Leveraging the Comparative Toxicogenomics Database to Fill in Knowledge Gaps for Environmental Health: A Test Case for Air Pollution-induced Cardiovascular Disease

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.

Transforming environmental health datasets from the comparative toxicogenomics database into chord diagrams to visualize molecular mechanisms

In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as "CGPD-tetramers," wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum.

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.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

Network pharmacology and experimental evidence: MAPK signaling pathway is involved in the anti-asthma roles of Perilla frutescens leaf

Perilla frutescens (PF) leaf is a traditional Chinese medicine and food with beneficial effects on allergic asthma. We sought to elucidate the active compounds, the targets, and underlying mechanisms of PF leaf in the treatment of allergic asthma by using experimental pharmacology and network pharmacology. An OVA-allergic asthma murine model was constructed to evaluate the effect of PF leaf on allergic asthma. And the network pharmacology and western blotting were performed to evaluate its underlying mechanisms in allergic asthma. PF leaf treatment significantly improved the lung function of OVA model mice and mitigated lung injury by significantly reducing of OVA-specific immunoglobulin E in serum, and interleukin 4, interleukin 5 and tumor necrosis factor alpha in the bronchoalveolar lavage fluid. 50 core targets were screened based on 8 compounds (determined by high performance liquid chromatography) through compound-target- disease network. Furthermore, MAPK signaling pathway was identified as the pathway mediated by PF leaf with the most potential against allergic asthma. And the WB results showed that PF leaf could down-regulate the expression of p-ERK, p-JNK and p-p38, which was highly consistent with the predicted targets and pathway network. In conclusion, this study provides the evidence to support the molecular mechanisms of PF leaf on the treatment of allergic asthma using network pharmacology and in vivo experiments.

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health

The molecular mechanisms connecting environmental exposures to adverse endpoints are often unknown, reflecting knowledge gaps. At the Comparative Toxicogenomics Database (CTD), we developed a bioinformatics approach that integrates manually curated, literature-based interactions from CTD to generate a "CGPD-tetramer": a 4-unit block of information organized as a step-wise molecular mechanism linking an initiating Chemical, an interacting Gene, a Phenotype, and a Disease outcome. Here, we describe a novel, user-friendly tool called CTD Tetramers that generates these evidence-based CGPD-tetramers for any curated chemical, gene, phenotype, or disease of interest. Tetramers offer potential solutions for the unknown underlying mechanisms and intermediary phenotypes connecting a chemical exposure to a disease. Additionally, multiple tetramers can be assembled to construct detailed modes-of-action for chemical-induced disease pathways. As well, tetramers can help inform environmental influences on adverse outcome pathways (AOPs). We demonstrate the tool's utility with relevant use cases for a variety of environmental chemicals (eg, perfluoroalkyl substances, bisphenol A), phenotypes (eg, apoptosis, spermatogenesis, inflammatory response), and diseases (eg, asthma, obesity, male infertility). Finally, we map AOP adverse outcome terms to corresponding CTD terms, allowing users to query for tetramers that can help augment AOP pathways with additional stressors, genes, and phenotypes, as well as formulate potential AOP disease networks (eg, liver cirrhosis and prostate cancer). This novel tool, as part of the complete suite of tools offered at CTD, provides users with computational datasets and their supporting evidence to potentially fill exposure knowledge gaps and develop testable hypotheses about environmental health.

Perfluoroalkyl substances promote breast cancer progression via ERα and GPER mediated PI3K/Akt and MAPK/Erk signaling pathways

Perfluoroalkyl substances (PFASs) are a classic environmental endocrine disruptor with carcinogenic risk. Epidemiological studies have shown that PFASs contamination is associated with breast cancer development, but the mechanism remains largely unknown. This study first obtained complex biological information about PFASs-induced breast cancer through the comparative toxicogenomics database (CTD). The Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were utilized to investigate molecular pathways. The ESR1 and GPER expression levels at different pathological stages and the prognosis of Breast Cancer patients were confirmed using the Cancer Genome Atlas (TCGA) database. Furthermore, we verified this by cellular experiments and the results showed breast cancer cell migration and invasion were promoted by PFOA. Two estrogen receptors (ER), ERα and G protein-coupled estrogen receptor (GPER), mediated the promoting effects of PFOA by activating MAPK/Erk and PI3K/Akt signaling pathways. These pathways were regulated by ERα and GPER in MCF-7 cells or independently by GPER in MDA-MB-231 cells. Overall, our study provides a better overview of the mechanisms associated with PFASs-induced breast cancer development and progression.

