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The rise of resistances against antibiotics in bacteria is a major threat for public health and demands the development of novel antibacterial therapies. Infections with Pseudomonas aeruginosa are a severe problem for hospitalized patients and for patients suffering from cystic fibrosis. These bacteria can form biofilms and thereby increase their resistance towards antibiotics. The bacterial lectin LecB was shown to be necessary for biofilm formation and the inhibition with its carbohydrate ligands resulted in reduced amounts of biofilm. The natural ligands for LecB are glycosides of D-mannose and L-fucose, the latter displaying an unusual strong affinity. Interestingly, although mannosides are much weaker ligands for LecB, they do form an additional hydrogen bond with the protein in the crystal structure. To analyze the individual contributions of the methyl group in fucosides and the hydroxymethyl group in mannosides to the binding, we designed and synthesized derivatives of these saccharides. We report glycomimetic inhibitors that dissect the individual interactions of their saccharide precursors with LecB and give insight into the biophysics of binding by LecB. Furthermore, theoretical calculations supported by experimental thermodynamic data suggest a perturbed hydrogen bonding network for mannose derivatives as molecular basis for the selectivity of LecB for fucosides. Knowledge gained on the mode of interaction of LecB with its ligands at ambient conditions will be useful for future drug design.

Transcriptomic analysis is a powerful method in the utilization of New Approach Methods (NAMs) for identifying mechanisms of toxicity and application to hazard characterization. With this regard, mapping toxicological events to time of exposure would be helpful to characterize early events. Here, we investigated time-dependent changes in gene expression levels in iPSC-derived renal proximal tubular-like cells (PTL) treated with five diverse compounds using TempO-Seq transcriptomics with the aims to evaluate the application of PTL for toxicity prediction and to report on temporal effects for the activation of cellular stress response pathways. PTL were treated with either 50 μM amiodarone, 10 μM sodium arsenate, 5 nM rotenone, or 300 nM tunicamycin over a temporal time course between 1 and 24 h. The TGFβ-type I receptor kinase inhibitor GW788388 (1 μM) was used as a negative control. Pathway analysis revealed the induction of key stress-response pathways, including Nrf2 oxidative stress response, unfolding protein response, and metal stress response. Early response genes per pathway were identified much earlier than 24 h and included HMOX1 , ATF3 , DDIT3 , and several MT1 isotypes. GW788388 did not induce any genes within the stress response pathways above, but showed deregulation of genes involved in TGFβ inhibition, including downregulation of CYP24A1 and SERPINE1 and upregulation of WT1 . This study highlights the application of iPSC-derived renal cells for prediction of cellular toxicity and sheds new light on the temporal and early effects of key genes that are involved in cellular stress response pathways.

In this paper we present our reworked approach to generate ligand protonation states with our structure preparation tool SPORES (Structure PrOtonation and REcognition System). SPORES can be used for the preprocessing of proteins and protein–ligand complexes as e.g. taken from the Protein Data Bank as well as for the setup of 3D ligand databases. It automatically assigns atom and bond types, generates different protonation, tautomeric states as well as different stereoisomers. In the revised version, pKa calculations with the ChemAxon software MARVIN are used either to determine the likeliness of a combinatorial generated protonation state or to determine the titrable atoms used in the combinatorial approach. Additionally, the MARVIN software is used to predict microspecies distributions of ligand molecules. Docking studies were performed with our recently introduced program PLANTS (Protein–Ligand ANT System) on all protomers resulting from the three different selection methods for the well established CCDC/ASTEX clean data set demonstrating the usefulness of especially the latter approach.

Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects.

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

Nanosafety assessment, which seeks to evaluate the risks from exposure to nanoscale materials, spans materials synthesis and characterisation, exposure science, toxicology, and computational approaches, resulting in complex experimental workflows and diverse data types. Managing the data flows, with a focus on provenance (who generated the data and for what purpose) and quality (how was the data generated, using which protocol with which controls), as part of good research output management, is necessary to maximise the reuse potential and value of the data. Instance maps have been developed and evolved to visualise experimental nanosafety workflows and to bridge the gap between the theoretical principles of FAIR (Findable, Accessible, Interoperable and Re-usable) data and the everyday practice of experimental researchers. Instance maps are most effective when applied at the study design stage to associate the workflow with the nanomaterials, environmental conditions, method descriptions, protocols, biological and computational models to be used, and the data flows arising from study execution. Application of the InstanceMaps tool (described herein) to research workflows of increasing complexity is presented to demonstrate its utility, starting from (i) documentation of a nanomaterial’s synthesis, functionalisation, and characterisation, over (ii) assessment of a nanomaterial’s transformations in complex media, (iii) description of the culturing of ecotoxicity model organisms Daphnia magna and their use in standardised tests for nanomaterials ecotoxicity assessment, and (iv) visualisation of complex workflows in human immunotoxicity assessment using cell lines and primary cellular models, to (v) the use of the instance map approach for the coordination of materials and data flows in complex multipartner collaborative projects and for the demonstration of case studies. Finally, areas for future development of the instance map approach and the tool are highlighted.

Extracts of the predatory bacterium Myxococcus fulvus HKI 722 showed promising antimicrobial activities in the agar diffusion assay. A combined chemical and computational analysis led to the identification of five thiazole-containing antibiotics. Two of the isolated compounds represent previously unrecognized members of the myxothiazol family of natural products. Their antibiotic properties were determined in comparison with those of the known myxothiazols A and Z.

Apart from their central role during 3D structure determination of proteins the backbone chemical shift assignment is the basis for a number of applications, like chemical shift perturbation mapping and studies on the dynamics of proteins. This assignment is not a trivial task even if a 3D protein structure is known and needs almost as much effort as the assignment for structure prediction if performed manually. We present here a new algorithm based solely on 4D [ 1 H, 15 N]-HSQC-NOESY-[ 1 H, 15 N]-HSQC spectra which is able to assign a large percentage of chemical shifts (73–82 %) unambiguously, demonstrated with proteins up to a size of 250 residues. For the remaining residues, a small number of possible assignments is filtered out. This is done by comparing distances in the 3D structure to restraints obtained from the peak volumes in the 4D spectrum. Using dead-end elimination, assignments are removed in which at least one of the restraints is violated. Including additional information from chemical shift predictions, a complete unambiguous assignment was obtained for Ubiquitin and 95 % of the residues were correctly assigned in the 251 residue-long N-terminal domain of enzyme I. The program including source code is available at https://github.com/thomasexner/4Dassign .

In this paper we present our reworked approach to generate ligand protonation states with our structure preparation tool SPORES (Structure PrOtonation and REcognition System). SPORES can be used for the preprocessing of proteins and protein-ligand complexes as e.g. taken from the Protein Data Bank as well as for the setup of 3D ligand databases. It automatically assigns atom and bond types, generates different protonation, tautomeric states as well as different stereoisomers. In the revised version, pKa calculations with the ChemAxon software MARVIN are used either to determine the likeliness of a combinatorial generated protonation state or to determine the titrable atoms used in the combinatorial approach. Additionally, the MARVIN software is used to predict microspecies distributions of ligand molecules. Docking studies were performed with our recently introduced program PLANTS (Protein-Ligand ANT System) on all protomers resulting from the three different selection methods for the well established CCDC/ASTEX clean data set demonstrating the usefulness of especially the latter approach.

Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.