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Relationship: 1702

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

Interaction with the lung cell membrane leads to Increased proinflammatory mediators

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

Interaction with the lung cell membrane

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

Increased proinflammatory mediators

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 Do not cite	EAGMST Under Review
Interaction with lung resident cell membrane components leads to lung cancer	adjacent	Moderate	Moderate	Penny Nymark (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

Innate immune response is the first line of defence in any organism against invading infectious pathogens and toxic substances. It involves tissue triggered startle response to cellular stress and is described by a complex set of interactions between the toxic stimuli, soluble macromolecules and cells (reviewed in Nathan, 2002). The process culminates in a functional change defined as inflammation, purpose of which is to resolve infection and promote healing. In lungs, the interaction of toxic substances with resident cells results in cellular stress, death or necrosis leading to release of intracellular components such as alarmins (DAMPs, IL-1a, HMGB1). Released alarmins (danger sensors) bind cell surface receptors such as Interleukin 1 Receptor 1 (IL-1R1), Toll Like Receptors (TLRs) or others leading to activation of innate immune response signalling.

For example, binding of IL-1a to IL-1R1 can release Nuclear Factor (NF)-κb resulting in its translocation to nucleus and transactivation of pro-inflammatory genes including cytokines, growth factors and acute phase genes. The signalling also stimulates secretion of a variety of pro-inflammatory mediators. Overexpression of IL-1a in cells induces increased secretion of pro-inflammatory mediators. Products of necrotic cells are shown to stimulate the immune system in an IL-1R1-dependent manner (Chen et al., 2007).

The secreted alarmins activate resident cells pre-stationed in the tissues such as mast cells or macrophages leading to propagation of the already initiated immune response by releasing more eicosanoids, cytokines, chemokines and other pro-inflammatory mediators. Thus, secreted mediators signal the recruitment of neutrophils, which are the first cell types to be recruited in acute inflammatory conditions. Neutrophil influx in sterile inflammation is driven mainly by IL-1a (Rider P, 2011). IL-1 mediated signalling regulates neutrophil influx in silica-induced acute lung inflammation (Horning V, 2008). IL1 signalling also mediates neutrophil influx in other tissues and organs including liver and peritoneum. Other types of cells including macrophages, eosinophils, lymphocytes are also recruited in a signal-specific manner. Recruitment of leukocytesinduces critical cytokines associated with the Th2 immune response, including TNF-α, IL-1β, and IL-13.

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 relationship is high. There is a mechanistic relationship between the MIE (Event 1495) and KE1 (Event 1496) which has been evidenced in a number of both in vitro and in vivo model systems in response to stressors such as, asbestos, silica, bleomycin, carbon nanotubes, and metal oxide nanoparticles (Behzadi et al., 2017; Denholm & Phan 1990; Mossman & Churg 1998).

Increased expression of IL-1a or IL-1b following lung exposure to MWCNTs, bleomycin, micro silica particles, silica crystals, and polyhexamethyleneguanidine phosphate has been shown to be associated with neutrophil influx in rodents (Hornung et al., 2008; Girtsman et al., 2014; Gasse et al., 2007; Nikota et al., 2017; Suwara et al., 2013; Rabolli et al., 2014). Inhibition of IL-1 function by knocking out the expression of IL-1R1 using IL-1R1 KO mice or via treatment with IL-1a or IL-1b neutralising antibodies results in complete abrogation of lung neutrophilic influx following exposure to MWCNTs (Nikota et al, 2017), cigarette smoke (Halappanavar et al., 2013), silica crystals (Rabolli et al., 2014) and bleomycin (Gasse et al., 2017). In transgenic mice lacking IL1R1, Myd88 signalling or the IL-33 receptor St2, early inflammatory responses are suppressed following silica or bleomycin treatment (Dong, et al., 2014; Gasse et al., 2017).

