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Relationship: 1703

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

Increased proinflammatory mediators leads to Recruitment of inflammatory 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 Cataia Ives (send email) Under development: Not open for comment. Do not cite EAGMST Under Review
Decreased fibrinolysis and activated bradykinin system leading to hyperinflammation adjacent Cataia Ives (send email) Under development: Not open for comment. Do not cite Under Development
Frustrated phagocytosis leads to malignant mesothelioma adjacent High Not Specified Evgeniia Kazymova (send email) Under development: Not 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

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

Pro-inflammatory mediators are the chemical and biological molecules that initiate and regulate inflammatory reactions. They are secreted following inflammation or exposure to an inflammogen. Commonly measured pro-inflammatory mediators include IL-1 family cytokines, IL-4, IL-5, IL-6, TNFa, IFNg. (https://aopwiki.org/events/1496)

 Proinflammatory mediator increase is caused when there’s increased inflammation. This can be found in many ways, including bradykinin system activation or hypofibrinolysis (Koller, https://doi.org/10.1161/ATVBAHA.119.313536).With more proinflammatory mediators, this causes increased signaling from proinflammatory cytokines, which promotes leukocyte recruitment, which will differentiate into proinflammatory cells ( (Villenueve et al, https://doi.org/10.1093/toxsci/kfy047)). Increased proinflammatory mediators means this process happens more, which means increase recruitment of inflammatory cells.

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 of this KER is high. There are very well established functional relationships between the secreted signalling molecules and the chemotactic effects on pro-inflammatory cells (Harris, 1954; Petri & Sanz 2018).

Increased proinflammatory mediators means more proinflammatory cytokines, chemokines, vasoactive amines, and lipid mediators (Villenueve et al, https://doi.org/10.1093/toxsci/kfy047). Increased Signaling from these Cytokines and Chemokines promote leukocyte recruitment to areas of infection, including monocytes and neutrophil (Leick et al, doi: 10.1007/s00441-014-1809-9). The leukocytes will differentiate into mature proinflammatory cells, in response to mediators they encounter in the local tissue microenvironment (Villenueve et al, https://doi.org/10.1093/toxsci/kfy047). With higher levels of leukocytes from increased proinflammatory mediators, it causes an increase in proinflammatory cells (Libby, https://doi.org/10.1093/cvr/cvv188).

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

Attenuation or complete abrogation of KE1 (Event 1496) and KE2 (Event 1497) following inflammogenic stimuli is observed in rodents lacking functional IL-1R1 or other cell surface receptors that engage innate immune response upon stimulation. However, following exposure to MWCNTs, it has been shown that absence of IL-1R1 signalling is compensated for eventually and neutrophil influx is observed at a later post-exposure time point (Nikota et al., 2017). In another study, acute neutrophilic inflammation induced by MWCNT was suppressed at 24 hr in mice deficient in IL1R1 signalling; however, these mice showed exacerbated neutrophilic influx and fibrotic response at 28 days post-exposure (Girtsman et al., 2014). The early defence mechanisms involving DAMPs is fundamental for survival, which may necessitate activation of compensatory signalling pathways. As a result, inhibition of a single biological pathway mediated by an individual cell surface receptor may not be sufficient to completely abrogate the lung inflammatory response. Forced suppression of pro-inflammatory and immune responses early after exposure to substances that cannot be effectively cleared from lungs, may enhance the injury and initiate other pathways leading to exacerbated response.

Most of the studies evaluate one dose at different time points or one-time point at different concentrations. Moreover, some studies have demonstrated that a stressor can lead to the recruitment of pro-inflammatory cells, but the presence of pro-inflammatory mediators was not determined (Westphal et al., 2015).

Recruitment of pro-inflammatory cells is a key event that is complicated to replicate in vitro conditions as cell migration is induced by cooperative chemotactic mediators (Gouwy et al., 2015) which are produced and released from different cells. Therefore, more kinetics studies in co-culture techniques are needed to fill this gap.

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

Activated pro-inflammatory cells secrete pro-inflammatory mediators, and those mediators' goal is to cause signalling and response, which can lead to chronic inflammation (https://aopwiki.org/events/1497). Chronic inflammation means proinflammatory mediators increase and increased recruitment of inflammatory cells acts in a positive feedback loop, which continues a pro-inflammatory environment.

