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Relationship: 1906

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

Increase in RONS leads to Tissue resident cell activation

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Increased reactive oxygen and nitrogen species (RONS) leading to increased risk of breast cancer adjacent Moderate Not Specified Evgeniia Kazymova (send email) Under development: Not open for comment. Do not cite Under Development
Increased DNA damage leading to increased risk of breast cancer adjacent Moderate Not Specified Allie Always (send email) Under development: Not open for comment. Do not cite Under Development

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

Increased RONS leads to an increase in inflammation.

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 is Moderate. RONS can activate some inflammatory and anti-inflammatory pathways (TLR, TGF-β, NF-kB), and RONS are an essential part of multiple inflammatory and anti-inflammatory pathways (TLR4, TNF-a, TGF-β, NF-kB).

Empirical Evidence is Moderate. Both RONS and inflammation increase in response to agents that increase RONS or inflammation, and antioxidants reduce inflammation. Multiple studies show dose-dependent changes in both RONS and inflammation in response to stressors including ionizing radiation and antioxidants. RONS have been measured at the same or earlier time points as inflammatory markers, but additional studies are needed to characterize the inflammatory response at the earliest time points to support causation. Uncertainties come from the positive feedback from inflammation to RONS potentially interfering with attempts to establish causality, and from the large number of inflammation-related endpoints with differing responses to stressors and experimental variation.

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

Biological Plausibility is   Moderate. RONS can activate some inflammatory and anti-inflammatory pathways (TLR, TGF-β, NF-kB), and RONS are an essential part of multiple inflammatory and anti-inflammatory pathways (TLR4, TNF-a, TGF-β, NF-kB).

RONS activates or is essential to many inflammatory pathways including TGF-β  (Barcellos-Hoff and Dix 1996; Jobling, Mott et al. 2006), TNF (Blaser, Dostert et al. 2016), Toll-like receptor (TLR) (Park, Jung et al. 2004; Nakahira, Kim et al. 2006; Powers, Szaszi et al. 2006; Miller, Goodson et al. 2017; Cavaillon 2018), and NF-kB signaling (Gloire, Legrand-Poels et al. 2006; Morgan and Liu 2011). These interactions principally involve ROS, but RNS can indirectly activate TLRs and possibly NF-kB. Since inflammatory signaling and activated immune cells can also increase the production of RONS, positive feedback and feedforward loops can occur (Zhao and Robbins 2009; Ratikan, Micewicz et al. 2015; Blaser, Dostert et al. 2016).

Damage inflicted by RONS on cells activate TLRs and other receptors to promote release of cytokines (Ratikan, Micewicz et al. 2015). For example, oxidized lipids or oxidative stress-induced heat shock proteins can activate TLR4 (Miller, Goodson et al. 2017; Cavaillon 2018).

ROS is essential to TLR4 activation of downstream signals including NF-kB. Activation of TLR4 promotes the surface expression and movement of TLR4 into signal-promoting lipid rafts (Nakahira, Kim et al. 2006; Powers, Szaszi et al. 2006). This signal promotion requires NADPH-oxidase and ROS (Park, Jung et al. 2004; Nakahira, Kim et al. 2006; Powers, Szaszi et al. 2006). ROS is also required for the TLR4/TRAF6/ASK-1/p38 dependent activation of inflammatory cytokines (Matsuzawa, Saegusa et al. 2005). ROS therefore amplifies the inflammatory process.

RONS can also fail to activate or actively inhibit inflammatory pathways, and the circumstances determining response to RONS are not well known (Gloire, Legrand-Poels et al. 2006).

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 ROS can activate NF-KB (Gloire, Legrand-Poels et al. 2006), not all studies consistently show NF-kB activation after RONS stressor IR. It is possible that the link between ROS and NF-kB depends on the local environmental context, with different studies not adequately controlling all influential variables. One study offers a possible explanation based on temporal response: in macrophages, NF-kB was activated by shorter exposures to H2O2 (30 min), but the response disappeared with longer exposures (Nakao, Kurokawa et al. 2008).

While many models in vivo and in vitro showed a decreased inflammatory response to RONS stressors IR in combination with antioxidants, in endothelial cells in culture the increase in IL6 and IL8 after IR was not reduced by antioxidants, although a synergistic increase in those cytokines occurring with combined TNF-a and IR treatment was reduced by antioxidants (Meeren, Bertho et al. 1997). This is a reminder that multiple mechanisms can increase inflammation, that inflammatory factors participate in positive feedback loops, and that responses to stimuli vary between cells.

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

Since inflammatory signaling and activated immune cells can also increase the production of RONS, positive feedback and feedforward loops can occur (Zhao and Robbins 2009; Ratikan, Micewicz et al. 2015; Blaser, Dostert et al. 2016).

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

Ameziane-El-Hassani, R., M. Talbot, et al. (2015). "NADPH oxidase DUOX1 promotes long-term persistence of oxidative stress after an exposure to irradiation." Proceedings of the National Academy of Sciences of the United States of America 112(16): 5051-5056.