Empirical Evidence

In this section authors are encouraged to cite specific evidence that supports the idea that a change in the upstream KE (KEupstream) will lead to, or is associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. Given the likelihood that new empirical support will be developed over time, particularly as various AOPs are tested and applied, it is most practical to provide empirical support in the form of bulleted lists or tables that include a short description of the nature of the empirical support along with the corresponding reference(s).In particular, it is useful to cite evidence showing that stressors that perturb KEupstream also perturb KEdownstream. Because this section of the KER description cites evidence from specific studies, it is also helpful to provide as much detail about the toxicological and biological context in which the measurements were made, as is feasible, including the stressor(s) tested, the effective doses at each KE, etc. While the KER itself is not intended to be stressor-specific, those details can aid the overall assessment of the individual AOPs that include that KER. These details also help inform the question of consistency of supporting data, consistency across different biological contexts for which the KER is relevant, and the applicability domain of the KER. However, authors are cautioned that this evidence should focus on data that only relate KEupstream to KEdownstream, and should avoid reference to other KEs, KERs and AOPs as much as possible in order to maintain modularity of the KER. Please see pages 40-41 of the User Handbook for more information. More help

Empirical support for this KER is moderate. There are limited in vitro studies, which show a temporal and dose-dependent relationship between these two events, using the upregulation of specific surface receptors as a proxy for direct membrane interaction (Chan et al., 2018; Denholm & Phan, 1990; Roy et al., 2014). There are also studies that provide general support for the idea that an interaction with the lung resident cell membrane components leads to increased, secretion of pro-inflammatory and pro-fibrotic mediators (Table 1).

Dose-Response Evidence:

There are a few studies which provide evidence for a dose-response relationship in this KER. An in vitro study demonstrated a concentration-response relationship, in which silica exposure induced increases in pro-inflammatory cytokines through scavenger receptors in cultured bone marrow-derived murine mast cells. Cells were exposed to 6.25, 12.5, 25 or 50 mg/cm² SiO₂ for 24 h. Scavenger receptor MSR2 expression increased over time at 50 mg/cm² and in a concentration dependant relationship. Moreover, TNF-a, IL-13 and MCP-1 increased in a concentration-dependent manner (Brown et al., 2007). This provides indications that at higher concentrations of the stressor, the interaction with the lung resident cell membrane components (Event 1495) leads to an increased secretion of pro-inflammatory mediators (Event 1496).

Temporal Evidence:

In vitro and in vivo studies have demonstrated temporal concordance of the KEs.

Toll-like receptor 4 signal pathway was evaluated in differentiated macrophages exposed to silica at 2.5 mg/cm². After 16 and 24 h, the mRNA expression level of TLR4 increased. Moreover, the protein expression level of TLR-4 and related MyD88/TIRAP pathway increased at 24 h. Release of IL-1b, IL-6, IL-10, and TNF-a was induced by silica exposure at 24 h. Pre-treatment with TAK-242, an inhibitor of TLR4 signaling, suppressed the release of the cytokines (Chan et al., 2018).

Macrophages exposed to ZnO nanoparticles at 2.5 mg/mL for 24 h increased the expression level of TLR6 and MyD88, TRAF, and IRAK. At 24 h, they also observed an increase in the mRNA and protein levels of the pro-inflammatory cytokines IL-1b, IL-6, and TNF-a. These results demonstrated that ZnO nanoparticles induced pro-inflammatory mediators by TLR stimulation and MAPKs activation (Roy et al., 2014).

The pro-inflammatory IL-1b induced granulocyte migration and can be produced as a result of cellular detection of PAMPs. Mice exposed to 2.5 mg/mouse of silica by instillation showed an increase of mRNA expression of pro-IL-1b in BALF at 6, 12, and 24 h post-exposure in a time-dependent manner. At early time points (1 h, 3 h, 6 h), there was an increase in the release of an alarmin (IL-1a) which indicates that the alarmin was released due to cell damage leading to cytokine production and an inflammatory reaction. Moreover, at 24 h, the levels of mature IL-1b and neutrophil accumulation in BALF increased. Neutralization or deletion of IL-1a reduced the observed responses (Rabolli et al., 2014).

