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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

Increased proinflammatory mediators

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

Recruitment of inflammatory cells

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 Do not cite	EAGMST Under Review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).

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

The empirical support for this KER is moderate. There are many studies which show temporal and dose-dependent recruitment of immune cells following increases in pro-inflammatory mediators. However, these mediators exhibit pleiotropy, and knockdown or knockout of a single pathway or mediator can result in compensation and recruitment of immune cells at a later time, as is seen in Nikota et al.,. 2017. (Chen et al., 2016; Nikota et al., 2017; Schremmer et al., 2014) (Additional studies available in Table 1.).

Dose-Response Evidence:

Many studies provide dose-response evidence of this KER. For example, in vitro and in vivo studies testing stressors at different doses/concentrations have demonstrated a dose-response relationship; at the higher dose of the stressor, the pro-inflammatory mediators increased, leading to an increase of pro-inflammatory cell recruitment.

Ma, et al. (2016) studied inflammatory responses in male BALB/c mice exposed to multi-walled carbon nanotubes (MWCNT) administered intravenously at different doses (0.5-4 mg/kg) for 2 days. A dose-dependant relationship was found between the levels of the inflammatory mediators IL-6 and TNF-a and the MWCNT dose. At the highest dose, 4 mg/Kg, white blood cells, lymphocytes, and neutrophils levels increased.

Porter et al. (2020) have demonstrated that MWCNT caused dose-dependent and time-dependent pulmonary inflammation in male C57BL/6J mice. Animals received a single dose of 2.5, 10, or 40 mg/mouse. At 40 mg/mouse, IL-1b and IL-18 increased at one day post-exposure. Moreover, polymorphonuclear leukocyte increased on day 1, and after 7 days the number of inflammatory cells was higher.

Zinc oxide nanoparticles (NPs) can induce metal fume fever and acute inflammation. Female C57BL/6J mice were intratracheally instilled once at 11, 33, and 100 mg/kg with coated ZnO NPs. Inflammatory responses were evaluated after 1, 3, and 28 days of exposure. An increase in serum amyloid A3 mRNA in lung tissue was observed at 33 and 100 mg/kg. Neutrophils accumulated in BAL fluid after 28 days of exposure in a dose-dependant manner (Hadrup et al., 2019).

Polyhexamethyleneguanidine phosphate (PHMG-P) is used as a disinfectant. PHMG-P at 0.3, 0.9, and 1.5 mg/kg was instilled into the lungs of mice. At 7- and 14-days post-exposure an increase in the levels of pro-inflammatory markers (IL-1b, IL-6 and CXCL1) and an increase in mRNA levels of MCP1, MMP2, and MMP12 was seen. Moreover, on day 7, neutrophils were recruited to the inflamed site. These changes were observed in a dose-response manner (Song et al., 2014).

Bourdon et al. 2012 evaluated the toxicity of carbon black nanoparticles (CBNPs) in mouse lung and liver. C57BL/6 mice were exposed to Printex 90 CBNPs with 0.018, 0.054, or 0.162 mg, and after 1, 3, and 28 days of the single instillation, BAL fluid was analyzed. Polymorphonuclear cell counts in BAL increased in a dose-dependant manner with the strongest recruitment 1- and 3-days post-exposure and remained elevated at day 28. CBNP also increased the expression of Saa3 mRNA levels in lung tissue on days 1, 3, and 28 in a dose-dependant manner. Although this response decreased over time, the expression of Saa3 mRNA increased at all time points, which indicates a persistent acute phase response.

A study evaluated the mechanisms of toxicity after exposure to PM2.5 in a tri-culture system: A549 cells and THP-1 differentiated macrophages in the apical chamber; meanwhile, EA.hy926 endothelial cells were cultured in the basolateral chamber. The system was exposed to PM2.5 at three different concentrations 20, 60, and 180 mg/ml for 24 h. An increase in the pro-inflammatory mediators IL-6, IL-8, and TNF-a was observed, as well an increase in mRNA expression of MMP9, ICAM-1, and CAV-1. These genes are involved in the movement and recruitment of leukocytes in sites of inflammation. Changes were observed in a concentration-dependant manner (Wang et al., 2019).

