Development of Adverse Outcome Pathway for PPARÎ³ antagonism

Subscriber access provided by UNIV OF SOUTHERN INDIANA

Article

Development of Adverse Outcome Pathway for PPAR# antagonism leading to pulmonary fibrosis and chemical selection for its validation: ToxCast™ database and a deep learning artificial neural network model based approach Jaeseong Jeong, Natalia Reyero, Lyle Burgoon, Edward J. Perkins, Taehyun Park, Changheon Kim, Ji-Yeon Roh, and Jinhee Choi Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.9b00040 • Publication Date (Web): 10 May 2019 Downloaded from http://pubs.acs.org on May 10, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Chemical Research in Toxicology

Development

of

Adverse

Outcome

Pathway

for

PPARγ

antagonism leading to pulmonary fibrosis and chemical selection for its validation: ToxCast™ database and a deep learning artificial neural network model based approach Jaeseong Jeong1, Natàlia Garcia-Reyero2, Lyle Burgoon2, Edward Perkins2, Taehyun Park1, Changheon Kim3, Ji-Yeon Roh4, Jinhee Choi1* 1University

of Seoul, School of Environmental Engineering, 163 Seoulsiripdae-ro,

Dondaemun-gu, Seoul 02504, Republic of Korea 2US

Army ERDC Environmental Laboratory, 3909 Halls Ferry Rd, Vicksburg MS

39180, USA 3University

of Seoul, Department of Computer Science, 163 Seoulsiripdae-ro,

Dondaemun-gu, Seoul 02504, Republic of Korea 4Knoell

Korea, 37 Gukjegeumyung-ro 2-gil, Yeongdeungpo-gu, Seoul 07327, Republic

of Korea

Keywords: Adverse Outcome Pathway, pulmonary fibrosis, ToxCast, deep learning, chemical prioritization

1 ACS Paragon Plus Environment

Chemical Research in Toxicology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

for TOC only


Page 2 of 42

Page 3 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Abstract Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated by transforming growth factor β (TGF-β) and peroxisome proliferator-activated receptor gamma (PPARγ). Here, we developed an Adverse Outcome Pathway (AOP) to better define the linkage of PPARγ antagonism to the adverse outcome of pulmonary fibrosis. We then conducted systematic analysis to identify potential chemicals involved in this AOP, using the ToxCast database and deep learning artificial neural network models. We identified chemicals bearing potential inhalation hazard and exposure hazards from the database that could be related to this AOP. For chemicals that were not present in ToxCast database, multilayer perceptron models were developed based on the ToxCast assays related to the AOP. The reactivity of ToxCast untested chemicals was then predicted using these deep learning models. Both approaches identified a set of chemicals that could be used to validate the AOP. This study suggests that chemicals categorized using an existing database such as ToxCast can be used to validate an AOP, and that deep learning approaches can be used to characterize a range of potential active chemicals for an AOP of interest.



Introduction Humans are exposed to several environmental chemicals via inhalation through outdoor, indoor, or occupational exposure. Inhalation exposure of some chemicals can lead to pulmonary toxicity and lung diseases. Inhalation chambers are highly costly facilities used to test in vivo the potential inhalation toxicity of chemicals. These tests are not only very expensive, but also technically difficult and time consuming. Thus information on inhalation toxicity is available only for a limited number of chemicals1,2. In order to efficiently and rapidly screen the potential inhalation toxicity of a large number of chemicals, the development of new approach methodologies (NAMs) has been proposed. The Adverse Outcome Pathway (AOP) framework organizes existing knowledge across biological levels of organization to inform human and ecological risk assessment3. AOPs are toxicological constructs that connect mechanistic information to apical endpoints in a formalized way for regulatory purposes4. Nevertheless, even though the framework has been around since 2010, only a limited number of AOPs have been proposed for inhalation toxicity screening. Inhalation exposure to environmental chemicals can lead to various adverse health effects, including pulmonary fibrosis, one of the most common chronic pulmonary diseases5,6. Pulmonary fibrosis is a respiratory and potentially fatal disease characterized by accumulation of scar tissue in the lung insterstitium, which results in loss of alveolar function, destruction of normal lung architecture, and respiratory distress7. At the cellular level, fibrosis is characterized by the proliferation and accumulation of fibroblasts and scar-forming myofibroblasts with increased synthesis and deposition of extracellular matrix proteins including collagen and fibronectin7. Fibroblasts differentiate to


Page 4 of 42

Page 5 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


myofibroblasts after the appropriate stimuli, including the involvement of TGF- activation, and those have some of the characteristics of smooth muscle cells. The differentiation of fibroblasts to myofibroblasts leads to the development of fibroblastic foci, similar to the early stages of normal wound-healing and scar tissue formation. Scar formation, the accumulation of excess fibrous connective tissue, leads to thickening of the walls, and causes reduced oxygen supply in the blood. As a consequence, patients suffer from perpetual shortness of breath. Fibrosis is generally progressive and leads to the destruction of normal lung architecture, and has the potential to develop in other organs, such as skin, liver, kidney, and pancreas, where the mechanism is expected to be similar. Here, we developed an AOP for inhalation toxicity screening using pulmonary fibrosis as the adverse outcome (AO). Pulmonary fibrosis has been known to be regulated by transforming growth factor β (TGF-β) and peroxisome proliferator-activated receptor gamma (PPARγ)8–12. As the AOP is a conceptual framework, it needs to be validated by using appropriate stressors. Therefore, in the second part of this study, we conducted a systematic analysis for identifying potential chemicals involved in this AOP. To maximize applicability of this AOP to environmental inhalation toxicants, we selected inhalation-related chemicals for validation. To do this, we conducted a systematic categorization of potential inhalation toxicants using various databases that include environmental chemicals. By using a categorization of the information obtained from the databases, we identified a set of chemicals with potential to cause inhalation hazard.



In the AOP framework, chemicals physically interact with a molecular initiating event (MIE), which in turn, activates a set of key events (KEs). However, there is a possibility that the AO may be manifested in other pathways through the same MIE. Therefore, to identify potential chemicals showing activity for each of the MIE and KEs of our proposed AOP, we explored the U.S. Environmental Protection Agency Toxicity Forecaster (ToxCast) database. Positive chemicals on appropriate ToxCast assays related to the AOP were identified. Then deep learning models were developed for the potential inhalation toxicants not present in the ToxCast database. Their reactivity was predicted using deep learning artificial neural network models developed based on the related ToxCast assays. Using both approaches, a set of potential chemicals for the validation of the AOP was proposed.

Materials and Methods - Development of the Adverse Outcome Pathway The AOP was developed using information from the literature related to the mechanism of pulmonary fibrosis according to the AOP development guidelines13–16. We also performed a systematic assessment of confidence of the AOP using tailored BradfordHill considerations including (1) biological plausibility for Key Event Relationships (KERs), (2) essentiality of KEs and (3) empirical support for KERs17. Although various pathways and feed-back loops can trigger the AO, pulmonary fibrosis, we have constructed a simple AOP having one MIE and one AO for ease of future quantitative experimental validation of the AOP (AOP 206: Peroxisome proliferator-activated receptors γ inactivation leading to lung fibrosis, https://aopwiki.org/206).


Page 6 of 42

Page 7 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


- Chemical categorization We identified chemicals having potential to cause inhalation toxicity via the EU Biocides Regulation

528/2012

(EU-BPR)

biocidal

active

substances

(https://echa.europa.eu/information-on-chemicals/biocidal-active-substances)

and

US

Occupational Safety and Health Administration (OSHA) limits for air contaminants (https://www.osha.gov/laws-regs/federalregister/1993-06-30-0).

Inhalation

hazardous

chemicals were collected from the US Environmental Protection Agency Integrated Risk Information System’s (EPA IRIS, https://www.epa.gov/iris) inhalation category and OECD Test Guideline 403 (acute inhalation toxicity) test results from the OECD eChemPortal

(https://www.echemportal.org/echemportal/index.action).

