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Relationship: 2767

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

Cell injury/death leads to Decrease, Growth

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). 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

AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Uncoupling of oxidative phosphorylation leading to growth inhibition via increased cytosolic calcium adjacent Moderate Not Specified Cataia Ives (send email) Under development: Not open for comment. Do not cite Under Development
Uncoupling of oxidative phosphorylation leading to growth inhibition via ATP depletion associated cell death adjacent Moderate Not Specified Evgeniia Kazymova (send email) Open for citation & comment Under Development
Uncoupling of oxidative phosphorylation leading to growth inhibition via decreased Na-K ATPase activity adjacent Brendan Ferreri-Hanberry (send email) Under development: Not open for comment. Do not cite Under Development
Uncoupling of oxidative phosphorylation leading to growth inhibition via mitochondrial swelling adjacent Agnes Aggy (send email) Under development: Not open for comment. Do not cite Under Development
Reactive oxygen species leading to growth inhibition via lipid peroxidation and cell death adjacent High Moderate 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 injury/death adjacent 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
Reactive oxygen species leading to growth inhibition via oxidative DNA damage and cell death adjacent High Moderate Allie Always (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 Moderate NCBI
mammals mammals Moderate NCBI
fish fish Moderate NCBI
crustaceans Daphnia magna Moderate 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 causal and predictive relationship whereby an increase in cell injury and/or cell death leads to a decrease in growth. The upstream KE, cell injury/death, represents loss of cellular viability or severe cellular damage resulting in apoptosis, necrosis, or other forms of lethal cellular injury. The downstream KE, decreased growth, represents reduced accumulation of biomass, body size, length, cell density, tissue mass, or other growth-related endpoints at organ, organism, or population levels. The biological logic of the KER is that growth requires a positive balance between production of new cellular material and loss of existing cells. When cell injury/death is sufficiently frequent, persistent, or spatially distributed across growth-relevant tissues, net cell accumulation is reduced and tissue or organismal growth is impaired. In unicellular systems, increased cell death directly reduces viable cell density and biomass accumulation. In multicellular organisms, the relationship depends on the affected tissue, the ability to compensate through proliferation or regeneration, and the timing of injury relative to developmental or growth windows.

This relationship is not intended to imply that all decreases in growth are caused by cell death. Growth can also decrease through reduced cell proliferation, altered energy allocation, endocrine disruption, nutrient limitation, or developmental delay without overt lethality. Rather, the KER applies when increased cell injury/death is of sufficient magnitude or duration to reduce the viable cellular pool needed for growth or to damage growth-relevant tissues. Within the ROS-growth AOP network, this KER provides a terminal convergence relationship for pathways in which oxidative stress, DNA strand breaks, or ATP depletion produce cytotoxicity that contributes to reduced growth.

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 using the same AI-human hybrid strategy applied across the ROS-growth AOP network. Initial evidence identification used AOP-Wiki relationship and key event mapping, prior ROS-growth concordance tables, and targeted literature searches. Search terms combined upstream and downstream concepts such as “cell death”, “cell injury”, “cytotoxicity”, “apoptosis”, “necrosis”, “viability”, “growth inhibition”, “growth retardation”, “developmental delay”, “biomass”, “cell density”, “condition index”, and “organism growth”, together with taxa and stressor terms including algae, Daphnia, copepod, bivalve, fish embryo, mammalian embryo, paraquat, cadmium, methanol, rotenone, gamma radiation, and oxidative stress. AOP-Wiki was consulted to confirm that Relationship 2767 links Event 55 to Event 1521 and to identify related AOP reuse contexts.

Candidate studies were prioritized when they measured both cell injury/death and a growth-related outcome in the same biological system, reported dose or concentration and exposure duration, or provided information relevant to temporal, dose-response, or incidence concordance. Large language model assistance was used only as an auxiliary screening and structuring tool to extract study metadata, identify potentially relevant endpoints, and prioritize records for expert review. Final inclusion decisions, interpretation of endpoints, and weight-of-evidence judgments were made by manual expert curation against the original article text. Mechanistic reviews were used to support biological plausibility, while primary experimental studies were used preferentially to support empirical concordance.

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

Overall call: High. Growth at the level of a tissue, organ, organism, or cell population depends on net accumulation of cells and cellular biomass. Increased cell death directly lowers the number of viable cells and can reduce tissue mass, disrupt morphogenesis, or impair the capacity for biomass accumulation. This relationship is strongly supported by developmental and cell-size control principles showing that final tissue and organism size depend on the balance among cell growth, cell division, and cell death (Conlon and Raff, 1999). In embryos and developing organisms, excessive cell death can reduce cell number available for organ formation and growth, whereas in unicellular populations and cell cultures, cytotoxicity directly reduces viable cell density. The KER is therefore mechanistically plausible across taxa, although the magnitude of growth impairment depends on the tissue affected, compensatory proliferation, regeneration, and exposure duration.

