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Start date: 2021 × DDC: 570 × DDC: 610 × Publisher: London : BioMed Central ×

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Now showing 1 - 4 of 4
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    Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics
    (London : BioMed Central, 2016) Hoerr, Verena; Duggan, Gavin E.; Zbytnuik, Lori; Poon, Karen K.H.; Große, Christina; Neugebauer, Ute; Methling, Karen; Löffler, Bettina; Vogel, Hans J.
    Background: The emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in ‘omics’ studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs. Results: In an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative 1H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted. Conclusion: The metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.
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    Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research
    (London : BioMed Central, 2022) Vogt, Lars; Mikó, István; Bartolomaeus, Thomas
    Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.
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    Functionalization of Ti-40Nb implant material with strontium by reactive sputtering
    (London : BioMed Central, 2017-10-10) Göttlicher, Markus; Rohnke, Marcus; Moryson, Yannik; Thomas, Jürgen; Sann, Joachim; Lode, Anja; Schumacher, Matthias; Schmidt, Romy; Pilz, Stefan; Gebert, Annett; Gemming, Thomas; Janek, Jürgen
    Background: Surface functionalization of orthopedic implants with pharmaceutically active agents is a modern approach to enhance osseointegration in systemically altered bone. A local release of strontium, a verified bone building therapeutic agent, at the fracture site would diminish side effects, which could occur otherwise by oral administration. Strontium surface functionalization of specially designed titanium-niobium (Ti-40Nb) implant alloy would provide an advanced implant system that is mechanically adapted to altered bone with the ability to stimulate bone formation. Methods: Strontium-containing coatings were prepared by reactive sputtering of strontium chloride (SrCl2) in a self-constructed capacitively coupled radio frequency (RF) plasma reactor. Film morphology, structure and composition were investigated by scanning electron microscopy (SEM), time of flight secondary ion mass spectrometry (ToF-SIMS) and X-ray photoelectron spectroscopy (XPS). High-resolution transmission electron microscopy (HR-TEM) was used for the investigation of thickness and growth direction of the product layer. TEM lamellae were prepared using the focused ion beam (FIB) technique. Bioactivity of the surface coatings was tested by cultivation of primary human osteoblasts and subsequent analysis of cell morphology, viability, proliferation and differentiation. The results are correlated with the amount of strontium that is released from the coating in biomedical buffer solution, quantified by inductively coupled plasma mass spectrometry (ICP-MS). Results: Dense coatings, consisting of SrOxCly, of more than 100 nm thickness and columnar structure, were prepared. TEM images of cross sections clearly show an incoherent but well-structured interface between coating and substrate without any cracks. Sr2+ is released from the SrOxCly coating into physiological solution as proven by ICP-MS analysis. Cell culture studies showed excellent biocompatibility of the functionalized alloy. Conclusions: Ti-40Nb alloy, a potential orthopedic implant material for osteoporosis patients, could be successfully plasma coated with a dense SrOxCly film. The material performed well in in vitro tests. Nevertheless, the Sr2+ release must be optimized in future work to meet the requirements of an effective drug delivery system.
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    Semantic units: organizing knowledge graphs into semantically meaningful units of representation
    (London : BioMed Central, 2024) Vogt, Lars; Kuhn, Tobias; Hoehndorf, Robert
    Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.