Among

the

collected chemicals, only chemicals with occupational inhalation exposure information, or for which the consumer product is likely to be inhalation exposure (spray, aerosol, etc.) were selected for analysis.

- ToxCast assays relevant to AOPs We used the US EPA iCSS ToxCast Dashboard (https://actor.epa.gov/dashboard) and ToxCast and Tox21 summary files (https://www.epa.gov/chemical-research/exploringtoxcast-data-downloadable-data) to prioritize chemicals for validation of the AOP 206. ToxCast assays with the intended target corresponding to the MIE and KEs (PPARγ, TGF-, NF- κ B, IL-1 α , IL-6, TNF--6 MMP-2 and MMP-9) of the AOP 206 were identified and activity (Hitcall) data for the tested chemicals was collected.



- Deep learning models We built 25 deep learning artificial neural network models for the AOP 206 using 25 ToxCast in vitro assays data. The input data used for model development consisted of chemical names, the simplified molecular-input line-entry system (SMILES) code for each chemical (denotes structure) and whether or not the chemical was an active one. The chemical structure was transformed into Morgan Fingerprints with a radius of two bonds using an RDKit (http://www.rdkit.org/). We used Synthetic Minority Oversampling Technique (SMOTE) to oversample the class of chemicals18. The multilayer perceptron (MLP) has an input layer, a dense layer of the same size as the input, with a RELU activator, and a dense single neuron layer as the output with a sigmoidal activator. Model performance was assessed using a stratified 5-fold cross-validation, and all codes were developed in Python3. Keras and TensorFlow were used for the deep learning, and Scikit Learn was used to perform a stratified 5-fold cross-validation. The model building and analysis was performed in a Jupyter notebook. The model project can be downloaded from GitHub at https://github.com/UOSEST/AOP.

Results and Discussion - Description of the Adverse Outcome Pathway Based on literature review, here, we describe an AOP by simplifying the pathways, where antagonism of PPARγ leads to fibrosis (Figure 1). The MIE is defined as antagonism of PPARγ, which increases the profibrotic effect of TGF-β/Smad3 signaling19 (KE1). Then TGF-β signaling pathway and oxidative stress pathway lead to increased inflammatory cytokine production (KE2). Increased inflammatory response drives EMT (KE3), which


Page 8 of 42

Page 9 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


results in deposition of an interwoven network of fibrillar and non-fibrillar collagens (KE4)20. Increasing amounts of collagen lead to increased tissue stiffness, which in turn signals for increased production of extracellular matrix components (ECM) by mechanotransduction, through the rho-ROCK-actin pathways, leading to tissue damage and scarring or fibrosis, the AO. This AOP can be found in the AOP Wiki as AOP 206 (https://aopwiki.org/aops/206).

MIE (Inactivation of PPARγ) and KER with KE1 (Activation of TGF- signaling) Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors that belong to the nuclear hormone receptor family and regulate a wide range of physiological activities. Three different isoforms have been identified: PPARα, PPAR/δ, and PPARγ21. PPARγ is expressed in many types of lung cells, including fibroblasts, and has anti-inflammatory properties22. The Biological Plausibility is high as the anti-inflammatory effects of PPARγ ligands are well-described,7 and a number of studies have explored the effects of PPARγ ligands as potential antifibrotic agents23–26. The Essentiality of MIE is also high. TGF-β drives the differentiation of lung fibroblasts to myofibroblasts, a key step in fibrosis formation. On the other hand, PPARγ ligands differentiate fibroblasts to fat-storing adipocytes, suggesting that PPARγ agonists may oppose the fibrogenic effects of TGF-β7,11. Therefore, blocking PPARγ function would remove this opposing regulatory pathway, increasing the profibrotic effects of TGF-β19,27. However, the Empirical Support of KER is moderate. In several studies, the relationship between PPARγ and TGF-β was confirmed by experiments using PPARγ ligands, and the



PPARγ agonists inhibit the ability of TGF-β1 inducing myofibroblast differentiation and collagen secretion10,12,25,28,29. For the quantitative relationship, there were studies of PPARγ ligand decreased intrinsic expression of TGF-β1 and TGF-β induced phosphorylation of Akt, and α-SMA and fibronectin expression as a marker of myofibroblast differentiation, in a dose-dependent manner10,12,28.

KE1 (Activation of TGF- signaling) and KER with KE2 (Increase, Inflammation) Lung fibrosis is the end result of a TGF- activated signaling cascade in lung fibroblasts (Figure 2)30,31. In this pathway, TGF- activation leads to the phosphorylation and activation of Smad2 and Smad3, which heterodimerize with Smad4 and recruit the histone acetyltransferase p300 and the CREB binding-protein (CBP) at the promoters of Smad3 dependent genes to activate transcription32–37. Smad3 activation has been shown to be a necessary step in the progression of pulmonary fibrosis and myofibroblast differentiation, with knockout of Smad3 being sufficient to prevent pulmonary fibrosis and myofibroblast differentiation38. The Smad:p300 complex causes transcription of Smad3-dependent genes, including MLK1, a key regulator of fibroblast differentiation to myofibroblasts. The Biological Plausibility of the KER is high. The activated TGF-β signaling pathway stimulates the expression of multiple proinflammatory and fibrotic cytokines such as tumor necrosis factor-α (TNF-α), IL-1β, IL-6 and IL-13, promoting the fibrotic response39. The Essentiality of KE1 is high, as an antagonist of TGF- transduction signaling significantly reduced bleomycin induced lung fibrosis40,41. Furthermore, knockout and 10 ACS Paragon Plus Environment

Page 10 of 42

Page 11 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


knockdown experiments with MLK1 were sufficient to prevent cardiac fibrosis in mice42, reduced fibrogenesis in the lungs of rats with hypoxia induced pulmonary hypertension43, and prevented bleomycin induced lung fibrosis44 and skin fibrosis45. However, the Empirical Support between TGF- and inflammation is moderate. As a result of immunohistochemical staining for lung sections of pulmonary fibrosis patients, increased production of TGF- has been associated with chronic inflammatory and fibrotic diseases of humans and rodents46. There was limited research on quantitative relationships between TGF- and inflammation-related markers.

KE2 (Increase, Inflammation) and KER with KE3 (Induction, Epithelial Mesenchymal Transition) In general, the overproduction of cytokines leads to infiltration of inflammatory cells and proliferation of fibroblast-related interstitial cells47. The classical proinflammatory cytokines IL-6, IL-1β, and TNF-α released from innate immune cells like monocytes are also profibrotic. The Biological Plausibility is moderate. Th-2 cytokines (IL-4, IL-5 and IL-13) and proinflammatory cytokines (IL-1, IL-6 and TNF-α) are linked to MMP and fibrosis48. The Essentiality of KE2 is also high. Experiments in various animal models, such as TNF-α blockers or TNF-receptor deficient mice, have shown that cytokine TNF-α has pro-fibrosis properties. And IL-6 is an essential component of fibrosis, mainly resulting in reduced degradation of the matrix protein48. Also, the attenuation of cell-related inflammation leads to reduction of the activity of MMP49.



The Empirical Support is moderate. TNF-α induces the expression of vimentin and MMP, and inflammatory cytokines were associated with elevated EMT genes, indicating causal relationships50. For the quantitative relationship, there were studies that the proinflammatory cytokine, IL-17A induced dose-dependent downregulation of Ecadherin and upregulation of α-SMA51. And TNF-α only slightly decreases E-cadherin expression in concentration-dependent manner52.

KE3 (Induction, Epithelial Mesenchymal Transition) and KER with KE4 (Collagen Deposition) The EMT acquires mesenchymal markers, including neural cadherin (N-cadherin), vimentin, integrin, fibronectin, and MMPs while epithelial cells are gradually transformed into mesenchymal-like cells, losing epithelial functions and characteristics53. The Biological Plausibility is moderate. MMPs, the EMT markers, is able to degrade collagen type I and III, key collagens of the irreversible scar in hepatic cirrhosis54. The Essentiality of KE3 is high. Whereas EMT is necessary for proper reepithelialization and extracellular matrix (ECM) deposition, an uncontrolled continued transition from epithelial cells to myofibroblasts can result in fibrosis53,55. Inhibition of MMP activity leads to accumulation of matrix proteins like collagen in the extracellular space54. The Empirical Support is moderate. Bleomycin-induced fibrosis is reduced in transgenic animals30,56. There was limited research on quantitative relationships between EMT and collagen.


