Capturing time-dependent activation of genes and stress-response pathways using transcriptomics in iPSC-derived renal proximal tubule cells.

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 TGFß-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 TGFß 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.

European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials.

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.

Towards a qAOP framework for predictive toxicology - Linking data to decisions.

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

Temporal transcriptomic alterations of cadmium exposed human iPSC-derived renal proximal tubule-like cells.

Cadmium is a well-studied environmental pollutant where the kidney and particularly the proximal tubule cells are especially sensitive as they are exposed to higher concentrations of cadmium than other tissues. Here we investigated the temporal transcriptomic alterations (TempO-Seq) of human induced pluripotent stem cell (iPSC)-derived renal proximal tubule-like (PTL) cells exposed to 5 µM cadmium chloride for 1, 2, 4, 8, 12, 16, 20, 24, 72 and 168 h. There was an early activation (within 4 h) of the metal and oxidative stress responses (metal-responsive transcription factor-1 (MTF1) and nuclear factor erythroid-2-related factor 2 (Nrf2) genes). The Nrf2 response returned to baseline within 24 h. The Activator Protein 1 (AP-1) regulated genes HSPA6 and FOSL-1 followed the Nrf2 time course. While the MTF1 genes also spiked at 4 h, they remained strongly elevated over the entire exposure period. The data and cell culture model utilised will be useful in further research aimed at the refinement of safe human exposure limits for cadmium, other metals and their mixtures.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data.

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.

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods.

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.

A semi-automated workflow for adverse outcome pathway hypothesis generation: The use case of non-genotoxic induced hepatocellular carcinoma.

The utility of the Adverse Outcome Pathway (AOP) concept has been largely recognized by scientists, however, the AOP generation is still mainly done manually by screening through evidence and extracting probable associations. To accelerate this process and increase the reliability, we have developed an semi-automated workflow for AOP hypothesis generation. In brief, association mining methods were applied to high-throughput screening, gene expression, in vivo and disease data present in ToxCast and Comparative Toxicogenomics Database. This was supplemented by pathway mapping using Reactome to fill in gaps and identify events occurring at the cellular/tissue levels. Furthermore, in vivo data from TG-Gates was integrated to finally derive a gene, pathway, biochemical, histopathological and disease network from which specific disease sub-networks can be queried. To test the workflow, non-genotoxic-induced hepatocellular carcinoma (HCC) was selected as a case study. The implementation resulted in the identification of several non-genotoxic-specific HCC-connected genes belonging to cell proliferation, endoplasmic reticulum stress and early apoptosis. Biochemical findings revealed non-genotoxic-specific alkaline phosphatase increase. The explored non-genotoxic-specific histopathology was associated with early stages of hepatic steatosis, transforming into cirrhosis. This work illustrates the utility of computationally predicted constructs in supporting development by using pre-existing knowledge in a fast and unbiased manner.

Binding of HasA by its transmembrane receptor HasR follows a conformational funnel mechanism.

HasR in the outer membrane of Serratia marcescens binds secreted, heme-loaded HasA and translocates the heme to the periplasm to satisfy the cell's demand for iron. The previously published crystal structure of the wild-type complex showed HasA in a very specific binding arrangement with HasR, apt to relax the grasp on the heme and assure its directed transfer to the HasR-binding site. Here, we present a new crystal structure of the heme-loaded HasA arranged with a mutant of HasR, called double mutant (DM) in the following that seemed to mimic a precursor stage of the abovementioned final arrangement before heme transfer. To test this, we performed first molecular dynamics (MD) simulations starting at the crystal structure of the complex of HasA with the DM mutant and then targeted MD simulations of the entire binding process beginning with heme-loaded HasA in solution. When the simulation starts with the former complex, the two proteins in most simulations do not dissociate. When the mutations are reverted to the wild-type sequence, dissociation and development toward the wild-type complex occur in most simulations. This indicates that the mutations create or enhance a local energy minimum. In the targeted MD simulations, the first protein contacts depend upon the chosen starting position of HasA in solution. Subsequently, heme-loaded HasA slides on the external surface of HasR on paths that converge toward the specific arrangement apt for heme transfer. The targeted simulations end when HasR starts to relax the grasp on the heme, the subsequent events being in a time regime inaccessible to the available computing power. Interestingly, none of the ten independent simulation paths visits exactly the arrangement of HasA with HasR seen in the crystal structure of the mutant. Two factors which do not exclude each other could explain these observations: the double mutation creates a non-physiologic potential energy minimum between the two proteins and /or the target potential in the simulation pushes the system along paths deviating from the low-energy paths of the native binding processes. Our results support the former view, but do not exclude the latter possibility.

