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Relationship: 1726

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

CYP2E1 Activation leads to Oxidative Stress in Brain

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

CYP2E1 Activation

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

Oxidative Stress in Brain

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
CYP2E1 activation and formation of protein adducts leading to neurodegeneration	adjacent	Moderate	Moderate	Brendan Ferreri-Hanberry (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 Do not cite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

CYP2E1 is part of the cytochrome P450 family and can participate in the metabolism of endogenous, small and hydrophobic compounds using an oxidation reaction. When CYP2E1 is activated it can induce ROS formation. Activation of CYP2E1 will also lead to an increased expression of the enzyme itself, which will ultimately increase the formation of ROS. CYP2E1 is expressed at various parts in the human brain, such as cortex, cerebellum, hippocampus, thalamus and stratum. Since the level of defence mechanism in the brain against ROS is lower than in other parts in the body oxidative stress is reached faster.

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

The link between CYP2E1 activation and the formation of ROS, which ultimately leads to oxidative stress, was already made in the 90s. Hydroxyethyl free radicals where found in rat livers after the stimulation of CYP2E1, which eventually leads to liver damage. As already mentioned in the KE description, oxidative stress is defined as the moment when there is an imbalance between the ROS level and the defence mechanisms which leads to damage in the cell. Research done by Haorah et al. showed that CYP2E1 indeed produces ROS. ROS levels where measured in two different situations, neuron cells where induced with ethanol (inducer of CYP2E1) or neuron cells where induced with ethanol in combination with an inhibitor for CYP2E1. ROS levels in the neuron cells decreased significantly with the inhibitor for CYP2E1 when compared with the situation without the inhibitor for CYP2E1. Three other studies, using different cell types, showed that CYP2E1 KO mice resulted in an increased level of TBARS (marker for lipid peroxidation which is induced by ROS). Also in two of the three studies a higher level of GSH was detected in CYP2E1 KO mice, indicating a lower level of ROS since GSH is used as a defence mechanism against ROS. Furthermore recent research showed that CYP2E1 induction in granule neurons indeed results in ROS formation, but also that the inducement of CYP2E1 increased the expression of CYP2E1 itself. This was also shown in other studies, with the use of immunofluorescence detection techniques. Since activation of CYP2E1 also leads to a higher expression more ROS will be produced. This is also shown in the difference of CYP2E1 expression in alcoholics and non-drinkers, where the expression of CYP2E1 is far higher in alcoholic liver cells. Finally, oxidative stress is reached earlier in neuron cells because of the higher level of oxygen and the lower permeability of the blood vessels.

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 link between CYP2E1 activation oxidative stress is biological plausible.

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

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

Many studies are performed with ethanol, which is a well-known inducer of CYP2E1. But ethanol can also induce ROS formation by interfering in other biological pathways or inducing endoplasmic reticulum stress, which eventually can lead to neurotoxicity and neurodegeneration.On the other hand direct evidence is available with the studies described above that CYP2E1 induces ROS. Important studies performed are the WT/KO/KI mice and the detection of further CYP2E1 expression when CYP2E1 is activated.

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

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

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

Upadhya, S. C., Tirumalai, P. S., Boyd, M. R., Mori, T. & Ravindranath, V. Cytochrome P4502E (CYP2E) in brain: constitutive expression, induction by ethanol and localization by fluorescence in situ hybridization. Arch. Biochem. Biophys. 373, 23–34 (2000).

Garciá-Suástegui, W. A. et al. The Role of CYP2E1 in the Drug Metabolism or Bioactivation in the Brain. Oxidative Medicine and Cellular Longevity 2017, (2017).

Haorah, J. et al. Mechanism of alcohol-induced oxidative stress and neuronal injury. Free Radic. Biol. Med. 45, 1542–1550 (2008).

Valencia-Olvera, A. C., Morán, J., Camacho-Carranza, R., Prospéro-García, O. & Espinosa-Aguirre, J. J. CYP2E1 induction leads to oxidative stress and cytotoxicity in glutathione-depleted cerebellar granule neurons. Toxicol. Vitr. 28, 1206–1214 (2014).

Luo, J. Autophagy and ethanol neurotoxicity. Autophagy 10, 2099–2108 (2014).

Yang, L. & Cederbaum, A. CYP2E1, oxidative stress and MAPK signaling pathway in alcoholic liver disease. Curr. Top. Toxicol. 7, (2011).

Lakshman, M. R. et al. CYP2E1, Oxidative Stress, Post-translational Modifications and Lipid Metabolism. Subcell. Biochem. 67, 199–233 (2013).

Jimenez-Lopez, J. M. & Cederbaum, A. I. CYP2E1-dependent oxidative stress and toxicity: role in ethanol-induced liver injury. Expert Opin. Drug Metab. Toxicol. 1, 671–685 (2005).

Gonzalez, F. J. Role of cytochromes P450 in chemical toxicity and oxidative stress: Studies with CYP2E1. Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis 569, 101–110 (2005).

Albano, E. et al. Role of cytochrome P4502E1-dependent formation of hydroxyethyl free radical in the development of liver damage in rats intragastrically fed with ethanol. Hepatology 23, 155–163 (1996).

Albano, E. Alcohol, oxidative stress and free radical damage. Proc. Nutr. Soc. 65, 278–290 (2006).

Wu, D., Wang, X., Zhou, R., Yang, L. & Cederbaum, A. I. Alcohol steatosis and cytotoxicity: The role of cytochrome P4502E1 and autophagy. Free Radic. Biol. Med. 53, 1346–1357 (2012).

Cederbaum, A. I. Role of CYP2E1 in ethanol-induced oxidant stress, fatty liver and hepatotoxicity. Dig. Dis. 28, 802–811 (2010).

Lu, Y., Wu, D., Wang, X., Ward, S. C. & Cederbaum, A. I. Chronic alcohol-induced liver injury and oxidant stress are decreased in cytochrome P4502E1 knockout mice and restored in humanized cytochrome P4502E1 knock-in mice. Free Radic. Biol. Med. 49, 1406–1416 (2010).

Oneta, C. M. et al. Dynamics of cytochrome P4502E1 activity in man: induction by ethanol and disappearance during withdrawal phase. J. Hepatol. 36, 47–52 (2002).

Lieber, C. S. CYP2E1: From ASH to NASH. Hepatology Research 28, 1–11 (2004).

Emerit, J., Edeas, M. & Bricaire, F. Neurodegenerative diseases and oxidative stress. Biomedicine and Pharmacotherapy 58, 39–46 (2004).

Pereira, R. B., Andrade, P. B. & Valentão, P. A Comprehensive View of the Neurotoxicity Mechanisms of Cocaine and Ethanol. Neurotoxicity Research 28, 253–267 (2015).

Yang, F. & Luo, J. Endoplasmic reticulum stress and ethanol neurotoxicity. Biomolecules 5, 2538–2553 (2015).