To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:1706

Relationship: 1706


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

Activation of Th2 cells leads to Increased cellular proliferation and differentiation

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 High Low Cataia Ives (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 Do not cite EAGMST Under Review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

The wound healing process involves an inflammatory phase, during which the damage tissue/wound is provisionally filled with ECM. This phase is characterised by secretion of cytokines/chemokines, growth factors and recruitment of inflammatory cells, fibroblasts and endothelial cells. The activated Th1/Th2 response and increased pool of specific cytokines and growth factors such as IL-1b, IL-6, IL-13, and TGFβ, induce fibroblast proliferation. Th2 cells can directly stimulate fibroblasts to synthesise collagen with IL-1 and IL-13. Th2 cytokines IL-13 and IL-4, known to mediate the fibrosis process induce phenotypic transition of human fibroblasts (Hashimoto S, 2001). IL-13 is shown to inhibit MMP-mediated matrix degradation resulting in excessive collagen deposition by downregulating the synthesis and expression of matrix degrading MMPs. IL-13 is also suggested to induce TGFβ1 in macrophages and its absence results in reduced TGFβ1 expression and decrease in collagen deposition (Fichtner-Feigl et al., 2005). These cytokines are suggested to initiate polarisation of macrophages to the alternative M2 phenotype. Th2 cells that synthesise IL-4 and IL-13 induce synthesis of Arg-1 in M2 macrophages. The Arg-1 pathway stimulates synthesis of proline for collagen synthesis required for fibrosis (Barron and Wynn, 2011).

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 plausibility for this KER is high. There is a widely understood functional relationship between Th2 response related mediators, and their ability to induce proliferation and differentiation of fibroblasts (Shao et al., 2008; Wynn, 2012; 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

Due to multifarious functions of several cytokines involved in the process of inflammation and repair, the timing of when a pathway is intervened in an experiment is important in the assessment of the KER studies. For example, exposure to pro-fibrotic bleomycin stimulates IL-4 production during the acute inflammatory phase, which is suggested to limit the recruitment of T lymphocytes and production of damaging cytokines such as TNFα, IFNγ, and nitric oxide, playing a tissue protective role. However, production of IL- 4 during the chronic phase of tissue repair and healing, favors fibrosis manifestation. Treatment of IL4 -/- mice with low doses of bleomycin induced fewer fibrotic lesions compared to IL-4 +/+ mice. However, treatment of high doses of bleomycin induced more lethality in IL-4 -/- mice compared to the wild type mice (Huaux et al., 2003). Moreover, the KEs represented in AOP 173 can function in parallel in a positive feedback loop, perpetuating and magnifying the response at each stage. The resulting microenvironment may contain the same molecules in different proportions exhibiting different functions. Thus, the complexity of the process and the functional heterogeneity of the molecular players involved, makes it nearly impossible to establish KERs using a targeted deletion of one single gene or a pathway in a study, which is how most of the studies are designed.

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
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