Scaffold Effects on Halogen Bonding Strength.

Halogen bonds have become increasingly popular interactions in molecular design and drug discovery. One of the key features is the strong dependence of the size and magnitude of the halogen's σ-hole on the chemical environment of the ligand. The term σ-hole refers to a region of lower electronic density opposite to a covalent bond, e.g., the C-X bond. It is typically (but not always) associated with a positive electrostatic potential in close proximity to the extension of the covalent bond. Herein, we use a variety of 30 nitrogen-bearing heterocycles, halogenated systematically by chlorine, bromine, or iodine, yielding 468 different ligands that are used to exemplify scaffold effects on halogen bonding strength. As a template interaction partner, we have chosen N-methylacetamide representing the ubiquitously present protein backbone. Adduct formation energies were obtained at a MP2/TZVPP level of theory. We used the local maximum of the electrostatic potential on the molecular surface in close proximity to the σ-hole, V S,max, as a descriptor for the magnitude of the positive electrostatic potential characterizing the tuning of the σ-hole. Free optimization of the complexes gave reasonable correlations with V S,max but was found to be of limited use because considerable numbers of chlorinated and brominated ligands lost their halogen bond or showed significant secondary interactions. Thus, starting from a close to optimal geometry of the halogen bond, we used distance scans to obtain the best adduct formation energy for each complex. This approach provided superior results for all complexes exhibiting correlations with R2 > 0.96 for each individual halogen. We evaluated the dependence of V S,max from the molecular surface onto which the positive electrostatic potential is projected, altering the isodensity values from 0.001 au to 0.050 au. Interestingly, the best overall fit using a third-order polynomial function (R2 = 0.99, RMSE = 0.562 kJ/mol) with rather smooth transitions between all halogens was obtained for V S,max calculated from an isodensity surface at 0.014 au.

Predicting the Magnitude of σ-Holes Using VmaxPred, a Fast and Efficient Tool Supporting the Application of Halogen Bonds in Drug Discovery.

Halogen bonding as a modern molecular interaction has received increasing attention not only in materials sciences but also in biological systems and drug discovery. Thus, there is a growing demand for fast, efficient, and easily applicable tailor-made tools supporting the use of halogen bonds in molecular design and medicinal chemistry. The potential strength of a halogen bond is dependent on several properties of the σ-hole donor, e.g., a (hetero)aryl halide, and the σ-hole acceptor, a nucleophile with n or π electron density. Besides the influence of the interaction geometry and the type of acceptor, significant tuning effects on the magnitude of the σ-hole can be observed, caused by different (hetero)aromatic scaffolds and their substitution patterns. The most positive electrostatic potential on the isodensity surface ( Vmax), representing the σ-hole, has been widely used as the standard descriptor for the magnitude of the σ-hole and the strength of the halogen bond. Calculation of Vmax using quantum-mechanical methods at a reasonable level of theory is time-consuming and thus not applicable for larger numbers of compounds in drug discovery projects. Herein we present a tool for the prediction of this descriptor based on a machine-learned model with a speedup of 5 to 6 orders of magnitude relative to MP2 quantum-mechanical calculations. According to the test set, the squared correlation coefficient is greater than 0.94.

Nanodiscs for INPHARMA NMR Characterization of GPCRs: Ligand Binding to the Human A2A Adenosine Receptor.

G-protein-coupled-receptors (GPCRs) are of fundamental importance for signal transduction through cell membranes. This makes them important drug targets, but structure-based drug design (SBDD) is still hampered by the limitations for structure determination of unmodified GPCRs. We show that the interligand NOEs for pharmacophore mapping (INPHARMA) method can provide valuable information on ligand poses inside the binding site of the unmodified human A2A adenosine receptor reconstituted in nanodiscs. By comparing experimental INPHARMA spectra with back-calculated spectra based on ligand poses obtained from molecular dynamics simulations, a complex structure for A2A R with the low-affinity ligand 3-pyrrolidin-1-ylquinoxalin-2-amine was determined based on the X-ray structure of ligand ZM-241,358 in complex with a modified A2A R.

Automated assignment of NMR chemical shifts based on a known structure and 4D spectra.

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 .

Using Surface Scans for the Evaluation of Halogen Bonds toward the Side Chains of Aspartate, Asparagine, Glutamate, and Glutamine.

