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Relationship: 2959

Title

A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

Interaction with the lung cell membrane leads to Systemic acute phase response

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Interaction with the lung cell membrane
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). More help
Systemic acute phase response

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

AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Substance interaction with lung resident cell membrane components leading to atherosclerosis non-adjacent High Moderate Ulla Vogel (send email) Under development: Not open for comment. Do not cite Under Development

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) 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.  More help
Term Scientific Term Evidence Link
mouse Mus musculus High NCBI
human Homo sapiens High NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help
Sex Evidence
Male High
Female High

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER.  More help
Term Evidence
All life stages High

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. More help

This KER presents the association between the interaction of stressors with the lung resident cell membrane components (Key event 1495) and the induction of systematic acute phase response (Key event 1439). The evidence of the KER presented is based on animal studies (mice), controlled human studies and epidemiological studies.

Evidence Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings.  Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

Evidence Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help
Biological Plausibility
Addresses the biological rationale for a connection between KEupstream and KEdownstream.  This field 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.   More help

The biological plausibility is high. Pulmonary inflammation occurs when stressors interact with the airways (Moldoveanu et al., 2009) and acute phase response is induced during inflammatory conditions (Gabay & Kushner, 1999). It has been shown (see table below) that exposure to different stressors produces an increase of acute phase proteins in blood [i.e. C-reactive protein (CRP) and serum amyloid A (SAA)] in humans and mice.

Uncertainties and Inconsistencies
Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. More help

In the case of nanomaterials, it has been shown that physicochemical characteristics as size, surface area, surface functionalization, shape, composition, among others, affect the magnitude and duration of acute phase response in mice (Bengtson et al., 2017; Gutierrez et al., 2023; Poulsen et al., 2017).

It has been observed that in most controlled human studies, an increase in C-reactive protein (CRP) and/or serum amyloid A (SAA) was observed after exposure to particulate matter (Baumann et al., 2018; Monse et al., 2018; Monse et al., 2021; Walker et al., 2022; Wyatt, Devlin, Rappold, Case, & Diaz-Sanchez, 2020). However, in other human studies the exposure did not induce acute phase response (Andersen, Saber, Clausen, et al., 2018; Andersen, Saber, Pedersen, et al., 2018), maybe due to a low level of exposure (Andersen et al., 2019).

The following link presents inconsistencies for this KER, where substance interaction with lung resident cell membrane components has occurred, while systemic acute phase response was not observed. Exposure through the respiratory system (inhalation or intratracheal instillation) of stressors was considered as interaction with lung resident cell membrane components, while systemic acute phase response is measured as the concentration of acute phase protein in blood plasma or serum: Uncertainties KER6.

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references.  More help
Response-response Relationship
Provides sources of data that define the response-response relationships between the KEs.  More help

The interaction of insoluble nanomaterials with the lungs (Key event 1495) (measured in dosed surface area: dosed mass multiply by specific surface area) is correlated to serum amyloid A (SAA)3 and SAA1/2 plasma levels (Key event 1439) and the responses show a linear regression, in female C57BL/6J mice 1 day after intratracheal instillation (Gutierrez et al., 2023) (Figure 1 and Figure 2).

The Pearson’s correlation coefficient was 0.92 (p <0.001) between log-transformed dosed surface area (dosed mass multiply by specific surface area) and log-transformed SAA3 plasma levels  (Figure 1). The linear regression formula obtained was Log SAA3 = 0.9459 *Log Dosed surface area – 2.854 (p=0.01). In the case SAA1/2, the correlation coefficient was 0.83 (p<0.05) between log-transformed dosed surface area and log-transformed SAA1/2 plasma levels was, and the linear regression formula was Log SAA1/2 = 0.6368 *Log Dosed surface area +0.09524 (p=0.01) (Figure 2) (Gutierrez et al., 2023).

Figure 1. Correlations between pulmonary dosed surface area and SAA3 protein in plasma, 1 day after exposure to nanomaterials. Reproduced from Gutierrez et al. (2023).

Figure 2. Correlations between pulmonary dosed surface area and SAA1/2 protein in plasma, 1 day after exposure to nanomaterials. Reproduced from Gutierrez et al. (2023).

Time-scale
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?). More help

In mice, increased serum amyloid A (SAA) protein levels are observed 1 and 3 days after most exposures, however increased SAA levels are not frequently observed 28 or 90 days after exposure (Bourdon et al., 2012; Hadrup et al., 2019; Poulsen et al., 2017; Poulsen, Saber, Mortensen, et al., 2015; Poulsen, Saber, Williams, et al., 2015).

In humans, increased SAA and C-reactive protein has been observed 22h and 2 days after exposure to zinc oxide, but not 3 days after exposure (Monse et al., 2021). After exposure to zinc oxide, copper oxide or a mix both, SAA levels were elevated 24h after exposure in humans, but not 6h after exposure (Baumann et al., 2018).

Known Feedforward/Feedback loops influencing this KER
Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. More help

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured terms. More help

Systemic acute phase response is measured as elevation of acute phase proteins in humans (mainly C-reactive protein and serum amyloid A), and serum amyloid A in mice has been shown after exposure to several stressors (see Empirical evidence).