Azimzadeh, O., H. Scherthan, et al. (2011). "Rapid proteomic remodeling of cardiac tissue caused by total body ionizing radiation." Proteomics 11(16): 3299-3311.

Azimzadeh, O., W. Sievert, et al. (2015). "Integrative proteomics and targeted transcriptomics analyses in cardiac endothelial cells unravel mechanisms of long-term radiation-induced vascular dysfunction." J Proteome Res 14(2): 1203-1219.

Barcellos-Hoff, M. H. and T. A. Dix (1996). "Redox-mediated activation of latent transforming growth factor-beta 1." Mol Endocrinol 10(9): 1077-1083.

Berruyer, C., F. M. Martin, et al. (2004). "Vanin-1-/- mice exhibit a glutathione-mediated tissue resistance to oxidative stress." Mol Cell Biol 24(16): 7214-7224.

Black, A. T., M. K. Gordon, et al. (2011). "UVB light regulates expression of antioxidants and inflammatory mediators in human corneal epithelial cells." Biochem Pharmacol 81(7): 873-880.

Blaser, H., C. Dostert, et al. (2016). "TNF and ROS Crosstalk in Inflammation." Trends in cell biology 26(4): 249-261.

Cavaillon, J.-M. (2018). Damage-associated Molecular Patterns. Inflammation: From Molecular and Cellular Mechanisms to the Clinic. J.-M. Cavaillon and M. Singer, Wiley-VCHVerlagGmbH&Co.KGaA.: 57-80.

Das, U., K. Manna, et al. (2014). "Role of ferulic acid in the amelioration of ionizing radiation induced inflammation: a murine model." PLoS One 9(5): e97599.

Ezz, M. K., N. K. Ibrahim, et al. (2018). "The Beneficial Radioprotective Effect of Tomato Seed Oil Against Gamma Radiation-Induced Damage in Male Rats." J Diet Suppl 15(6): 923-938.

Gloire, G., S. Legrand-Poels, et al. (2006). "NF-kappaB activation by reactive oxygen species: fifteen years later." Biochem Pharmacol 72(11): 1493-1505.

Ha, Y. M., S. W. Chung, et al. (2010). "Molecular activation of NF-kappaB, pro-inflammatory mediators, and signal pathways in gamma-irradiated mice." Biotechnol Lett 32(3): 373-378.

Haddadi, G. H., A. Rezaeyan, et al. (2017). "Hesperidin as Radioprotector against Radiation-induced Lung Damage in Rat: A Histopathological Study." J Med Phys 42(1): 25-32.

Han, S. J., H. J. Min, et al. (2015). "HMGB1 in the pathogenesis of ultraviolet-induced ocular surface inflammation." Cell Death Dis 6: e1863.

Hiramoto, K., H. Kobayashi, et al. (2012). "Intercellular pathway through hyaluronic acid in UVB-induced inflammation." Exp Dermatol 21(12): 911-914.

Hung, S. J., S. C. Tang, et al. (2017). "Photoprotective Potential of Glycolic Acid by Reducing NLRC4 and AIM2 Inflammasome Complex Proteins in UVB Radiation-Induced Normal Human Epidermal Keratinocytes and Mice." DNA Cell Biol 36(2): 177-187.

Jobling, M. F., J. D. Mott, et al. (2006). "Isoform-specific activation of latent transforming growth factor beta (LTGF-beta) by reactive oxygen species." Radiation research 166(6): 839-848.

Kang, J. S., H. N. Kim, et al. (2007). "Regulation of UVB-induced IL-8 and MCP-1 production in skin keratinocytes by increasing vitamin C uptake via the redistribution of SVCT-1 from the cytosol to the membrane." J Invest Dermatol 127(3): 698-706.

Khan, A., K. Manna, et al. (2015). "Gossypetin ameliorates ionizing radiation-induced oxidative stress in mice liver--a molecular approach." Free Radic Res 49(10): 1173-1186.

Lee, E. J., M. S. Jeon, et al. (2010). "Capsiate inhibits ultraviolet B-induced skin inflammation by inhibiting Src family kinases and epidermal growth factor receptor signaling." Free radical biology & medicine 48(9): 1133-1143.

Lee, S. J., A. Dimtchev, et al. (1998). "A novel ionizing radiation-induced signaling pathway that activates the transcription factor NF-kappaB." Oncogene 17(14): 1821-1826.

Manna, K., U. Das, et al. (2015). "Naringin inhibits gamma radiation-induced oxidative DNA damage and inflammation, by modulating p53 and NF-kappaB signaling pathways in murine splenocytes." Free Radic Res 49(4): 422-439.

Martin, K., R. Sur, et al. (2008). "Parthenolide-depleted Feverfew (Tanacetum parthenium) protects skin from UV irradiation and external aggression." Arch Dermatol Res 300(2): 69-80.

