From Nose to Lung: Using Systems Biology to Fight Pathogens in the Human Respiratory Tract

In December 2019, the world was hit by the global SARS-CoV-2 pandemic. Two years later, the number of infections and deaths is still increasing, affecting everyday’s life. Although several vaccines have been successfully approved for SARS-CoV-2, therapeutic strategies are still minimal. As viruses rely on the host’s metabolism for replication, analyzing the viral reprogramming of the host cells might reveal potential antiviral targets. Such metabolic alterations can be evaluated and analyzed using genome-scale metabolic models (GEMs). These models represent large metabolic networks that connect metabolites with biochemical reactions facilitated by proteins and encoded by genes. With the help of genomic information, the so-called genotype, we can create metabolic models that can predict the phenotypic behavior of an organism. However, these GEMs can be used to analyze virus-host interactions and predict potential antiviral targets and understand the genotype-phenotype relationship of pathogens and commensals such as Staphylococcus aureus. High-quality models with a high predictive value help us to better understand an organism, determine metabolic capabilities in health and disease, identify potential targets for treatment interventions, and analyze the interplay between different cells and organisms. Such models can answer relevant and urgent questions of our time quickly and efficiently and become an indispensable constituent in future research. In my thesis, I demonstrate (I) how the quality and predictive value of an existing genome-scale metabolic model can be assessed, (II) how high-quality genome-scale metabolic models can be curated, and (III) how high-quality genome-scale metabolic models can be used for model-driven discoveries. All three points are addressed in the context of pathogens and commensals in the human respiratory tract. To assess the quality and predictive value of GEMs, we collected all currently available models of the pathogen Staphylococcus aureus, which colonizes the human nose. We evaluated the models concerning their validity, compliance with the FAIR data principle, quality, simulatability, and predictive value. Using high-quality models with a high predictive value enables model-driven hypotheses and discoveries. However, if no such model is available, one needs to curate a high-quality model. For this purpose, we developed a pipeline that focuses on the model curation of nasal pathogens and commensals. This pipeline is adaptable to incorporate other tools and bacteria, pathogens, or cells while maintaining certain community standards. We demonstrated the applicability of this pipeline by curating the first model of the nasal commensal Dolosigranulum pigrum. We showed how to use high-quality GEMs for model-driven discoveries by identifying novel antiviral targets. To do so, we virtually infected human alveolar macrophages in the lung with SARS-CoV-2

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice.

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

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

Abstract Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains