AOP-helpFinder 2.0: Integration of an event-event searches module

Connecting volatile organic compounds exposure to osteoporosis risk via oxidative stress based on adverse outcome pathway methodology

Existing evidence has demonstrated the association between exposure to volatile organic compounds (VOCs) and osteoporosis (OP) risk, but the underlying mechanistic framework remains unclear. This study aimed to explore potential pathways using adverse outcome pathway (AOP) analysis, and evidence this association in middle-to-old-aged American adults using the updated National Health and Nutrition Examination Survey data. Multivariable-adjusted general linear and weighted quantile sum models were employed to analyze associations of VOC metabolites (VOCMs), representing internal VOCs exposure levels, with OP-related phenotypes. An AOP framework based on network analysis was developed by extracting target genes and phenotypes. Among 3555 American adults aged ≥ 40 years (539 OP participants), we found that increasing urinary 3- and 4-methylhippuric acid, N-acetyl-S-(n-propyl)-l-cysteine (BPMA), and N-acetyl-S-(3-hydroxypropyl)-l-cysteine were associated with elevated OP odds with odds ratios (ORs) (95 % confidence intervals, 95 % CIs) being 1.254 (1.016 to 1.548), 1.182 (1.014 to 1.377), and 1.244 (1.029 to 1.505), respectively, per standard deviation. Urinary BPMA and N-acetyl-S-(2-cyanoethyl)-l-cysteine were inversely associated with lumbar spine bone mineral density (BMD), while urinary N-acetyl-S-(2-hydroxypropyl)-l-cysteine was positively associated with hip BMD. Additionally, OP odds increased by 46.0 % (95 % CI: 3.9 % to 105.1 %) per quartile increment in the VOC mixture. AOP analysis identified 53 target genes and 9 target phenotypes, and 5 of 9 target phenotypes were oxidative stress (OS)-related. Literature and the "AOP 482″ framework implied the core role of OS in the VOC exposure and prevalent OP association, with the interleukin-6 as the molecular initiating event. Our findings provided a theoretical basis for further investigation.

AOP-networkFinder-a versatile tool for the reconstruction and visualization of Adverse Outcome Pathway networks from AOP-Wiki

Motivation

The Adverse Outcome Pathways (AOP)-Wiki, a knowledge database for AOPs, requires an efficient way to present an overview of its content for the reconstruction of networks by experts in a given domain. We have developed the AOP-networkFinder, a user-friendly tool that retrieves AOPs of interest, allows network generation and cleaning, and finally visualizes networks built around the retrieved AOPs. Our tool constructs AOP networks by connecting AOPs that use the same Key Events (KEs) in a versatile but controlled manner. Genes related to these KEs are also displayed. The constructed networks can then be exported as images or to Cytoscape for further fine-tuning and statistical analysis.

Results

The AOP-networkFinder allows users to comprehensively identify relationships between KEs and visualize the overall structure of an AOP both quickly and easily. This is immensely beneficial to researchers who need to understand the complex interplay between different KEs and the overall pathway they are studying and helps them to build further networks of interest while logging relevant information about changes within the network. These efforts are in line with the Findable, Accessible, Interoperable, and Reusable principles, which are crucial attributes for any developed databases and tools for optimizing (re)use in a dynamically changing landscape of AOP-Wiki.

Availability and implementation

The AOP-networkFinder is an open-source application and is available online at aop-networkfinder.no, in the 'Computational Toxicology at Norwegian Institute of Public Health' Zenodo community at DOI 10.5281/zenodo.11068434, in the GitHub repository at github.com/folkehelseinstituttet/AOPnetworkFinder_v1, as well as in a Docker image at hub.docker.com/r/nurre123/aop_network_finder. The software is available under the GNU Affero General Public License (AGPL), v3.0. The tool uses the AOP-Wiki SPARQL endpoint to retrieve AOP data.