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. Alghsham R et al. Zinc oxide nanowires exposure induces a distinct inflammatory response via CCL11-mediated eosinophil recruitment. Frontiers in immunology, 2019, 10: 2604.
  2. Bourdon J et al. Carbon black nanoparticle instillation induces sustained inflammation and genotoxicity in mouse lung and liver. Particle and Fibre Toxicology, 2012, 9:5.
  3. Chen, S et al. No involvement of alveolar macrophages in the initiation of carbon nanoparticle induced acute lung inflammation in mice. Particle and Fibre Toxicology, 2016, 13:33, 1-15.
  4. Crouzier D et al. Carbon nanotubes induce inflammation but decrease the production of reactive oxygen species in lung. Toxicology, 2010, 272: 39.45.
  5. Driscoll K. Alveolar macrophage cytokine and growth factor production in a rat model of crocidolite-induced pulmonary inflammation and fibrosis, 1995, 46:2, 155-169.
  6. Girtsman, T., Beamer, C., Wu, N., Buford, M. and Holian, A. (2012). IL-1R signalling is critical for regulation of multi-walled carbon nanotubes-induced acute lung inflammation in C57BL/6 mice. Nanotoxicology, 8(1), pp.17-27.
  7. Gasse, P., Mary, C., Guenon, I., Noulin, N., Charron, S., Schnyder-Candrian, S., Schnyder, B., Akira, S., Quesniaux, V., Lagente, V., Ryffel, B. and Couillin, I. (2007). IL-1R1/MyD88 signaling and the inflammasome are essential in pulmonary inflammation and fibrosis in mice. Journal of Clinical Investigation.
  8. Gouwy, M et al. Serum amyloid A chemoattracts immature dendritic cells and indirectly provokes monocyte chemotaxis by induction of cooperating CC and CXC chemokines. Eur. J. Immunol. 2015. 45:101-112.
  9. Hadrup N et al. Acute phase response and inflammation following pulmonary exposure to low doses of zinc oxide nanoparticles in mice. Nanotoxicology, 2019, 13(9): 1275-1292.
  10. Halappanavar S et al. Pulmonary response to surface-coated nanotitanium dioxide particles includes induction of acute phase response genes, inflammatory cascades, and changes in MicroRNAs: A toxicogenomic study. Environmental and molecular mutagenesis, 2011, 52: 425-439.
  11. 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.
  12. HARRIS H. (1954). Role of chemotaxis in inflammation. Physiological reviews, 34(3), 529–562.
  13. Ho C et al. Quantum dot 705, a cadmium-based nanoparticle, induces persistent inflammation and granuloma formation in the mouse lung. Nanotoxicology, 2013, 7(1): 105-115.
  14. Hornung, V., Bauernfeind, F., Halle, A., Samstad, E., Kono, H., Rock, K., Fitzgerald, K. and Latz, E. (2008). Silica crystals and aluminum salts activate the NALP3 inflammasome through phagosomal destabilization. Nature Immunology, 9(8), pp.847-856.
  15. Husain M et al. Carbon black nanoparticles induce biphasic gene expression changes associated with inflammatory responses in the lungs of C57BL/6 mice following a single intratracheal instillation. Toxicology and applied pharmacology, 2015, 289: 573-588.
  16. Jardine L et al. Lipopolysaccharide inhalation recruits monocytes and dendritic cell subsets to the alveolar airspace. Nature communications, 2019, 10, 1999. Https//doi.org/10.1038/s41467.
  17. Kamata H et al. Carbon black nanoparticles enhance bleomycin-induced lung inflammatory and fibrotic changes in mice. Experimental Biology and Medicine, 2011, 236, 315-324.
  18. Khatri M et al. Chronic upper airway inflammation and systemic oxidative stress from nanoparticles in photocopier operators: Mechanistic insights. NanoImpact, 2017, 5: 133-145.
  19. Lee, S et al., Nickel oxide nanoparticles can recruit eosinophils in the lungs of rats by the direct release of intracellular eotaxin. Particle and Fibre Toxicology, 2016, 13:30, 1-11.
  20. Leick, M. Azcutia, V. Newton, G. Luscinskas, F. Leukocyte Recruitment in Inflammation: Basic Concepts and New Mechanistic Insights Based on New Models and Microscopic Imaging Technologies. Cell Tissue Res. 2014 Mar; 355(3): 647–656. doi: 10.1007/s00441-014-1809-9
  21. Liao D et al. Persistent pleural lesions and inflammation by pulmonary exposure of multiwalled carbon nanotubes. Chem Res Toxicol, 2018, 31(10): 1025-1031.
  22. Libby, P. Fanning the flames: inflammation in cardiovascular diseases. Cardiovascular Research, Volume 107, Issue 3, August, 2015. Pages 307–309, https://doi.org/10.1093/cvr/cvv188
  23. Ma, J et al. Carbon nanotubes stimulate synovial inflammation by inducing systemic pro-inflammatory cytokines. Nanoscale, 2016, 8, 18070-18086.
