Chemicals are a critical aspect of modern agriculture and residues of these chemicals are commonly consumed by humans. Consumption, inhalation, or topical exposure to agricultural chemicals can pose a risk for human health through a variety of mechanisms. Similarly, exposures to radiation, nutrient consumption, and many other environmental entities can impact health and thus a wide array of research has been pursued to better understand the mechanisms and impacts of environmental exposures. While extensive exposure research has been conducted and the data stored in environmental health databases, the ability to computationally assess these findings in the larger context of biomedical research to inform our knowledge for improved human health is still challenging. We developed an integrative exposure-disease model based on the Exposure Ontology (ExO) upper level ontology and established four Dead Simple OWL Design Patterns (DOSDP) for Mondo Disease Ontology. These patterns offer coordination of exposure event and exposure stimulus terms with disease terms, utilizing content from Open Biological Ontologies. Our model and pattern set can leverage logical axioms from integrated ontologies including the Food Ontology and the Environmental Conditions, Treatments, and Exposures Ontology (ECTO) for greater data and knowledge enrichment. Development of exposure event component terms and related logical axioms can facilitate the standardization needed for exposure modeling. Exposure content and our model can be utilized for the development of integrative knowledge graphs of exposure health data. Additionally, this model serves as a resource to aid the integration of common exposure data sources such as self-reported survey tools. Future work is needed to incorporate essential exposure data components into a comprehensive model, such as estimated or known exposure values, temporality of exposures, and biologically active exposure dosages that incur toxic effects.
For decades, chemicals such as fertilizers, pesticides, herbicides, and insecticides have been used as
an essential component to modern agriculture [
Previously, we developed the Environmental Conditions, Treatments, and Exposures Ontology
(ECTO) to address these use cases. ECTO’s terms represent a variety of stimuli and environmental
conditions, including experimental and non-experimental exposures to humans, plants, and animals
[
To facilitate encoding of environmental exposures and their impact on health, our adaptable
exposure-disease model was developed to include exposures, food products, crop plants, mechanism of
action, phenotypes, and disease. This model was the outcome of multiple workshops and community
coordination, which included ExO, ECTO, and Mondo developers [
Figure 1 depicts the three-part progression of our model including the upper ExO ontology (Figure
1A), our adaptable exposure-disease model (Figure 1B), and an application of the model using an
instance level example of chlorpyrifos residue ingestion on an apple. Chlorpyrifos, an
organophosphorus insecticide, is a common agricultural chemical used for production of produce and
other crops within the US and beyond [
As seen in Figure 1, ExO describes an ‘exposure stimulus’ as ‘an agent, stimulus, activity, or event that causes stress or tension on an organism and interacts with an exposure receptor during an exposure event’. A ‘exposure receptor’ is defined as ‘an entity (e.g., a human, human population, or a human organ) that interacts with an exposure stimulus during an exposure event’. An ‘exposure outcome’ is defined as ‘entity that results from the interaction between an exposure receptor and an exposure stimulus during an exposure event’ and represents the negative or positive outcomes of having been exposed to the stimulus. It is important to recognize that it is the axioms encoded within the model that connect exposure information to a variety of knowledge that then allows the potential inference of a candidate stimulus or a predicted outcome in response to one. disease presentation in the individual. Documented relationships are seen with solid lines and inferred relationships are seen in dashed lines.
By documenting not only food items that are the mediums for the exposure to chlorpyrifos, but also including the mechanism of action, known phenotypes, and disease states, our example schema of chlorpyrifos exposure offers access points in which further information can be inferred. For example, if another chemical served as an acetylcholinesterase inhibitor within humans, by inclusion of that chemical exposure and known regulatory activity, one could infer that the second chemical exposure may also be related to cognitive disorders, or that the chemicals composition may be similar to chlorpyrifos.
To support the exposure-disease model described above, we utilize ontology relationship axioms and structures. Some examples include logical axioms in ECTO and FoodOn.
ECTO terms are developed as pre-composed classes. Exposure terms are inherently coordinated with the relevant ontology term for the chemical, environmental stimulus, or condition the ECTO term label refers to. Each pre-composed ECTO class includes a reference to another ontology term. For example, with the ECTO term ‘exposure to fertilizer’ (ECTO:9000091) the equivalence axiom for this term includes a reference to the ChEBI term ‘fertilizer’ CHEBI:33287. This logic provides the ‘has exposure stimulus’ relationship and aligns the exposure term with the detailed content of the referenced ChEBI term.
Class: ‘exposure to fertilizer’ Equivalence axiom: 'exposure event' and 'has exposure stimulus' some fertilizer"
Existing logic from FoodOn is also included in our model. Within FoodOn, the source ontology for food terminology, foods produced directly from a crop include a logical axiom. For example, the FoodOn term ‘orange (whole)’ (FOODON:03315106) has the logical axiom shown below that references the plant term ‘hesperidium fruit’ (PO:0030109) and the taxon ‘Citrus sinensis’ (NCBITaxon:2711).
