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Relationship: 1705

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

Loss of alveolar capillary membrane integrity leads to Activation of Th2 cells

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
Substance interaction with the lung resident cell membrane components leading to lung fibrosis adjacent Moderate Low Sabina Halappanavar (send email) Under development: Not open for comment. Do not cite EAGMST Under Review

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

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

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

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

During the tissue injury-mediated immune response, naïve CD4+ Th cells differentiate into two major functional subsets: Th1 and Th2 type. Both Th1 and Th2 secrete distinct cytokines that promote proliferation and differentiation of their respective T cell population and inhibit proliferation and differentiation of the opposing subset. Th2 cytokines including pro-inflammatory and fibrotic mediators such as GATA-3, IL-13 and Arg-1 are increased in lung-irradiation induced fibrosis (Wynn, 2004; Brush et al., 2007; Han et al., 2011). Th2 immune response is implicated in allergen-mediated lung fibrosis. Meta-analysis of gene expression data collected from lungs of mice exposed to various fibrogenic substances including MWCNTs, showed that the expression and function of Th2 response associated genes and pathways are altered in fibrotic lungs (Nikota et al., 2016). Exposure of mice lacking STAT6 transcription factor to MWCNTs resulted in abrogated expression of Th2 genes and reduced lung fibrosis (Nikota et al., 2017). IL-4, the archetypal Th2 cytokine is a pro-fibrotic cytokine and is elevated in IPF and lung fibrosis. Overexpression of pro-fibrotic Th2 cytokine IL-13 results in sub-epithelial fibrosis with eosinophilic inflammation (Wilson and Wynn, 2009). In silica-induced pulmonary fibrosis in mice, T regulatory lymphocytes are recruited to the lungs where they increase expression of platelet-derived growth factor (PDGF) and TGF-β (Maggi et al., 2005). Chemokines associated with the Th2 response in airway epithelial cells include CCL1, CCL17, CCL20, and CCL22 (Lekkerkerker et al., 2012).

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
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 biological plasubility of this KER is high as there is a mechanistic relationship between ACM injury (tissue damage), and the induction of a Th2 response (responsible for wound healing) (Gieseck et al., 2018; Wynn, 2004).

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

Exogenous delivery of TNFα to mouse lungs with established fibrosis, reduced the fibrotic burden. Exogenous treatment with TNFα slowed the M2 macrophage polarisation. TNFα deficient mice showed prolonged pro-fibrotic response and M2 polarisation following bleomycin treatment (Redente et al., 2014).

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
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
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
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

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
  1. Brush, J., Lipnick, S., Phillips, T., Sitko, J., McDonald, J. and McBride, W. (2007). Molecular Mechanisms of Late Normal Tissue Injury. Seminars in Radiation Oncology, 17(2), pp.121-130.
  2. Caldwell, C. C., Chen, Y., Goetzmann, H. S., Hao, Y., Borchers, M. T., Hassett, D. J., Young, L. R., Mavrodi, D., Thomashow, L., & Lau, G. W. (2009). Pseudomonas aeruginosa exotoxin pyocyanin causes cystic fibrosis airway pathogenesis. The American journal of pathology, 175(6), 2473–2488.
  3. Chang, X., Zhu, A., Liu, F., Zou, L., Su, L., Li, S., & Sun, Y. (2017). Role of NF-κB activation and Th1/Th2 imbalance in pulmonary toxicity induced by nano NiO. Environmental toxicology, 32(4), 1354–1362.
  4. Gieseck, R. L., 3rd, Wilson, M. S., & Wynn, T. A. (2018). Type 2 immunity in tissue repair and fibrosis. Nature reviews. Immunology, 18(1), 62–76.
  5. Haczku, A., Cao, Y., Vass, G., Kierstein, S., Nath, P., Atochina-Vasserman, E. N., Scanlon, S. T., Li, L., Griswold, D. E., Chung, K. F., Poulain, F. R., Hawgood, S., Beers, M. F., & Crouch, E. C. (2006). IL-4 and IL-13 form a negative feedback circuit with surfactant protein-D in the allergic airway response. Journal of immunology (Baltimore, Md. : 1950), 176(6), 3557–3565.
  6. Han, G., Zhang, H., Xie, C. H., & Zhou, Y. F. (2011). Th2-like immune response in radiation-induced lung fibrosis. Oncology reports26(2), 383–388.

