To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:2450

Relationship: 2450

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Oxidative Stress leads to FOXJ1 Protein, Decreased

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Oxidative stress [MIE] Leading to Decreased Lung Function [AO] adjacent Moderate Moderate Karsta Luettich (send email) Open for comment. Do not cite

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
Homo sapiens Homo sapiens High NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence
Mixed

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
All life stages

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Oxidative stress (such as that caused by cigarette smoke exposure or irradiation) leads to decreased forkhead box J1 (FOXJ1) gene and protein expression, as well as to decreased FOXJ1 target gene expression (Brekman et al., 2014; Garcia-Arcos et al., 2016; Ishikawa and Ito, 2017; Milara et al., 2012; Valencia-Gattas et al., 2016). FOXJ1 is a key factor of multiple motile cilia assembly in the respiratory airways (Zhou and Roy, 2015). Thus oxidative stress blocks the multiple ciliogenesis program in the airway epithelium.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

Cigarette smoke-induced oxidative stress downregulates FOXJ1 expression at both the gene and protein levels in human lung cells in vitro (Milara et al., 2012; Brekman et al., 2014; Valencia-Gattas et al., 2016; Ishikawa and Ito, 2017). Oxidative stress induced by human respiratory syncytial virus reduces FOXJ1 mRNA levels, which can be restored by treatment with antioxidants or the phosphodiesterase 4 inhibitor roflumilast N-oxide (Akaike et al., 1990; Geiler et al., 2010; Mata et al., 2012). In mice, thoracic irradiation results in free radical generation and subsequent reduction in FOXJ1 mRNA expression (Bernard et al., 2012). Many genes that are transcriptionally regulated by FOXJ1 are also downregulated following exposure to cigarette smoke, which implies a reduction in FOXJ1 transcriptional activity (Brekman et al., 2014).

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

The negative association between cigarette smoke exposure and FOXJ1 levels in airways was shown in multiple studies and can be estimated as a strong linkage. Yet, the notion that oxidative stress as a result of cigarette smoke exposure is leading to decreased FOXJ1 levels is not well demonstrated. As a complex mixture of thousands of chemicals, cigarette smoke exposure could lead to reduced FOXJ1 levels via different routes. Indirect evidence, such as antioxidant molecules that restore cigarette smoke exposure-reduced FOXJ1 levels, as well evidences from other oxidative stress generating insults that decrease FOXJ1 levels add confidence to this KER. However, studies showing a link between oxidative stress generating agents and reduced FOXJ1 levels are scarce. Collectively, the empirical evidence and uncertainties of the linkage imply a moderate ranking for the KER.

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

Schamberger et al. did not find any alterations in FOXJ1 mRNA levels or FOXJ1 target gene (DNAI1, DNALI1, SPAG6, TEKT1) transcription upon 2.5% or 5% CSE exposure of HBECs for 28 days. However, in this study, cigarette smoke exposure reduced ciliated cell numbers (Schamberger et al., 2015).

The evidences listed suggest several mechanisms on how oxidative stress could lead to decreased FOXJ1 levels, including EGFR-, MCIDAS- or IL-13-mediated mechanisms. Most of the studies, however, do not corroborate on how oxidative stress mechanistically leads to reduced FOXJ1 levels. Since there are several other factors (GMNC, NOTCH, ULK4 etc.) known to regulate FOXJ1 levels, further pathways might be involved in passing the oxidative stress signal to FOXJ1.  

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help

Normal HBECs were exposed to whole cigarette smoke from 3R4F research grade cigarettes using the Vitrocell® VC 10® Smoking Robot (35-mL puff volume, 2 s duration and 1 min between puffs or air as a control). For differentiated cells, treatment was done every 2 d for 5 d (3 exposures) and samples were collected 48 h after treatment. Differentiating cells were exposed 3 times per week to smoke from 1 cigarette, and samples were collected after 14, 21 and 27 days. FOXJ1 protein and mRNA level changes were decreased 2.5-fold in differentiated and 2-fold in differentiating NHBE cells. There was a significantly lower percentage of FoxJ1 positive cells in the WCS exposed cells at 27 d of differentiation (4.3 +/- 4.2% vs. 13.0 +/- 7.3%, air)  (Valencia-Gattas et al., 2016).

