This report presents the highlights of the 7th European Meeting on Molecular Diagnostics held in Scheveningen, The Hague, The Netherlands, 12-14 October 2011. The areas covered included molecular diagnostics applications in medical microbiology, virology, pathology, hemato-oncology,clinical genetics and forensics. Novel real-time amplification approaches, novel diagnostic applications and new technologies, such as next-generation sequencing, PCR lectrospray-ionization TOF mass spectrometry and techniques based on the detection of proteins or other molecules, were discussed. Furthermore, diagnostic companies presented their future visions for molecular diagnostics in human healthcare.
DOCUMENT
pH-sensitive gels: By using a cyclohexane-based scaffold to which various amino acid based substituents can be connected, low-molecular-weight compounds were obtained that can gelate water at very low concentrations. Their modular design (see picture: AA = amino acid(s), X = hydrophilic substituent, dark purple = hydrophobic region, light purple = hydrophilic region), allows tuning of the thermally and pH-induced reversible gel-to-sol transition of their gels.
DOCUMENT
Summary: Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.
DOCUMENT
Despite tremendous efforts, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. SARS-CoV-2 envelope is a key structural component of the virion that encapsulates viral RNA. It is composed of three structural proteins, spike, membrane (M), and envelope, which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns. These results are in good agreement with current experimental data, demonstrating a generic and versatile approach to model the structure of a virus de novo.
DOCUMENT
BackgroundThe ROS1 G2032R mutation is the most common on-target resistance mutation in crizotinib treated ROS1-positive lung cancer patients. The aim of our study was to investigate resistance mechanisms in SCL34A2-ROS1G2032R positive Ba/F3 cells against second line treatment with lorlatinib.MethodsBa/F3 SLC34A2-ROS1G2032R cells were subjected to N-ethyl-N-nitrosourea (ENU) mutagenesis and clones were selected upon treatment with 1000 nM lorlatinib for 4 weeks. Resistant clones were analyzed for presence of on-target resistant mutations using Sanger sequencing. In addition, we generated subclones expressing SLC34A2-ROS1L2026M+G2032R and SLC34A2-ROS1L2026M in Ba/F3 cells. Sensitivity to ROS1 TKIs was determined by measuring cell viability and ROS1 phosphorylation. Molecular Dynamic simulations of the ATP binding pocket were performed for all ROS1 variants.ResultsThe ENU-screen of 41 lorlatinib resistant clones revealed one with a mutation in the kinase domain: L2026M. Cell viability assays of the ENU-induced resistant cell line and the Ba/F3 cells transfected with the mutant SCL34A2-ROS1 fusion gene constructs revealed a decreased sensitivity of SLC34A2-ROS1L2026M+G2032R cells for lorlatinib, crizotinib, entrectinib and repotrectinib compared to the single mutants. Consistent with these findings, we observed phosphorylation of ROS1 fusion protein in the double mutant cells which was not inhibited upon treatment with ROS1 TKIs. The single mutant cells showed as expected a clear reduction in phosphorylated ROS1 fusion protein . Molecular modeling to unravel the effect of the mutations demonstrated that the volume of the ATP-binding pocket was reduced in single and double mutants compared to wild type. The double L2026M+G2032R mutant displayed the smallest pocket.ConclusionsWe identified a novel on-target mutation after inducing lorlatinib resistance in SLC34A2-ROS1G2032R Ba/F3 cells. This SLC34A2-ROS1L2026M+G2032R cell line was also resistant to crizotinib, entrectinib and repotrectinib. The resistance can be explained by a smaller ATP binding pocket in the mutated ROS1 fusion protein preventing effective binding of the investigated TKIs.
DOCUMENT
Understanding taste is key for optimizing the palatability of seaweeds and other non-animal-based foods rich in protein. The lingual papillae in the mouth hold taste buds with taste receptors for the five gustatory taste qualities. Each taste bud contains three distinct cell types, of which Type II cells carry various G protein-coupled receptors that can detect sweet, bitter, or umami tastants, while type III cells detect sour, and likely salty stimuli. Upon ligand binding, receptor-linked intracellular heterotrimeric G proteins initiate a cascade of downstream events which activate the afferent nerve fibers for taste perception in the brain. The taste of amino acids depends on the hydrophobicity, size, charge, isoelectric point, chirality of the alpha carbon, and the functional groups on their side chains. The principal umami ingredient monosodium l-glutamate, broadly known as MSG, loses umami taste upon acetylation, esterification, or methylation, but is able to form flat configurations that bind well to the umami taste receptor. Ribonucleotides such as guanosine monophosphate and inosine monophosphate strongly enhance umami taste when l-glutamate is present. Ribonucleotides bind to the outer section of the venus flytrap domain of the receptor dimer and stabilize the closed conformation. Concentrations of glutamate, aspartate, arginate, and other compounds in food products may enhance saltiness and overall flavor. Umami ingredients may help to reduce the consumption of salts and fats in the general population and increase food consumption in the elderly.
MULTIFILE
Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
DOCUMENT
To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
DOCUMENT
From the publisher: "Background: The introduction of whole new foods in a population may lead to sensitization and food allergy. This constitutes a potential public health problem and a challenge to risk assessors and managers as the existing understanding of the pathophysiological processes and the currently available biological tools for prediction of the risk for food allergy development and the severity of the reaction are not sufficient. There is a substantial body of in vivo and in vitro data describing molecular and cellular events potentially involved in food sensitization. However, these events have not been organized in a sequence of related events that is plausible to result in sensitization, and useful to challenge current hypotheses. The aim of this manuscript was to collect and structure the current mechanistic understanding of sensitization induction to food proteins by applying the concept of adverse outcome pathway (AOP). Main body: The proposed AOP for food sensitization is based on information on molecular and cellular mechanisms and pathways evidenced to be involved in sensitization by food and food proteins and uses the AOPs for chemical skin sensitization and respiratory sensitization induction as templates. Available mechanistic data on protein respiratory sensitization were included to fill out gaps in the understanding of how proteins may affect cells, cell-cell interactions and tissue homeostasis. Analysis revealed several key events (KE) and biomarkers that may have potential use in testing and assessment of proteins for their sensitizing potential. Conclusion: The application of the AOP concept to structure mechanistic in vivo and in vitro knowledge has made it possible to identify a number of methods, each addressing a specific KE, that provide information about the food allergenic potential of new proteins. When applied in the context of an integrated strategy these methods may reduce, if not replace, current animal testing approaches. The proposed AOP will be shared at the www.aopwiki.org platform to expand the mechanistic data, improve the confidence in each of the proposed KE and key event relations (KERs), and allow for the identification of new, or refinement of established KE and KERs." Authors: Jolanda H. M. van BilsenEmail author, Edyta Sienkiewicz-Szłapka, Daniel Lozano-Ojalvo, Linette E. M. Willemsen, Celia M. Antunes, Elena Molina, Joost J. Smit, Barbara Wróblewska, Harry J. Wichers, Edward F. Knol, Gregory S. Ladics, Raymond H. H. Pieters, Sandra Denery-Papini, Yvonne M. Vissers, Simona L. Bavaro, Colette Larré, Kitty C. M. Verhoeckx and Erwin L. Roggen
LINK
Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
DOCUMENT