Page 12 of 42

Page 13 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


KE4 (Collagen Deposition) and KER with AO (Pulmonary fibrosis) Collagen deposition is a part of the tissue healing process induced by epithelial cell injury. The Biological Plausibility is high. The total amount of collagen deposited by fibroblasts is a controlled balance between collagen synthesis and catabolism. In the remodeling phase, when this balance is disrupted and collagen deposition increases, scar formation, organs or peri-implantational fibrosis occur57. The Essentiality of KE4 is moderate. The disruption of the epithelial layer integrity can enhance inflammatory cell infiltration and in turn, worsen the fibrotic process58. The Empirical Support is moderate. Morphological analysis in the bleomycin-treated meprinβ KO mice revealed decrease collagen deposition and tissue density in lung fibrosis58. There were limited understanding on quantitative relationships between collagen and fibrosis. - Identification of potential chemicals for validation of AOP 206 After development of the AOP, we then tried to identify potential chemicals for validation. A total of 10 chemicals to validate AOP 206 were selected considering both inhalation exposure and hazard using a tiered approach, as shown in Figure 3. First, 654 chemicals having potential for inhalation hazard and exposure were identified using databases including EU-BPR, OSHA, EPA IRIS and OECD eChemPortal (Step 1). Twenty five ToxCast assays related to pathways involved in AOP 206 were then identified (Step 2). For chemicals not tested in selected ToxCast assays, 25 MLP models were developed based on the ToxCast assays to predict each chemical’s activity (Step 3). Among the chemicals having potential inhalation exposure and hazard identified in Step



1, those chemicals which were active when tested in the aforementioned ToxCast assays were identified and, if not tested, the activity was predicted using the MLP models (Step 4). Lastly, a set of chemicals for the validation of AOP206 was proposed (Step 5).

Step 1. In the EU-BPR and OSHA chemical inventory, 136 and 554 chemicals with vapor, gas, dust and mist properties were selected, respectively, as potential inhalation exposure chemicals. Seventy chemicals from the US EPA IRIS (inhalation toxicity category) and 72 chemicals from the OECD eChemPortal (OECD TG 403) were selected as potential compounds to cause inhalation hazard. A total of 654 unique chemicals were identified and a summary of the chemical list is presented in Table 1. A full list of chemicals and their exposure and usage information is found in Supplementary Table 1.

Step 2. ToxCast assays relevant to the MIE and KEs of AOP 206 were identified. In total 25 assays were selected, including four PPARγ assays, five TGF-β assays, three NF-κB assays, ten Inflammation (IL-1α, IL-6, and TNF-α) assays, and three EMT (MMP-2 and MMP-9) assays. The PPARγ assays were related to the identification of MIE, whereas other assays were related to pathways involved in KEs. A brief description of each assay is presented in Table 2.

Step 3.


Page 14 of 42

Page 15 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


To predict potential activity of the chemicals for each MIE and KEs, we developed MLP models using chemical structure and their activity59. We obtained the chemical structure information (SMILES) from PubChem (https://pubchem.ncbi.nlm.nih.gov) and ToxCast in vitro assay data from the iCSS ToxCast Dashboard. We built 25 models for AOP 206 targets (i.e. 4 PPARγ, 5 TGF-β, 3 NF-κB, 10 Inflammation, and 3 EMT). A summary of accuracy and the number of active chemicals used for learning each model is presented in Table 3. The active / inactive ratio of the ToxCast assay dataset was unbalanced, for example, TOX21_PPARg_BLA_Agonist_ratio (AEID 802) dataset consisted of 372 PPARγ active (agonist) and 7575 inactive chemicals. Since there are large differences between the number of active and inactive chemicals, the active chemicals were oversampled to the inactive chemicals using the SMOTE algorithm18 for all datasets. RDKit identified 2048-bit fingerprints across these chemicals, and 100 epochs were performed for each fold in the stratified 5-fold cross validation. The final models had an accuracy of ranging from 86.73% to 99.82% based on 5-fold cross validation, with true positive rate ranging from 0.91 to 1.00 and false positive rate ranging from 0.00 to 0.17.

Step 4. We investigated whether or not chemicals selected in Step 1 were tested in the selected ToxCast assays. Active, tested chemicals were identified for each assay, whereas for chemicals not present in ToxCast, their activity status was predicted using the MLP models (Table 4). For PPARγ, 17.4% of the chemicals were found active in ToxCast, and 17.0% were predicted to be active in the MLP models; similar results were shown in TGF-β and EMT. Related to inflammation, the most abundant active chemicals were



identified, showing 38.5% in ToxCast and 28.3% in MLP. In contrast, there were the least active chemicals identified in NF-κB, showing 8.2% in both ToxCast and MLP.

Step 5. Potential chemicals for the validation of AOP 206 were selected using inhalation hazard and exposure information from the database, experimental result from ToxCast, or predicted results from the deep learning model (Table 5). Among the 654 chemicals, 299 chemicals were identified as active chemicals in at least one event of AOP 206 (i.e. PPARγ, TGF-, NF-κB, inflammation and EMT). Fourteen chemicals were identified as affecting all events in AOP 206, and six chemicals (heptachlor, aldrin, n-isopropyl aniline, hexachlorobutadiene, disulfiram and endosulfan) potentially affected only KEs (i.e. TGF-, NF-κB, inflammation, EMT). The full results of all 654 chemicals are in Supplementary Table 2. Finally, we selected five active chemicals from ToxCast and five active chemicals from the MLP model for validation of AOP 206 based on the number of active assays and coverage of AOP 206 and the use category. The five chemicals identified from ToxCast were glutaraldehyde, rotenone, disulfiram, 1,2benzisothiazol-3(2H)-one, and 1,2,3,4,5,5-hexachloro-1,3-cyclopentadiene, and the five chemicals predicted from the MLP models were cetyl pyridinium chloride, pentachlorophenol, poly(oxy-1,2-ethanediyl), alpha-sulfo-omega-(dodecyloxy)-sodium salt, graphite and n-isopropyl aniline. Experimental validation of the AOP 206 using these prioritized chemicals is ongoing.

Conclusions 16 ACS Paragon Plus Environment

Page 16 of 42

Page 17 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In this study, we describe an AOP where antagonism of PPARγ leads to pulmonary fibrosis for the development of an inhalation toxicity screening platform. Although there may be a number of pathways leading to pulmonary fibrosis, the MIE target is set to PPARγ, whose activity is determined by ligand binding, thus making various in silico approaches including molecular docking applicable. This allows a quick and simple screening of AO, pulmonary fibrosis, using in silico approaches for chemicals suspected to cause inhalation toxicity. This, of course, requires a quantitative relationship as well as qualitative relationship between the KEs. To date, however, there is limited evidence of quantitative relationships in the literature. Also, the overall Biological Plausibility and Essentiality of each KE are high, but the Empirical support and quantitative relationships are moderate or weak. Therefore, it is necessary to qualitatively and quantitatively validate the KERs by selecting chemicals that follow this AOP as a future study. However, finding appropriate chemicals for validating each event of an AOP is not an easy task. Here we proposed a tiered approach for selecting chemicals by combining inhalation-related database, ToxCast database and deep learning artificial neural network models. The complexity of the fibrosis formation network, the number of potential interacting MIEs, as well as the number of pathways that could affect it highlight the importance of assays and databases such as ToxCast to explore potential MIEs and KEs. This study also highlights the fact that toxicity prediction based on deep learning models has potential to accurately classify a range of chemicals for AOP validation.

