To initiate or not to initiate: A critical assessment of eIF2A, eIF2D, and MCT-1·DENR to deliver initiator tRNA to ribosomes.

Selection of the correct start codon is critical for high-fidelity protein synthesis. In eukaryotes, this is typically governed by a multitude of initiation factors (eIFs), including eIF2·GTP that directly delivers the initiator tRNA (Met-tRNAi Met ) to the P site of the ribosome. However, numerous reports, some dating back to the early 1970s, have described other initiation factors having high affinity for the initiator tRNA and the ability of delivering it to the ribosome, which has provided a foundation for further work demonstrating non-canonical initiation mechanisms using alternative initiation factors. Here we provide a critical analysis of current understanding of eIF2A, eIF2D, and the MCT-1·DENR dimer, the evidence surrounding their ability to initiate translation, their implications in human disease, and lay out important key questions for the field. This article is categorized under: RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes Translation > Mechanisms Translation > Regulation.

Translation reinitiation after uORFs does not fully protect mRNAs from nonsense-mediated decay.

It is estimated that nearly 50% of mammalian transcripts contain at least one upstream open reading frame (uORF), which are typically one to two orders of magnitude smaller than the downstream main ORF. Most uORFs are thought to be inhibitory as they sequester the scanning ribosome, but in some cases allow for translation reinitiation. However, termination in the 5' UTR at the end of uORFs resembles premature termination that is normally sensed by the nonsense-mediated mRNA decay (NMD) pathway. Translation reinitiation has been proposed as a method for mRNAs to prevent NMD. Here, we test how uORF length influences translation reinitiation and mRNA stability in HeLa cells. Using custom 5' UTRs and uORF sequences, we show that reinitiation can occur on heterologous mRNA sequences, favors small uORFs, and is supported when initiation occurs with more initiation factors. After determining reporter mRNA half-lives in HeLa cells and mining available mRNA half-life data sets for cumulative predicted uORF length, we conclude that translation reinitiation after uORFs is not a robust method for mRNAs to prevent NMD. Together, these data suggest that the decision of whether NMD ensues after translating uORFs occurs before reinitiation in mammalian cells.

Confidence in Inactive and Active Predictions from Structural Alerts.

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays.

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships.

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Vision of a near future: Bridging the human health-environment divide. Toward an integrated strategy to understand mechanisms across species for chemical safety assessment.

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.

Neural network activation similarity: a new measure to assist decision making in chemical toxicology.

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093 and ROC-AUC 0.96 ± 0.04). A new molecular similarity measure, Neural Network Activation Similarity, has been developed, based on signal propagation through the network. This is complementary to standard Tanimoto similarity, and the combined use increases confidence in the computer's prediction of activity for new chemicals by providing a greater understanding of the underlying justification. The in silico prediction of these human molecular initiating events is central to the future of chemical safety risk assessment and improves the efficiency of safety decision making.

Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events.

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

Using 2D Structural Alerts to Define Chemical Categories for Molecular Initiating Events.

Molecular initiating events (MIEs) are important concepts for in silico predictions. They can be used to link chemical characteristics to biological activity through an adverse outcome pathway (AOP). In this work, we capture chemical characteristics in 2D structural alerts, which are then used as models to predict MIEs. An automated procedure has been used to identify these alerts, and the chemical categories they define have been used to provide quantitative predictions for the activity of molecules that contain them. This has been done across a diverse group of 39 important pharmacological human targets using open source data. The alerts for each target combine into a model for that target, and these models are joined into a tool for MIE prediction with high average model performance (sensitivity = 82%, specificity = 93%, overall quality = 93%, Matthews correlation coefficient = 0.57). The result is substantially improved from our previous study (Allen, T. E. H., Goodman, J. M., Gutsell, S., and Russell, P. J. 2016. A history of the molecular initiating event. Chem. Res. Toxicol. 29, 2060-2070) for which the mean sensitivity for each target was only 58%. This tool provides the first step in an AOP-based risk assessment, linking chemical structure to toxicity endpoint.

Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors.