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. Ando, M., Miyazaki, E., Fukami, T., Kumamoto, T. and Tsuda, T. (1999). Interleukin-4-producing cells in idiopathic pulmonary fibrosis: An immunohistochemical study. Respirology, 4(4), pp.383-391.
  2. Barron, L. and Wynn, T. (2011). Fibrosis is regulated by Th2 and Th17 responses and by dynamic interactions between fibroblasts and macrophages. American Journal of Physiology-Gastrointestinal and Liver Physiology, 300(5), pp.G723-G728
  3. Cao H et al. Hydrogen sulfide protects against bleomycin-induced pulmonary fibrosis in rats by inhibiting NF-B expression and regulating Th1/Th2 balance. Toxicology Letters, 2014, 224, 387-394
  4. Dong, J., Porter, D., Batteli, L., Wolfarth, M., Richardson, D. and Ma, Q. (2014). Pathologic and molecular profiling of rapid-onset fibrosis and inflammation induced by multi-walled carbon nanotubes. Archives of Toxicology, 89(4), pp.621-633
  5. Dong J and Ma Qiang. In vivo activation of a T helper 2-driven innate immune response in lung fibrosis induced by multi-walled carbon nanotubes. Arch Toxicol, 2016, 90, 9: 2231-2248
  6. Fichtner-Feigl, S., Strober, W., Kawakami, K., Puri, R. and Kitani, A. (2005). IL-13 signaling through the IL-13α2 receptor is involved in induction of TGF-β1 production and fibrosis. Nature Medicine, 12(1), pp.99-106.
  7. Gibbons M. Ly6Chi Monocytes direct alternatively activated profibrotic macrophage regulation of lung fibrosis. Am J Respir Crit Care Med, 2011, 184, 569-581
  8. Halappanavar, S., Nikota, J., Wu, D., Williams, A., Yauk, C. and Stampfli, M. (2013). IL-1 Receptor Regulates microRNA-135b Expression in a Negative Feedback Mechanism during Cigarette Smoke–Induced Inflammation. The Journal of Immunology, 190(7), pp.3679-3686
  9. Hashimoto, S., Gon, Y., Takeshita, I., Maruoka, S. and Horie, T. (2001). IL-4 and IL-13 induce myofibroblastic phenotype of human lung fibroblasts through c-Jun NH2-terminal kinase–dependent pathway. Journal of Allergy and Clinical Immunology, 107(6), pp.1001-1008
  10. Huaux, F., Liu, T., McGarry, B., Ullenbruch, M. and Phan, S. (2003). Dual Roles of IL-4 in Lung Injury and Fibrosis. The Journal of Immunology, 170(4), pp.2083-2092
  11. Kurosaka, H., Kurosaka, D., Kato, K., Mashima, Y., & Tanaka, Y. (1998). Transforming growth factor-beta 1 promotes contraction of collagen gel by bovine corneal fibroblasts through differentiation of myofibroblasts. 
  12. Lee, C. G., Homer, R. J., Zhu, Z., Lanone, S., Wang, X., Koteliansky, V., Shipley, J. M., Gotwals, P., Noble, P., Chen, Q., Senior, R. M., & Elias, J. A. (2001). Interleukin-13 induces tissue fibrosis by selectively stimulating and activating transforming growth factor beta(1). The Journal of experimental medicine, 194(6), 809–821.
  13. Liu T et al. FIZZ2/RELM- induction and role in pulmonary fibrosis. The Journal of Immunology, 2011, 187:450-461.
  14. Lo Re, S et al. Platelet-derived growth factor-producing CD4+Foxp3+ regulatory T lymphocytes promote lung fibrosis. Am J Respir Crit Care Med, 2011. 184, 1270-1281.
  15. Meziani L et al. CSF1R inhibition prevents radiation pulmonary fibrosis by depletion of interstitial macrophages. Eur Respir J, 2018, 51: 1702120
  16. Nikota, J., Banville, A., Goodwin, L. R., Wu, D., Williams, A., Yauk, C. L., Wallin, H., Vogel, U., & Halappanavar, S. (2017). 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. Particle and fibre toxicology, 14(1), 37.
  17. Postlethwaite, A., Holness, M., Katai, H. and Raghow, R. (1992). Human fibroblasts synthesize elevated levels of extracellular matrix proteins in response to interleukin 4. Journal of Clinical Investigation, 90(4), pp.1479-1485
  18. Sempowski, G. D., Beckmann, M. P., Derdak, S., & Phipps, R. P. (1994). Subsets of murine lung fibroblasts express membrane-bound and soluble IL-4 receptors. Role of IL-4 in enhancing fibroblast proliferation and collagen synthesis. Journal of immunology (Baltimore, Md. : 1950), 152(7), 3606–3614.
  19. Shao, D. D., Suresh, R., Vakil, V., Gomer, R. H., & Pilling, D. (2008). Pivotal Advance: Th-1 cytokines inhibit, and Th-2 cytokines promote fibrocyte differentiation. Journal of leukocyte biology, 83(6), 1323–1333.
  20. Redington, A., Madden, J., Frew, A., Djukanovic, R., Roche, W., Holgate, S. and Howarth, P. (1997). Transforming Growth Factor- β 1 in Asthma. American Journal of Respiratory and Critical Care Medicine, 156(2), pp.642-647.
  21. Wallace, W. and Howie, S. (1999). Immunoreactive interleukin 4 and interferon-? expression by type II alveolar epithelial cells in interstitial lung disease. The Journal of Pathology, 187(4), pp.475-480.
  22. Wynes M et al. IL-4 induced macrophage-derived IGF-1 protects myofibroblasts from apoptosis following growth factor withdrawal. Journal of Leukocyte Biology, 2004, 76, 1019-1027
  23. Wynn, T. Fibrotic disease and the TH1/TH2 paradigm. Nat Rev Immunol 4, 583–594 (2004).
  24. Wynn, T. A., & Ramalingam, T. R. (2012). Mechanisms of fibrosis: therapeutic translation for fibrotic disease. Nature medicine, 18(7), 1028–1040.
  25. Yin H et al. IL-33 accelerates cutaneous wound healing involved in upregulation of alternatively activated macrophages. Molecular immunology, 2013, 56, 347-353