Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation

Transcriptomic dose-response by UVC and heavy ion radiation reveal pathways to immune impairment

Irradiation-induced immune impairment has been linked to human immune diseases, such as myelodysplastic syndromes (MDS) and leukemia. Global molecular responses to genome instability in immune cells can be identified by using transcriptomics. However, it is hard to link the molecular mechanism to the disease outcomes in the previous mechanistic studies. Here, transcriptomic dose-responses in human CD4+ T lymphocytes exposed to ultraviolet and heavy ion radiation were revealed by identification of the gene expression patterns of differential expression genes (DEGs) and calculating the point of departure (POD) of each DEG and molecular pathway, which provided an opportunity for quantitively illustrating the biological process of irradiation-induced immune impairments. Two potential adverse outcome pathways (AOPs) to irradiation-related leukemia were identified by mapping the molecular pathways into the biological event cascades, which provided phenotypic anchoring for the toxicological mechanisms. In addition, this study also revealed that NOP14/ NOP14-AS1 could be potential biomarkers of irradiation-induced immune impairment. Our works strengthen the use of AOP network in the next-generation risk assessment of irradiation-related diseases.

Novel Approach to Screen Endocrine-Disrupting Chemicals via Endocrine-Enhanced Reduced Human Transcriptome

Endocrine-disrupting chemicals (EDCs) can interfere with multiple pathways and trigger different modes of action. Thus, the traditional EDC in vitro screening processes often require a battery of bioassays to cover multiple target pathways. Here we developed an endocrine-enhanced reduced human transcriptome (ERHT) focused on hormone receptor signaling induced by the EDCs regulating specific genes. ERHT was developed based on 1200 prioritized genes covering 110 endocrine-related biological pathways across eight potential adverse outcomes. The ability of this approach to identify EDCs was derived from machine learning of 1068 dose-dependent transcriptome profiles and enhanced by quantifying chemical-induced critical pathway responses, and thus, it demonstrated excellent classification performance (AUC = 0.84 ± 0.03) in internal cross-validation. We ultimately applied this approach to known EDCs and inactive substances to validate the reliability of this approach. Through external validation on 210 chemicals, the extrapolation accuracy exceeded 80%, demonstrating the outstanding practical performance of this approach. Meanwhile, the pathway responses induced by the same chemical were consistent with the experimental results reported by multiple sequencing platforms, highlighting the robustness of this approach. The above results demonstrate that this approach can provide novel insights for EDCs' high-throughput screening and comprehensive toxic mechanisms through biological pathways.

Transcriptomic characterization of 2D and 3D human induced pluripotent stem cell-based in vitro models as New Approach Methodologies for developmental neurotoxicity testing

The safety and developmental neurotoxicity (DNT) potential of chemicals remain critically understudied due to limitations of current in vivo testing guidelines, which are low throughput, resource-intensive, and hindered by species differences that limit their relevance to human health. To address these issues, robust New Approach Methodologies (NAMs) using deeply characterized cell models are essential. This study presents the comprehensive transcriptomic characterization of two advanced human-induced pluripotent stem cell (hiPSC)-derived models: a 2D adherent and a 3D neurosphere model of human neural progenitor cells (hiNPCs) differentiated up to 21 days. Using high-throughput RNA sequencing, we compared gene expression profiles of 2D and 3D models at three developmental stages (3, 14, and 21 days of differentiation). Both models exhibit maturation towards post-mitotic neurons, with the 3D model maturing faster and showing a higher prevalence of GABAergic neurons, while the 2D model is enriched with glutamatergic neurons. Both models demonstrate broad applicability domains, including excitatory and inhibitory neurons, astrocytes, and key endocrine and especially the understudied cholinergic receptors. Comparison with human fetal brain samples confirms their physiological relevance. This study provides novel in-depth applicability insights into the temporal and dimensional aspects of hiPSC-derived neural models for DNT testing. The complementary use of these two models is highlighted: the 2D model excels in synaptogenesis assessment, while the 3D model is particularly suited for neural network formation as observed as well in previous functional studies with these models. This research marks a significant advancement in developing human-relevant, high-throughput DNT assays for regulatory purposes.

Causal, predictive or observational? Different understandings of key event relationships for adverse outcome pathways and their implications on practice

The Adverse Outcome Pathways (AOPs) framework is pivotal in toxicology, but the, terminology describing Key Event Relationships (KERs) varies within AOP guidelines.This study examined the usage of causal, observational and predictive terms in AOP, documentation and their adaptation in AOP development. A literature search and text, analysis of key AOP guidance documents revealed nuanced usage of these terms, with KERs often described as both causal and predictive. The adaptation of, terminology varies across AOP development stages. Evaluation of KER causality often, relies targeted blocking experiments and weight-of-evidence assessments in the, putative and qualitative stages. Our findings highlight a potential mismatch between,terminology in guidelines and methodologies in practice, particularly in inferring,causality from predictive models. We argue for careful consideration of terms like, causal and essential to facilitate interdisciplinary communication. Furthermore, integrating known causality into quantitative AOP models remains a challenge.