CSE was obtained from one Marlboro Red commercial cigarette bubbled in 12.5 mL of differentiation medium that was then filtered (0.2-µm pore filter). The absorbance was measured at 320 nm on a spectrophotometer, and the optical density of 1 was defined as 100%. HBECs were differentiated at the air-liquid interface while being exposed to 0, 3, and 6% CSE between days 5 and 28. 3% and 6% CSE treatment reduced FOXJ1 mRNA levels to approx. 65% and 55% of control levels, respectively. Treatment of differentiating cultures with 3% CSE reduced FOXJ1 protein levels by 2-fold by day 28 (Brekman et al., 2014).

The smoke of one 2R4F research cigarette was bubbled into a flask containing 25 mL of pre-warmed (37°C) differentiation medium using a respiratory pump model (Harvard Apparatus Rodent Respirator 680, Harvard Apparatus, Holliston, MA, USA) that generates three puffs min−1; 35 mL per each puff of 2 s duration with a volume of 0.5 cm above the filter. The solution was then filtered (0.22 µm pore size) to remove particles and the tar phase. The resulting sterile solution was defined as 100% CSE and used within 30 min of preparation. Treatment of differentiated human bronchial epithelial cells with 10% CSE decreased FOXJ1 expression by about 40% at 24 h and 70% at 72 h exposure (Milara et al., 2012).

3R4F reference cigarettes were smoked in accordance with the ISO smoking protocol (35-mL puffs of 2 s each minute). Whole CS, generated by a VC10 smoking robot, was released into a mixing device in 2.8-s exhaust and diluted with humidified clean air at 1.0 L/min dilution flow. Diluted smoke was introduced into the CULTEX RFS module and guided into the exposure chamber (5 mL/min) using a vacuum pump. FOXJ1 mRNA levels were reduced to 60% and 40% of the control after exposure to CS from 1 or 4 cigarettes, respectively (Ishikawa and Ito, 2017).

RSV infection elicits ROI-mediated effects manifested by changes in the expression of NRF2 and HMOX-1 genes (approx. 6- and 9-fold increase at day 15 post-RSV infection, respectively), H2O2 generation (7-fold increase in intracellular levels at day 15 post-infection) and severe reduction in total antioxidant capacity. These data together indicate the presence of oxidative stress following infection which leads to decreased FOXJ1 mRNA levels (ca. 25% of control at 15 days post-RSV infection (Mata et al., 2012) and ca. 45% of control at 10 days after RSV infection (Mata et al., 2013).

FOXJ1 mRNA levels were reduced by 50% in murine lungs 14 days after thoracic irradiation at 15 Gy (Bernard et al., 2012).

Time-scale
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

Whole cigarette smoke exposure from one 3R4F research grade cigarette using the Vitrocell® VC 10® Smoking Robot (35-mL puff volume, 2 s duration and 1 min between puffs or air as a control) once a day on alternate days for 5 days decreased FOXJ1 mRNA levels by 2.5-fold in differentiated HBECs (Valencia-Gattas et al., 2016).

Whole cigarette smoke exposure from one 3R4F research grade cigarette using the Vitrocell® VC 10® Smoking Robot (35-mL puff volume, 2 s duration and 1 min between puffs or air as a control) for 3 times per week for 4 weeks (27 days) decreased FOXJ1 mRNA levels by 2-fold in differentiating HBECs (Valencia-Gattas et al., 2016).

Treatment of differentiating HBECs between days 5 and 28 with 3% CSE reduced FOXJ1 protein levels by 2-fold by day 28. Treatment of differentiating HBECs between days 5 and 28 with 6% CSE reduced FOXJ1 protein levels by approx. 55% by day 28 (Brekman et al., 2014).

Treatment of differentiated human bronchial epithelial cells with 10% CSE decreased FOXJ1 expression by about 40% at 24 h and 70% at 72 h (Milara et al., 2012). 

Repeated exposure of 3D bronchial epithelial cultures to whole smoke of 4 cigarettes (every other day, treatment started on ALI culture day 7) resulted in 2.5-fold decrease of FOXJ1 mRNA levels by day 21 (Ishikawa and Ito, 2017).

At 15 days post-RSV infection, FOXJ1 mRNA levels were four-fold reduced compared to untreated samples (Mata et al., 2012). In another study from the same research group, FOXJ1 mRNA levels were reduced to 45% of the FOXJ1 levels in uninfected sample 10 days post-RSV infection (Mata et al., 2013).

At 7 days and 14 day after 15 GY thoracic irradiation, FOXJ1 mRNA levels were reduced to approx. 70% and 50% of controls, respectively (Bernard et al., 2012).

Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

Unknown

Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Unknown

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help
  • Akaike, T., Ando, M., Oda, T., Doi, T., Ijiri, S., Araki, S., et al. (1990). Dependence on O2- generation by xanthine oxidase of pathogenesis of influenza virus infection in mice. J. Clin. Investig. 85(3), 739-745. 
  • Azzam, E.I., Jay-Gerin, J.P., and Pain, D. (2012). Ionizing radiation-induced metabolic oxidative stress and prolonged cell injury. Cancer Lett 327(1-2), 48-60. 
  • Baumung, C., Rehm, J., Franke, H., and Lachenmeier, D.W. (2016). Comparative risk assessment of tobacco smoke constituents using the margin of exposure approach: the neglected contribution of nicotine. Sci Rep 6, 35577.
  • Bernard, M.E., Kim, H., Rajagopalan, M.S., Stone, B., Salimi, U., Rwigema, J.C., et al. (2012). Repopulation of the irradiation damaged lung with bone marrow-derived cells. In Vivo 26(1), 9-18.
  • Brekman, A., Walters, M.S., Tilley, A.E., and Crystal, R.G. (2014). FOXJ1 prevents cilia growth inhibition by cigarette smoke in human airway epithelium in vitro. Am. J. Respir. Cell Mol. Biol. 51(5), 688-700.
  • Garcia-Arcos, I., Geraghty, P., Baumlin, N., Campos, M., Dabo, A.J., Jundi, B., et al. (2016). Chronic electronic cigarette exposure in mice induces features of COPD in a nicotine-dependent manner. Thorax 71(12), 1119-1129. 
  • Ishikawa, S., and Ito, S. (2017). Repeated whole cigarette smoke exposure alters cell differentiation and augments secretion of inflammatory mediators in air-liquid interface three-dimensional co-culture model of human bronchial tissue. Toxicol. In Vitro 38, 170-178.
  • Koc, M., Taysi, S., Buyukokuroglu, M.E., and Bakan, N. (2003). Melatonin protects rat liver against irradiation-induced oxidative injury. J Radiat Res 44(3), 211-215. 
  • Mata, M., Martinez, I., Melero, J.A., Tenor, H., and Cortijo, J. (2013). Roflumilast inhibits respiratory syncytial virus infection in human differentiated bronchial epithelial cells. PLoS One 8(7), e69670. 
  • Mata, M., Sarrion, I., Armengot, M., Carda, C., Martinez, I., Melero, J.A., et al. (2012). Respiratory syncytial virus inhibits ciliagenesis in differentiated normal human bronchial epithelial cells: effectiveness of N-acetylcysteine. PloS one 7(10), e48037.
  • Milara, J., Armengot, M., Bañuls, P., Tenor, H., Beume, R., Artigues, E., et al. (2012). Roflumilast N-oxide, a PDE4 inhibitor, improves cilia motility and ciliated human bronchial epithelial cells compromised by cigarette smoke in vitro. Br. J. Pharmacol. 166(8), 2243-2262. 
  • Rodrigues-Moreira, S., Moreno, S.G., Ghinatti, G., Lewandowski, D., Hoffschir, F., Ferri, F., et al. (2017). Low-Dose Irradiation Promotes Persistent Oxidative Stress and Decreases Self-Renewal in Hematopoietic Stem Cells. Cell Rep 20(13), 3199-3211. 
  • Schamberger, A.C., Staab-Weijnitz, C.A., Mise-Racek, N., and Eickelberg, O. (2015). Cigarette smoke alters primary human bronchial epithelial cell differentiation at the air-liquid interface. Scientific reports 5, 8163.
  • Shirazi, A., Mihandoost, E., Ghobadi, G., Mohseni, M., and Ghazi-Khansari, M. (2013). Evaluation of radio-protective effect of melatonin on whole body irradiation induced liver tissue damage. Cell J 14(4), 292-297.
  • Valencia-Gattas, M., Conner, G.E., and Fregien, N.L. (2016). Gefitinib, an EGFR Tyrosine Kinase inhibitor, Prevents Smoke-Mediated Ciliated Airway Epithelial Cell Loss and Promotes Their Recovery. PloS one 11(8), e0160216. 
  • Zhou, F., and Roy, S. (2015). SnapShot: Motile Cilia. Cell 162(1), 224-224 e221.