The Ames mutagenicity assay is a long established in vitro test to measure the mutagenicity potential of a new chemical used in regulatory testing globally. One of the key computational approaches to modeling of the Ames assay relies on the formation of chemical categories based on the different electrophilic compounds that are able to react directly with DNA and form a covalent bond. Such approaches sometimes predict false positives, as not all Michael acceptors are found to be Ames-positive. The formation of such covalent bonds can be explored computationally using density functional theory transition state modeling. We have applied this approach to mutagenicity, allowing us to calculate the activation energy required for α,ß-unsaturated carbonyls to react with a model system for the guanine nucleobase of DNA. These calculations have allowed us to identify that chemical compounds with activation energies greater than or equal to 25.7 kcal/mol are not able to bind directly to DNA. This allows us to reduce the false positive rate for computationally predicted mutagenicity assays. This methodology can be used to investigate other covalent-bond-forming reactions that can lead to toxicological outcomes and learn more about experimental results.

A History of the Molecular Initiating Event.

The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Using Molecular Initiating Events To Generate 2D Structure-Activity Relationships for Toxicity Screening.

Molecular initiating events (MIEs) can be boiled down to chemical interactions. Chemicals that interact must have intrinsic properties that allow them to exhibit this behavior, be these properties stereochemical, electronic, or otherwise. In an attempt to discover some of these chemical characteristics, we have constructed structural alert-style structure-activity relationships (SARs) to computationally predict MIEs. This work utilizes chemical informatics approaches, searching the ChEMBL database for molecules that bind to a number of pharmacologically important human toxicology targets, including G-protein coupled receptors, enzymes, ion channels, nuclear receptors, and transporters. By screening these compounds to find common 2D fragments and combining this approach with a good understanding of the literature, bespoke 2D structural alerts have been written. These SARs form the beginning of a tool for screening novel chemicals to establish the kind of interactions that they may be able to make in humans. These SARs have been run through an internal validation to test their quality, and the results of this are also discussed. MIEs have proven to be difficult to find and characterize, but we believe we have taken a key first step with this work.

Mechanistic understanding of molecular initiating events (MIEs) using NMR spectroscopy.

Toxicological risk assessments in the 21st century are increasingly being driven by the Adverse Outcome Pathways (AOP) conceptual framework in which the Molecular Initiating Event (MIE) is of fundamental importance to pathway progression. For those MIEs that involve covalent chemical reactions, such as protein haptenation, determination of relative rates and mechanisms of reactions is a prerequisite for their understanding. The utility of NMR spectroscopy as an experimental technique for effectively providing reaction rate and mechanistic information for early assessment of likely MIE(s) has been demonstrated. To demonstrate the concept, model systems exemplifying common chemical reactions involved in the covalent modification of proteins were utilized; these involved chemical reactions of electrophilic species (representing different mechanistic classes) with simple amine and thiol nucleophiles acting as surrogates for the reactive groups of lysine and cysteine protein side chains respectively. Such molecular interactions are recognized as critical mechanisms in a variety of chemical and drug toxicities, including respiratory and skin sensitization and liver toxicity as well as being the key mechanism of action for a number of therapeutic agents.

Defining molecular initiating events in the adverse outcome pathway framework for risk assessment.

Consumer and environmental safety decisions are based on exposure and hazard data, interpreted using risk assessment approaches. The adverse outcome pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine current risk assessment practices in ecotoxicology. This framework can also be applied to human toxicology and is explored on the basis of investigating the molecular initiating events (MIEs) of compounds. The precise definition of the MIE has yet to reach general acceptance. In this work we present a unified MIE definition: an MIE is the initial interaction between a molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway. Case studies are presented, and issues with current definitions are addressed. With the development of a unified MIE definition, the field can look toward defining, classifying, and characterizing more MIEs and using knowledge of the chemistry of these processes to aid AOP research and toxicity risk assessment. We also present the role of MIE research in the development of in vitro and in silico toxicology and suggest how, by using a combination of biological and chemical approaches, MIEs can be identified and characterized despite a lack of detailed reports, even for some of the most studied molecules in toxicology.

Similarity analyses of chromatographic herbal fingerprints: a review.