In another study female C57BL/6 mice were exposed to 18, 54, or 162 mg of MWCNT/mouse via single intratracheal instillation. An increased gene expression of Cxcl1, IL-6, Mt2, Saa1, and Saa2 was observed in a dose-dependent manner at 24 h post-exposure. Moreover, an increase in the recruitment of pro-inflammatory cells was observed in a dose-dependent manner (Poulsen et al., 2013).

Temporal Evidence:

There is significant evidence of the temporal relationship between the two KES. In vitro and in vivo studies have demonstrated that pro-inflammatory mediators (Event 1496) increased prior to the recruitment of pro-inflammatory cells (Event 1497).

Female C57BL/6J mice were exposed to carbon nanoparticles at 20 mg/mouse via intratracheal instillation. An increase in the levels of cytokines CXCL1, CXCL2, and CXCL5 at 3 h post-exposure was observed, with peaks after 12 and 18 h post-exposure. These pro-inflammatory mediators preceded neutrophil recruitment (12 and 24 h post-exposure) (Chen et al., 2016). Alveolar macrophages (AM) were isolated from lungs 3 to 12 h after CNP instillation, but they did not show a pro-inflammatory response. The authors suggest that AM are not involved in the initiation of the inflammatory response. Meanwhile, ATII cells induced the highest CXCL levels and acute neutrophilic inflammation.

Nickel oxide NPs intratracheally instilled at one single dose 200 cm²/rat into female Wistar rats induced an increase of pro-inflammatory cytokines in BALF, at 24 and 74 h for CINC-3 and eotaxin, respectively. At 24 h and 48 h, neutrophils were observed, and after 72 h, the levels of neutrophils, eosinophils, and macrophages increased (Lee et al., 2016).

Porter et al. (2002) have shown pulmonary inflammation in rats exposed to crystalline silica aerosol at a concentration of 15 mg/m³ (6h/day, 5 days/week) for 116 days. Lung disease was linked to TNF-a and IL-10 production in a timely response (10-116 days). The number of polymorphonuclear cells in the BALF increased progressively from day 41 - 116.

One study has demonstrated a dose-response and temporal relationship for these two KEs (Patowary et al., 2020). Female Wistar rats were exposed to oleoresin capsicum (OC) sprays at 2, 6, and 10%, and after 1, 3, and 24 h post-exposure, blood cell and BALF cytokines were evaluated. The pro-inflammatory cytokine TNF-a increased in a dose-dependant manner, and polymorphonuclear cells increased in a time-dependant manner.

Schremmer et al. (2014) have reported the time course of chemotaxis in vitro in response to the challenge of biopersistent particles and their relation to inflammatory mediators. NR8383 rat alveolar macrophages were challenged with different types of particles for 1, 4, and 16 h. The cell supernatants obtained from different time points were used to evaluate the chemotaxis of unexposed NR8383 macrophages. They found that nanosized silica at 16 mg/cm² induced an elevated transcription of CCL4, CXCL1, CXCL3, and TNF-a in a time-dependant manner. The pro-inflammatory cytokines present in the supernatants induced chemotaxis of unexposed macrophages at 4 and 16 h post-exposure.

Husain et al. (2015) found increased expression of genes related to chemotactic recruitment of pro-inflammatory cells at 3 h and 1 day after exposure to 162 mg/mouse carbon black nanoparticles in female C57BL/6 mice. They observed an increase in the gene expression of pro-inflammatory mediators at day 1 (Cxcl2, Ccl2), day 3 (IL-17, IL-33), day 14 (Cd2), and day 42 (Cxcl) post-exposure. The KE2 (Event 1497) increased over time with the maximum levels of neutrophils, macrophages, eosinophils, and lymphocytes at 4- and 5-days post-exposure. This response suggests chronic inflammation occurs because of an incomplete resolution of acute inflammation.

