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Relationship: 269

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

Alkylation, Protein leads to Cell injury/death

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
Protein Alkylation leading to Liver Fibrosis adjacent Moderate Brigitte Landesmann (send email) Open for citation & comment WPHA/WNT Endorsed

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
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
Rattus norvegicus Rattus norvegicus High NCBI

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

Alkylating agents are highly reactive chemicals that may produce cellular damage by covalently binding to cellular macromolecules to form adducts and thereby preventing their proper functioning. Covalent protein alkylation by reactive electrophiles was identified as a key triggering event in chemical toxicity; it disturbs the cellular redox balance - contributing also to the development of oxidative stress - through interaction with glutathione, which leads to disruption of multiple biochemical pathways in exposed cells and is associated with mitochondrial dysfunction, which in turn, can trigger the death of exposed cells via either apoptosis and/or necrosis. [1][2][3][4][5]

For example, Acrolein, the metabolite of Allyl Alcohol is a highly reactive electrophilic aldehyde and rapidly binds to cellular nucleophiles like glutathione. Thiol redox balance is critical for numerous cell functions Acrolein has been identified as both a product and initiator of lipid peroxidation. [6] The high toxic potential of Acrolein reflects its possession of two strongly electrophilic centres which ensure it readily reacts with nucleophilic groups on biological molecules including glutathione and proteins. These reactions typically proceed via Michael addition of nucleophiles to the a,b-unsaturated bond of Acrolein, generating carbonyl-retaining adducts with the ability to undergo further crosslinking. Reaction of the carbonyl group in the first instance to form Schiff base adducts is typically much less preferred. Adduction of a diverse range of targets, in addition to disruption of the cellular redox balance, appears to underlie the disruption of multiple biochemical pathways in Acrolein-exposed cells. Such events can trigger the death of exposed cells via either apoptosis and/or necrosis. [7]

It has been suggested that the alkylation of nucleophilic groups of cellular macromolecules effected by Acrolein after glutathione depletion is the event actually leading to cell injury.[8]

Another example for an alkylating agent is Carbon Tetrachloride (CCl4), for which consensus has emerged that its toxicity is a mutifactorial process involving the generation of CCl4-derived free radicals, lipid peroxidation, covalent binding to macromolecules, loss of calcium homeostasis, nucleic acid hypomethylation and inflammatory cytokines. CCl4-derived free radicals are highly reactive species that are able to alkylate proteins and nucleic acids to generate CCl4-derived adducts. [9]

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

Cell injury caused by covalent binding is biologically plausible. The mechanistic relationship between MIE and KE 1 consistent with established biological knowledge. [10] [11][6]

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

Though covalent protein alkylation by reactive electrophiles was identified as a key triggering event in chemical toxicity already over 40 years ago and despite the intense effort expended over the past few years, our understanding of the mechanism and consequences of protein modification by reactive intermediates – both oxidizing and alkylating agents - is still quite limited. Covalent protein alkylation is a feature of many hepatotoxic drugs and the overall extent of binding does not adequately distinguish toxic from non-toxic binding. Directly relating covalent binding to hepatotoxicity is likely an oversimplification of the process whereby adduct formation ultimately leads to toxicity. Understanding underlying complexities (e.g., which macromolecules are important covalent binding targets) will be essential to any understanding of the problem of metabolism-dependent hepatotoxicity and predicting toxicity from in vitro experiments. [30][31] Data from Codreanu et al. suggest that non-toxic covalent binding may largely be survivable damage to cytoskeletal components and other highly reactive protein targets, whereas toxic covalent binding produces lethal injury by targeting protein synthesis and catabolism and possibly mitochondrial electron transport. Future studies with appropriate probe molecules for toxic and non-toxic drugs could test these hypotheses and provide a better mechanistic basis for interpreting protein alkylation in drugsafety evaluation [10]

For this AOP it is not known whether protein alkylation to certain proteins is required and whether particular proteins and various binding sites influence the further downstream process. Further we do not know whether there is a threshold and if this threshold would refer to the number of alkylation of a single protein or of a threshold number of proteins.

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

human:[23] rat:[29]