SOLA: dissecting dose-response patterns in multi-omics data using a semi-supervised workflow

An increasing number of ecotoxicological studies have used omics-data to understand the dose-response patterns of environmental stressors. However, very few have investigated complex non-monotonic dose-response patterns with multi-omics data. In the present study, we developed a novel semi-supervised network analysis workflow as an alternative to benchmark dose (BMD) modelling. We utilised a previously published multi-omics dataset generated from Daphnia magna after chronic gamma radiation exposure to obtain novel knowledge on the dose-dependent effects of radiation. Our approach combines 1) unsupervised co-expression network analysis to group genes with similar dose responses into modules; 2) supervised classification of these modules by relevant response patterns; 3) reconstruction of regulatory networks based on transcription factor binding motifs to reveal the mechanistic underpinning of the modules; 4) differential co-expression network analysis to compare the discovered modules across two datasets with different exposure periods; and 5) pathway enrichment analysis to integrate transcriptomics and metabolomics data. Our method unveiled both known and novel effects of gamma radiation, provide insight into shifts in responses from low to high dose rates, and can be used as an alternative approach for multi-omics dose-response analysis in future. The workflow SOLA (Semi-supervised Omics Landscape Analysis) is available at https://gitlab.com/wanxin.lai/SOLA.git.

Multi-omics integration analysis: Tools and applications in environmental toxicology

Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.

A novel method to derive a human safety limit for PFOA by gene expression profiling and modelling

Perfluorooctanoic acid (PFOA) is a persistent environmental contaminant that can accumulate in the human body due to its long half-life. This substance has been associated with liver, pancreatic, testicular and breast cancers, liver steatosis and endocrine disruption. PFOA is a member of a large group of substances also known as "forever chemicals" and the vast majority of substances of this group lack toxicological data that would enable their effective risk assessment in terms of human health hazards. This study aimed to derive a health-based guidance value for PFOA intake (ng/kg BW/day) from in vitro transcriptomics data. To this end, we developed an in silico workflow comprising five components: (i) sourcing in vitro hepatic transcriptomics concentration-response data; (ii) deriving molecular points of departure using BMDExpress3 and performing pathway analysis using gene set enrichment analysis (GSEA) to identify the most sensitive molecular pathways to PFOA exposure; (iii) estimating freely-dissolved PFOA concentrations in vitro using a mass balance model; (iv) estimating in vivo doses by reverse dosimetry using a PBK model for PFOA as part of a quantitative in vitro to in vivo extrapolation (QIVIVE) algorithm; and (v) calculating a tolerable daily intake (TDI) for PFOA. Fourteen percent of interrogated genes exhibited in vitro concentration-response relationships. GSEA pathway enrichment analysis revealed that "fatty acid metabolism" was the most sensitive pathway to PFOA exposure. In vitro free PFOA concentrations were calculated to be 2.9% of the nominal applied concentrations, and these free concentrations were input into the QIVIVE workflow. Exposure doses for a virtual population of 3,000 individuals were estimated, from which a TDI of 0.15 ng/kg BW/day for PFOA was calculated using the benchmark dose modelling software, PROAST. This TDI is comparable to previously published values of 1.16, 0.69, and 0.86 ng/kg BW/day by the European Food Safety Authority. In conclusion, this study demonstrates the combined utility of an "omics"-derived molecular point of departure and in silico QIVIVE workflow for setting health-based guidance values in anticipation of the acceptance of in vitro concentration-response molecular measurements in chemical risk assessment.

Effects of petrogenic pollutants on North Atlantic and Arctic Calanus copepods: From molecular mechanisms to population impacts

Oil and gas industries in the Northern Atlantic Ocean have gradually moved closer to the Arctic areas, a process expected to be further facilitated by sea ice withdrawal caused by global warming. Copepods of the genus Calanus hold a key position in these cold-water food webs, providing an important energetic link between primary production and higher trophic levels. Due to their ecological importance, there is a concern about how accidental oil spills and produced water discharges may impact cold-water copepods. In this review, we summarize the current knowledge of the toxicity of petroleum on North Atlantic and Arctic Calanus copepods. We also review how recent development of high-quality transcriptomes from RNA-sequencing of copepods have identified genes regulating key biological processes, like molting, diapause and reproduction in Calanus copepods, to suggest linkages between exposure, molecular mechanisms and effects on higher levels of biological organization. We found that the available ecotoxicity threshold data for these copepods provide valuable information about their sensitivity to acute petrogenic exposures; however, there is still insufficient knowledge regarding underlying mechanisms of toxicity and the potential for long-term implications of relevance for copepod ecology and phenology. Copepod transcriptomics has expanded our understanding of how key biological processes are regulated in cold-water copepods. These advances can improve our understanding of how pollutants affect biological processes, and thus provide the basis for new knowledge frameworks spanning the effect continuum from molecular initiating events to adverse effects of regulatory relevance. Such efforts, guided by concepts such as adverse outcome pathways (AOPs), enable standardized and transparent characterization and evaluation of knowledge and identifies research gaps and priorities. This review suggests enhancing mechanistic understanding of exposure-effect relationships to better understand and link biomarker responses to adverse effects to improve risk assessments assessing ecological effects of pollutant mixtures, like crude oil, in Arctic areas.