Rahman et al. 2017 evaluated whether different TiO₂ NPs induce lung inflammation. C57BL/6 mice were exposed to 18, 54, 162, or 486 mg/mouse of TiO₂ NPs via single intratracheal instillation. At 1-day post-exposure, gene expression analysis showed more changes in genes associated with inflammation and fibrosis. Moreover, after 1- and 28-days post-exposure, an increase in cell counts in BALF was observed in a dose-dependant manner.

Ho et al., 2013 evaluated the inflammatory response in mice exposed to coated quantum dots (QD705-PEG, QD705-COOH) at 12 or 60 mg/mouse. At 2-, 17- and 90-days post-exposure, an increase in the level of TNF-a, IL-1b, IL-6, CXCL1, CCL2, CCL1, CCL17, and CXCL13 mRNA levels in lungs was observed and the amount of polymorphonuclear cells in BALF increased in a dose-dependent manner at day 7 post-exposure. The inflammatory response increased on days 2 and 17, but on day 90 decreased. QD705-COOH induced granulomas persistently presented from 2 to 90 days.

Morimoto et al. 2010 examined the different kinds of cytokines related to lung inflammation by nickel oxide exposure. Rats were intratracheally exposed to 0.33 mg/Kg and 0.66 mg/kg nickel oxide NPs and were sacrificed at day 3, after 1 week, 1, 3, and 6 months post exposure. Infiltration of alveolar macrophages in lung tissue and BALF was observed from day 3 to 3 months post exposure, with higher levels after 1 and 3 months. Before the recruitment of inflammatory cells, an increase in the level of pro-inflammatory cells as MCP-1 and IL-1b in BALF was observed. Nickel oxide nanoparticles induced a persistent inflammatory effect.

Kamata et al. (2011) studied the impact of carbon black nanoparticles on susceptible subjects with predisposing lung disease and the effects of nanoparticles on inflammation and fibrotic changes. To achieve this goal, female C57BL/6J mice were intratracheally administered with bleomycin 20 mg/mouse and carbon black nanoparticles 10 mg/mouse. Evaluations were performed post-exposure at different time points. An increase of IL-6 and CCL2 in BALF was observed at days 2 and 7. After 7- and 14-days, a recruitment of pro-inflammatory cells was observed. Oxidant injury (evaluated as nitrotyrosine expression) was observed after 7 days and 14 days. The levels of TGF-b₁increased over time with the highest level at day 14. Finally, they observed an increase in lung collagen deposition, and a decrease in lung compliance at day 21.

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.

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

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

Alghsham R et al. Zinc oxide nanowires exposure induces a distinct inflammatory response via CCL11-mediated eosinophil recruitment. Frontiers in immunology, 2019, 10: 2604.
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.
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.
Crouzier D et al. Carbon nanotubes induce inflammation but decrease the production of reactive oxygen species in lung. Toxicology, 2010, 272: 39.45.
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.
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.
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.
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.
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.
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.
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.
HARRIS H. (1954). Role of chemotaxis in inflammation. Physiological reviews, 34(3), 529–562.
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.
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.
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.
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.
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.
Khatri M et al. Chronic upper airway inflammation and systemic oxidative stress from nanoparticles in photocopier operators: Mechanistic insights. NanoImpact, 2017, 5: 133-145.
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.
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
Liao D et al. Persistent pleural lesions and inflammation by pulmonary exposure of multiwalled carbon nanotubes. Chem Res Toxicol, 2018, 31(10): 1025-1031.
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
Ma, J et al. Carbon nanotubes stimulate synovial inflammation by inducing systemic pro-inflammatory cytokines. Nanoscale, 2016, 8, 18070-18086.
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.
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