Epithelial damage can lead to the release of alarmins. In this stead, conditioned media from primary human bronchial epithelial cells (PBECs) exposed to thapsigargin was able to induce a pro-inflammatory response in primary human lung fibroblasts. PBECs were exposed to thapsigargin 20 mM for 2 h. After that, the cell culture medium was replaced, and cells were incubated for 24 h. At this time, the medium was recovered and used to culture lung fibroblast for 5 h. This conditioned media from epithelial cells damage contains the alarmin IL-1a, which induced increased gene expression of IL-6, IL-8, MCP-1, and GMC-CSF in fibroblasts. These responses were reduced with anti-IL-1a treatment (Suwara et al., 2014).

Heijink et al. 2015 conducted a similar strategy to identify the relationship between DAMPs and pro-inflammatory mediator release after exposure to cigarette smoke (CS). Neutrophils treated with CS bubbled for 1 min, released high levels of HMGB1 as a consequence of necrotic cell death. The cell-free supernatant, which contains HMGB1, was used to culture human bronchial epithelial cells, and after 24 h it promoted the production of CXCL 8 by lung epithelial cells. Pharmacological inhibitors, such as 1-palmitoyl-2-arachidonyl-sn-glycero-3-phosphorylcholine (OxPAPC) and RAGE antagonist peptide (RAP), reduced the effect of CXC8L release.

HMGB1 and HSP-70 can be released by damaged hepatocytes. In a study, mice were treated with acetaminophen 350 mg/Kg for 3 and 6 h. At these time points, the liver perfusate was obtained and an increase in HSP-70 and HMGB1 protein levels was observed. RAW 264.7 cells treated with the liver perfusate exhibited increased mRNA expression levels of MCP-1 and IL-1b (Brittany et al. 2010).

Female mice were intratracheally administered with bleomycin at 5 mg/kg to represent idiopathic pulmonary fibrosis. IL-33, a molecule that can act as a DAMP, increased in lungs after 3 and 7 days of treatment. In serum, at 7-, 14- and 28-days post-exposure, IL-4 and IL-13 increased. It was concluded that IL-33/ST2 signaling pathway is involved in pulmonary fibrosis by bleomycin (Xu et al., 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

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 signaling 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.

Quantitative Understanding of the Linkage

The quantitative understanding section of the KER description is intended to capture information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. Empirical support addresses whether data between the two KEs are consistent with the patterns that are expected if the upstream event is causing the downstream event to occur, while the quantitative understanding section helps to define the precision with which the state of the downstream KE can be predicted from knowledge of the state of the upstream KE. These quantitative relationships may be defined in terms of correlations, response-response relationships, dose-dependent transitions or points of departure (i.e., a threshold of change in KEupstream needed to elicit a change in KEdownstream), etc. They may take the form of simple mathematical equations or sophisticated biologically-based computational models that consider other modulating factors such as compensatory responses, or interactions with other biological or environmental variables. Regardless of form, the idea is to briefly describe what is known regarding the quantitative relationship between the KEs and cite appropriate literature that defines those relationships and/or provides support for them.Data that confer quantitative understanding of a KER are not necessarily mutually exclusive from those addressing other weight of evidence considerations. In that respect, the quantitative understanding section of the KER description is not intended to be redundant with the other WoE sections. Rather, it is intended to aid application of the AOP by allowing a reader to rapidly identify the relationships that would support a quantitative prediction of the probability or magnitude of change in KEdownstream based on a known state of KEupstream. For transparency, the toxicological and biological context in which the quantitative relationships were defined should be indicated within the description. However, the ultimate goal is to identify quantitative relationships that generalize across the entire applicability domain of the two KEs being linked via the KER. More help

A majority of the in vivo studies are conducted with only one dose and thus, it is difficult to derive quantitative dose-response relationships based on the existing data. However, it is clear from the studies referenced above that greater concentrations or doses of pro-fibrotic substances results in higher release of alarmins, and consequently, higher pro- inflammatory signalling. The above studies also demonstrate strong temporal relationships between the individual KEs.