Martinez, R. M., F. A. Pinho-Ribeiro, et al. (2016). "Topical formulation containing hesperidin methyl chalcone inhibits skin oxidative stress and inflammation induced by ultraviolet B irradiation." Photochem Photobiol Sci 15(4): 554-563.

Matsuzawa, A., K. Saegusa, et al. (2005). "ROS-dependent activation of the TRAF6-ASK1-p38 pathway is selectively required for TLR4-mediated innate immunity." Nat Immunol 6(6): 587-592.

Meeren, A. V., J. M. Bertho, et al. (1997). "Ionizing radiation enhances IL-6 and IL-8 production by human endothelial cells." Mediators Inflamm 6(3): 185-193.

Miller, M. F., W. H. Goodson, et al. (2017). "Low-Dose Mixture Hypothesis of Carcinogenesis Workshop: Scientific Underpinnings and Research Recommendations." Environmental health perspectives 125(2): 163-169.

Morgan, M. J. and Z. G. Liu (2011). "Crosstalk of reactive oxygen species and NF-kappaB signaling." Cell Res 21(1): 103-115.

Nakahira, K., H. P. Kim, et al. (2006). "Carbon monoxide differentially inhibits TLR signaling pathways by regulating ROS-induced trafficking of TLRs to lipid rafts." J Exp Med 203(10): 2377-2389.

Nakao, N., T. Kurokawa, et al. (2008). "Hydrogen peroxide induces the production of tumor necrosis factor-alpha in RAW 264.7 macrophage cells via activation of p38 and stress-activated protein kinase." Innate Immun 14(3): 190-196.

Narayanan, P. K., K. E. LaRue, et al. (1999). "Alpha particles induce the production of interleukin-8 by human cells." Radiation research 152(1): 57-63.

Ozyurt, H., O. Cevik, et al. (2014). "Quercetin protects radiation-induced DNA damage and apoptosis in kidney and bladder tissues of rats." Free Radic Res 48(10): 1247-1255.

Park, H. S., H. Y. Jung, et al. (2004). "Cutting edge: direct interaction of TLR4 with NAD(P)H oxidase 4 isozyme is essential for lipopolysaccharide-induced production of reactive oxygen species and activation of NF-kappa B." J Immunol 173(6): 3589-3593.

Park, L. J., S. M. Ju, et al. (2006). "The enhanced monocyte adhesiveness after UVB exposure requires ROS and NF-kappaB signaling in human keratinocyte." J Biochem Mol Biol 39(5): 618-625.

Powers, K. A., K. Szaszi, et al. (2006). "Oxidative stress generated by hemorrhagic shock recruits Toll-like receptor 4 to the plasma membrane in macrophages." J Exp Med 203(8): 1951-1961.

Ratikan, J. A., E. D. Micewicz, et al. (2015). "Radiation takes its Toll." Cancer Lett 368(2): 238-245.

Ren, X., Y. Shi, et al. (2016). "Naringin protects ultraviolet B-induced skin damage by regulating p38 MAPK signal pathway." J Dermatol Sci 82(2): 106-114.

Saltman, B., D. H. Kraus, et al. (2010). "In vivo and in vitro models of ionizing radiation to the vocal folds." Head Neck 32(5): 572-577.

Sharma, S. D., S. M. Meeran, et al. (2007). "Dietary grape seed proanthocyanidins inhibit UVB-induced oxidative stress and activation of mitogen-activated protein kinases and nuclear factor-kappaB signaling in in vivo SKH-1 hairless mice." Molecular cancer therapeutics 6(3): 995-1005.

Sinha, M., D. K. Das, et al. (2011). "Leaf extract of Moringa oleifera prevents ionizing radiation-induced oxidative stress in mice." J Med Food 14(10): 1167-1172.

Sinha, M., D. K. Das, et al. (2012). "Epicatechin ameliorates ionising radiation-induced oxidative stress in mouse liver." Free Radic Res 46(7): 842-849.

Soltani, B., N. Ghaemi, et al. (2016). "Redox maintenance and concerted modulation of gene expression and signaling pathways by a nanoformulation of curcumin protects peripheral blood mononuclear cells against gamma radiation." Chem Biol Interact 257: 81-93.

Straub, J. M., J. New, et al. (2015). "Radiation-induced fibrosis: mechanisms and implications for therapy." J Cancer Res Clin Oncol 141(11): 1985-1994.

Zetner, D., L. P. Andersen, et al. (2016). "Melatonin as Protection Against Radiation Injury: A Systematic Review." Drug Res (Stuttg) 66(6): 281-296.

Zhang, Q., L. Zhu, et al. (2017). "Ionizing radiation promotes CCL27 secretion from keratinocytes through the cross talk between TNF-alpha and ROS." J Biochem Mol Toxicol 31(3).

Zhao, W. and M. E. Robbins (2009). "Inflammation and chronic oxidative stress in radiation-induced late normal tissue injury: therapeutic implications." Curr Med Chem 16(2): 130-143.