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Relationship: 1704

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

Recruitment of inflammatory cells leads to Loss of alveolar capillary membrane integrity

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 Moderate 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 Do not cite EAGMST Under Review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

Acute lung injury followed by normal repair of the ACM results in rapid resolution of the tissue injury and restoration of tissue integrity and function. The irreversible loss of alveolar membrane integrity occurs when 1) acute inflammation is not able to get rid of the toxic substance or invading pathogen (this happens following exposure to a toxic substance that is persistent or when the host is repeatedly exposed to the substance over a long period of time, 2) acute inflammation, originally incited to protect the host from external stimuli and to maintain normal homeostasis, by itself damages the host, resulting in tissue injury, and 3) the host fails to initiate a resolution response, which is essential to override the self-perpetuating inflammation response (Nathan, 2002). Loss of type-1 epithelial cells and endothelial cells, the collapse of alveolar structures and fusion of basement membranes, and persistent proliferation of type II alveolar epithelial cells on a damaged ECM, mark this phase (Strieter and Mehrad, 2009). The lung tissues from patients diagnosed with idiopathic pulmonary fibrosis show ultrastructural damage to the ACM with type-1 pneumocyte and endothelial cell injury (Strieter and Mehrad, 2009). In rodents treated with bleomycin, the damaged ACM resembles that seen in the fibrotic human lung (Grande et al., 1998).

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 is a mechanistic relationship between an increase in pro-inflammatory cells and mediators, and damage to the ACM (Bhalla et al., 2009; Ward 2003; Zemans et al., 2009).

Exposure to high doses of insoluble nanomaterials can impair the macrophage-mediated clearance process, initiating chronicity of inflammation characterized by cytokine release, ROS synthesis and the tissue damage cascade (Palecanda and Kobzik, 2001) and subsequently leading to tissue injury. For example, exposure to crystalline silica generates oxidative stress, increased release of pro-inflammatory cytokines (e.g. TNF-α, IL-1, IL-6), activation of transcription factors (e.g. NF-κB, AP-1), and other cell signalling pathways including MAP and ERK kinase (Hubbard et al., 2001; Hubbard et al., 2002; Fubini and Hubbard 2003). In silicosis, TNF-α is suggested to play a critical role in the observed pathogenicity (Castranova et al., 2004), which in turn, is dependent on activation of NF-κB and ROS synthesis (Shi et al.,1998; Cassel et al.,2008; Kawasaki et al., 2015). It has been proposed that IPF is a disorder of elevated oxidative stress, with the existence of an oxidant-antioxidant imbalance in distal alveolar air spaces (MacNee, 2001). Several studies have reported that anti-oxidant treatment attenuates the bleomycin-induced oxidative burden and subsequent pulmonary fibrosis (Wang et al., 2002; Serrano-Mollar et al., 2003; Punithavathi, et al., 2000).

Mice deficient in Nalp3 showed reduced inflammation, lower cytokine production and dampened fibrotic response following exposure to asbestos or silica (Dostert et al., 2008). SWCNT exposure induces alveolar macrophage activation, enhanced oxidative stress, increased and persistent expression of pro-inflammatory mediators associated with chronic inflammation and severe granuloma formation in mice (Chou et al., 2008). Bleomycin treatment induces increased lung weight, epithelial cell death, inflammation, increased hydroxyproline content, collagen accumulation and fibrotic lesions in mice, all of which were elevated in mice deficient in Nrf2 (Cho et al., 2004). MWCNT-induced fibrotic response is the result of interplay between oxidative stress and inflammation, which determines the severity of the fibrotic pathology. Mice lacking Nrf2 (the nuclear factor erythroid 2-related factor 2), that is associated with mounting anti-oxidant defense against oxidative stress, exhibit exuberant fibrotic responses to MWCNT (Dong and Ma, 2016).

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

Although there is enough evidence to suggest a role for persistent inflammation and oxidative stress in ACM integrity loss, a direct relationship is hard to establish as studies involving inhibition of early pro-inflammatory cellular influx alter other immune cell types, thereby altering the end outcome.

