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Relationship: 3632

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

Increase, Oxidative Stress leads to Increase, Protein oxidation

Upstream event

The causing Key Event (KE) in a Key Event Relationship (KER). More help

Increase, Oxidative Stress

Downstream event

The responding Key Event (KE) in a Key Event Relationship (KER). More help

Increase, Protein oxidation

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
Excessive reactive oxygen species leading to growth inhibition via protein oxidation and cell injury/death	adjacent	High		Evgeniia Kazymova (send email)	Under development: Not open for comment. Do not cite
Excessive reactive oxygen species leading to growth inhibition via fatty acid oxidation and cell injury/death	adjacent			Arthur Author (send email)	Under development: Not open for comment. Do not cite
Excessive reactive oxygen species leading to growth inhibition via fatty acid oxidation and reduced cell growth	adjacent			Agnes Aggy (send email)	Under development: Not open for comment. Do not cite
Excessive reactive oxygen species leading to growth inhibition via fatty acid oxidation and reduced cell proliferation	adjacent			Allie Always (send email)	Under development: Not open for comment. Do not cite
Excessive reactive oxygen species leading to growth inhibition via protein oxidation and cell cycle disruption	adjacent			Cataia Ives (send email)	Under development: Not open for comment. Do not cite
Reactive oxygen species leading to growth inhibition via protein oxidation and decreased cell proliferation	adjacent	High	Moderate	Evgeniia Kazymova (send email)	Under development: Not open for comment. Do not cite
Reactive oxygen species leading to growth inhibition via protein oxidation and cell death	adjacent	High	Moderate	Cataia Ives (send email)	Under development: Not open for comment. Do not cite

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
humans	Homo sapiens	High	NCBI
mammals	mammals	High	NCBI
fish	fish	High	NCBI
crustaceans	Daphnia magna	High	NCBI
green algae	Ulva compressa	Moderate	NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help

Sex	Evidence
Unspecific	Moderate

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER. More help

Term	Evidence
All life stages	Moderate

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 describes the relationship by which an increase in oxidative stress leads to increased protein oxidation. Oxidative stress represents a state in which oxidant generation or antioxidant depletion shifts the biological system toward a pro-oxidant condition. Under these conditions, reactive oxygen and nitrogen species, lipid-derived reactive aldehydes, metal-catalyzed oxidants and oxidized thiol/disulfide systems can modify proteins directly or indirectly. Protein oxidation includes irreversible modifications such as protein carbonyl formation, oxidation of aromatic and sulfur-containing amino acids, backbone fragmentation, crosslinking and aggregation, as well as reversible or regulatory modifications such as disulfide formation, S-glutathionylation, S-nitrosylation and other redox post-translational modifications (Stadtman and Levine, 2003; Dalle-Donne et al., 2006; Davies, 2016).

The relationship is biologically plausible because proteins are abundant cellular targets and many amino acid side chains react with oxidants or with secondary products of oxidative stress. Increased oxidative stress raises the probability that susceptible proteins will undergo oxidation, particularly when antioxidant defenses, reductive repair systems, proteasomal degradation or protein turnover cannot maintain proteostasis. The downstream KE therefore reflects a measurable biochemical consequence of the upstream oxidative-stress state.

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 for this KER was assembled from the ROS-growth AOP network literature review and data-mining workflow, targeted searches of the primary literature, AOP-Wiki records, and mechanistic reviews of protein oxidation chemistry. The evidence-collection process followed the AOP-Wiki KER page template, including structured consideration of biological applicability, KER description, evidence collection strategy, evidence supporting the KER, modulating factors, quantitative understanding, domain of applicability and references.

The search strategy focused on studies that measured oxidative stress and protein oxidation in the same biological system, or that provided strong mechanistic support for the transition from oxidative stress to protein oxidation. Search concepts included "oxidative stress", "reactive oxygen species", "protein oxidation", "protein carbonyl", "carbonylation", "oxidized proteins", "protein glutathionylation", "thiol oxidation", "redox proteomics", "AOPP", "hydrogen peroxide", "cadmium", "copper", "paraquat", "temperature stress", "salinity stress", "bivalve", "fish", "Chlamydomonas", "human cells", and "mammalian cells". PubMed, Web of Science, Google Scholar, AOP-Wiki, and the curated ROS-growth concordance table were consulted.