Class: orange (whole) Logical axiom: ‘hesperidium fruit and derives from some Citrus sinensis’ While these relationships and components of our model are already represented in ontology structures, the critical relationship between exposure and disease outcomes was not yet well defined.
To facilitate the integration and modeling of environmental exposures and human disease, we have
developed and implemented four patterns for disease terms with a known exposure basis for the Mondo
Disease Ontology. Creation of these patterns established logical axioms within Mondo disease terms
that coordinate with environmental exposure ECTO terms. This Mondo-ECTO term relationship can
then be directly implemented into our exposure-disease model. Within Mondo, as well as other
ontologies, Dead Simple OWL Design Patterns (DOSDP) are frequently used to develop new terms
with logical axioms in a consistent and easily maintained manner [
The disease ‘radiodermatitis’ (MONDO:0043771) conforms to the Mondo ‘realized_in_response_to_environmental_exposure‘ design pattern (https://github.com/monarchinitiative/mondo/blob/master/src/patterns/dosdppatterns/realized_in_response_to_environmental_exposure.yaml). This pattern uses the relation ‘realized in response to’ to link diseases to the exposures (represented by ECTO classes) causing the disease. The logical axiom utilized for this pattern is: '%s and (''realized in response to'' some %s)' Vars: • Disease • Exposure
Within this logical axiom template are the variable (vars) fields, represented by ‘%s’. For each vars, a variable term is required to complete the axiom statement. In this instance, the vars are ‘Disease’ and ‘Exposure’. These variable terms will be identified from Mondo and ECTO and will be used to fill in the first and second fields respectively. For example, the logical axiom for ‘radiodermatitis’ is represented as: radiodermatitis and ('realized in response to' some ‘exposure to electromagnetic radiation’)
For the variety of diseases that may be caused by or initiated via an environmental exposure or external entity, we have created multiple DOSDPs for Mondo that support general and specific exposure-based disease terms. Their content and applications are described in Table 1. At this time, over 390 terms have been implemented using these patterns, with 46 terms including logical axioms referencing 17 unique ECTO exposure terms.
Building models for exposure risk and disease causality has been challenging due to the heterogeneity and lack of interoperability across agricultural, toxicological, and clinical data [25,26]. The model outlined in here is a preliminary foundation for how exposure influenced diseases can be described in a computable fashion. The four patterns presented here can be used to establish exposure based disease classes for Mondo Disease Ontology, and similar approaches could likely be translated into other ontologies.
Our modeling structure can be used for chemical, nutrient, and other environmental exposures and their impact on phenotypes, disease, and gene function. This modular approach supports adaptation of exposure source and types while also allowing for multiple different exposures to be integrated for a comprehensive mapping of exposures to outcomes. In addition to the proposed model, work to include variables for comprehensive exposure to health modeling such as estimated or known exposure values (e.g. residual agricultural chemicals consumed in average diet), estimated or known temporality of exposures, and biologically active exposure dosages for toxic effects are needed. We plan to utilize this semantic framework for integrating a wide range of dietary and other exposures for predictive analytics, inference of causality, and to inform mitigation of exposures. The goal is to be able to integrate clinical data and biomarkers of exposure with data collected via self-reported surveys, which are commonly used for dietary data collection and estimation tools for personal environmental exposures. Additionally, harmonization of this model with other existing resources for describing adverse outcome pathways and ecotoxicology such those presented by Myklebust et al. [27] would offer substantial data integration for inference development.
Using this semantic framework, we will be able to populate a knowledge graph that would leverage content found in numerous biomedical ontologies alongside instance level data from surveys, clinical data, and more. Future efforts will be focused on improving the accuracy with which exposure events can be documented to include temporality, dosage, and resulting environmental and health outcomes. In turn, these efforts are intended to support methods for risk estimations of disease and phenotype outcomes given predicted or known environmental exposures.
The ECTO repository: https://github.com/EnvironmentOntology/environmental-exposureontology
The Exposure-Disease wiki: https://github.com/EnvironmentOntology/environmentalexposure-ontology/wiki/Exposure-disease-model 6. References [25] L. Chan, N. Vasilevsky, A. Thessen, J. McMurry, M. Haendel, The landscape of nutri-informatics: a review of current resources and challenges for integrative nutrition research. Database. (2021). doi:10.1093/database/baab003 [26] T. Hartung, R. E. FitzGerald, P. Jennings, G. R. Mirams, M. C. Peitsch, A. Rostami-Hodjegan, et al., Systems Toxicology: Real World Applications and Opportunities. Chem Res Toxicol. 30 (2017): 870–882. [27] E. B. Myklebust, E. Jimenez-Ruiz, J. Chen, R. Wolf, Tera: the toxicological effect and risk assessment knowledge graph. arXiv preprint arXiv. (2019). URL: https://arxiv.org/abs/1908.10128