The human nose harbors various microbes that decisively influence the wellbeing 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 GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the 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 uses case illustrates how to incorporate genuine GEM 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.

The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus, which occasionally turn into pathogens causing infectious diseases, while the contribution of many nasal microorganisms to human health remains undiscovered. To better understand the composition of the nasal microbiome community, we create a workflow of the community model, which mimics the human nasal environment. To address this challenge, constraint-based reconstruction of biochemically accurate genome-scale metabolic models (GEMs) networks of microorganisms is mandatory. Our workflow applies constraint-based modeling (CBM), simulates the metabolism between species in a given microbiome, and facilitates generating novel hypotheses on microbial interactions. Utilizing this workflow, we hope to gain a better understanding of interactions from the metabolic modeling perspective. This article presents nasal community modeling workflow (NCMW)—a python package based on GEMs of species as a starting point for understanding the composition of the nasal microbiome community. The package is constructed as a step-by-step mathematical framework for metabolic modeling and analysis of the nasal microbial community. Using constraint-based models reduces the need for culturing species in vitro, a process that is not convenient in the environment of human noses.Availability: NCMW is freely available on the Python Package Index (PIP) via pip install NCMW. The source code, documentation, and usage examples (Jupyter Notebook and example files) are available at https://github.com/manuelgloeckler/ncmw.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types. The recently emerged human coronavirus SARS-CoV-2 spread worldwide, causing severe challenges in health care, the economy, and society. Developing new vaccines and therapies is essential to prevent the next pandemic efficiently. However, vaccines have the disadvantage of decreased immunity over time, while they lose their efficacy against subsequent mutations and variants. Hence, effective pandemic preparedness requires discovering broadly acting antivirals with high resistance barriers. Here, we accelerate the detection of antiviral drug candidates against RNA viruses by analyzing metabolic changes in infected cells. Viruses rely on the host to acquire all macromolecules needed for their replication, re-programming the cellular metabolism according to their needs. For this reason, host-directed approaches are of great importance. We develop software to reconstruct constraint-based models, simulate infections of a cell, and identify host-based metabolic pathways that can be inhibited to suppress viral replication. We identify promising targets with inhibitory effects across multiple variants facilitating further in vitro and in vivo experiments. Our workflow can be applied to any RNA virus and aims to rapidly identify antiviral targets to better prepare for the next pandemic.

The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

The complex interplay between pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism Yersinia entercolitica (Ye) is one of the five top frequent causes of bacterial gastroenteritis based on the Epidemiological Bulletin of the Robert Koch Institute (RKI), 10 September 2020. A fundamental challenge in predicting the course of an infection is to understand whether co-infection with two Yersinia strains, differing only in their capacity to resist killing by the host immune system, may decrease the overall virulence by competitive exclusion or increase it by acting cooperatively. Herein, we study the primary interactions among Ye, the host immune system and the microbiota, and their influence on Yersinia population dynamics. The employed model considers commensal bacterial in two host compartments (the intestinal mucosa the and lumen), the co-existence of wt and mut Yersinia strains, and the host immune responses. We determine four possible equilibria: disease-free, wt-free, mut-free, and co-existence of wt and mut in equilibrium. We also calculate the reproduction number for each strain as a threshold parameter to determine if the population may be eradicated or persist within the host. We conclude that the infection should disappear if the reproduction numbers for each strain fall below one, and the commensal bacteria growth rate exceeds the pathogen’s growth rate. These findings will help inform medical control strategies. The supplement includes the MATLAB source script, Maple workbook, and figures.