Substantiate a read-across hypothesis by using transcriptome data—A case study on volatile diketones

This case study explores the applicability of transcriptome data to characterize a common mechanism of action within groups of short-chain aliphatic α-, β-, and γ-diketones. Human reference in vivo data indicate that the α-diketone diacetyl induces bronchiolitis obliterans in workers involved in the preparation of microwave popcorn. The other three α-diketones induced inflammatory responses in preclinical in vivo animal studies, whereas beta and gamma diketones in addition caused neuronal effects. We investigated early transcriptional responses in primary human bronchiolar (PBEC) cell cultures after 24 h and 72 h of air-liquid exposure. Differentially expressed genes (DEGs) were assessed based on transcriptome data generated with the EUToxRisk gene panel of Temp-O-Seq®. For each individual substance, genes were identified displaying a consistent differential expression across dose and exposure duration. The log fold change values of the DEG profiles indicate that α- and β-diketones are more active compared to γ-diketones. α-diketones in particular showed a highly concordant expression pattern, which may serve as a first indication of the shared mode of action. In order to gain a better mechanistic understanding, the resultant DEGs were submitted to a pathway analysis using ConsensusPathDB. The four α-diketones showed very similar results with regard to the number of activated and shared pathways. Overall, the number of signaling pathways decreased from α-to β-to γ-diketones. Additionally, we reconstructed networks of genes that interact with one another and are associated with different adverse outcomes such as fibrosis, inflammation or apoptosis using the TRANSPATH-database. Transcription factor enrichment and upstream analyses with the geneXplain platform revealed highly interacting gene products (called master regulators, MRs) per case study compound. The mapping of the resultant MRs on the reconstructed networks, visualized similar gene regulation with regard to fibrosis, inflammation and apoptosis. This analysis showed that transcriptome data can strengthen the similarity assessment of compounds, which is of particular importance, e.g., in read-across approaches. It is one important step towards grouping of compounds based on biological profiles

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Publisher Copyright: © 2021, The Author(s).Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.Peer reviewe

Development of a QSAR model to predict respiratory irritation by individual constituents

The RespiraTox project, funded by NC3R CrackIT, develops a QSAR model, which predicts the potential of individual compounds to cause irritation in the respiratory tract. We distinguished two mode of actions, i) “sensory irritation”, characterized by a decrease in breathing rates, and ii) “tissue irritation” characterized by primarily histopathological findings. QSAR models rely on high quality datasets. We based the classification “irritating to respiratory tract” on several data types from in vivo studies with inhalation exposure. In a tiered approach, we considered information from i) studies with acute exposure from the ECHA CHEM database (DB), ii) the Hazardous Substance DB, iii) the harmonized classification and labelling inventory from ECHA, and iv) repeated dose studies from the Fraunhofer RepDose DB. For later stage validation, we withhold human data from Fraunhofer Breath DB. The final dataset includes about 2500 irritating and 800 non-irritating compounds. Prior to model development, the CAS numbers and compound structures were quality controlled and corrected. Two kinds of information were generated from the compounds structures: i) structural descriptors (ECFPS), and ii) physico-chemical properties. We explored several machine learning algorithms including Logistic Regression (LR), Random Forests (RF), and Gradient Boosted Decision Trees (BT) to derive a classification model. The internal validation procedure employed stratified k-fold cross-validation (k=5). The overall approach adheres to the five OECD principles. The criteria used to measure performance of a given model is the Area Under ROC-Curve (AUC). The AUC for LR using the combined feature set is 0.71. The optimal performance for both RF and BT is 0.78. The applicability domain is determined by features with highest impact on the final model. The current approach will be further refined and improved (e.g. by differentiating sensory and tissue irritation). The final model will be provided online as user-friendly interface to promote its use by toxicologists, regulators, and overall to reduce the testing of animals

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action

International audienceRead-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE lipid accumulation. KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed

Beiträge zum 29. Darmstädter Geotechnik-Kolloquium am 7. März 2024

Themenschwerpunkte: 1. Klimawandelfolgen und Nachhaltigkeit 2. Digitalisierung und künstliche Intelligenz 3. Planung und Bemessung von Infrastrukturprojekten 4. Großprojekt