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
  1. Tanel, A. et al. (2007), Activation of the death receptor pathway of apoptosis by the aldehyde Acrolein, Free Radic Biol Med, vol. 42, no. 6, pp. 798-810.
  2. 2.0 2.1 Boll, M. et al. (2001), Pathogenesis of Carbon Tetrachloride-induced hepatocyte injury bioactivation of CCI4 by cytochrome P450 and effects on lipid homeostasis. Z Naturforsch C, vol. 56, no. 1-2, pp. 111-121.
  3. Manibusan, M.K., M. Odin and D.A. Eastmond (2007), Postulated carbon tetrachloride mode of action: a review, J Environ Sci Health C Environ Carcinog Ecotoxicol Rev, vol. 25, no. 3, pp. 185 - 209.
  4. Rudolph, T.K. and B.A. Freeman (2009), Transduction of redox signaling by electrophile-protein reactions, Sci Signal, vol. 2, no. 90, re7.
  5. Schopfer, F.J. et al. (2011), Formation and Signaling Actions of Electrophilic Lipids, Chem Rev, vol. 111, no. 10, pp. 5997–6021.
  6. 6.0 6.1 Kehrer, J.P. and S. Biswal (2000), The Molecular Effects of Acrolein, Toxicol. Sciences, vol. 57, no. 1, pp. 6-15.
  7. 7.0 7.1 Thompson, C.A. and P.C. Burcham (2008), Protein alkylation, transcriptional responses and cytochrome c release during Acrolein toxicity in A549 cells: influence of nucleophilic culture media constituents, Toxicol In Vitro, vol. 22, no. 4, pp. 844-853.
  8. Pompella, A. et al. (1991), Loss of membrane protein thiols and lipid peroxidation in allyl alcohol hepatotoxicity, Biochemical Pharmacology, vol. 41, no. 8, pp. 255-1259.
  9. Knockaert, L. et al. (2012), Carbon tetrachloride-mediated lipid peroxidation induces early mitochondrial alterations in mouse liver, Lab Invest, vol. 92, no. 3, pp. 396-410.
  10. 10.0 10.1 10.2 10.3 Codreanu, S.G. et al. (2014), Alkylation damage by lipid electrophiles targets functional protein systems, Molecular & Cellular Proteomics, vol. 13, no. 3, pp.849–859.
  11. Liebler, D.C. (2008), Protein Damage by Reactive Electrophiles: Targets and Consequences, Chem Res Toxicol, vol. 21, no. 1, pp. 117-128.
  12. Golla, U., G. Bandi and R.S.Tomar (2015), Molecular cytotoxicity mechanisms of allyl alcohol (acrolein) in budding yeast, Chem Res Toxicol. vol. 28, no. 6, pp.1246-1264.
  13. Nardini, M. et al. (2002),Acrolein-induced cytotoxicity in cultured human bronchial epithelial cells. Modulation by alpha-tocopherol and ascorbic acid, Toxicology, vol.170, no. 3, pp. 173-185.
  14. Randall, M.J., M. Hristova and A. van der Vliet (2013), Protein alkylation by the α,β-unsaturated aldehyde acrolein. A reversible mechanism of electrophile signaling? FEBS Lett, vol. 587, no. 23, pp. 3808-3814.
  15. Reddy, S. et al. (2002), Identification of glutathione modifications by cigarette smoke, Free Radic Biol Med, vol. 33, no.11, pp. 1490-1498.
  16. Moghe, A. et al. (2015), Molecular mechanisms of acrolein toxicity: relevance to human disease, Toxicol Sci, vol. 143, no. 2, pp. 242-255.
  17. Dong, L. et al. (2013), Magnolol protects against oxidative stress-mediated neural cell damage by modulating mitochondrial dysfunction and PI3K/Akt signaling, J Mol Neurosci, vol. 50, no. 3, pp. 469-481.
  18. Roy, J. et al. (2009), Acrolein induces a cellular stress response and triggers mitochondrial apoptosis in A549 cells, Chem Biol Interact, vol. 181, no. 2, pp. 154-167.
  19. Tanel, A. and D.A. Averill-Bates (2005), The aldehyde acrolein induces apoptosis via activation of the mitochondrial pathway, Biochim Biophys Acta, vol. 1743, no. 3, pp. 255-267.
  20. Kern, J.C. and J.P. Kehrer (2002), Acrolein-induced cell death: a caspase-influenced decision between apoptosis and oncosis/necrosis, Chem Biol Interact, vol.139, no.1, pp.79-95.
  21. Hristova, M. et al. (2012), The tobacco smoke component, acrolein, suppresses innate macrophage responses by direct alkylation of c-Jun N-terminal kinase, Am J Respir Cell Mol Biol, vol. 46, no. 1, pp. 23-33.
  22. Mohammad, M.K. et al. (2012), Acrolein cytotoxicity in hepatocytes involves endoplasmic reticulum stress, mitochondrial dysfunction and oxidative stress, Toxicol Appl Pharmacol, vol. 265, no. 1, pp. 73-82.
  23. 23.0 23.1 23.2 Schwend, T. et al. (2008), Alkylation of adenosine deaminase and thioredoxin by acrylamide in human cell cultures, Z Naturforsch, vol. 64, no. 5-6, pp. 447-453.
  24. Cai, Y. et al. (2005), Apoptosis initiated by carbon tetrachloride in mitochondria of rat primary cultured hepatocytes, Acta Pharmacol Sin, vol. 26, no. 8, pp. 969-975.
  25. Perrissoud, D. et al. (1980), The effect of carbon tetrachloride on isolated rat hepatocytes,Virchows Archiv B, vol. 35, no 1, pp.83-91.
  26. Johnston, D.E. and C. Kroening, Mechanism of early carbon tetrachloride toxicity in cultured rat hepatocytes, Pharmacol Toxicol. vol. 83, no.6, pp. 231-239.
  27. Boll, M. et al., 2001, Mechanism of carbon tetrachloride-induced hepatotoxicity. Hepatocellular damage by reactive carbon tetrachloride metabolites, Z Naturforsch C, vol. 56, no. 7-8, pp. 649-659.
  28. Zwilling, R. and C. Balduini (eds) (1992), Biology of Aging, vol. 1, Springer Berlin Heidelberg.
  29. 29.0 29.1 Boot, J.H. (1996), Hepatotoxic effects of SH-reagents in human and rat hepatocyte cultures and in situ perfused rat livers, Cell Structure and Function, vol. 2, no. 4, pp. 221-229.
  30. Bauman, J.N. et al. (2008), Comparison of the bioactivation potential of the antidepressant and hepatotoxin nefazodone with aripiprazole, a structural analog and marketed drug, Drug Metab. Dispos, vol. 36, no. 6, pp. 1016–1029.
  31. Bauman, J.N. et al. (2009), Can in vitro metabolism-dependent covalent binding data distinguish hepatotoxic from nonhepatotoxic drugs? An analysis using human hepatocytes and liver S-9 fraction, Chem Res Toxicol, vol. 22, no. 2, pp. 332-340.