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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

Recruitment of inflammatory cells

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

Loss of alveolar capillary membrane integrity

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 Do not cite	EAGMST Under Review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).

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 is both temporal and dose-response evidence to suggest that an increased amount of pro-inflammatiory immune cells potentiates alveolar capillary damage. However, few studies assessing these KEs include multiple concentrations and timepoints, and as such, these KEs are typically reported as occurring together (i.e. damage is detected along with an increase in cell abundance) (Umbright et al., 2017; Zeidler-Erdley et al., 2011; Additional references available in Table 1).

Dose-Response Evidence:

There are some studies that 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/concentration of the stressor, the recruitment of pro-inflammatory cells increased leading to loss of alveolar-capillary membrane integrity.

Microvascular endothelial and the human lung adenocarcinoma cell lines in co-culture were exposed to 6-600 mg/ml silica nanoparticles (NPs). After 4 h of exposure and 20 h recovery, siCAM-1 (intracellular adhesion molecule 1), IL-6, and IL-8 increased in a concentration-dependant manner. These cytokines increased the recruitment and regulation of neutrophils. The study suggests that there is crosstalk of both cell types in co-culture that leads to basolateral cytokine secretion. Moreover, TEER decreased in a concentration-dependant manner after 4 h exposure, and changes in the assembly of cell-cell junctions were observed (Kasper J et al. 2011).

Polyhexamethylene guanidine phosphate is used for the prevention of microorganism growth in humidifiers. To evaluate the inflammation response after the exposure to this chemical, three human lung cells (Calu-3, differentiated THP-1, and HMC-1 cells) were culture and exposed at the air-liquid interface at 2.2, 4.4, 8.8, and 17.6 mg/ml for 1, 6 and 24 h. An increase in chemoattractant cytokine IL-8 release was observed in a concentration- and time-dependant manner. This response preceded the alveolar-capillary membrane integrity loss, which occurred at 24 h at the highest concentration. These changes were followed by reactive oxygen species (ROS) generation, an increase in the levels of MMP-2, TIMP-1, MMP-9, TIMP-2 mRNA expression, and the release of TNF-a, IL-6 and TGF-b1. Moreover, inflammatory cell infiltration, fibrosis, and the release of cytokines was observed in lung sections in Sprague-Dawley rats following a 3 week exposure (Kim et al., 2015).

Arras et al. (2001) studied the effect of IL-9 on the development of lung fibrosis after crystalline silica particle (DQ12) exposure. Transgenic Tg5 mice expressing high levels of IL-9 and wild type (WT) FVB mice were exposed to DQ12 particles by intratracheal instillation at 1 or 5 mg, and mice were sacrificed 2, 4, and 6 months after instillation. Recruitment of pro-inflammatory cells and an increase in the level of LDH and proteins were observed in BALF after 2 and 4 months at the highest exposure (5 mg). Hydroxyproline content in the lung increased over time with the highest levels seen 4 months after instillation. Moreover, IL-4 increased in a dose-dependent manner after 2 months of exposure. In contrast, IFN-g decreased after 2 months and 4 months after the highest exposure dose. In Tg5 mice, fibrosis was less severe than in WT mice. Moreover, intraperitoneal injection of IL-9 in C57BL/6 reduced the amplitude of silica-induced lung fibrosis associated with a recruitment of B lymphocytes in the lung parenchyma.

Morimoto et al. (2015) studied the inflammatory response of cerium oxide NPs in the acute and chronic phases. Male F344 rats were exposed once to 0.2 mg or 1 mg of NPs. Total cell counts in BALF increased at 1 mg after one week, and decreased after 1 and 3 months. Only neutrophils increased at the lowest dose at 3 days and 1 week after exposure and dropped after 1, 3, and 6 months. LDH activity in BALF increased after 3 days of exposure and decreased over time (1 week to 3 months). In the chronic phase, the total cell counts increased 3-days post-exposure, with no effects after 1 and 3 months. LDH increased after 3 days of administration and decreased in a time-dependant manner. The effects were more severe at the highest dose.

Temporal Evidence:

There is strong evidence of a temporal relationship between the two KES. In vitro and in vivo studies have demonstrated that the recruitment of pro-inflammatory cells increased prior to loss of alveolar-capillary membrane integrity.

Mice exposed to aerosolized multiwalled carbon nanotubed (MWCNT) at 10mg/m³ (5 h per day for 2, 4, 8 or 12 days) developed pulmonary inflammation 1-day post-exposure. There was an increase in the recruitment of polymorphonuclear cells, neutrophil chemoattractant, albumin concentration, and LDH activity in the whole lung fluid in a time-dependant manner (Porter et al., 2013).

Research has showb that crystalline silica induces pulmonary toxicity. Rats exposed to this stressor at 15 mg/m³, 6 h/day, 5 days/week for 3, 6, and 12 weeks, showed an accumulation of macrophages and neutrophils in BALF. Moreover, an increase in BALF LDH activity and albumin content was also observed (Umbright et al., 2017).

Gautam et al. (1998) studied the effect of a chemotactic stimulant, fMLP, on polymorphonuclear cells resting on an endothelial cell monolayer in the upper compartment. Bovine aorta endothelial cells exposed to 10^-7 M fMLP in the upper compartment induced adhesion of polymorphonuclear (PMN) cells followed by a decrease in transendothelial electrical resistance and an increase in protein permeability for 10-50 min in a time-dependant manner. The findings indicate that PMN are activated by fMLP and adhere to the endothelium which induces an increase in cytosolic free Ca²⁺. This lead to a decrease in electrical resistance, leading to a structural rearrangement of endothelial cells which impairing their barrier integrity.