AOP-helpFinder and preliminary text-mining were used to identify co-occurrence of event-related terms, followed by overlap analysis to remove redundant records and exclude taxa-irrelevant or low-priority literature. Large language model-assisted screening was used only to extract candidate metadata and prioritize abstracts and full-text records. Final inclusion, interpretation and weight-of-evidence calls were made manually by expert review. Mechanistic reviews were used to support biological plausibility, while primary experimental studies were prioritized for empirical support, especially when oxidative stress and protein oxidation endpoints were measured in the same exposure context.

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

Biological plausibility of this KER is high. Oxidative stress produces or reflects oxidizing conditions that can modify proteins through multiple well-established chemical mechanisms. Hydroxyl radicals, peroxyl radicals, singlet oxygen, hypochlorous acid, peroxynitrite and metal-catalyzed oxidants can oxidize amino acid side chains, while secondary products of lipid peroxidation, such as reactive aldehydes, can form protein adducts and carbonyl derivatives. These processes produce measurable protein carbonyls, oxidized methionine, oxidized cysteine residues, disulfides, protein hydroperoxides, crosslinks and fragmented or aggregated proteins (Stadtman and Levine, 2003; Dalle-Donne et al., 2006; Davies, 2016).

The structural and functional relationship between the two KEs is direct. The upstream KE increases the oxidizing chemical environment, and the downstream KE is the covalent modification of protein targets under that oxidizing environment. Because proteins are abundant and essential for enzyme activity, signaling, structural integrity and energy metabolism, protein oxidation is a broadly expected consequence of oxidative stress when protective and repair mechanisms are insufficient.

Empirical Evidence

Provides specific (citable) evidence that supports the idea of a change in the upstream KE (KEupstream) leading to, or being associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. More help

Empirical support for this KER is moderate to high. Multiple studies in diverse systems show that oxidative-stress conditions coincide with or precede increases in protein oxidation markers, especially protein carbonylation, oxidized thiols or glutathionylated proteins. The strongest evidence comes from experiments in which oxidative stress biomarkers and protein oxidation endpoints were measured in the same biological system and exposure context. However, the empirical evidence is not uniformly high because many studies measure protein oxidation alone as an oxidative damage endpoint, without direct upstream ROS or redox measurements in the same time course.

Biological system	Stressor or condition	Evidence relevant to KER	Interpretation
Chlamydomonas reinhardtii	Cadmium or hydrogen peroxide / oxidative stress conditions	Proteomic analyses identified protein carbonylation and redox modifications including glutathionylation of photosynthetic and metabolic proteins under oxidative stress conditions (Zaffagnini et al., 2012).	Supports occurrence of protein oxidation under oxidative-stress conditions in photosynthetic eukaryotes.
Zebrafish brain	Acute cold exposure	Protein carbonyls increased by 38% within 1 h after cold exposure, with increased antioxidant response markers over the same early time frame (Tseng et al., 2011).	Supports temporal association between oxidative stress response and protein oxidation in fish.
Freshwater fish Channa punctata	Deltamethrin, endosulfan and paraquat	Protein carbonyls were proposed and measured as biomarkers of exposure to oxidative-stress-inducing pesticides (Parvez and Raisuddin, 2005).	Supports stressor-induced protein oxidation in fish exposed to pro-oxidant pesticides.
Mytilus galloprovincialis hemocytes	Cadmium or 17 beta-estradiol	Redox parameters were altered by micromolar concentrations of stressors, consistent with oxidative stress and linked signaling processes in mussel hemocytes (Koutsogiannaki et al., 2014).	Supports relevance of oxidative stress/protein-oxidation processes in molluscan immune cells.
Mammalian / human cell systems	Hydrogen peroxide and related oxidants	Live-cell fluorescent detection approaches demonstrate oxidative stress-induced carbonylation of biomolecules, including proteins (Mukherjee et al., 2015).	Supports direct oxidative stress-induced carbonylation in mammalian cell systems.

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

A major uncertainty is that protein oxidation comprises many different chemical modifications with different reversibility, biological consequences and measurement approaches. Protein carbonyls are widely used as relatively stable markers, but they represent only one subset of oxidative protein damage. Thiol oxidation, methionine oxidation and glutathionylation may be reversible or regulatory, while carbonylation and aggregation are often associated with irreversible damage. Therefore, different studies may use different operational definitions of protein oxidation, making quantitative comparison difficult (Dalle-Donne et al., 2006; Davies, 2016).