Integrative Data Mining Approach: Case Study with Adverse Outcome Pathway Network Leading to Pulmonary Fibrosis

An adverse outcome pathway (AOP) framework can be applied as an efficient tool for the rapid screening of environmental chemicals. For the development of an AOP, a database mining approach can support an expert derivation approach by gathering a wider range of evidence than in a literature review. In this study, data from various databases were integrated and analyzed to supplement the AOP leading to pulmonary fibrosis by analyzing additional evidence using a data mining approach and establishing an application domain for chemicals. First, we collected chemicals, genes, and phenotypes that were studied and related to pulmonary fibrosis through the Comparative Toxicogenomics Database (CTD). CGPD-tetramers constructed by linking each related chemical, gene, phenotype, and disease can provide the basic components for the assembly of putative AOPs. Next, an AOP network was established by connecting eight existing AOPs for pulmonary fibrosis developed by expert derivation from the AOP Wiki. Finally, the pulmonary fibrosis AOP network was proposed by integrating the AOP network from AOP Wiki and the CGPD-tetramers from the CTD. To prioritize potential chemical stressors in the AOP network, 61 chemicals were ranked using the relevance of the chemical to the AOP and chemical exposure information from the CompTox Chemicals Dashboard. The approach proposed in this study can guide the utilization of available evidence from various databases as well as the literature in constructing AOP networks related to specific diseases.

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond

BACKGROUND

Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery.

METHODS AND FINDINGS

ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS).

CONCLUSIONS

We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps.

Tanshinone IIA promotes apoptosis by downregulating BCL2 and upregulating TP53 in triple-negative breast cancer

Tanshinone IIA (Tan IIA) was mainly used for cardiovascular disease treatment. Recent studies have demonstrated the role of Tan IIA for tumor treatment, but its mechanism remains unclear. At the first, the inhibitory effect of Tan IIA on 4T1 breast cancer cells was determined by CCK8 and colony formation assay. Then, a 4T1 BALB/c model of breast cancer was established to evaluate the anti-cancer effect of Tan IIA in vivo. Flow cytometry analysis and the TUNEL test were used to detect cell apoptosis in vitro and in vivo, respectively. The related targets and mechanisms of Tan IIA were predicted through network-based systems biology. At last, molecular docking and the molecular biological techniques were used to evaluate the predicted targets. Tan IIA displayed encouraging inhibitory influences on 4T1 cells after incubation for 24 h and showed a half-maximal inhibitory concentration (IC50) of 49.78 μM after 48-h incubation. After 23 days of treatment, the relative tumor volumes in the Tan IIA group were 65.53% inhibited compared with the control group. Furthermore, Tan IIA induced 4T1 cell apoptosis both in vivo and in vitro. The possible targets of Tan IIA for TNBC treatment were predicted with network-based systems biology, and results showed that TP53, NF-κB, AKT, MYC, and BCL-2 were the hub targets. The mechanism against breast cancer may be based on the P53 signaling pathway, the PI3K/Akt pathway, the MAPK signaling pathway, and the mTOR signaling pathways. Molecular docking analysis reveals that Tan IIA has a high affinity for p53, Bcl-2, and NF-κB1; the binding energies were - 6.92, - 6.07, and - 6.28 kcal/mol, respectively. The predicted proteins were further validated using Western blotting. Increased expression of phosphorylated p53 and p53 and decreased expression of Bcl-2 were found in Tan IIA-treated 4T1 cells. Tan IIA is potentially effective for the treatment of 4T1 breast cancer, and the molecular mechanism may be through enhancing the activity of p53 and decreasing Bcl-2 to suppress proliferation and promote apoptosis.