Paraquat is a herbicide that induces pulmonary toxicity. Chronic exposure to Paraquat results in inflammation, damage to alveolar epithelial cells, and fibrosis. In sheep exposed to Paraquat at 5 mg/Kg (intramuscular), there was an increase in the number of granulocytes after 1, 2, and 3 weeks in a dose-dependent manner. After 3 weeks of paraquat administration alveolar wall thickening was observed, and the concentration of lung malondialdehyde increased as an indicator of lipid peroxidation. These results indicate that the recruitment of pro-inflammatory cells precedes alveolar damage (Shinozaki et al., 1992).

Exposure to nickel NPs can induce oxidative stress and lung inflammation. In a dose-response study, mice were intratracheally instilled with 0, 10, 20, 50, and 100 mg per mouse of nickel NPs and sacrificed at day 3 post-exposure. There was an increase in the neutrophil count in BALF, LDH activity, and total protein in BALF in a dose-dependent manner; the highest response was observed at 50 mg per mouse.

In a time-response study, mice were intratracheally instilled with 50 mg per mouse of nickel NPs and sacrificed at days 1, 3, 7, 14, 28, and 42 post-instillation. The recruitment of neutrophils increased at day 1 and 3 post-exposure. The levels of TBARS and 8-OHdG increased after 3 days of instillation. LDH activity and total protein in BALF increased at day 3 and 7 post-exposure. These responses decreased after 14 days post-exposure. However, at 42-day post-exposure, an increase in the level of hydroxyproline content was observed in lung tissues exposed to NPs (Mo et al., 2019).

Park et al. (2009) used BALF indicators as a tool for evaluating radiation-induced lung damage. Sprague-Dawley rats received 20 Gy of radiation to the right lung. At 3, 7, 14, 28, and 56 days after radiation, rats were sacrificed. Total cells in the BALF increased at 14 and 28 days, with the highest levels at day 56. Meanwhile, total protein in BALF increased after 7 days of radiation and peaked at 28 days post-radiation. The levels of TGF-b increase after 56 days of radiation.

Sapoznikov et al. (2019) studied the role of neutrophils in the early disruption of the alveolar-capillary barrier in a ricin-induced ARDS mouse model. Female CD-1 mice were administered intranasally with crude ricin (50 ml; 7mg/Kg diluted in PBS), and after 3, 6, 24, 48, and 72 h animals were sacrificed. The neutrophil count increased at 24, 48, and 72 h post-exposure. Alveolar capillary membrane integrity loss was evaluated as Evans blue dye extravasation and the protein expression of VE-cadherin claudin 18, claudin 5, connexin 43, and occludin. After 6 h post-exposure, alveolar permeability increased in a time-dependent manner. From 3 h post-exposure, the decrease of junction proteins was evident. Animals treated with anti-ricin antibody, anti-Ly6G (neutrophil depletion), and marismat (MMP inhibition) showed less severity in alveolar membrane integrity loss.

Wan et al. (2017) studied the genotoxic effects of cobalt NPs (Nano-Co) and their capacity for causing oxidative stress and inflammation. Gpt delta transgenic mice were exposed to 50 μg/mouse of Nano-Co by intratracheal instillation, and animals were sacrificed at days 1, 3, 7, and 28 post exposure, as well as 4 months post exposure. The levels of CXCL1/KC and neutrophils increased at 1, 3, and 7 days post-exposure, but they decreased at day 28. LDH activity and protein content in BALF also increased, but their levels dropped after 28 days post-exposure. Four months after instillation, 8-OHdG levels were measured and were found in high levels. Moreover, histological changes were observed 7 days (infiltration of a large amount of neutrophils and macrophages in the alveolar space and septa, focal alveolar epithelial cell hyperplasia, and thickening of the alveolar wall) and 4 months (interstitial fibrosis, bronchiolization of the alveoli and collagen deposition in the alveolar septa) after the exposure.

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.

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

In the context of lung fibrosis, data supporting quantitative dose-response relationships between the individual KEs is scarce. A majority of the mechanistic studies investigating the role of inflammation in lung fibrosis report acute neutrophilic inflammation and how altering neutrophil influx acutely after exposure to a toxic substance alters the end fibrotic outcome. However, these studies do not characterise the impact on immediate downstream KEs including the loss of ACM integrity or chronic inflammation in the absence of acute neutrophilia. Few studies have shown such concordance. For example, in mice exposed to different doses of bleomycin, total number of cells in BALF increased in a dose-dependent manner with predominant neutrophil phenotype at 7 days post-exposure and macrophage dominance at 24 days post-exposure (Kim et al., 2010). Other studies have shown that upon onset of chronic inflammation, secondary stimuli such as persisting toxic substance can make the injured tissue highly sensitive to acute inflammatory stimuli and may in turn fuel the ongoing chronic inflammation and affect the disease process (Ma et al., 2016).

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

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.
Barosova H et al. Use of epiAlveolar lung model to predict fibrotic potential of multiwalled carbon nanotubes. ACSNANO, 2020, 14(4): 3941-3956.
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.
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.
Caielli, S., Banchereau, J. and Pascual, V. (2012). Neutrophils come of age in chronic inflammation. Current Opinion in Immunology, 24(6), pp.671-677.
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.
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.
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.
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.
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.
Cui A et al. VCAM-1 mediated neutrophil infiltration exacerbates ambient fine particle-induced lung injury. Toxicology Letters, 2018, 1;302: 60-74.
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.
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.
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.
Gautam N et al. Kinetics of leukocyte-induced changes in endothelial barrier function. British Journal of Pharmacology, 1998, 125, 1109-1114.
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.
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.
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.
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.
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.
Kasai T et al. Lung carcinogenicity of inhaled multi-walled carbon nanotube in rats. Particle and Fibre Toxicology, 2016, 13:53.
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