ASSOCIATED CONTENT Supporting Information 17 ACS Paragon Plus Environment


The following files are available free of charge. Table S1. A full list of chemicals having inhalation exposure and hazardous potential (MS Excel). Table S2. A full analysis results of all 654 chemicals using ToxCast database and the deep learning model (MS Excel).

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]

ORCID Jaeseong Jeong: 0000-0002-3860-0648 Lyle Burgoon: 0000-0003-4977-5352 Edward Perkins: 0000-0003-1693-7714 Jinhee Choi: 0000-0003-3393-7505

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Funding This work was supported by the Korean Ministry of Environment under the ‘Environmental Health R&D Program’ (2017001370001).

Notes The authors declare no competing interest.


Page 18 of 42

Page 19 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


References (1)

Rovida, C.; Hartung, T. Re-Evaluation of Animal Numbers and Costs for in Vivo Tests to Accomplish REACH Legislation Requirements for Chemicals - A Report by the Transatlantic Think Tank for Toxicology (T4). ALTEX 2009, 26 (3), 187–208. https://doi.org/10.14573/altex.2009.3.187.

(2)

Clippinger, A. J.; Allen, D.; Jarabek, A. M.; Corvaro, M.; Gaça, M.; Gehen, S.; Hotchkiss, J. A.; Patlewicz, G.; Melbourne, J.; Hinderliter, P.; et al. Alternative Approaches for Acute Inhalation Toxicity Testing to Address Global Regulatory and Non-Regulatory Data Requirements: An International Workshop Report. Toxicol. Vitr. 2018, 48 (December 2017), 53–70. https://doi.org/10.1016/j.tiv.2017.12.011.

(3)

Ankley, G. T.; Bennett, R. S.; Erickson, R. J.; Hoff, D. J.; Hornung, M. W.; Johnson, R. D.; Mount, D. R.; Nichols, J. W.; Russom, C. L.; Schmieder, P. K.; et al. Adverse Outcome Pathways: A Conceptual Framework to Support Ecotoxicology Research and Risk Assessment. Environ. Toxicol. Chem. 2010, 29 (3), 730–741. https://doi.org/10.1002/etc.34.

(4)

Leist, M.; Ghallab, A.; Graepel, R.; Marchan, R.; Hassan, R.; Bennekou, S. H.; Limonciel, A.; Vinken, M.; Schildknecht, S.; Waldmann, T.; et al. Adverse Outcome Pathways: Opportunities, Limitations and Open Questions. Arch. Toxicol. 2017, 91 (11), 3477–3505. https://doi.org/10.1007/s00204-0172045-3.



(5)

Taskar, V. S.; Coultas, D. B. Is Idiopathic Pulmonary Fibrosis an Environmental Disease? Proc. Am. Thorac. Soc. 2006, 3 (4), 293–298. https://doi.org/10.1513/pats.200512-131TK.

(6)

Yoshida, T.; Ohnuma, A.; Horiuchi, H.; Harada, T. Pulmonary Fibrosis in Response to Environmental Cues and Molecular Targets Involved in Its Pathogenesis. J. Toxicol. Pathol. 2011, 24 (1), 9–24. https://doi.org/10.1293/tox.24.9.

(7)

Lakatos, H. F.; Thatcher, T. H.; Kottmann, R. M.; Garcia, T. M.; Phipps, R. P.; Sime, P. J. The Role of PPARs in Lung Fibrosis. PPAR Res. 2007, 2007, 71323. https://doi.org/10.1155/2007/71323.

(8)

Burgess, H. A.; Daugherty, L. E.; Thatcher, T. H.; Lakatos, H. F.; Ray, D. M.; Redonnet, M.; Phipps, R. P.; Sime, P. J. PPARγ Agonists Inhibit TGF-β Induced Pulmonary Myofibroblast Differentiation and Collagen Production: Implications for Therapy of Lung Fibrosis. Am. J. Physiol. Cell. Mol. Physiol. 2005, 288 (6), L1146–L1153. https://doi.org/10.1152/ajplung.00383.2004.

(9)

Tan, X.; Dagher, H.; Hutton, C. A.; Bourke, J. E. Effects of PPARγ Ligands on TGF-Β1-Induced Epithelial-Mesenchymal Transition in Alveolar Epithelial Cells. Respir. Res. 2010, 11, 1–13. https://doi.org/10.1186/1465-9921-11-21.

(10) Kulkarni, A. A.; Thatcher, T. H.; Olsen, K. C.; Maggirwar, S. B.; Phipps, R. P.; Sime, P. J. PPAR-γ Ligands Repress TGFβ-Induced Myofibroblast Differentiation by Targeting the PI3K/Akt Pathway: Implications for Therapy


Page 20 of 42

Page 21 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


of Fibrosis. PLoS One 2011, 6 (1), e15909. https://doi.org/10.1371/journal.pone.0015909. (11) Deng, Y. L.; Xiong, X. Z.; Cheng, N. S. Organ Fibrosis Inhibited by Blocking Transforming Growth Factor-β Signaling via Peroxisome ProliferatorActivated Receptor γ Agonists. Hepatobiliary Pancreat. Dis. Int. 2012, 11 (5), 467–478. https://doi.org/10.1016/S1499-3872(12)60210-0. (12) Nuwormegbe, S. A.; Sohn, J. H.; Kim, S. W. A PPAR-Gamma Agonist Rosiglitazone Suppresses Fibrotic Response in Human Pterygium Fibroblasts by Modulating the P38 MAPK Pathway. Investig. Opthalmology Vis. Sci. 2017, 58 (12), 5217–5226. https://doi.org/10.1167/iovs.17-22203. (13) OECD. Guidance Document on Developing and Assessing Adverse Outcome Pathways; 2013; Vol. 184. https://doi.org/No. 184. (14) Villeneuve, D. L.; Crump, D.; Garcia-Reyero, N.; Hecker, M.; Hutchinson, T. H.; LaLone, C. A.; Landesmann, B.; Lettieri, T.; Munn, S.; Nepelska, M.; et al. Adverse Outcome Pathway (AOP) Development I: Strategies and Principles. Toxicol. Sci. 2014, 142 (2), 312–320. https://doi.org/10.1093/toxsci/kfu199. (15) Villeneuve, D. L.; Crump, D.; Garcia-Reyero, N.; Hecker, M.; Hutchinson, T. H.; LaLone, C. A.; Landesmann, B.; Lettieri, T.; Munn, S.; Nepelska, M.; et al. Adverse Outcome Pathway Development II: Best Practices. Toxicol. Sci. 2014, 142 (2), 321–330. https://doi.org/10.1093/toxsci/kfu200. (16) Perkins, E. J.; Gayen, K.; Shoemaker, J. E.; Antczak, P.; Burgoon, L.; Falciani, F.; Gutsell, S.; Hodges, G.; Kienzler, A.; Knapen, D.; et al. Chemical Hazard 21 ACS Paragon Plus Environment


Prediction and Hypothesis Testing Using Quantitative Adverse Outcome Pathways. ALTEX 2019, 36 (1), 91–102. https://doi.org/10.14573/altex.1808241. (17) Becker, R. A.; Ankley, G. T.; Edwards, S. W.; Kennedy, S. W.; Linkov, I.; Meek, B.; Sachana, M.; Segner, H.; Van Der Burg, B.; Villeneuve, D. L.; et al. Increasing Scientific Confidence in Adverse Outcome Pathways: Application of Tailored Bradford-Hill Considerations for Evaluating Weight of Evidence. Regul. Toxicol. Pharmacol. 2015, 72 (3), 514–537. https://doi.org/10.1016/j.yrtph.2015.04.004. (18) Raftery Adrian E.; Kass Robert E. Bayes Factors. J. Am. Stat. Assoc. 1995, 90 (430), 773–795. (19) Ferguson, H. E.; Kulkarni, A.; Lehmann, G. M.; Garcia-Bates, T. M.; Thatcher, T. H.; Huxlin, K. R.; Phipps, R. P.; Sime, P. J. Electrophilic Peroxisome ProliferatorActivated Receptor-γ Ligands Have Potent Antifibrotic Effects in Human Lung Fibroblasts. Am. J. Respir. Cell Mol. Biol. 2009, 41 (6), 722–730. https://doi.org/10.1165/rcmb.2009-0006OC. (20) Cox, T. R.; Erler, J. T. Remodeling and Homeostasis of the Extracellular Matrix: Implications for Fibrotic Diseases and Cancer. Dis. Model. Mech. 2011, 4 (2), 165–178. https://doi.org/10.1242/dmm.004077. (21) Blanquart, C.; Barbier, O.; Fruchart, J. C.; Staels, B.; Glineur, C. Peroxisome Proliferator-Activated Receptors: Regulation of Transcriptional Activities and