Comparative Toxicogenomics Database (CTD): update 2023

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) harmonizes cross-species heterogeneous data for chemical exposures and their biological repercussions by manually curating and interrelating chemical, gene, phenotype, anatomy, disease, taxa, and exposure content from the published literature. This curated information is integrated to generate inferences, providing potential molecular mediators to develop testable hypotheses and fill in knowledge gaps for environmental health. This dual nature, acting as both a knowledgebase and a discoverybase, makes CTD a unique resource for the scientific community. Here, we report a 20% increase in overall CTD content for 17 100 chemicals, 54 300 genes, 6100 phenotypes, 7270 diseases and 202 000 exposure statements. We also present CTD Tetramers, a novel tool that computationally generates four-unit information blocks connecting a chemical, gene, phenotype, and disease to construct potential molecular mechanistic pathways. Finally, we integrate terms for human biological media used in the CTD Exposure module to corresponding CTD Anatomy pages, allowing users to survey the chemical profiles for any tissue-of-interest and see how these environmental biomarkers are related to phenotypes for any anatomical site. These, and other webpage visual enhancements, continue to promote CTD as a practical, user-friendly, and innovative resource for finding information and generating testable hypotheses about environmental health.

Study on the mechanism of Cortex Lycii on lung cancer based on network pharmacology combined with experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Xie Bai San is a Chinese medicine prescription that has been used to treat lung cancer in China for a long time. It has been proven to alleviate the symptoms and extend the survival time of lung cancer patients. Xie Bai San comprises Cortex Lycii, Cortex Mori, and Radix Glycyrrhizae Preparata. The effects and mechanisms of Cortex Mori and Glycyrrhizae on lung cancer have been reported, whereas the underlying mechanism of Cortex Lycii remains unknown.

MATERIAL AND METHODS

Network pharmacology was used to explore the unknown mechanisms underlying the effect of Cortex Lycii on lung cancer. Molecular docking was used to predict the binding of a compound to the protein. The fingerprint of Cortex Lycii was obtained by HPLC. Cell counting Kit-8 assay was used to determine the appropriate concentration of Cortex Lycii extract for human lung adenocarcinoma cells, A549 and H1299. Wound healing assay and Matrigel invasion assay were used to detect the influence of Cortex Lycii extract on the migration and invasion ability of A549 and H1299. The protein expression level was detected by western blot and immunohistochemical staining.

RESULTS

Using network pharmacology, 38 components of Cortex Lycii and 79 possible lung cancer-related target genes of Cortex Lycii were obtained. The targets were assigned to 35 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and the PI3K-AKT signaling pathway contained the most targets and had the second-lowest P-value. The molecular docking showed the components of Cortex Lycii bound to HSP90AB1. Among them, 6 components bound to HSP90AB1 in which HSP90AB1 binds to and phosphorylates AKT. The functional experiments showed that Cortex Lycii suppressed the migration and invasion of human lung cancer cells in a dose-dependent manner. Cortex Lycii up-regulated E-Cadherin and down-regulated N-Cadherin, Vimentin, and MMP2. Furthermore, Cortex Lycii made no change in the total AKT and mTOR protein levels, but caused the down-regulation of p-AKT and p-mTOR in human lung cancer cells, which was reversed by Terazosin, an agonist of HSP90. Moreover, acacetin and apigenin, two components of Cortex Lycii, reduced the protein level of p-AKT and p-mTOR, and the reduction was also inhibited by Terazosin.