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

One publication examined the timescale of KE induction with relation to this KER, in the context of AOP 173. Mo et al., 2019 found that KE2 (Event 1497) (1 and 3 days post-exposure) precedes KE3 (Event 1498) (3 and 7 days post-exposure) in mice exposed to 50 μg per mouse of nickel nanoparticles by intratracheal instillation.

Reference

In vitro/in vivo/population study

Design

KE1 (Event 1496)

  KE2 (Event 1497)

KE3 (Event 1498)

KE6 (Event 1501)

Mo Y et al., 2019

In vivo

Mice C57BL/6, 50 mg per mouse intratracheal instillation

CXCL1/KC

1- and 3-days post-exposure

Neutrophil content

1 and 3 days

Post-exposure

LDH activity, oxidative stress protein content

3- and 7-days post-exposure

Hydroxyproline content

42 days post-exposure

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. Arras M et al. Interleukine-9 reduces lung fibrosis and type 2 immune polarization induced by silica particles in a murine model. Am J Respir Cell Mol Biol, 2001, 24, 368-375.
  2. Barosova H et al. Use of epiAlveolar lung model to predict fibrotic potential of multiwalled carbon nanotubes. ACSNANO, 2020, 14(4): 3941-3956.
  3. Bhalla, D. K., Hirata, F., Rishi, A. K., & Gairola, C. G. (2009). Cigarette smoke, inflammation, and lung injury: a mechanistic perspective. Journal of toxicology and environmental health. Part B, Critical reviews, 12(1), 45–64.
  4. Blum et al. Short-term inhalation of cadmium oxide nanparticles alters pulmonary dynamics associated with lung injury, inflammation, and repair in a mouse model. Inhalation toxicology, 2014, 26(1): 48-58.
  5. Caielli, S., Banchereau, J. and Pascual, V. (2012). Neutrophils come of age in chronic inflammation. Current Opinion in Immunology, 24(6), pp.671-677.
  6. Cassel, S., Eisenbarth, S., Iyer, S., Sadler, J., Colegio, O., Tephly, L., Carter, A., Rothman, P., Flavell, R. and Sutterwala, F. (2008). The Nalp3 inflammasome is essential for the development of silicosis. Proceedings of the National Academy of Sciences, 105(26), pp.9035- 9040.
  7. Castranova, V. (2004). Signaling Pathways Controlling The Production Of Inflammatory Mediators in Response To Crystalline Silica Exposure: Role Of Reactive Oxygen/Nitrogen Species. Free Radical Biology and Medicine, 37(7), pp.916-925.
  8. Chaudhary, N., Schnapp, A. and Park, J. (2006). Pharmacologic Differentiation of Inflammation and Fibrosis in the Rat Bleomycin Model. American Journal of Respiratory and Critical Care Medicine, 173(7), pp.769-776.
  9. Cho, H., Reddy, S., Yamamoto, M. and Kleeberger, S. (2004). The transcription factor NRF2 protects against pulmonary fibrosis. The FASEB Journal, 18(11), pp.1258-1260.
  10. Chou, C., Hsiao, H., Hong, Q., Chen, C., Peng, Y., Chen, H. and Yang, P. (2008). Single-Walled Carbon Nanotubes Can Induce Pulmonary Injury in Mouse Model. Nano Letters, 8(2), pp.437-445.
  11. Cui A et al. VCAM-1 mediated neutrophil infiltration exacerbates ambient fine particle-induced lung injury. Toxicology Letters, 2018, 1;302: 60-74.
  12. Dong, J. and Ma, Q. (2016). In vivo activation of a T helper 2-driven innate immune response in lung fibrosis induced by multi-walled carbon nanotubes. Archives of Toxicology, 90(9), pp.2231-2248.
  13. Dostert, C., Petrilli, V., Van Bruggen, R., Steele, C., Mossman, B. and Tschopp, J. (2008). Innate Immune Activation Through Nalp3 Inflammasome Sensing of Asbestos and Silica. Science, 320(5876), pp.674-677.