A second uncertainty is that oxidative stress is often inferred from antioxidant enzyme activity, glutathione status or damage endpoints rather than directly measured ROS flux. As a result, some empirical studies demonstrate co-occurrence of oxidative-stress markers and protein oxidation but cannot establish the exact sequence of events. Conversely, protein oxidation may arise secondarily from lipid peroxidation products, inflammation, metal-catalyzed reactions or impaired protein turnover, so the upstream oxidative-stress KE should be interpreted as a redox-state driver rather than a single chemical species. No strong contradictory evidence was identified for the general relationship that oxidative stress can increase protein oxidation.

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

Modulating factor	Details	Influence on KER	Supporting evidence
Antioxidant and reductive capacity	Glutathione, thioredoxin, glutaredoxin, peroxiredoxins, catalase, superoxide dismutase and related systems.	Higher antioxidant/reductive capacity decreases the probability or magnitude of protein oxidation for a given oxidative challenge; depletion increases sensitivity.	Sies et al., 2017; Rouhier et al., 2015; Zaffagnini et al., 2012.
Metal availability	Iron, copper, cadmium and other redox-active or thiol-reactive metals.	Transition metals and thiol-reactive metals can promote site-specific oxidation, protein carbonylation or altered thiol redox state.	Stadtman and Levine, 2003; Parvez and Raisuddin, 2005; Koutsogiannaki et al., 2014.
Protein composition and localization	Proteins rich in cysteine, methionine, aromatic residues or metal-binding sites; mitochondrial, chloroplast and membrane-associated proteins.	Susceptible proteins and proteins located near ROS sources are more likely to undergo oxidation.	Davies, 2016; Dalle-Donne et al., 2006.
Proteostasis and repair capacity	Proteasome activity, autophagy, methionine sulfoxide reductases, thiol-disulfide exchange systems and protein turnover.	Efficient repair and degradation can reduce accumulation of oxidized proteins even when oxidative stress occurs.	Dalle-Donne et al., 2006; Davies, 2016.
Exposure duration and intensity	Acute versus chronic oxidative stress; pulse versus sustained oxidant generation.	Longer or more intense oxidative stress increases accumulation of stable oxidative protein damage, especially carbonyls and aggregates.	Mukherjee et al., 2015; Tseng et al., 2011.

Quantitative Understanding of the Linkage

Captures 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. More help

Quantitative understanding of this KER is moderate. The qualitative biochemical relationship between oxidative stress and protein oxidation is well established, and response-response relationships exist in some experimental systems.

Response-response Relationship

Provides sources of data that define the response-response relationships between the KEs. More help

However, a general quantitative function predicting the magnitude of protein oxidation from a given oxidative-stress measurement has not been established across taxa, tissues, protein classes, stressors and assay methods. Quantitative prediction is complicated because the upstream KE can be measured by multiple endpoints, including ROS probes, glutathione status, antioxidant enzyme responses or pathway activation, while the downstream KE can be measured by protein carbonyls, oxidized thiols, methionine oxidation, glutathionylation, AOPP or redox proteomics.

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

The time scale of the linkage can range from minutes to days. Oxidation of susceptible amino acid residues may occur rapidly during an acute oxidant pulse, whereas accumulation of stable carbonylated proteins, protein aggregates or proteomic changes may require longer exposure or exceed the capacity of repair and degradation systems. In zebrafish exposed to acute cold stress, protein carbonylation increased within 1 h, showing that the downstream KE can occur rapidly in vivo under oxidative-stress conditions (Tseng et al., 2011). In Chlamydomonas and mammalian cell systems, protein oxidation and carbonylation are also detectable under defined pro-oxidant exposure conditions (Zaffagnini et al., 2012; Mukherjee et al., 2015).

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

The linkage is expected to be nonlinear and threshold-dependent. Low or transient oxidative stress may lead to reversible redox signaling or repairable thiol modifications, whereas stronger or persistent oxidative stress is more likely to cause irreversible carbonylation, aggregation or loss of protein function. Quantitative evaluation is therefore strongest when upstream oxidative stress and downstream protein oxidation are measured in the same biological system across multiple exposure concentrations and time points.