Network-based investigation of petroleum hydrocarbons-induced ecotoxicological effects and their risk assessment

Petroleum hydrocarbons (PHs) are compounds composed mostly of carbon and hydrogen, originating from crude oil and its derivatives. PHs are primarily released into the environment through the diffusion of oils, resulting from anthropogenic activities like transportation and offshore drilling, and accidental incidents such as oil spills. Once released, these PHs can persist in different ecosystems and cause long-term detrimental ecological impacts. While the hazards associated with such PH contaminations are often assessed by the concentrations of total petroleum hydrocarbons in the environment, studies focusing on the risks associated with individual PHs are limited. In this study, different network-based frameworks were utilized to explore and understand the adverse ecological effects associated with PH exposure. First, a list of 320 PHs was systematically curated from published reports. Next, biological endpoint data from toxicological databases was systematically integrated, and a stressor-centric adverse outcome pathway (AOP) network, linking 75 PHs with 177 ecotoxicologically-relevant high confidence AOPs within AOP-Wiki, was constructed. Further, stressor-species networks, based on reported toxicity concentrations and bioconcentration factors data within ECOTOX, were constructed for 80 PHs and 28 PHs, respectively. It was found that crustaceans are documented to be affected by many of these PHs. Finally, the aquatic toxicity data within ECOTOX was used to construct species sensitivity distributions for PHs, and their corresponding hazard concentrations (HC05), that are harmful to 5% of species in the aquatic ecosystem, were derived. Further, the predicted no-effect concentrations (PNECs) and risk quotients for the US EPA priority polycyclic aromatic hydrocarbons (PAHs) were computed by using their environmental concentration data for Indian coastal and river waters. Overall, this study highlights the importance of using network-based approaches and risk assessment methods to understand the PH-induced toxicities effectively.

Comprehensive analysis of adverse outcome pathway, potency, human exposure supports carcinogenicity of polyhexamethylene guanidine phosphate in lung cancer

In this study, we investigated the potential mechanisms by which polyhexamethylene guanidine phosphate (PHMG-p), a known respiratory irritant, may contribute to lung cancer development. Using the adverse outcome pathway (AOP) framework, we analyzed established databases (such as AOP-Wiki) and employed AI tools (AOP-helpFinder) to identify key events (KEs) associated with lung carcinogenesis. Our analysis indicates that chronic inhalation of PHMG-p triggers a non-genotoxic pathway, characterized by cell membrane disruption, inflammation, and oxidative stress, with a point of departure (POD) of 0.0018 mg/m³, suggesting carcinogenic potential. Additionally, a human exposure assessment revealed that most claimants were exposed to PHMG-p levels exceeding the estimated inhalation reference concentration (RfC) of 0.018 µg/m³. While downstream KEs, such as DNA damage, mutation, and cell proliferation, require further investigation, our findings, supported by the AOP framework and potency and exposure assessments, strongly suggest that PHMG-p exposure could induce lung cancer in individuals affected by humidifier disinfectants. These results underscore the importance of a comprehensive risk assessment approach for evaluating the carcinogenicity of PHMG-p.

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives

Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are intentionally added to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP-Wiki. Based on these plastic additive-AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into ten priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives-AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/ . Finally, we showed the utility of the constructed plastic additives-AOP network by identifying highly relevant AOPs associated with benzo[a]pyrene (B[a]P), bisphenol A (BPA), and bis(2-ethylhexyl) phthalate (DEHP) and thereafter, explored the associated toxicity pathways in humans and aquatic species. Overall, the constructed plastic additives-AOP network will assist regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.