Quantitative relationships of FAM50B and PTCHD3 methylation with reduced intelligence quotients in school aged children exposed to lead: Evidence from epidemiological and in vitro studies

At present, the application of DNA methylation (DNAm) biomarkers in environmental health risk assessment (EHRA) is more challenging due to the unclearly quantitative relationship between them. We aimed to explore the role of FAM50B and PTCHD3 at the level of signaling pathways, and establish the quantitative relationship between them and children's intelligence quotients (IQs). DNAm of target regions was measured in multiple cell models and was compared with the human population data. Then the dose-response relationships of lead exposure with neurotoxicity and DNAm were established by benchmark dose (BMD) model, followed by potential signaling pathway screening. Results showed that there was a quantitative linear relationship between children's IQs and FAM50B/PTCHD3 DNAm (DNAm between 51.40 % - 78.78 % and 31.41 % - 74.19 % for FAM50B and PTCHD3, respectively), and this relationship was more significant when children's IQs > 90. The receiver operating characteristic (ROC) and calibration curves showed that FAM50B/PTCHD3 DNAm had a satisfying accuracy and consistency in predicting children's IQs, which was confirmed by sensitivity analysis of gender and CpG site grouping data. In cell experiments, there was also a quantitative linear relationship between FAM50B DNAm and reactive oxygen species (ROS) production, which was mediated by PI3K-AKT signaling pathway. In addition, the lead BMD of ROS was close to that of FAM50B DNAm, suggesting that FAM50B DNAm was a suitable biomarker for the risk assessments of adverse outcomes induced by lead. Taken collectively, these results suggest that FAM50B/PTCHD3 can be applied to EHRA and the prevention/intervention of adverse effects of lead on children's IQs.

Editorial trend: adverse outcome pathway (AOP) and computational strategy - towards new perspectives in ecotoxicology

The adverse outcome pathway (AOP) has been conceptualized in 2010 as an analytical construct to describe a sequential chain of causal links between key events, from a molecular initiating event leading to an adverse outcome (AO), considering several levels of biological organization. An AOP aims to identify and organize available knowledge about toxic effects of chemicals and drugs, either in ecotoxicology or toxicology, and it can be helpful in both basic and applied research and serve as a decision-making tool in support of regulatory risk assessment. The AOP concept has evolved since its introduction, and recent research in toxicology, based on integrative systems biology and artificial intelligence, gave it a new dimension. This innovative in silico strategy can help to decipher mechanisms of action and AOP and offers new perspectives in AOP development. However, to date, this strategy has not yet been applied to ecotoxicology. In this context, the main objective of this short article is to discuss the relevance and feasibility of transferring this strategy to ecotoxicology. One of the challenges to be discussed is the level of organisation that is relevant to address for the AO (population/community). This strategy also offers many advantages that could be fruitful in ecotoxicology and overcome the lack of time, such as the rapid identification of data available at a time t, or the identification of "data gaps". Finally, this article proposes a step forward with suggested priority topics in ecotoxicology that could benefit from this strategy.

Isoorientin ameliorates H2O2-induced apoptosis and oxidative stress in chondrocytes by regulating MAPK and PI3K/Akt pathways

Osteoarthritis (OA) is a chronic and complicated degenerative disease for which there is currently no effective treatment. Isoorientin (ISO) is a natural plant extract that has antioxidant activity and could be used to treat OA. However, due to a lack of research, it has not been widely used. In this study, we investigated the protective effects and molecular mechanisms of ISO on H₂O₂-induced chondrocytes, a widely used cell model for OA. Based on RNA-seq and bioinformatics, we discovered that ISO significantly increased the activity of chondrocytes induced by H₂O₂, which was associated with apoptosis and oxidative stress. Furthermore, the combination of ISO and H₂O₂ significantly reduced apoptosis and restored mitochondrial membrane potential (MMP), which may be achieved by inhibiting apoptosis and mitogen-activated protein kinase (MAPK) signaling pathways. Moreover, ISO increased superoxide dismutase (SOD), heme oxygenase 1 (HO-1) and quinone oxidoreductase 1 (NQO-1) and reduced malondialdehyde (MDA) levels. Finally, ISO inhibited H₂O₂-induced intracellular reactive oxygen species (ROS) in chondrocytes by activating the nuclear factor erythroid 2-related factor 2 (Nrf2) and phosphatidylinositol 3 kinase/protein kinase B (PI3K/Akt) signaling pathways. This study establishes a theoretical framework for ISO's ability to inhibit OA in vitro models.