Biology subjects

Draeger, A.

Publications and source records attributed to Draeger, A..

7 recordsLinked to original sources

bioRxiv2023

Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions

Staphylococcus epidermidis, a commensal bacterium inhabiting collagen-rich areas, like human skin, has gained significance due to its probiotic potential in the nasal microbiome and as a leading cause of nosocomial infections. While infrequently leading to severe illnesses, S. epidermidis exerts a significant influence, particularly in its close association with implant-related infections and its role as a classic opportunistic biofilm former. Understanding its opportunistic nature is crucial for developing novel therapeutic strategies, addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a powerful tool to elucidate the lifestyle and capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organisms growth conditions within diverse habitats by reconstructing and analyzing a manually curated and experimentally validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051 metabolites, and 705 genes, adhering to established community standards and modeling guidelines. Benchmarking with the MEMOTE test suite yields a high score, highlighting the models high semantic quality. Following the FAIR data principles, iSep23 becomes a valuable and publicly accessible asset for subsequent studies. Growth simulations and carbon source utilization predictions align with experimental results, showcasing the models predictive power. This metabolic model advances our understanding of S. epidermidis as a commensal and potential probiotic and enhances insights into its opportunistic pathogenicity against other microorganisms. Author summaryStaphylococcus epidermidis, a bacterium commonly found on human skin, has shown probiotic effects in the nasal microbiome and is a notable causative agent of hospital-acquired infections. While typically causing non-life-threatening diseases, the economic ramifications of S. epidermidis infections are substantial, with annual costs reaching billions of dollars in the United States. To unravel its opportunistic nature, we utilized genome-scale metabolic modeling, creating a detailed mathematical network that elucidates S. epidermidiss lifestyle and capabilities. This model, encompassing over a thousand reactions, metabolites, and genes, adheres rigorously to established standards and guidelines, evident in its commendable benchmarking scores. Adhering to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable), the model stands as a valuable resource for subsequent investigations. Growth simulations and predictions align closely with experimental results, showcasing the models predictive accuracy. This metabolic model not only enhances our understanding of S. epidermidis as a skin commensal and potential probiotic but also sheds light on its opportunistic pathogenicity, particularly in competition with other microorganisms.

systems biology↗

bioRxiv2023

Genome-Scale Modeling of Rothia mucilaginosa Reveals Insights into Metabolic Capabilities and Therapeutic Strategies for Cystic Fibrosis

BackgroundCystic fibrosis (CF) is an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, resulting in the production of sticky and thick mucosal fluids. This leads to an environment that facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may have a positive influence on the disease process. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled the anti-inflammatory properties of R. mucilaginosa using in vitro three-dimensional (3-D) lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from a potentially beneficial anti-inflammatory role in chronic lung diseases, R. mucilaginosa has been associated with severe infections. This dual nature highlights the bacteriums complexity and diverse impact on health and disease. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. ResultsTo gain insights into the cellular metabolism and genetic content of R. mucilaginosa, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetic experiments and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the models effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knock-outs and mutant transposon libraries. ConclusionOverall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosas metabolism for desired performance.

systems biology↗

bioRxiv2023

Exploring the metabolic profiling of A. baumannii for antimicrobial development using genome-scale modeling

With the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms urgently needing new antibiotics, with the carbapenem-resistant Acinetobacter baumannii designated as "critical". Such isolates, frequently detected in healthcare settings, pose a global pandemic threat. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics is to apply constraint-based modelling. Here, we developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a novel metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our model i ACB23LX could recapitulate cellular metabolic phenotypes observed during in vitro experiments, while positive biomass production rates were observed and experimentally validated in various growth media. We further defined a minimal set of compounds that increase A. baumannii s cellular biomass and identified putative essential genes with no human counterparts, offering novel candidates for future antimicrobial development. Finally, we assembled and curated the first collection of reconstructions for distinct A. baumannii strains and analysed their growth characteristics. The presented models are in a standardised and well-curated format, enhancing their usability for multi-strain network reconstruction.

systems biology↗

bioRxiv2021

An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.

systems biology↗

bioRxiv2020

Model-based prediction of bacterial population dynamics in gastrointestinal infection

The complex interplay of a pathogen with the host immune response and the endogenous microbiome determines the course and outcome of gastrointestinal infection (GI). Expansion of a pathogen within the gastrointestinal tract implies an increased risk to develop systemic infection. Through computational modeling, we aimed to calculate bacterial population dynamics in GI in order to predict infection course and outcome. For the implementation and parameterization of the model, oral mouse infection experiments with Yersinia enterocolitica were used. Our model takes into account pathogen specific characteristics, such as virulence, as well as host properties, such as microbial colonization resistance or immune responses. We were able to confirm the model calculations in these scenarios by experimental mouse infections and show that it is possible to computationally predict the infection course. Far future clinical application of computational modeling of infections may pave the way for personalized treatment and prevention strategies of GI.

microbiology↗

bioRxiv2019

Community standards to facilitate development and address challenges in metabolic modeling

Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remains challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of metabolic modeling by conducting a community-wide survey. We used this feedback to (1) outline the major challenges that our field faces and to propose solutions and (2) identify a set of features that defines what a \"gold standard\" metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community-driven outline will help the long-term development of community-inspired resources as well as produce high-quality, accessible models. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure continued development of both practical, usable standards and reproducible, knowledge-rich models.

systems biology↗

bioRxiv2018

Visualizing metabolic network dynamics through time-series metabolomics data

New technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems--the human platelet and erythrocyte under cold storage for use in transfusion medicine.\n\nThe results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.\n\nIn conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.\n\nAUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

systems biology↗