  7. Jackson, J., Friberg, I., Little, S. and Bradley, J. (2009). Review series on helminths, immune modulation and the hygiene hypothesis: Immunity against helminths and immunological phenomena in modern human populations: coevolutionary legacies?. Immunology, 126(1), pp.18-27
  8. Kikuchi, N., Ishii, Y., Morishima, Y., Yageta, Y., Haraguchi, N., Itoh, K., Yamamoto, M., & Hizawa, N. (2010). Nrf2 protects against pulmonary fibrosis by regulating the lung oxidant level and Th1/Th2 balance. Respiratory research, 11(1), 31.
  9. Kolb, M., Margetts, P., Anthony, D., Pitossi, F. and Gauldie, J. (2001). Transient expression of IL-1β induces acute lung injury and chronic repair leading to pulmonary fibrosis. Journal of Clinical Investigation, 107(12), pp.1529-1536.
  10. N. Lekkerkerker, A., Aarbiou, J., van Es, T. and A.J. Janssen, R. (2012). Cellular Players in Lung Fibrosis. Current Pharmaceutical Design, 18(27), pp.4093-4102
  11. Li, J., Zhao, L., Zhang, Y., Li, W., Duan, X., Chen, J., Guo, Y., Yang, S., Sun, G., & Li, B. (2017). Imbalanced immune responses involving inflammatory molecules and immune-related pathways in the lung of acute and subchronic arsenic-exposed mice. Environmental research, 159, 381–393.
  12. Maggi, E., Cosmi, L., Liotta, F., Romagnani, P., Romagnani, S. and Annunziato, F. (2005). Thymic regulatory T cells. Autoimmunity Reviews, 4(8), pp.579-586.
  13. Miyazaki, Y., Araki, K., Vesin, C., Garcia, I., Kapanci, Y., Whitsett, J., Piguet, P. and Vassalli, P. (1995). Expression of a tumor necrosis factor-alpha transgene in murine lung causes lymphocytic and fibrosing alveolitis. A mouse model of progressive pulmonary fibrosis. Journal of Clinical Investigation, 96(1), pp.250-25
  14. Nikota, J., Williams, A., Yauk, C. L., Wallin, H., Vogel, U., & Halappanavar, S. (2016). Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials. Particle and fibre toxicology, 13(1), 25. https://doi.org/10.1186/s12989-016-0137-5
  15. Nikota J, Banville A, Goodwin LR, Wu D, Williams A, Yauk CL, Wallin H, Vogel U, Halappanavar S. Stat-6 signaling pathway and not Interleukin-1 mediates multi-walled carbon nanotube-induced lung fibrosis in mice: insights from an adverse outcome pathway framework. Part Fibre Toxicol. 2017 Sep 13;14(1):37.
  16. Ortiz, L., Lasky, J., Hamilton, R., Holian, A., Hoyle, G., Banks, W., Peschon, J., Brody, A., Lungarella, G. and Friedman, M. (1998). Expression of TNF and the Necessity of TNF Receptors in Bleomycin-Induced Lung Injury in Mice. Experimental Lung Research, 24(6), pp.721-743
  17. Piguet, P. (1989). Tumor necrosis factor/cachectin plays a key role in bleomycin-induced pneumopathy and fibrosis. Journal of Experimental Medicine, 170(3), pp.655-663
  18. Redente, E., Keith, R., Janssen, W., Henson, P., Ortiz, L., Downey, G., Bratton, D. and Riches, D. (2014). Tumor Necrosis Factor-α Accelerates the Resolution of Established Pulmonary Fibrosis in Mice by Targeting Profibrotic Lung Macrophages. American Journal of Respiratory Cell and Molecular Biology, 50(4), pp.825-837
  19. Venosa, A., Malaviya, R., Choi, H., Gow, A. J., Laskin, J. D., & Laskin, D. L. (2016). Characterization of Distinct Macrophage Subpopulations during Nitrogen Mustard-Induced Lung Injury and Fibrosis. American journal of respiratory cell and molecular biology, 54(3), 436–446.
  20. Wilson, M. and Wynn, T. (2009). Pulmonary fibrosis: pathogenesis, etiology and regulation. Mucosal Immunology, 2(2), pp.103-121
  21. Wynn, T. (2004). Fibrotic disease and the TH1/TH2 paradigm. Nature Reviews Immunology, 4(8), pp.583-594.
  22. Zhang K. (1994). Myofibroblasts and Their Role in Lung Collagen Gene Expression during Pulmonary Fibrosis. American Journal of Pathology, Vol. 145, No. 1.