Page 22 of 42

Page 23 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Roles in Inflammation. J. Steroid Biochem. Mol. Biol. 2003, 85 (2–5), 267–273. https://doi.org/10.1016/S0960-0760(03)00214-0. (22) Rizzo, G.; Fiorucci, S. PPARs and Other Nuclear Receptors in Inflammation. Curr. Opin. Pharmacol. 2006, 6 (4), 421–427. https://doi.org/10.1016/j.coph.2006.03.012. (23) Kawaguchi, K.; Sakaida, I.; Tsuchiya, M.; Omori, K.; Takami, T.; Okita, K. Pioglitazone Prevents Hepatic Steatosis, Fibrosis, and Enzyme-Altered Lesions in Rat Liver Cirrhosis Induced by a Choline-Deficient L-Amino Acid-Defined Diet. Biochem. Biophys. Res. Commun. 2004, 315 (1), 187–195. https://doi.org/10.1016/j.bbrc.2004.01.038. (24) Uto, H.; Nakanishi, C.; Ido, A.; Hasuike, S.; Kusumoto, K.; Abe, H.; Numata, M.; Nagata, K.; Hayashi, K.; Tsubouchi, H. The Peroxisome Proliferator-Activated Receptor-γ Agonist, Pioglitazone, Inhibits Fat Accumulation and Fibrosis in the Livers of Rats Fed a Choline-Deficient, l-Amino Acid-Defined Diet. Hepatol. Res. 2005, 32 (4), 235–242. https://doi.org/10.1016/J.HEPRES.2005.05.008. (25) Dantas, A. T.; Pereira, M. C.; de Melo Rego, M. J.; da Rocha, L. F. J.; Pitta Ida, R.; Marques, C. D.; Duarte, A. L.; Pitta, M. G. The Role of PPAR Gamma in Systemic Sclerosis. PPAR Res. 2015, 2015, 124624. https://doi.org/10.1155/2015/124624. (26) Ruzehaji, N.; Frantz, C.; Ponsoye, M.; Avouac, J.; Pezet, S.; Guilbert, T.; Luccarini, J. M.; Broqua, P.; Junien, J. L.; Allanore, Y. Pan PPAR Agonist IVA337 Is Effective in Prevention and Treatment of Experimental Skin Fibrosis. Ann. 23 ACS Paragon Plus Environment


Rheum. Dis. 2016, 75 (12), 2175–2183. https://doi.org/10.1136/annrheumdis-2015-208029. (27) Yoon, Y. S.; Kim, S. Y.; Kim, M. J.; Lim, J. H.; Cho, M. S.; Kang, J. L. PPARγ Activation Following Apoptotic Cell Instillation Promotes Resolution of Lung Inflammation and Fibrosis via Regulation of Efferocytosis and Proresolving Cytokines. Mucosal Immunol. 2015, 8 (5), 1031–1046. https://doi.org/10.1038/mi.2014.130. (28) Milam, J. E.; Keshamouni, V. G.; Phan, S. H.; Hu, B.; Gangireddy, S. R.; Hogaboam, C. M.; Standiford, T. J.; Thannickal, V. J.; Reddy, R. C. PPAR-γ Agonists Inhibit Profibrotic Phenotypes in Human Lung Fibroblasts and Bleomycin-Induced Pulmonary Fibrosis. Am. J. Physiol. Cell. Mol. Physiol. 2007, 294 (5), L891–L901. https://doi.org/10.1152/ajplung.00333.2007. (29) Wu, M.; Melichian, D. S.; Chang, E.; Warner-Blankenship, M.; Ghosh, A. K.; Varga, J. Rosiglitazone Abrogates Bleomycin-Induced Scleroderma and Blocks Profibrotic Responses through Peroxisome Proliferator-Activated Receptor-β. Am. J. Pathol. 2009, 174 (2), 519–533. https://doi.org/10.2353/ajpath.2009.080574. (30) Kang, H. R.; Soo, J. C.; Chun, G. L.; Homer, R. J.; Elias, J. A. Transforming Growth Factor (TGF)-Β1 Stimulates Pulmonary Fibrosis and Inflammation via a BaxDependent, Bid-Activated Pathway That Involves Matrix Metalloproteinase12. J. Biol. Chem. 2007, 282 (10), 7723–7732. https://doi.org/10.1074/jbc.M610764200. 24 ACS Paragon Plus Environment

Page 24 of 42

Page 25 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(31) Wei, J.; Bhattacharyya, S.; Varga, J. Peroxisome Proliferator-Activated Receptor γ: Innate Protection from Excessive Fibrogenesis and Potential Therapeutic Target in Systemic Sclerosis. Curr. Opin. Rheumatol. 2010, 22 (6), 671–676. https://doi.org/10.1097/BOR.0b013e32833de1a7. (32) Massagué, J.; Seoane, J.; Wotton, D. Smad Transcription Factors. Trends Biochem. Sci. 2005, 19 (23), 2783–2810. https://doi.org/10.1101/gad.1350705. (33) Chen, S. J.; Yuan, W.; Mori, Y.; Levenson, A.; Trojanowska, M.; Varga, J. Stimulation of Type I Collagen Transcription in Human Skin Fibroblasts by TGF-β: Involvement of Smad 3. J. Invest. Dermatol. 1999, 112 (1), 49–57. https://doi.org/10.1046/j.1523-1747.1999.00477.x. (34) Mori, Y.; Chen, S. J.; Varga, J. Modulation of Endogenous Smad Expression in Normal Skin Fibroblasts by Transforming Growth Factor-β. Exp. Cell Res. 2000, 258 (2), 374–383. https://doi.org/10.1006/excr.2000.4930. (35) Ghosh, A. K.; Yuan, W.; Mori, Y.; Varga, J. Smad-Dependent Stimulation of Type I Collagen Gene Expression in Human Skin Fibroblasts by TGF-β Involves Functional Cooperation with P300/CBP Transcriptional Coactivators. Oncogene 2000, 19 (31), 3546–3555. https://doi.org/10.1038/sj.onc.1203693. (36) Ghosh, A. K.; Yuan, W.; Mori, Y.; Chen, S. J.; Varga, J. Antagonistic Regulation of Type I Collagen Gene Expression by Interferon-γ and Transforming Growth Factor-β: Integration at the Level of P300/CBP Transcriptional Coactivators. J. 25 ACS Paragon Plus Environment