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

One study has demonstrated a response-response relationship for this KER.

Human intervertebral disc cells were treated with 0, 0.5, 1, or 2 mg/ml of recombinant HMGB1 for 24 h. Protein levels were determined in cell medium supernatant by ELISA. HMGB1 stimulates the expression of IL-6 and MMP-1 in a response-response relationship. A strong correlation was observed by Spearman’s rank correlation coefficient between HMGB1 treatment and IL-6 or MMP-1 levels (Shah et al., 2018).

Other reports have studied both KEs, but they do not indicate if the response-response relationship was linear or not (coefficient or correlation is not shown) (Fukuda et al. 2017; Kim et al., 2020, Piazza et al., 2013; Yang et al., 2012; Chakraborty et al., 2017).

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

Some studies have described how long after a change in the MIE (Event 1495; interaction substance and components), KE1 (Event 1496;pro-inflammatory mediators are secreted) is impacted (Table 3.).

Table 3. Time-scale related studies relevant to the MIE (Event 1495) - KE1 (Event 1496) relationship.

Reference	In vitro/in vivo/population study	Design	MIE (Event 1495)	KE1 (Event 1496)
Reference	In vitro/in vivo/population study	Design	Timepoint	Timepoint
Xu et al., 2016	In vivo	40 Female Kunming strain mice Bleomycin was intratracheally administered 5 mg/Kg. Days post-exposure	IL-33 3, 7 days	IL-4, IL-13 7, 14, and 28 days
Roy et al., 2014	In vitro	Primary mice macrophages exposed to 2.5 mg/ml ZnO for 24 hrs.	Increased TLR6 expression 0.5, 3, 6, 12, and 24 h	Increased IL-6, TNF-a 24 h
Rabollli et al., 2014	In vivo	Female C57BL/6 mice Exposed to silica 2.5 mg/mouse by instillation	Increased the release of IL-1 a 1, 3, and 6 h	Increased mRNA expression of pro IL-1b 6, 12, and 24 h

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

Pancreatic cancer cells stimulated with S100A8 and S100A9 released pro-inflammatory cytokines IL-8, TNF-a, and FGF. Cancer cell-derived conditioned media and the individual cytokines (TNF-a and TGF-b) induced the protein expression of S100A8 and S100A9 in HL-60 monocytic cell line and primary human monocytes (Nedjadi et al. 2018).