  14. Fubini, B. and Hubbard, A. (2003). Reactive oxygen species (ROS) and reactive nitrogen species (RNS) generation by silica in inflammation and fibrosis. Free Radical Biology and Medicine, 34(12), pp.1507-1516.
  15. Gautam N et al. Kinetics of leukocyte-induced changes in endothelial barrier function. British Journal of Pharmacology, 1998, 125, 1109-1114.
  16. Grande, N. R., Peao, M. N. ., de Sa, C. M., & Aguas, A. P. (1998). Lung Fibrosis Induced by Bleomycin: Structural Changes and Overview of Recent Advances. Scanning Microsc, 12(3), 487–494.
  17. Hubbard, A., Timblin, C., Rincon, M. and Mossman, B. (2001). Use of Transgenic Luciferase Reporter Mice To Determine Activation of Transcription Factors and Gene Expression by Fibrogenic Particles. Chest, 120(1), pp.S24-S25.
  18. Hubbard, A., Timblin, C., Shukla, A., Rincón, M. and Mossman, B. (2002). Activation of NF-κB-dependent gene expression by silica in lungs of luciferase reporter mice. American Journal of Physiology-Lung Cellular and Molecular Physiology, 282(5), pp.L968-L975.
  19. Inoue H et al. Ultrastructural changes of the air-blood barrier in mice after intratracheal instillation of lipopolysaccharide and ultrafine carbon black particles. Experimental and toxicology pathology, 2009, 61: 51-58.
  20. Janga H et al. Site-specific and endothelial-mediated dysfunction of the alveolar-capillary barrier in response to lipopolysaccharides. J Cell Mol Med, 2018, 22(2): 982-998.
  21. Kasai T et al. Lung carcinogenicity of inhaled multi-walled carbon nanotube in rats. Particle and Fibre Toxicology, 2016, 13:53.
  22. Kasper J et al. Inflammatory and cytotoxic responses of an alveolar-capillary co-culture model to silica nanoparticles: Comparison with conventional monocultures. Particle and Fibre Toxicology, 2011,8:6.
  23. Kawasaki, H. (2015). A mechanistic review of silica-induced inhalation toxicity. Inhalation Toxicology, 27(8), pp.363-377.
  24. Kim H et al. Polyhexamethylene guanidine phosphate aerosol particles induce pulmonary inflammatory and fibrotic responses. Arch Toxicol. 2015, 90(3): 617-32.
  25. Kim, S., Lee, J., Yang, H., Cho, J., Kwon, S., Kim, Y., Her, J., Cho, K., Song, C. and Lee, K. (2010). Dose-response Effects of Bleomycin on Inflammation and Pulmonary Fibrosis in Mice. Toxicological Research, 26(3), pp.217-222.
  26. Koli, K., Myllärniemi, M., Keski-Oja, J. and Kinnula, V. (2008). Transforming Growth Factor-β Activation in the Lung: Focus on Fibrosis and Reactive Oxygen Species. Antioxidants & Redox Signaling, 10(2), pp.333-342.
  27. Ma, B., Whiteford, J., Nourshargh, S. and Woodfin, A. (2016). Underlying chronic inflammation alters the profile and mechanisms of acute neutrophil recruitment. The Journal of Pathology, 240(3), pp.291-303.
  28. MacNee, W. (2001). Oxidative stress and lung inflammation in airways disease. European Journal of Pharmacology, 429(1-3), pp.195-207.
  29. Marcus B et al. Loss of endothelial barrier function requires neutrophil adhesion. Surgery, 1997: 420-427
  30. Mo Y et al. Comparative mouse lung injury by nickel nanoparticles with differential surface modification. Journal of Nanobiotechnology, 2019, 17:2.
  31. Morimoto Y et al. Pulmonary toxicity of well-dispersed cerium oxide nanoparticles following intratracheal instillation and inhalation. J Nanopart Res, 2015, 17:442.
  32. Nathan, C. (2002). Points of control in inflammation. Nature, 420(6917), pp.846-852.
  33. Nemmar A et al. Chronic exposure to water-pipe smoke induces alveolar enlargement, DNA damage and impairment of lung function. Cell Physiol Biochem, 2016, 38:382-992.
