such as Prevotella and Veillonella are associated with improved pneumonia outcomes and activate several immune defense pathways in the lower airway. Colonizers of the nasal cavity, including Corynebacterium and Dolosigranulum, directly impact the growth and virulence of lung pathogens, aligning with robust clinical correlations between their upper airway abundance and reduced respiratory tract infection risk. Here, we highlight recent work identifying respiratory tract bacteria that promote airway health and resilience against disease, with a focus on lung infections and the underlying mechanisms driving RTM-protective benefits.

Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum

Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated. Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Hierarchical modelling of microbial communities

SUMMARY

The human body harbours a plethora of microbes that play a fundamental role in the well-being of the host. Still, the contribution of many microorganisms to human health remains undiscovered. To understand the composition of their communities, the accurate genome-scale metabolic network models of participating microorganisms are integrated to construct a community that mimics the normal bacterial flora of humans. So far, tools for modelling the communities have transformed the community into various optimization problems and model compositions. Therefore, any knockout or modification of each submodel (each species) necessitates the up-to-date creation of the community to incorporate rebuildings. To solve this complexity, we refer to the context of SBML in a hierarchical model composition, wherein each species's genome-scale metabolic model is imported as a submodel in another model. Hence, the community is a model composed of submodels defined in separate files. We combine all these files upon parsing to a so-called 'flattened' model, i.e., a comprehensive and valid SBML file of the entire community that COBRApy can parse for further processing. The hierarchical model facilitates the analysis of the whole community irrespective of any changes in the individual submodels.

AVAILABILITY AND IMPLEMENTATION

The module is freely available at https://github.com/manuelgloeckler/ncmw.