Biol. Chem. 2001, 276 (14), 11041–11048. https://doi.org/10.1074/jbc.M004709200. (37) Ghosh, A. K.; Bhattacharyya, S.; Varga, J. The Tumor Suppressor P53 Abrogates Smad-Dependent Collagen Gene Induction in Mesenchymal Cells. J. Biol. Chem. 2004, 279 (46), 47455–47463. https://doi.org/10.1074/jbc.M403477200. (38) Gu, L.; Zhu, Y. J.; Yang, X.; Guo, Z. J.; Xu, W. B.; Tian, X. L. Effect of TGF-β/Smad Signaling Pathway on Lung Myofibroblast Differentiation. Acta Pharmacol. Sin. 2007, 28 (3), 382–391. https://doi.org/10.1111/j.1745-7254.2007.00468.x. (39) Kang, H. Role of Micrornas in TGF-β Signaling Pathway-Mediated Pulmonary Fibrosis. Int. J. Mol. Sci. 2017, 18 (12). https://doi.org/10.3390/ijms18122527. (40) Giri, S. N.; Hyde, D. M.; Hollinger, M. A. Effect of Antibody to Transforming Growth Factor /B on Bleomycin Induced Accumulation of Lung Collagen in Mice. Thorax 1993, 48 (10), 959–966. https://doi.org/10.1136/thx.48.10.959. (41) Nakao, A.; Fujii, M.; Matsumura, R.; Kumano, K.; Saito, Y.; Miyazono, K.; Iwamoto, I. Transient Gene Transfer and Expression of Smad7 Prevents Bleomycin-Induced Lung Fibrosis in Mice. J. Clin. Invest. 1999, 104 (1), 5–11. https://doi.org/10.1172/jci6094. (42) Small, E. M.; Thatcher, J. E.; Sutherland, L. B.; Kinoshita, H.; Gerard, R. D.; Richardson, J. A.; Dimaio, J. M.; Sadek, H.; Kuwahara, K.; Olson, E. N. Myocardin-Related Transcription Factor-a Controls Myofibroblast Activation


Page 26 of 42

Page 27 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and Fibrosis in Response to Myocardial Infarction. Circ. Res. 2010, 107 (2), 294–304. https://doi.org/10.1161/CIRCRESAHA.110.223172. (43) Yuan, Z.; Chen, J.; Chen, D.; Xu, G.; Xia, M.; Xu, Y.; Gao, Y. Megakaryocytic Leukemia 1 (MKL1) Regulates Hypoxia Induced Pulmonary Hypertension in Rats. PLoS One 2014, 9 (3), e83895. https://doi.org/10.1371/journal.pone.0083895. (44) Zhou, Y.; Huang, X.; Hecker, L.; Kurundkar, D.; Kurundkar, A.; Liu, H.; Jin, T. H.; Desai, L.; Bernard, K.; Thannickal, V. J. Inhibition of Mechanosensitive Signaling in Myofibroblasts Ameliorates Experimental Pulmonary Fibrosis. J. Clin. Invest. 2013, 123 (3), 1096–1108. https://doi.org/10.1172/JCI66700. (45) Shiwen, X.; Stratton, R.; Nikitorowicz-Buniak, J.; Ahmed-Abdi, B.; Ponticos, M.; Denton, C.; Abraham, D.; Takahashi, A.; Suki, B.; Layne, M. D.; et al. A Role of Myocardin Related Transcription Factor-A (MRTF-A) in Scleroderma Related Fibrosis. PLoS One 2015, 10 (5), 1–20. https://doi.org/10.1371/journal.pone.0126015. (46) Khalil, N.; O’Connor, R. N.; Unruh, H. W.; Warren, P. W.; Flanders, K. C.; Kemp, A.; Bereznay, O. H.; Greenberg, A. H. Increased Production and Immunohistochemical Localization of Transforming Growth Factor-Beta in Idiopathic Pulmonary Fibrosis. Am. J. Respir. Cell Mol. Biol. 1991, 5 (2), 155– 162. (47) Miyazaki, Y.; Araki, K.; Vesin, C.; Garcia, I.; Kapanci, Y.; Whitsett, J. A.; Piguet, P. F.; Vassalli, P. Expression of a Tumor Necrosis Factor-Alpha Transgene in 27 ACS Paragon Plus Environment


Murine Lung Causes Lymphocytic and Fibrosing Alveolitis. A Mouse Model of Progressive Pulmonary Fibrosis. J. Clin. Invest. 1995, 96 (1), 250–259. https://doi.org/10.1172/jci118029. (48) Mack, M. Inflammation and Fibrosis. Matrix Biol. 2017, 68–69, 106–121. https://doi.org/10.1016/j.matbio.2017.11.010. (49) Underwood, D. C.; Osborn, R. R.; Bochnowicz, S.; Webb, E. F.; Rieman, D. J.; Lee, J. C.; Romanic, A. M.; Adams, J. L.; Hay, D. W. P.; Griswold, D. E. SB 239063, a P38 MAPK Inhibitor, Reduces Neutrophilia, Inflammatory Cytokines, MMP-9, and Fibrosis in Lung. Am. J. Physiol. Cell. Mol. Physiol. 2000, 279 (5), L895– L902. https://doi.org/10.1152/ajplung.2000.279.5.l895. (50) Yan, C.; Grimm, W. A.; Garner, W. L.; Qin, L.; Travis, T.; Tan, N.; Han, Y. P. Epithelial to Mesenchymal Transition in Human Skin Wound Healing Is Induced by Tumor Necrosis Factor-α through Bone Morphogenic Protein-2. Am. J. Pathol. 2010, 176 (5), 2247–2258. https://doi.org/10.2353/ajpath.2010.090048. (51) Vittal, R.; Fan, L.; Greenspan, D. S.; Mickler, E. A.; Gopalakrishnan, B.; Gu, H.; Benson, H. L.; Zhang, C.; Burlingham, W.; Cummings, O. W.; et al. IL-17 Induces Type V Collagen Overexpression and EMT via TGF-β-Dependent Pathways in Obliterative Bronchiolitis. Am. J. Physiol. Cell. Mol. Physiol. 2013, 304 (6), L401–L414. https://doi.org/10.1152/ajplung.00080.2012.


Page 28 of 42

Page 29 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(52) Kasai, H.; Allen, J. T.; Mason, R. M.; Kamimura, T.; Zhang, Z. TGF-Β1 Induces Human Alveolar Epithelial to Mesenchymal Cell Transition (EMT). Respir. Res. 2005, 6, 1–15. https://doi.org/10.1186/1465-9921-6-56. (53) Stone, R. C.; Pastar, I.; Ojeh, N.; Chen, V.; Liu, S.; Garzon, K. I.; Tomic-Canic, M. Epithelial-Mesenchymal Transition in Tissue Repair and Fibrosis. Cell Tissue Res. 2016, 365 (3), 495–506. https://doi.org/10.1007/s00441-016-2464-0. (54) Roeb, E. Matrix Metalloproteinases and Liver Fibrosis (Translational Aspects). Matrix Biol. 2018, 68–69, 463–473. https://doi.org/10.1016/j.matbio.2017.12.012. (55) Rout-Pitt, N.; Farrow, N.; Parsons, D.; Donnelley, M. Epithelial Mesenchymal Transition (EMT): A Universal Process in Lung Diseases with Implications for Cystic Fibrosis Pathophysiology. Respir. Res. 2018, 19 (1), 1–10. https://doi.org/10.1186/s12931-018-0834-8. (56) Cabrera, S.; Gaxiola, M.; Arreola, J. L.; Ramírez, R.; Jara, P.; D’Armiento, J.; Richards, T.; Selman, M.; Pardo, A. Overexpression of MMP9 in Macrophages Attenuates Pulmonary Fibrosis Induced by Bleomycin. Int. J. Biochem. Cell Biol. 2007, 39 (12), 2324–2338. https://doi.org/10.1016/j.biocel.2007.06.022. (57) Chen, C. Z. C.; Raghunath, M. Focus on Collagen: In Vitro Systems to Study Fibrogenesis and Antifibrosis_state of the Art. Fibrogenes. Tissue Repair 2009, 2 (1), 1–10. https://doi.org/10.1186/1755-1536-2-7. (58) Biasin, V.; Wygrecka, M.; Marsh, L. M.; Becker-Pauly, C.; Brcic, L.; Ghanim, B.; Klepetko, W.; Olschewski, A.; Kwapiszewska, G. Meprin β Contributes to 29 ACS Paragon Plus Environment


Page 30 of 42

Collagen Deposition in Lung Fibrosis. Sci. Rep. 2017, 7 (1), 1–12. https://doi.org/10.1038/srep39969. (59) Burgoon, L.; Perkins, E. J.; Garcia-Reyero, N. Manuscript in Preparation.