CONCLUSION

Cortex Lycii suppressed epithelial-mesenchymal transition (EMT) in lung cancer cells through the PI3K-AKT-mTOR signaling pathway, possibly by targeting HSP90AB1 and inhibiting HSP90AB1-AKT binding.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis

Considering that some researchers point to a possible influence of air pollution on COVID-19 transmission, severity, and death rate, the aim of our in silico study was to determine the relationship between the key air pollutants [sulphur dioxide (SO), carbon monoxide (CO), ₂particulate matter (PM_x), nitrogen dioxide (NO₂), and ozone (O₃)] and COVID-19 complications using the publicly available toxicogenomic analytical and prediction tools: (i) Comparative Toxicogenomic Database (CTD) to identify genes common to air pollutants and COVID-19 complications; (ii) GeneMANIA to construct a network of these common and related genes; (iii) ToppGene Suite to extract the most important biological processes and molecular pathways; and (iv) DisGeNET to search for the top gene-disease pairs. SO₂, CO, PM_x, NO₂, and O₃ interacted with 6, 6, 18, 9, and 12 COVID-19-related genes, respectively. Four of these are common for all pollutants (IL10, IL6, IL1B, and TNF) and participate in most (77.64 %) physical interactions. Further analysis pointed to cytokine binding and cytokine-mediated signalling pathway as the most important molecular function and biological process, respectively. Other molecular functions and biological processes are mostly related to cytokine activity and inflammation, which might be connected to the cytokine storm and resulting COVID-19 complications. The final step singled out the link between the CEBPA gene and acute myelocytic leukaemia and between TNFRSF1A and TNF receptor-associated periodic fever syndrome. This indicates possible complications in COVID-19 patients suffering from these diseases, especially those living in urban areas with poor air quality.

Advancing the Adverse Outcome Pathway for PPARγ Inactivation Leading to Pulmonary Fibrosis Using Bradford-Hill Consideration and the Comparative Toxicogenomics Database

Pulmonary fibrosis is regulated by transforming growth factor-β (TGF-β) and peroxisome proliferator-activated receptor-gamma (PPARγ). An adverse outcome pathway (AOP) for PPARγ inactivation leading to pulmonary fibrosis has been previously developed. To advance the development of this AOP, the confidence of the overall AOP was assessed using the Bradford-Hill considerations as per the recommendations from the Organisation for Economic Co-operation and Development (OECD) Users' Handbook. Overall, the essentiality of key events (KEs) and the biological plausibility of key event relationships (KERs) were rated high. In contrast, the empirical support of KERs was found to be moderate. To experimentally evaluate the KERs from the molecular initiating event (MIE) and KE1, PPARγ (MIE) and TGF-β (KE1) inhibitors were used to examine the effects of downstream events following inhibition of their upstream events. PPARγ inhibition (MIE) led to TGF-β activation (KE1), upregulation in vimentin expression (KE3), and an increase in the fibronectin level (KE4). Similarly, activated TGF-β (KE1) led to an increase in vimentin (KE3) and fibronectin expression (KE4). In the database analysis using the Comparative Toxicogenomics Database, 31 genes related to each KE were found to be highly correlated with pulmonary fibrosis, and the top 21 potential stressors were suggested. The AOP for pulmonary fibrosis evaluated in this study will be the basis for the screening of inhaled toxic substances in the environment.

Investigation of molecular regulation mechanism under the pathophysiology of subarachnoid hemorrhage

This study aimed to investigate the molecular mechanism under the pathophysiology of subarachnoid hemorrhage (SAH) and identify the potential biomarkers for predicting the risk of SAH. Differentially expressed mRNAs (DEGs), microRNAs, and lncRNAs were screened. Protein-protein interaction (PPI), drug-gene, and competing endogenous RNA (ceRNA) networks were constructed to determine candidate RNAs. The optimized RNAs signature was established using least absolute shrinkage and selection operator and recursive feature elimination algorithms. A total of 124 SAH-related DEGs were identified, and were enriched in inflammatory response, TNF signaling pathway, and others. PPI network revealed 118 hub genes such as TNF, MMP9, and TLR4. Drug-gene network revealed that chrysin targeted more genes, such as TNF and MMP9. JMJD1C-AS-hsa-miR-204-HDAC4/SIRT1 and LINC01144-hsa-miR-128-ADRB2/TGFBR3 regulatory axes were found from ceRNA network. From these networks, 125 candidate RNAs were obtained. Of which, an optimal 38 RNAs signatures (2 lncRNAs, 1 miRNA, and 35 genes) were identified to construct a Support Vector Machine classifier. The predictive value of 38 biomarkers had an AUC of 0.990. Similar predictive performance was found in external validation dataset (AUC of 0.845). Our findings provided the potential for 38 RNAs to serve as biomarkers for predicting the risk of SAH. However, their application values should be further validated in clinical.

