The Peptide Antibiotic Corramycin Adopts a β-Hairpin-like Structure and Is Inactivated by the Kinase ComG

The rapid development of antibiotic resistance, especially among difficult-to-treat Gram-negative bacteria, is recognized as a serious and urgent threat to public health. The detection and characterization of novel resistance mechanisms are essential to better predict the spread and evolution of antibiotic resistance. Corramycin is a novel and modified peptidic antibiotic with activity against several Gram-negative pathogens. We demonstrate that the kinase ComG, part of the corramycin biosynthetic gene cluster, phosphorylates and thereby inactivates corramycin, leading to the resistance of the host. Remarkably, we found that the closest structural homologues of ComG are aminoglycoside phosphotransferases; however, ComG shows no activity toward this class of antibiotics. The crystal structure of ComG in complex with corramycin reveals that corramycin adopts a β-hairpin-like structure and allowed us to define the changes leading to a switch in substrate from sugar to peptide. Bioinformatic analyses suggest a limited occurrence of ComG-like proteins, which along with the absence of cross-resistance to clinically used drugs positions corramycin as an attractive antibiotic for further development.

Nitroxoline resistance is associated with significant fitness loss and diminishes in vivo virulence of Escherichia coli

IMPORTANCE

Antimicrobial resistance (AMR) poses a global threat and requires the exploration of underestimated treatment options. Nitroxoline, an effective broad-spectrum antibiotic, does not suffer from high resistance rates in the clinics but surprisingly, it is not heavily used yet. Our findings provide compelling evidence that Nitroxoline resistance renders bacteria unable to cause an infection in vivo, thereby reinvigorating the potential of Nitroxoline in combating AMR.

Evaluation of extraction methods for untargeted metabolomic studies for future applications in zebrafish larvae infection models

Sample preparation in untargeted metabolomics should allow reproducible extractions of as many molecules as possible. Thus, optimizing sample preparation is crucial. This study compared six different extraction procedures to find the most suitable for extracting zebrafish larvae in the context of an infection model. Two one-phase extractions employing methanol (I) and a single miscible phase of methanol/acetonitrile/water (II) and two two-phase methods using phase separation between chloroform and methanol/water combinations (III and IV) were tested. Additional bead homogenization was used for methods III and IV (III_B and IV_B). Nine internal standards and 59 molecules of interest (MoInt) related to mycobacterial infection were used for method evaluation. Two-phase methods (III and IV) led to a lower feature count, higher peak areas of MoInt, especially amino acids, and higher coefficients of variation in comparison to one-phase extractions. Adding bead homogenization increased feature count, peak areas, and CVs. Extraction I showed higher peak areas and lower CVs than extraction II, thus being the most suited one-phase method. Extraction III and IV showed similar results, with III being easier to execute and less prone to imprecisions. Thus, for future applications in zebrafish larvae metabolomics and infection models, extractions I and III might be chosen.

An object-relational model for structured representation of medical knowledge

Domain specific knowledge is often not static but continuously evolving. This is especially true for the medical domain. Furthermore, the lack of standardized structures for presenting knowledge makes it difficult or often impossible to assess new knowledge in the context of existing knowledge. Possibilities to compare knowledge easily and directly are often not given. It is therefore of utmost importance to create a model that allows for comparability, consistency and quality assurance of medical knowledge in specific work situations. For this purpose, we have designed on object-relational model based on structured knowledge elements that are dynamically reusable by different multi-media-based tools for case-based documentation, disease course simulation, and decision support. With this model, high-level components, such as patient case reports or simulations of the course of a disease, and low-level components (e.g., diagnoses, symptoms or treatments) as well as the relationships between these components are modeled. The resulting schema has been implemented in AMOS II, on object-relational multi-database system supporting different views with regard to search and analysis depending on different work situations.

A database generator for human brain imaging

Sharing scientific data containing complex information requires new concepts and new technology. NEUROGENERATOR is a database generator for the neuroimaging community. A database generator is a database that generates new databases. The scientists submit raw PET and fMRI data to NEUROGENERATOR, which then processes the data in a uniform way to create databases of homogeneous data suitable for data sharing, met-analysis and modelling the human brain at the systems level. These databases are then distributed to the scientists.

A DATA MANAGER for the health information system Berlin

The needs for permanently changing the logical and physical structure of a medical datebase during the development of a health information system have initiated the project of implementing a DATA MANAGER. The concept of the DATA MANAGER covers facilities for the development of the logical data structure model including documentation of the model and programming support for application programs accessing the health information system (HIS) database. The outstanding facilities of the INTERLISP system have been found to be appropriate for writing the DATA MANAGER. A first data structure model, on which the DATA MANAGER will operate, is roughly outlined.