References

List of the literature that was cited for this KER description. More help

Andersen, M. H. G., Frederiksen, M., Saber, A. T., Wils, R. S., Fonseca, A. S., Koponen, I. K., . . . Vogel, U. (2019). Health effects of exposure to diesel exhaust in diesel-powered trains. Part Fibre Toxicol, 16(1), 21. doi:10.1186/s12989-019-0306-4

Andersen, M. H. G., Saber, A. T., Clausen, P. A., Pedersen, J. E., Lohr, M., Kermanizadeh, A., . . . Vogel, U. (2018). Association between polycyclic aromatic hydrocarbon exposure and peripheral blood mononuclear cell DNA damage in human volunteers during fire extinction exercises. Mutagenesis, 33(1), 105-115. doi:10.1093/mutage/gex021

Andersen, M. H. G., Saber, A. T., Pedersen, J. E., Pedersen, P. B., Clausen, P. A., Lohr, M., . . . Moller, P. (2018). Assessment of polycyclic aromatic hydrocarbon exposure, lung function, systemic inflammation, and genotoxicity in peripheral blood mononuclear cells from firefighters before and after a work shift. Environ Mol Mutagen, 59(6), 539-548. doi:10.1002/em.22193

Baumann, R., Gube, M., Markert, A., Davatgarbenam, S., Kossack, V., Gerhards, B., . . . Brand, P. (2018). Systemic serum amyloid A as a biomarker for exposure to zinc and/or copper-containing metal fumes. J Expo Sci Environ Epidemiol, 28(1), 84-91. doi:10.1038/jes.2016.86

Bengtson, S., Knudsen, K. B., Kyjovska, Z. O., Berthing, T., Skaug, V., Levin, M., . . . Vogel, U. (2017). Differences in inflammation and acute phase response but similar genotoxicity in mice following pulmonary exposure to graphene oxide and reduced graphene oxide. PLoS One, 12(6), e0178355. doi:10.1371/journal.pone.0178355

Bourdon, J. A., Halappanavar, S., Saber, A. T., Jacobsen, N. R., Williams, A., Wallin, H., . . . Yauk, C. L. (2012). Hepatic and pulmonary toxicogenomic profiles in mice intratracheally instilled with carbon black nanoparticles reveal pulmonary inflammation, acute phase response, and alterations in lipid homeostasis. Toxicol Sci, 127(2), 474-484. doi:10.1093/toxsci/kfs119

Gabay, C., & Kushner, I. (1999). Acute-phase proteins and other systemic responses to inflammation. N Engl J Med, 340(6), 448-454. doi:10.1056/NEJM199902113400607

Gutierrez, C. T., Loizides, C., Hafez, I., Brostrom, A., Wolff, H., Szarek, J., . . . Vogel, U. (2023). Acute phase response following pulmonary exposure to soluble and insoluble metal oxide nanomaterials in mice. Part Fibre Toxicol, 20(1), 4. doi:10.1186/s12989-023-00514-0

Hadrup, N., Knudsen, K. B., Berthing, T., Wolff, H., Bengtson, S., Kofoed, C., . . . Vogel, U. (2019). Pulmonary effects of nanofibrillated celluloses in mice suggest that carboxylation lowers the inflammatory and acute phase responses. Environ Toxicol Pharmacol, 66, 116-125. doi:10.1016/j.etap.2019.01.003

Moldoveanu, B., Otmishi, P., Jani, P., Walker, J., Sarmiento, X., Guardiola, J., . . . Yu, J. (2009). Inflammatory mechanisms in the lung. J Inflamm Res, 2, 1-11.

Monse, C., Hagemeyer, O., Raulf, M., Jettkant, B., van Kampen, V., Kendzia, B., . . . Merget, R. (2018). Concentration-dependent systemic response after inhalation of nano-sized zinc oxide particles in human volunteers. Part Fibre Toxicol, 15(1), 8. doi:10.1186/s12989-018-0246-4

Monse, C., Raulf, M., Jettkant, B., van Kampen, V., Kendzia, B., Schurmeyer, L., . . . Bunger, J. (2021). Health effects after inhalation of micro- and nano-sized zinc oxide particles in human volunteers. Arch Toxicol, 95(1), 53-65. doi:10.1007/s00204-020-02923-y

Poulsen, S. S., Knudsen, K. B., Jackson, P., Weydahl, I. E., Saber, A. T., Wallin, H., & Vogel, U. (2017). Multi-walled carbon nanotube-physicochemical properties predict the systemic acute phase response following pulmonary exposure in mice. PLoS One, 12(4), e0174167. doi:10.1371/journal.pone.0174167

Poulsen, S. S., Saber, A. T., Mortensen, A., Szarek, J., Wu, D., Williams, A., . . . Vogel, U. (2015). Changes in cholesterol homeostasis and acute phase response link pulmonary exposure to multi-walled carbon nanotubes to risk of cardiovascular disease. Toxicol Appl Pharmacol, 283(3), 210-222. doi:10.1016/j.taap.2015.01.011

Poulsen, S. S., Saber, A. T., Williams, A., Andersen, O., Kobler, C., Atluri, R., . . . Vogel, U. (2015). MWCNTs of different physicochemical properties cause similar inflammatory responses, but differences in transcriptional and histological markers of fibrosis in mouse lungs. Toxicol Appl Pharmacol, 284(1), 16-32. doi:10.1016/j.taap.2014.12.011

Walker, E. S., Fedak, K. M., Good, N., Balmes, J., Brook, R. D., Clark, M. L., . . . Peel, J. L. (2022). Acute differences in blood lipids and inflammatory biomarkers following controlled exposures to cookstove air pollution in the STOVES study. Int J Environ Health Res, 32(3), 565-578. doi:10.1080/09603123.2020.1785402

Wyatt, L. H., Devlin, R. B., Rappold, A. G., Case, M. W., & Diaz-Sanchez, D. (2020). Low levels of fine particulate matter increase vascular damage and reduce pulmonary function in young healthy adults. Part Fibre Toxicol, 17(1), 58. doi:10.1186/s12989-020-00389-5