Integration of data from the in vitro long-term exposure study on human endothelial cells and the in silico analysis: A case of dibutyl phthalate-induced vascular dysfunction

General population is exposed to dibutyl phthalate (DBP) through continuous use of various consumer products. DBP exhibits its effects mainly on the endocrine and reproductive system but it can also affect the function of the vasculature; however, the underlying mechanisms behind DBP-induced vascular dysfunction are not fully understood. To infer pathways, molecular functions, biological processes, and human diseases associated with DBP exposure, we integrated the toxicogenomic data obtained from the 4-week-long exposure of human vascular endothelial cells (ECs) to three environmentally relevant concentrations of DBP with the in silico analysis. Nine genes were affected by DBP exposure: six of the integrin family, VCAM1, ICAM1, and MMP2. As shown by the in silico analysis, changes in DBP-affected genes could affect extracellular matrix and binding of molecules and cells to ECs, thereby altering cell adhesion and migration. Several pathways, molecular functions, and biological processes were further identified to provide insight into the DBP-vascular disease relationships and the potential mechanism of action. The top three human disease categories associated with DBP exposure and vascular dysfunction include cardiovascular, cerebrovascular, and immune system diseases. Integration of experimental and in silico approaches may offer better understanding of the potential human health risks associated with DBP exposure.

Integration of data from the cell-based ERK1/2 ELISA and the Comparative Toxicogenomics Database deciphers the potential mode of action of bisphenol A and benzo[a]pyrene in lung neoplasm

Chemicals can activate a variety of signaling pathways, initiating changes in gene expression and cellular functions. Here, we combined experimental data on the chemical-induced extracellular signal-regulated kinase 1/2 (ERK1/2) activation with the Comparative Toxicogenomics Database (CTD) to connect signaling, genes, and phenotypes to reveal the potential chemical's mode of action (MOA) responsible for the disease state. Experimental data on ERK1/2 activation were derived from the cell-based phospho-ERK1/2 ELISA on human alveolar epithelial cells A549. A549 cells were exposed to bisphenol A (BPA), benzo[a]pyrene (BaP), tributyltin (TBT), and ibuprofen from 10^-12 M to 10^-5 M. Results show that BPA, BaP, and TBT can activate ERK1/2 in A549 cells. We selected BPA and BaP to elucidate the molecular events connecting chemical exposure, ERK1/2 signaling, phenotypes, and lung neoplasm (LN) using CTD. CTD analysis showed that BPA and BaP share 26 mitogen-activated protein kinase 1/3 (MAPK1/3) signaling genes associated with LN. Phenotype prioritization revealed 37 BPA, 10 BaP, and 11 shared key phenotypes associated with LN. Alignment of MAPK1/3 signaling genes and phenotypes showed that ERK1/2 and oxidative stress, EGFR gene, and positive regulation of cell proliferation and migration could be the shared key events (KE) for BPA and BaP. This analysis also identified protein kinase B and ERK1/2 signaling, FGF9, FGFR1 and FGFR2 genes, positive regulation of cell proliferation and angiogenesis as KE in MOA for BPA, whereas ERK1/2 signaling, IL6 and DAB2IP genes, negative regulation of cell proliferation and inflammatory response were identified as KE in MOA for BaP.

Predicting molecular mechanisms, pathways, and health outcomes induced by Juul e-cigarette aerosol chemicals using the Comparative Toxicogenomics Database

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Vaping chemicals of Juul aerosols were analyzed using CTD.