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

This KER is broadly applicable to aerobic biological systems in which oxidative stress and protein oxidation can be measured. It is particularly relevant to tissues and cellular compartments exposed to high oxidant flux, including mitochondria, chloroplasts, peroxisomes, inflammatory cells, gill and digestive tissues, nervous tissues and rapidly metabolizing cells. The relationship is expected to be conserved because it is based on fundamental redox chemistry and protein chemistry rather than on a taxon-specific receptor or signaling pathway.

The KER should be applied with greatest confidence when upstream oxidative stress is assessed using direct or mechanistically interpretable redox endpoints and downstream protein oxidation is measured using specific markers such as protein carbonyls, oxidized thiols, methionine oxidation, AOPP, or redox proteomics. Applicability is weaker when protein oxidation is inferred only from broad stress responses or when oxidative stress and protein oxidation are not measured in the same biological context. Species, life stage and sex should be considered mainly as modifiers of sensitivity rather than determinants of whether the relationship can occur.

References

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

AOP-Wiki. Relationship 3632: Increase, Oxidative Stress leads to Increase, Protein oxidation. https://aopwiki.org/relationships/3632. Accessed 14 May 2026.

Dalle-Donne I, Aldini G, Carini M, Colombo R, Rossi R, Milzani A. 2006. Protein carbonylation, cellular dysfunction, and disease progression. Journal of Cellular and Molecular Medicine 10(2):389-406. https://doi.org/10.1111/j.1582-4934.2006.tb00407.x.

Davies MJ. 2016. Protein oxidation and peroxidation. Biochemical Journal 473(7):805-825. https://doi.org/10.1042/BJ20151227.

Halliwell B, Gutteridge JMC. 2015. Free Radicals in Biology and Medicine. 5th ed. Oxford: Oxford University Press.

Koutsogiannaki S, Franzellitti S, Fabbri E, Kaloyianni M. 2014. Oxidative stress parameters induced by exposure to either cadmium or 17 beta-estradiol on Mytilus galloprovincialis hemocytes: the role of signaling molecules. Aquatic Toxicology 146:186-195. https://doi.org/10.1016/j.aquatox.2013.11.005.

Mukherjee K, Chio TI, Sackett DL, Bane SL. 2015. Detection of oxidative stress-induced carbonylation in live mammalian cells using a hydrazine-based fluorescent probe. Free Radical Biology and Medicine 84:11-21. https://doi.org/10.1016/j.freeradbiomed.2015.03.011.

Parvez S, Raisuddin S. 2005. Protein carbonyls: novel biomarkers of exposure to oxidative stress-inducing pesticides in freshwater fish Channa punctata (Bloch). Environmental Toxicology and Pharmacology 20(1):112-117. https://doi.org/10.1016/j.etap.2004.11.002.

Rouhier N, Cerveau D, Couturier J, Reichheld JP, Rey P. 2015. Involvement of thiol-based mechanisms in plant development. FEBS Letters 589(1):37-44. https://doi.org/10.1016/j.febslet.2014.11.021.

Schieber M, Chandel NS. 2014. ROS function in redox signaling and oxidative stress. Current Biology 24(10):R453-R462. https://doi.org/10.1016/j.cub.2014.03.034.

Sies H, Berndt C, Jones DP. 2017. Oxidative stress. Annual Review of Biochemistry 86:715-748. https://doi.org/10.1146/annurev-biochem-061516-045037.

Stadtman ER, Levine RL. 2003. Free radical-mediated oxidation of free amino acids and amino acid residues in proteins. Amino Acids 25(3-4):207-218. https://doi.org/10.1007/s00726-003-0011-2.

Tseng YC, Chen RD, Lucassen M, Schmidt MM, Dringen R, Abele D, Hwang PP. 2011. Exploring uncoupling proteins and antioxidant mechanisms under acute cold exposure in brains of fish. PLoS ONE 6(3):e18180. https://doi.org/10.1371/journal.pone.0018180.

Zaffagnini M, Bedhomme M, Groni H, Marchand CH, Puppo C, Gontero B, Cassier-Chauvat C, Decottignies P, Lemaire SD. 2012. Glutathionylation in the photosynthetic model organism Chlamydomonas reinhardtii: a proteomic survey. Molecular & Cellular Proteomics 11(2):M111.014142. https://doi.org/10.1074/mcp.M111.014142.