Herbal medicines are becoming again more popular in the developed countries because being "natural" and people thus often assume that they are inherently safe. Herbs have also been used worldwide for many centuries in the traditional medicines. The concern of their safety and efficacy has grown since increasing western interest. Herbal materials and their extracts are very complex, often including hundreds of compounds. A thorough understanding of their chemical composition is essential for conducting a safety risk assessment. However, herbal material can show considerable variability. The chemical constituents and their amounts in a herb can be different, due to growing conditions, such as climate and soil, the drying process, the harvest season, etc. Among the analytical methods, chromatographic fingerprinting has been recommended as a potential and reliable methodology for the identification and quality control of herbal medicines. Identification is needed to avoid fraud and adulteration. Currently, analyzing chromatographic herbal fingerprint data sets has become one of the most applied tools in quality assessment of herbal materials. Mostly, the entire chromatographic profiles are used to identify or to evaluate the quality of the herbs investigated. Occasionally only a limited number of compounds are considered. One approach to the safety risk assessment is to determine whether the herbal material is substantially equivalent to that which is either readily consumed in the diet, has a history of application or has earlier been commercialized i.e. to what is considered as reference material. In order to help determining substantial equivalence using fingerprint approaches, a quantitative measurement of similarity is required. In this paper, different (dis)similarity approaches, such as (dis)similarity metrics or exploratory analysis approaches applied on herbal medicinal fingerprints, are discussed and illustrated with several case studies.

Dynamic contour tonometer and Goldmann applanation tonometer performance in a developing world setting: intraocular pressure measurement acquisition and precision.

PURPOSE: To examine tonometer usability and intraocular pressure (IOP) measurement precision in a general ophthalmology clinic in the developing world. METHODS: A total of 100 eyes of 100 participants attending a charity ophthalmology walk-in clinic in Ghana, West Africa, had IOP measurements made with the Goldmann applanation tonometer (GAT) and the dynamic contour tonometer (DCT) in a randomized order by 2 clinicians. Both clinicians had extensive experience in using the GAT but were relatively inexperienced in using the DCT. The repeatability coefficient was calculated to determine intraobserver variability. Reproducibility of interobserver IOP measurements was calculated using Bland-Altman analysis. RESULTS: IOP could not be measured in 3% of eyes using the GAT and in 16% of eyes using the DCT. The repeatability coefficient for the GAT and DCT were 2.5 and 3.0 mm Hg, respectively. The DCT repeatability coefficient was 2.3 mm Hg when only "good quality" measurements were considered. The interobserver mean difference (limits of agreement) were -0.8 mm Hg (±3.9 mm Hg) for the GAT and 0.3 mm Hg (±3.3 mm Hg) for the DCT. DCT IOP measurements were unobtainable in eyes with corneal surface irregularities or excessive eye or lid movements. CONCLUSIONS: The DCT shows good measurement precision with comparable repeatability and superior reproducibility compared with the GAT. The DCT score is useful in its objectivity and improving repeatability. However, patient and ocular surface factors may impede DCT measurements, impacting upon its general usability in a high volume, walk-in community clinic.

A validated bioanalytical method in mouse, rat, rabbit and human plasma for the quantification of one of the steroid glycosides found in Hoodia gordonii extract.

Hoodia gordonii extract contains steroid glycosides, fatty acids, plant sterols and polar organic material. Certain steroid glycosides show appetite suppressant activities following oral ingestion. This study describes the validation of a bioanalytical method for the quantification of one of the steroid glycosides, H.g.-12 (≈ 10% (w/w) of the extract), in mouse, rat, rabbit and human plasma. The method utilises a liquid-liquid extraction with methyl-tert-butyl ether followed by chromatographic separation on a 2.1 × 50 mm C(18) Genesis high performance liquid chromatography (HPLC) column and detection on a triple quadrupole mass spectrometer. Detection of H.g.-12 and its stable isotope internal standards is performed using positive TurboIonspray™ ionisation in multiple reaction monitoring mode. The validation procedure demonstrated assay sensitivity, linearity, accuracy, precision and selectivity over the calibration range of 0.5-150 ng/mL in human plasma (500 µL sample volume), 1.0-100 ng/mL in rat and rabbit plasma (150 µL sample volume) and 1.0-250 ng/mL in mouse plasma (150 µL sample volume) with good recoveries (≥ 77%). H.g.-12 was stable in plasma for ≥ 6 months at -20°C, for up to 4h at ambient temperature (ca22°C) and after 3 freeze-thaw cycles. Plasma extracts were stable for up to 24h at ambient temperature.