  24. Marchini T et al. Acute exposure to air pollution particulate matter aggravates experimental myocardial infarction in mice by potentiating cytokine secretion from lung macrophages. Basic Res Cardiol, 2016, 111:44.
  25. Morimoto Y et al. Expression of inflammation-related cytokines following intratracheal instillation of nickel oxide nanoparticles. Nanotoxicology, 2010, 4(2): 161-176.
  26. Nikota, J., Banville, A., Goodwin, L., Wu, D., Williams, A., Yauk, C., Wallin, H., Vogel, U. and 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).
  27. Patowary, P et al. Innate inflammatory response to acute inhalation exposure of riot control agent oleoresin capsicum in female rats: An interplay between neutrophil mobilization and inflammatory markers. Experimental lung research, 2020, 46:3-4, 81-97.
  28. Petri, B., Sanz, MJ. Neutrophil chemotaxis. Cell Tissue Res 371, 425–436 (2018).
  29. Porter, D et al. Time course of pulmonary response of rats to inhalation of crystalline silica: NF-kB activation, inflammation, cytokine production, and damage. Inhalation Toxicology, 2002, 14:349-367, 349-367.
  30. Porter, D et al. Mouse pulmonary dose-and time course-responses induced by exposure to nitrogen-doped multi-walled carbon nanotubes. Inhalation toxicology, 2020, 32:1, 24-38.
  31. Poulsen S et al. Transcriptomic analysis reveals novel mechanistic insight into murine biological responses to multi-walled carbon nanotubes in lungs and cultured lung epithelial cells. Plos one, 2013, 8,11, e80452.
  32. Rabolli, V., Badissi, A., Devosse, R., Uwambayinema, F., Yakoub, Y., Palmai-Pallag, M., Lebrun, A., De Gussem, V., Couillin, I., Ryffel, B., Marbaix, E., Lison, D. and Huaux, F. (2014). The alarmin IL-1α is a master cytokine in acute lung inflammation induced by silica micro- and nanoparticles. Particle and Fibre Toxicology, 11(1).
  33. Rahman L et al. Toxicogenomics analysis of mouse lung responses following exposure to titanium dioxide nanomaterials reveal their disease potential at high doses. Mutagenesis, 2017, 32, 59-76.
  34. Rahman L et al. Multi-walled carbon nanotube-induced genotoxic, inflammatory and pro-fibrotic responses in mice: investigating the mechanisms of pulmonary carcinogenesis. Mutat Res Gen Tox En, 2017, 823: 28-44.
  35. Rider, P., Carmi, Y., Guttman, O., Braiman, A., Cohen, I., Voronov, E., White, M., Dinarello, C. and Apte, R. (2011). IL-1α and IL-1β Recruit Different Myeloid Cells and Promote Different Stages of Sterile Inflammation. The Journal of Immunology, 187(9), pp.4835-4843.
  36. Riva D et al. Low dose of fine particulate matter (PM2.5) can induce acute oxidative stress, inflammation and pulmonary impairment in healthy mice. Inhalation toxicology, 2011, 23(5): 257-267.
  37. Saito F et al. Role of interleukin-6 in bleomycin-induced lung inflammatory changes in mice. Am J Respir Cell Mol Biol, 2008, 38, 566-571.
  38. Schremmer, I et al. Kinetics of chemotaxis; cytokine; and chemokine release of NR8383 macrophages after exposure to inflammatory and inert granular insoluble particles. Toxicology Letters, 2014, http://dx.doi.org/10.1016/j.toxlet.2016.08.2014.
  39. Shvedova A et al. Unusual inflammatory and fibrogenic pulmonary responses to single-walled carbon nanotubes in mice. Am J Physiol Lung Cell Mol Physiol, 2005, 289: L698-L708.
  40. Song J et al. Polyhexamethyleneguanidine phosphate induces severe lung inflammation, fibrosis, and thymic atrophy. Food and chemical toxicology, 2014, 69: 267-275.
  41. Suwara, M., Green, N., Borthwick, L., Mann, J., Mayer-Barber, K., Barron, L., Corris, P., Farrow, S., Wynn, T., Fisher, A. and Mann, D. (2014). IL-1α released from damaged epithelial cells is sufficient and essential to trigger inflammatory responses in human lung fibroblasts. Mucosal Immunology, 7(3), pp.684-693.
  42. Villeneuve D., Landesmann B., Allavena P., Ashley N., Bal-Price, A., Corsini E., Halappanavar S., Hussell T.,  Laskin D.,  Lawrence T., Nikolic-Paterson D., Pallardy M., Paini A., Pieters R., Roth R., Tschudi-Monnet F. Representing the Process of Inflammation as Key Events in Adverse Outcome Pathways. Toxicological Sciences, Volume 163, Issue 2, June 2018, Pages 346–352, https://doi.org/10.1093/toxsci/kfy047
  43. Wang G et al. Ambient fine particulate matter induce toxicity in lung epithelial-endothelial co-culture models. Toxicol Lett, 2019, 301: 133-145.
  44. Westphal, GA et al. Particle induced cell migration assay (PICMA): a new in vitro assay for inflammatory particle effects based on permanent cell lines. Toxicol. In vitro, 2015, 29(5):997-1005.