Tables: Table 1. Number of chemicals from each category for AOP validation Category

Database

Number

Inhalation exposure

EU-BPR (vapor, gas, dust and mist)

136

chemicals

OSHA (vapor, gas, dust and mist)

554

Inhalation hazard chemicals

EPA IRIS (Inhalation category)

70

OECD eChemPortal (OECD TG403)

72


Page 31 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Table 2. Brief description of ToxCast assays related to AOP 206 AOP206 (25)

Target family

Target subfamily

PPARγ (4)

Nuclear receptor

Nonsteroidal

TGF-β (5)

NF-κB (3)

Inflamma tion (10)

Growth factor

DNA binding

Cytokine

Transformin g growth factor beta

NF-kappa B

Interleukins

Assay

Model

Name ATG_PPARg_TRANS_up

Format

AEI D 134

HepG2

24-well plate

Time point

Function

Detection

24 h

Reporter gene

1536well plate

24 h

Reporter gene

RT-PCR and Capillary electrophoresis GAL4 blactamase reporter gene

384-well plate 24-well plate

2h

Binding

24 h

Reporter gene

TOX21_PPARg_BLA_Ag onist_ratio TOX21_PPARg_BLA_ant agonist_ratio NVS_NR_hPPARg

802

HEK293T

1127

HEK293

719

NA

ATG_TGFb_CIS_dn ATG_TGFb_CIS_up

1497 112

HepG2

BSK_BE3C_TGFb1_down

199

96-well plate

24 h

BSK_KF3CT_TGFb1_dow n BSK_KF3CT_TGFb1_up

271

Bronchial epithelial cells Keratinocytes and foreskin fibroblasts

96-well plate

ATG_NF_kB_CIS_dn ATG_NF_kB_CIS_up

1458 94

HepG2

TOX21_NFkB_BLA_agon ist_ratio

1346

ME-180

BSK_BE3C_IL1a_down BSK_BE3C_IL1a_up

187 188

BSK_KF3CT_IL1a_down BSK_KF3CT_IL1a_up

261 262

BSK_LPS_IL1a_down

281

Bronchial epithelial cells Keratinocytes and foreskin fibroblasts Umbilical

272


Readout

Positive control GW7647 Rosiglitazo ne GW9662 Ciglitazone

Signaling

Fluorescence Polarization RT-PCR and Capillary electrophoresis ELISA

24 h

Signaling

ELISA

colchicine

24-well plate

24 h

Reporter gene

NA

1536well plate 96-well plate

24 h

Reporter gene

24 h

Signaling

RT-PCR and Capillary electrophoresis GAL4 blactamase reporter gene ELISA

colchicine

96-well plate

24 h

Signaling

ELISA

colchicine

96-well

24 h

Signaling

ELISA

colchicine

NA colchicine

TNFa

Chemical Research in Toxicology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

EMT (3)

Protease

BSK_LPS_IL1a_up

282

BSK_CASM3C_IL6_down BSK_CASM3C_IL6_up

209 210

Inflammator y factor

BSK_LPS_TNFa_down BSK_LPS_TNFa_up

295 296

Matrix metalloprote inase

NVS_ENZ_hMMP2

491

NVS_ENZ_hMMP9

497

BSK_KF3CT_MMP9_dow n

267

vein endothelium and peripheral blood mononuclear cells Coronary artery smooth muscle cells Umbilical vein endothelium and peripheral blood mononuclear cells Cell-free Keratinocytes and foreskin fibroblasts

Page 32 of 42

plate

96-well plate

24 h

Signaling

ELISA

colchicine

96-well plate

24 h

Signaling

ELISA

colchicine

96-well plate

1h

Enzymatic activity

Fluorescence

Galardin (GM6001)

96-well plate

24 h

Signaling

ELISA

colchicine

Table 3. Accuracy of deep learning model based on ToxCast assays related to AOP 206 AOP206

Assay

ToxCast AEID

Number of chemicals

Active chemicals

Average model accuracy (min – max)

True positiv e rate

False positive rate

True negativ e rate

PPARγ

TOX21_PPARg_BLA_Agonist_ratio

802

7947

372

1.00

0.02

0.98

TOX21_PPARg_BLA_antagonist_ratio

1127

7202

565

98.35 (97.82 - 98.81) 96.73 (96.01 – 98.00)

False negativ e rate 0.00

0.99

0.04

0.96

0.01


Page 33 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


TGF-β

NF-κB

Inflammat ion

ATG_PPARg_TRANS_up

134

3422

939

NVS_NR_hPPARg

719

782

180

ATG_TGFb_CIS_dn

1497

3422

124

ATG_TGFb_CIS_up

112

3422

138

BSK_BE3C_TGFb1_down

199

1445

230

BSK_KF3CT_TGFb1_down

271

1445

294

BSK_KF3CT_TGFb1_up

272

1456

12

ATG_NF_kB_CIS_dn

1458

3422

281

ATG_NF_kB_CIS_up

94

3422

141

TOX21_NFkB_BLA_agonist_ratio

1346

7202

243

BSK_BE3C_IL1a_down

187

1445

271

BSK_BE3C_IL1a_up

188

1456

18

BSK_KF3CT_IL1a_down

261

1445

298

BSK_KF3CT_IL1a_up

262

1456

28

BSK_LPS_IL1a_down

281

1445

311

BSK_LPS_IL1a_up

282

1445

22

BSK_CASM3C_IL6_down

209

1445

210

BSK_CASM3C_IL6_up

210

1445

49


87.83 (84.99 - 89.41) 90.28 (88.79 - 91.25) 98.33 (98.02 - 98.63) 98.44 (97.71 - 99.16) 94.35 (92.38 - 96.29) 99.34 (98.26 - 99.65) 92.43 (91.3 - 94.34) 96.46 (95.38 - 97.45) 98.44 (98.32 - 98.62) 98.85 (98.56 - 99.06) 92.75 (89.97 - 93.81) 99.82 (99.65 - 100) 91.92 (90.39 - 93.44) 99.29 (98.94 - 99.47) 92.01 (90.28 - 94.48) 99.64 (99.29 - 100) 95.62 (92.9 - 97.97) 99.06 (98.74 - 99.64)

0.94

0.16

0.84

0.06

0.96

0.13

0.87

0.04

0.99

0.02

0.98

0.01

0.99

0.02

0.98

0.01

0.98

0.13

0.87

0.02

1.00

0.01

0.99

0.00

0.95

0.13

0.87

0.05

0.99

0.06

0.94

0.01

1.00

0.02

0.98

0.00

1.00

0.05

0.95

0.00

0.98

0.15

0.85

0.02

1.00

0.00

1.00

0.00

0.96

0.15

0.85

0.04

1.00

0.01

0.99

0.00

0.94

0.11

0.89

0.06

0.99

0.01

0.99

0.01

0.98

0.05

0.95

0.02

1.00

0.01

0.99

0.00


EMT

BSK_LPS_TNFa_down

295

1445

294

BSK_LPS_TNFa_up

296

1445

77

NVS_ENZ_hMMP2

491

1113

42

NVS_ENZ_hMMP9

497

2007

125

BSK_KF3CT_MMP9_down

267

1445

392

Page 34 of 42

92.08 (90 - 93.91) 98.02 (96.34 - 98.9) 98.73 (97.65 - 99.76) 98.37 (97.99 - 98.93) 86.73 (84.28 - 89.07)

0.95

0.10

0.90

0.05

0.99

0.04

0.96

0.01

1.00

0.03

0.97

0.00

1.00

0.03

0.97

0.00

0.91

0.17

0.83

0.09

Table 4. Number of active and inactive chemicals based on ToxCast and deep learning model AOP206

PPARγ

Assay

ToxCast AEID

Active chemicals (%) 6 (2.0)

Number of chemicals 314

Active chemicals (%) 15 (4.8)

Inactive chemicals

TOX21_PPARg_BLA_Agonist_ratio

802

TOX21_PPARg_BLA_antagonist_ratio

1127

290

11 (3.8)

319

8 (2.5)

590

ATG_PPARg_TRANS_up

134

208

34 (16.3)