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

Anderson D et al. Evaluation of protease inhibitors and an antioxidant for treatment of sulfur mustard-induced toxic lung injury. Toxicology, 2009, 263: 41-46.
Andersohn A et al. Aggregated and hyperstable damage-associated molecular patterns are released during ER stress to modulate immune function. Frontiers in cell and development biology, 2019, 7, 198. DOI: 10.3389/fcell.2019.00198.
Andón F et al. Hollow carbon spheres trigger inflammasome-dependent IL-1b secretion in macrophages. Carbon, 2017, 113: 243-251.
Brittany V et al. The role of damage associated molecular pattern molecules in acetaminophen-induced liver injury in mice. Toxicol Lett. 2010, 192(3): 387-94.
Brown et al. Silica-Directed mast cell activation is enhanced by scavenger receptors. Am J Respir Cell Mol Biol, 2007, 36, 43-52.
Cassel S et al. The Nalp3 inflammasome is essential for the development of silicosis. PNAS, 2008, 105(26): 90-35-9040.
Chakraborty D et al. Alarmin S100A8 activates alveolar epithelial cells in the context of acute lung injury in a TLR4-dependent manner. Front Immunol, 2017, 8:1493.
Chan et al. Regulation of TLR4 in silica-induced inflammation: An underlying mechanism of silicosis. Int. J. Med. Sci. 2018, 15 (10): 986-991.
Chen, C., Kono, H., Golenbock, D., Reed, G., Akira, S. and Rock, K. (2007). Identification of a key pathway required for the sterile inflammatory response triggered by dying cells. Nature Medicine, 13(7), pp.851-856.
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.
Denholm, E. M., & Phan, S. H. (1990). Bleomycin binding sites on alveolar macrophages. Journal of leukocyte biology, 48(6), 519–523.
Dostert C et al. Innate immune activation through Nalp3 inflammasome sensing of asbestos and silica. 2008, 320: 674-677.
Fukuda K et al. Cytokine expression and barrier disruption in human corneal epithelial cells induced by alarmin released from necrotic cells. Jpn J Ophthalmol, 2017, 61(5): 415-422.
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.
Girtsman, T., Beamer, C., Wu, N., Buford, M. and Holian, A. (2014). 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.
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.
Heijink I et al. Cigarette smoke-induced damage-associated molecular pattern release from necrotic neutrophils triggers pro-inflammatory mediator release. American Journal of Respiratory Cell and Molecular Biology, 2015, Volume 52, number 5: 554-562
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.
Hu et al. Mitochondrial damage-associated molecular patterns (MTDs) are released during hepatic ischemia reperfusion and induce inflammatory responses. Plos one, 2015, 10(10): e0140105.
Jia J et al. Propofol inhibits the release of interleukin-6, 8 and tumor necrosis factor-a correlating with high-mobility group box 1 expression in lipopolysaccharides-stimulated RAW 264.7 cells. BMC anesthesiology, 2017, 17:148.
Jiraviriyakul et al. Honokiol-enhanced cytotoxic T lymphocyte activity against cholangiocarcinoma cells mediated by dendritic cells pulsed with damage-associated molecular patterns. World j Gastroenterol, 2019, 25(9): 3941-3955.
Kato R and Uetrecht J. Supernatant from hepatocyte cultures with drugs that cause idiosyncratic liver injury activates macrophage inflammasomes. Chem. Res. Toxicol. 2017, 30, 1327-1332.
Kim D et al. Suppressive effects of S100A8 and S100A9 on neutrophil epithelial cells in asthma. International Journal of Medical Sciences, 2020, 17(4):498-509.
Liang Y et al. Elevated IL-33 promotes expression of MMP2 and MMP9 via activating STAT3 in alveolar macrophages during LPS-induced acute lung injury. Cellular & Molecular Biology Letters, 2018, 23:52.
Maslanik T et al. The inflammasome and danger associated molecular patterns (DAMPs) are implicated in cytokine and chemokine responses following stressor exposure. Brain, Behavior, and Immunity 2013, 28, 54-62.
Nathan, C. (2002). Points of control in inflammation. Nature, 420(6917), pp.846-852.
Nedgadi F. S100A8 and S100A9 proteins from part of a paracrine feedback loop between pancreatic cancer cells and monocytes. BMC Cancer, 2018, 18: 1255.
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).
Osei E et al. Interleukin-1a drives the dysfunctional cross talk of the airway epithelium and lung fibroblasts in COPD. Eur Respir J. 2016, 48: 359-369.
Peeters P et al. Silica-induced NLRP3 inflammasome activation in vitro and in rat lungs. Particle and Fibre Toxicology, 2014, 11:58.
Piazza, O et al. S100B induces the release of pro-inflammatory cytokines in alveolar type I-like cells. International Journal of Immunopathology and Pharmacology, 2013, 26(2), 383-391.
Pouwels S et al. Cigarette smoke-induced necroptosis and DAMP release trigger neutrophilic airway inflammation in mice. 2015. Am J Physiol Lung Cell Mol Physiol. 310: L377-L386.
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