  34. Pacheco Y et al. Granulomatous lung inflammation is nanoparticle type-dependent. Experimental lung research, 2018, 44(1): 25-39.
  35. Palecanda, A. and Kobzik, L (2001). Receptors for Unopsonized Particles: The Role of Alveolar Macrophage Scavenger Receptors. Current Molecular Medicine, 1(5), pp.589-595.
  36. Park K et al. Bronchoalveolar lavage findings of radiation induced lung damage in rats. J. Radiat. Res., 2009, 50: 177-182.
  37. Porter D et al. Acute pulmonary dose-responses to inhaled multi-walled carbon nanotubes. Nanotoxicology, 2013, 7(7): 1179-1194.
  38. Punithavathi, D., Venkatesan, N. and Babu, M. (2000). Curcumin inhibition of bleomycin-induced pulmonary fibrosis in rats. British Journal of Pharmacology, 131(2), pp.169-172.
  39. Sapoznikov A et al. Early disruption of the alveolar-capillary barrier in a ricin-induced ARDS mouse model: neutrophil-dependent and -independent impairment of junction proteins. Am J Physiol Lung Cell Mol Physiol, 2019, 316(1): L255-L268.
  40. Sellamuthu R et al. Molecular mechanisms of pulmonary response progression in crystalline silica exposed rats. Inhalation toxicology, 2017, 29(2): 53-64.
  41. Serrano-Mollar, A., Closa, D., Prats, N., Blesa, S., Martinez-Losa, M., Cortijo, J., Estrela, J., Morcillo, E. and Bulbena, O. (2003). In vivoantioxidant treatment protects against bleomycin-induced lung damage in rats. British Journal of Pharmacology, 138(6), pp.1037-1048.
  42. Shi, X., Castranova, V., Halliwell, B. and Vallyathan, V. (1998). Reactive oxygen species and silicainduced carcinogenesis. Journal of Toxicology and Environmental Health, Part B, 1(3), pp.181-197.
  43. Shinozaki S et al. Pulmonary hemodynamics and lung function during chronic paraquat poisoning in sheep. Am Rev Respir Dis, 1992, 146:775-780.
  44. Soehnlein, O., Steffens, S., Hidalgo, A. and Weber, C. (2017). Neutrophils as protagonists and targets in chronic inflammation. Nature Reviews Immunology, 17(4), pp.248-261.
  45. Strieter, R. and Mehrad, B. (2009). New Mechanisms of Pulmonary Fibrosis. Chest, 136(5), pp.1364-1370.
  46. Umbright, C., Sellamuthu, R., Roberts, J. R., Young, S. H., Richardson, D., Schwegler-Berry, D., McKinney, W., Chen, B., Gu, J. K., Kashon, M., & Joseph, P. (2017). Pulmonary toxicity and global gene expression changes in response to sub-chronic inhalation exposure to crystalline silica in rats. Journal of toxicology and environmental health. Part A, 80(23-24), 1349–1368.
  47. Wan et al. Cobalt nanoparticles induce lung injury, DNA damage and mutations in mice. Particle and Fibre Toxicology, 2017, 14:38.
  48. Ward P. A. (2003). Acute lung injury: how the lung inflammatory response works. The European respiratory journal. Supplement, 44, 22s–23s.
  49. Wang, H., Yamaya, M., Okinaga, S., Jia, Y., Kamanaka, M., Takahashi, H., Guo, L., Ohrui, T. and Sasaki, H. (2002). Bilirubin Ameliorates Bleomycin-Induced Pulmonary Fibrosis in Rats. American Journal of Respiratory and Critical Care Medicine, 165(3), pp.406-411.
  50. Zeidler-Erdely, P. C., Battelli, L. A., Stone, S., Chen, B. T., Frazer, D. G., Young, S. H., Erdely, A., Kashon, M. L., Andrews, R., & Antonini, J. M. (2011). Short-term inhalation of stainless steel welding fume causes sustained lung toxicity but no tumorigenesis in lung tumor susceptible A/J mice. Inhalation toxicology, 23(2), 112–120.
  51. Zemans, R. L., Colgan, S. P., & Downey, G. P. (2009). Transepithelial migration of neutrophils: mechanisms and implications for acute lung injury. American journal of respiratory cell and molecular biology, 40(5), 519–535.