AOP-based framework for predicting the joint action mode of di-(2-ethylhexyl) phthalate and bisphenol A co-exposure on autism spectrum disorder

Autism spectrum disorder (ASD), also known as autism, is a common, highly hereditary and heterogeneous neurodevelopmental disorder. The global prevalence of ASD among children continues to rise significantly, which is partially attributed to environmental pollution. It has been reported that pre- or post-natal exposure to di-(2-ethylhexyl) phthalate (DEHP) or bisphenol A (BPA), two prevalent environmental endocrine disruptors, increases the risk of ASD in offspring. Yet, the joint action mode linking DEHP and BPA with ASD is incompletely understood. This study aims to unravel the joint action mode of DEHP and BPA co-exposure on the development of ASD. An adverse outcome pathway (AOP) framework was employed to integrate data from multiple public database and construct chemical-gene-phenotype-disease networks (CGPDN) for DEHP- and BPA-related ASD. Topological analysis and comprehensive literature exploration of the CGPDN were performed to build the AOP. By analysis of shared key events (KEs) or phenotypes within the AOP or the CGPDN, we uncovered two AOPs, decreased N-methyl-D-aspartate receptor (NMDAR) and estrogen antagonism that were likely linked to ASD, both with moderate confidence. Our analysis further predicted that the joint action mode of DEHP and BPA related ASD was possibly an additive or synergistic action. Thus, we propose that the co-exposure to BPA and DEHP perhaps additively or synergistically increases the risk of ASD.

Chemical respiratory sensitization-Current status of mechanistic understanding, knowledge gaps and possible identification methods of sensitizers

Respiratory sensitization is a complex immunological process eventually leading to hypersensitivity following re-exposure to the chemical. A frequent consequence is occupational asthma, which may occur after long latency periods. Although chemical-induced respiratory hypersensitivity has been known for decades, there are currently no comprehensive and validated approaches available for the prospective identification of chemicals that induce respiratory sensitization, while the expectations of new approach methodologies (NAMs) are high. A great hope is that due to a better understanding of the molecular key events, new methods can be developed now. However, this is a big challenge due to the different chemical classes to which respiratory sensitizers belong, as well as because of the complexity of the response and the late manifestation of symptoms. In this review article, the current information on respiratory sensitization related processes is summarized by introducing it in the available adverse outcome pathway (AOP) concept. Potentially useful models for prediction are discussed. Knowledge gaps and gaps of regulatory concern are identified.

Transcriptome-centric approach to the derivation of adverse outcome pathway networks of vascular dysfunction after long-term low-level exposure of human endothelial cells to dibutyl phthalate

Dibutyl phthalate (DBP) is an endocrine disruptor that adversely affects reproduction; however, evidence suggests it can also impact other systems, including vascular function. The mechanisms underlying DBP-induced vascular dysfunction, particularly after long-term low-level exposure of endothelial cells to this phthalate, remain largely unknown. To address this gap, we used experimentally derived data on differentially expressed genes (DEGs) obtained after 12 weeks of exposure of human vascular endothelial cells EA.hy926 to the concentrations of DBP to which humans are routinely exposed (10-9 M, 10-8 M, and 10-7 M) and various computational tools and manual data curation to build the first adverse outcome pathway (AOP) network relevant to DBP-induced vascular toxicity. DEGs were used to infer transcription factors (molecular initiating events) and molecular functions and biological processes (key events, KEs) using the Enrichr database. The AOP-helpFinder 2.0, an artificial intelligence-based web tool, was used to link genes and KEs and assign confidence scores to co-occurred terms. We constructed the AOP networks using Cytoscape and then manually arranged KEs to depict the flow of mechanistic information across different levels of network organization. An AOP network was created for each DBP concentration, revealing several distinct high-confidence subnetworks that could be involved in DBP-induced vascular toxicity: the insulin-like growth factor subnetwork for 10-7 M DBP, the CXCL8-dependent chemokine subnetwork for 10-8 M DBP, and the fatty acid subnetwork for 10-9 M DBP. We also developed an AOP network providing a mechanistic insight into the dose-dependent effects of DBP in endothelial cells leading to vascular dysfunction. In summary, we present novel putative AOP networks describing the mechanistic flow of information involved in DBP-induced vascular dysfunction in a long-term low-level exposure scenario.

The application of natural language processing for the extraction of mechanistic information in toxicology

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).

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium from five exposome-relevant databases using a data-centric approach, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs). By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.

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.