401

38 (9.5)

537

NVS_NR_hPPARg

719

75

15 (20.0)

526

25 (4.8)

561

298

52 (17.4)

312

53 (17.0)

505

588

ATG_TGFb_CIS_dn

1497

208

5 (2.4)

401

15 (3.7)

589

ATG_TGFb_CIS_up

112

208

6 (2.9)

401

38 (9.5)

565

BSK_BE3C_TGFb1_down

199

130

17 (13.1)

473

49 (10.4)

537

BSK_KF3CT_TGFb1_down

271

130

23 (17.7)

473

25 (5.3)

555

BSK_KF3CT_TGFb1_up

272

130

0 (0)

473

6 (1.3)

597

208

36 (17.3)

401

85 (21.2)

488

208

17 (8.2)

401

25 (6.2)

567

TGF-β Total NF-κB

Deep learning

Number of chemicals 295

PPARγ Total TGF-β

ToxCast

ATG_NF_kB_CIS_dn

1458


Page 35 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


ATG_NF_kB_CIS_up

94

208

2 (1.0)

401

7 (1.7)

600

TOX21_NFkB_BLA_agonist_ratio

1346

290

9 (3.1)

319

4 (1.3)

596

294

24 (8.2)

316

26 (8.2)

560

NF-κB Total Inflammati on

BSK_BE3C_IL1a_down

187

130

17 (1.31)

473

38 (8)

548

BSK_BE3C_IL1a_up

188

130

1 (0.8)

473

2 (0.4)

600

BSK_KF3CT_IL1a_down

261

130

22 (16.9)

473

47 (9.9)

534

BSK_KF3CT_IL1a_up

262

130

1 (0.8)

474

7 (1.5)

596

BSK_LPS_IL1a_down

281

130

25 (19.2)

473

56 (11.8)

522

BSK_LPS_IL1a_up

282

130

0 (0)

473

5 (1.1)

598

BSK_CASM3C_IL6_down

209

130

17 (13.1)

473

37 (7.8)

549

BSK_CASM3C_IL6_up

210

130

5 (3.8)

473

2 (0.4)

596

BSK_LPS_TNFa_down

295

130

25 (19.2)

473

35 (7.4)

543

BSK_LPS_TNFa_up

296

130

11 (8.5)

473

17 (3.6)

575

130

50 (38.5)

474

134 (28.3)

420

Inflammation Total EMT

NVS_ENZ_hMMP2

491

122

1 (0.8)

481

14 (2.9)

588

NVS_ENZ_hMMP9

497

166

5 (3.0)

440

29 (6.6)

572

BSK_KF3CT_MMP9_down

267

130

25 (19.2)

473

37 (7.8)

541

168

29 (17.3)

438

69 (15.8)

508

EMT Total

Table 5. Summary of selected chemicals for validation of AOP 206, based on BPR, OSHA, IRIS and OECD eChemPortal database and ToxCast assay CAS No.

Chemical name

Exposure related source BPR OSHA

Toxicity related source IRIS eChme Portal

Use category

HTS in vitro data Active/ Total


Target (AEID)

Predictive result Active/ Total

Target (AEID)


(TG403) 111-30-8

Glutaraldehyde

83-79-4

Rotenone

97-77-8

Disulfiram

2634-33-5

1,2Benzisothiazol3(2H)-one

77-47-4

1,2,3,4,5,5Hexachloro-1,3cyclopentadiene

9004-82-4

Poly(oxy-1,2ethanediyl), .alpha.-sulfo.omega.(dodecyloxy)-, sodium salt Graphite

7782-42-5

√

√

ACUTE TOX. 2

assay

assay

Disinfectant

6/25

√

Insecticide

5/24

√

Processing aids

8/25

Preservative s and Antioxidants

12/24

Pesticides and flame retardants

10/25

Surfactants

0/0

12/25

Manufacturi ng and electronic products

0/0

14/25

√

√

√

ACUTE TOX. 2

√

√

Page 36 of 42

√


PPARγ (802), TGF-β (271), NF-κB (1458), Inflammation (261; 295), EMT (267) PPARγ (1127), NFκB (94), Inflammation (281; 295), EMT (267) TGF-β (199; 271), NF-κB (1458), Inflammation (187; 261; 281; 295), EMT (267) PPARγ (802; 1127), TGF-β (199; 271), NF-κB (1458; 1346), Inflammation (187; 261; 281; 209; 295), EMT (267) PPARγ (1127), TGFβ (199; 271), NF-κB (1458), Inflammation (187; 261; 281; 209; 295), EMT (267)

0/0

0/1

0/0

0/1

0/0

PPARγ (134; 802; 1127; 719), TGF-β (1497; 112; 199; 271), NF-κB (1458), Inflammation (187; 261), EMT (267) PPARγ (134), TGF-β (1497; 199; 271; 272), NF-κB (94; 1346), Inflammation (187; 261; 281; 209; 295),

Page 37 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


EMT (497; 267) 123-03-5

Cetyl pyridinium chloride

√

87-86-5

Pentachlorophe nol

√

768-52-5

N-Isopropyl aniline

√

√

Antimicrobial product

1/3

√

Herbicide

0/0

7/25

√

Dye and chemical intermediate

0/0

8/25


PPARγ (1127)

12/22

PPARγ (134), TGF-β (1497; 199; 271; 272), NF-κB (1458), Inflammation (187; 261; 281; 209; 295), EMT (267) PPARγ (134; 1127), TGF-β (112; 199), NFκB (94), Inflammation (187), EMT (267) TGF-β (199), NF-κB (1458), Inflammation (187; 261; 281; 209; 295), EMT (267)


Figure legend: Figure 1. Adverse Outcome Pathway (AOP) for PPARγ inactivation leading to fibrosis. Molecular initiating event (MIE) is an inactivation of peroxisome proliferator-activated receptor gamma (PPARγ) signaling. Key event (KE) 1 is an activate Transforming Growth Factor beta (TGF-β) signaling. KE2 is an increase inflammatory response. KE3 is an increase epithelial mesenchymal transition (EMT). KE4 is an increase in the amount of collagen. The AO or adverse outcome as the result of progression through the MIE and KEs is pulmonary fibrosis.

Figure 2. Overview of key steps in the interaction between Transforming Growth Factor beta (TGF-β) and Peroxisome proliferator-activated receptor gamma (PPARγ) signaling and pulmonary fibrosis. TGFBRI: transforming growth factor-beta type I receptor; TGFBRII: transforming growth factor-beta type II receptor; Smad 2/3: mothers against decapentaplegic homolog 2/3; SMAD4: mothers against decapentaplegic homolog 4; CBP: CREB-binding protein; MLK1: mitogen-activated protein kinase kinase mlk-1; ROCK1: Rho-associated protein kinase 1; SRF: serum response factor; TRPV4: transient receptor potential cation channel subfamily V member 4; COL1A2: collagen type I alpha 2 chain. A. Phosphorylation of Smad 2 or 3 by TGF-β receptor. B. Formation of the Smad2/3/4, p300, CPB complex in nucleus. C. Ligand activation of PPARγ. D. Formation of PPARγ, p300, CPB complex for PPARγ-dependent transcription. E. Transcription of Smad-regulated genes. F. Globular actin binding and inhibition of MLK1 nuclear transport. G. Activation of Rho-ROCK signaling. H. Formation of actin fibers, release of MLK1, and transport of MLK1 into the nucleus. I. MLK1-Smad3


Page 38 of 42

Page 39 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


regulated transcription important in the differentiation of fibroblasts to myofibroblasts. J. MLK1-SRF regulated transcription important in the production of extra cellular matrix materials leading to fibrosis.

Figure 3. Graphical representation of the workflow of identification of potential chemicals for validation of AOP 206 using ToxCast and deep learning model.



Figures:

Figure 1


Page 40 of 42

Page 41 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2



Figure 3


Page 42 of 42

Development of Adverse Outcome Pathway for PPARÎ³ antagonism

Recommend Documents