Using halogen-specific Connolly type molecular surfaces, we herein invented a new type of surface-based interaction analysis employed for the study of halogen bonding toward model systems of biologically relevant carboxylates (ASP/GLU) and carboxamides (ASN/GLN). Database mining and statistical assessment of the PDB revealed that such interactions are widely underrepresented at the moment. We observed important distance-dependent adaptions of the binding modes of halobenzenes from a preferential oxygen-directed to a bifurcated interaction geometry of the carboxylate. In addition, halogen···π contacts perpendicular to the nitrogen atom of the carboxamide become increasingly important for the lighter halogens. Our analysis on a MP2/TZVPP level of theory is backed by CCSD(T)/CBS reference calculations. To put the vast interaction energies into perspective, we also performed COSMO-RS calculations of the solvation free energy. Facilitating the visualization of our results mapped onto any binding site of choice, we aim to inspire more design studies showcasing these underrepresented interactions.

Exploiting Transient Protein States for the Design of Small-Molecule Stabilizers of Mutant p53.

The destabilizing p53 cancer mutation Y220C creates an extended crevice on the surface of the protein that can be targeted by small-molecule stabilizers. Here, we identify different classes of small molecules that bind to this crevice and determine their binding modes by X-ray crystallography. These structures reveal two major conformational states of the pocket and a cryptic, transiently open hydrophobic subpocket that is modulated by Cys220. In one instance, specifically targeting this transient protein state by a pyrrole moiety resulted in a 40-fold increase in binding affinity. Molecular dynamics simulations showed that both open and closed states of this subsite were populated at comparable frequencies along the trajectories. Our data extend the framework for the design of high-affinity Y220C mutant binders for use in personalized anticancer therapy and, more generally, highlight the importance of implementing protein dynamics and hydration patterns in the drug-discovery process.

On-the-Fly Integration of Data from a Spin-Diffusion-Based NMR Experiment into Protein-Ligand Docking.

INPHARMA (interligand nuclear Overhauser enhancement for pharmacophore mapping) determines the relative orientation of two competitive ligands in the protein binding pocket. It is based on the observation of interligand transferred NOEs mediated by spin diffusion through protons of the protein and is, therefore, sensitive to the specific interactions of each of the two ligands with the protein. We show how this information can be directly included into a protein-ligand docking program to guide the prediction of the complex structures. Agreement between the experimental and back-calculated spectra based on the full relaxation matrix approach is translated into a score contribution that is combined with the scoring function ChemPLP of our docking tool PLANTS. This combined score is then used to predict the poses of five weakly bound cAMP-dependent protein kinase (PKA) ligands. After optimizing the setup, which finally also included trNOE data and optimized protonation states, very good success rates were obtained for all combinations of three ligands. For one additional ligand, no conclusive results could be obtained due to the ambiguous electron density of the ligand in the X-ray structure, which does not disprove alternative ligand poses. The failures of the remaining ligand are caused by suboptimal locations of specific protein side chains. Therefore, side-chain flexibility should be included in an improved INPHARMA-PLANTS version. This will reduce the strong dependence on the used protein input structure leading to improved scores overall, not only for this last ligand.

Performance of protein-ligand docking with simulated chemical shift perturbations.

Protein chemical shift perturbations (CSPs) that result from the binding of a ligand to the protein contain structural information about the complex. Therefore, the CSP data, typically obtained during library screening from two-dimensional (2D) nuclear magnetic resonance (NMR) spectra, are often available before attempts to solve the experimental structure of the complex are started, and can be used to solve the complex structure with CSP-based docking. Here, we compare the performance of the post-docking filter and the guided-docking approaches using either amide or α-proton CSPs with 10 protein-ligand complexes. We show that the comparison of experimental CSPs with CSPs simulated for virtual ligand positions can be used to evidence protein conformational change upon binding and possibly improve the CSP-based docking.

Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes.

NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3-0.6 ppm and correlation coefficients (r(2)) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization.

A biophysical study with carbohydrate derivatives explains the molecular basis of monosaccharide selectivity of the Pseudomonas aeruginosa lectin LecB.

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.

Halogen-enriched fragment libraries as chemical probes for harnessing halogen bonding in fragment-based lead discovery.

Halogen bonding has recently experienced a renaissance, gaining increased recognition as a useful molecular interaction in the life sciences. Halogen bonds are favorable, fairly directional interactions between an electropositive region on the halogen (the σ-hole) and a number of different nucleophilic interaction partners. Some aspects of halogen bonding are not yet understood well enough to take full advantage of its potential in drug discovery. We describe and present the concept of halogen-enriched fragment libraries. These libraries consist of unique chemical probes, facilitating the identification of favorable halogen bonds by sharing the advantages of classical fragment-based screening. Besides providing insights into the nature and applicability of halogen bonding, halogen-enriched fragment libraries provide smart starting points for hit-to-lead evolution.