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CTD can be used to predict disease associations of Juul aerosol chemicals.

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Predictive pathways that relate vaping aerosols to diseases are described.

There is a critical need to understand the health risks associated with vaping e-cigarettes, which has reached epidemic levels among teens. Juul is currently the most popular type of e-cigarette on the market. Using the Comparative Toxicogenomics Database (CTD; http://ctdbase.org), a public resource that integrates chemical, gene, phenotype and disease data, we aimed to analyze the potential molecular mechanisms of eight chemicals detected in the aerosols generated by heating Juul e-cigarette pods: nicotine, acetaldehyde, formaldehyde, free radicals, crotonaldehyde, acetone, pyruvaldehyde, and particulate matter. Curated content in CTD, including chemical-gene, chemical-phenotype, and chemical-disease interactions, as well as associated phenotypes and pathway enrichment, were analyzed to help identify potential molecular mechanisms and diseases associated with vaping. Nicotine shows the most direct disease associations of these chemicals, followed by particulate matter and formaldehyde. Together, these chemicals show a direct marker or mechanistic relationship with 400 unique diseases in CTD, particularly in the categories of cardiovascular diseases, nervous system diseases, respiratory tract diseases, cancers, and mental disorders. We chose three respiratory tract diseases to investigate further, and found that in addition to cellular processes of apoptosis and cell proliferation, prioritized phenotypes underlying Juul-associated respiratory tract disease outcomes include response to oxidative stress, inflammatory response, and several cell signaling pathways (p38MAPK, NIK/NFkappaB, calcium-mediated).

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

A toxicity pathway-based approach for modeling the mode of action framework of lead-induced neurotoxicity

BACKGROUND

The underlying mechanisms of lead (Pb) toxicity are not fully understood, which makes challenges to the traditional risk assessment. There is growing use of the mode of action (MOA) for risk assessment by integration of experimental data and system biology. The current study aims to develop a new pathway-based MOA for assessing Pb-induced neurotoxicity.

METHODS

The available Comparative Toxicogenomic Database (CTD) was used to search genes associated with Pb-induced neurotoxicity followed by developing toxicity pathways using Ingenuity Pathway Analysis (IPA). The spatiotemporal sequence of disturbing toxicity pathways and key events (KEs) were identified by upstream regulator analysis. The MOA framework was constructed by KEs in biological and chronological order.

RESULTS

There were a total of 71 references showing the relationship between lead exposure and neurotoxicity, which contained 2331 genes. IPA analysis showed that the neuroinflammation signaling pathway was the core toxicity pathway in the enriched pathways relevant to Pb-induced neurotoxicity. The upstream regulator analysis demonstrated that the aryl hydrocarbon receptor (AHR) signaling pathway was the upstream regulator of the neuroinflammation signaling pathway (11.76% overlap with upstream regulators, |Z-score|=1.451). Therefore, AHR activation was recognized as the first key event (KE1) in the MOA framework. The following downstream molecular and cellular key events were also identified. The pathway-based MOA framework of Pb-induced neurotoxicity was built starting with AHR activation, followed by an inflammatory response and neuron apoptosis.

CONCLUSION

Our toxicity pathway-based approach not only advances the development of risk assessment for Pb-induced neurotoxicity but also brings new insights into constructing MOA frameworks of risk assessment for new chemicals.

CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies

The Comparative Toxicogenomics Database (CTD) is a freely available public resource that curates and interrelates chemical, gene/protein, phenotype, disease, organism, and exposure data. CTD can be used to address toxicological mechanisms for environmental chemicals and facilitate the generation of testable hypotheses about how exposures affect human health. At CTD, manually curated interactions for chemical-induced phenotypes are enhanced with anatomy terms (tissues, fluids, and cell types) to describe the physiological system of the reported event. These same anatomy terms are used to annotate the human media (e.g., urine, hair, nail, blood, etc.) in which an environmental chemical was assayed for exposure. Currently, CTD uses more than 880 unique anatomy terms to contextualize over 255,000 chemical-phenotype interactions and 167,000 exposure statements. These annotations allow chemical-phenotype interactions and exposure data to be explored from a novel, anatomical perspective. Here, we describe CTD's anatomy curation process (including the construction of a controlled, interoperable vocabulary) and new anatomy webpages (that coalesce and organize the curated chemical-phenotype and exposure data sets). We also provide examples that demonstrate how this feature can be used to identify system- and cell-specific chemical-induced toxicities, help inform exposure data, prioritize phenotypes for environmental diseases, survey tissue and pregnancy exposomes, and facilitate data connections with external resources. Anatomy annotations advance understanding of environmental health by providing new ways to explore and survey chemical-induced events and exposure studies in the CTD framework.

Regulatory Status of Pesticide Residues in Cannabis: Implications to Medical Use in Neurological Diseases

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Movement disorders are the most common neurological category of qualifying conditions in the U.S.

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The number and action levels of regulated pesticides in cannabis differ vastly in 33 states and Washington, D.C.

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Network analysis reveals potential interactions of insecticides, cannabinoids, and seizure at a functional level.

Medical cannabis represents a potential route of pesticide exposure to susceptible populations. We compared the qualifying conditions for medical use and pesticide testing requirements of cannabis in 33 states and Washington, D.C. Movement disorders were the most common neurological category of qualifying conditions, including epilepsy, certain symptoms of multiple sclerosis, Parkinson’s Disease, and any cause of symptoms leading to seizures or spasticity. Different approaches of pesticide regulation were implemented in cannabis and cannabis-derived products. Six states imposed the strictest U.S. EPA tolerances (i.e. maximum residue levels) for food commodities on up to 400 pesticidal active ingredients in cannabis, while pesticide testing was optional in three states. Dimethomorph showed the largest variation in action levels, ranging from 0.1 to 60 ppm in 5 states. We evaluated the potential connections between insecticides, cannabinoids, and seizure using the Comparative Toxicogenomics Database. Twenty-two insecticides, two cannabinoids, and 63 genes were associated with 674 computationally generated chemical-gene-phenotype-disease (CGPD) tetramer constructs. Notable functional clusters included oxidation-reduction process (183 CGPD-tetramers), synaptic signaling pathways (151), and neuropeptide hormone activity (46). Cholinergic, dopaminergic, and retrograde endocannabinoid signaling pathways were linked to 10 genetic variants of epilepsy patients. Further research is needed to assess human health risk of cannabinoids and pesticides in support of a national standard for cannabis pesticide regulations.

Comparative Toxicogenomics Database (CTD): update 2021

The public Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is an innovative digital ecosystem that relates toxicological information for chemicals, genes, phenotypes, diseases, and exposures to advance understanding about human health. Literature-based, manually curated interactions are integrated to create a knowledgebase that harmonizes cross-species heterogeneous data for chemical exposures and their biological repercussions. In this biennial update, we report a 20% increase in CTD curated content and now provide 45 million toxicogenomic relationships for over 16 300 chemicals, 51 300 genes, 5500 phenotypes, 7200 diseases and 163 000 exposure events, from 600 comparative species. Furthermore, we increase the functionality of chemical-phenotype content with new data-tabs on CTD Disease pages (to help fill in knowledge gaps for environmental health) and new phenotype search parameters (for Batch Query and Venn analysis tools). As well, we introduce new CTD Anatomy pages that allow users to uniquely explore and analyze chemical-phenotype interactions from an anatomical perspective. Finally, we have enhanced CTD Chemical pages with new literature-based chemical synonyms (to improve querying) and added 1600 amino acid-based compounds (to increase chemical landscape). Together, these updates continue to augment CTD as a powerful resource for generating testable hypotheses about the etiologies and molecular mechanisms underlying environmentally influenced diseases.

Community Approaches for Integrating Environmental Exposures into Human Models of Disease

BACKGROUND

A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.

OBJECTIVES

Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.

DISCUSSION

There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215.