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

The main uncertainty is that decreased growth is an integrative endpoint and can arise through several mechanisms that do not require overt cell death. Reduced proliferation, ATP depletion, endocrine disruption, altered energy allocation, nutrient limitation, delayed development, or behavioral effects can all reduce growth. For this reason, cell injury/death should be interpreted as a sufficient but not always necessary contributor to decreased growth. A second uncertainty is that many studies measure cytotoxicity and growth at different times or in different tissues, which limits direct evaluation of temporal concordance. In some algal studies, growth inhibition occurs at lower concentrations than overt cell death, suggesting that non-lethal impairment of proliferation, photosynthesis, or energy metabolism may precede cell death. Conversely, mild or localized cell injury may be compensated by repair or proliferation and may not lead to measurable growth reduction. These uncertainties support a moderate, rather than high, empirical call for this KER.

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

Relevant details

Effect on the KER

Supporting references

Developmental stage

Embryonic and larval stages, rapid growth phases

Increases sensitivity because rapid tissue growth requires high net cell accumulation; cell death during development can disproportionately impair growth.

Abbott et al., 1995; Conlon and Raff, 1999

Tissue regenerative capacity

Capacity for compensatory proliferation or tissue repair

Reduces probability that cell death will translate into growth impairment when surviving cells can replace lost cells.

Conlon and Raff, 1999

Exposure duration and timing

Acute versus chronic exposures; timing relative to growth window

Longer or developmentally timed exposures increase probability of growth effects from cell loss.

Jamers and De Coen, 2010; Melo et al., 2015

Energy and nutritional status

Energy budget, food availability, metabolic reserve

Can increase or decrease impact of cell death on growth by altering compensatory capacity and resource allocation.

Sokolova, 2013; Cherkasov et al., 2006

Environmental stressors

Temperature, oxygen availability, salinity, co-exposures

Can amplify cytotoxicity or reduce compensatory growth responses, modifying downstream growth effects.

Cherkasov et al., 2006; Won and Lee, 2014

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

In cell populations and unicellular organisms, the quantitative relationship can be relatively direct because viable cell density is part of the growth measurement. In multicellular organisms, the relationship is less direct because growth can continue despite localized cell death if compensatory proliferation or tissue repair occurs. Some data show concordance between cytotoxicity and growth inhibition, but these data are generally insufficient to define universal thresholds. Therefore, quantitative understanding should be considered low to moderate for broad AOP-Wiki application, with higher confidence possible for specific model systems where cell viability and growth rate are measured in the same assay and time course.

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

Domain of Applicability

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

The KER is applicable to biological systems in which growth depends on maintenance or expansion of viable cell number or biomass. This includes unicellular populations, developing embryos, juvenile organisms, growing tissues, and adult organisms in which tissue condition or somatic growth is assessed. Taxonomic applicability is broad across eukaryotes, but empirical support is strongest for algae, aquatic invertebrates, mollusks, fish, and mammalian embryo or cell models. The KER is not sex-specific, but sex, endocrine status, life stage, and environmental context may modulate sensitivity. The relationship is most relevant when cell injury/death is sufficiently extensive, sustained, or located in growth-relevant tissues. It is less predictive when growth is reduced by upstream mechanisms that suppress proliferation or metabolism without substantial cell death.

References

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

Abbott, B. D., Harris, M. W., & Birnbaum, L. S. (1995). Cell death in rat and mouse embryos exposed to methanol in whole embryo culture: Evaluation of the role of the p53 tumor suppressor gene. Teratogenesis, Carcinogenesis, and Mutagenesis, 15(3), 147–169.

Cherkasov, A. S., Biswas, P. K., Ridings, D. M., Ringwood, A. H., & Sokolova, I. M. (2006). Effects of acclimation temperature and cadmium exposure on cellular energy budgets in the marine mollusk Crassostrea virginica: Linking cellular and mitochondrial responses. Journal of Experimental Biology, 209(7), 1274–1284.

Conlon, I., & Raff, M. (1999). Size control in animal development. Cell, 96(2), 235–244.

Jamers, A., & De Coen, W. (2010). Effect assessment of the herbicide paraquat on a green alga using differential gene expression and biochemical biomarkers. Environmental Toxicology and Chemistry, 29(4), 893–901.

Knops, M., Altenburger, R., & Segner, H. (2001). Alterations of physiological energetics, growth and reproduction of Daphnia magna under toxicant stress. Aquatic Toxicology, 53(2), 79–90.

Melo, K. M., Oliveira, R., Grisolia, C. K., Domingues, I., Pieczarka, J. C., de Souza Filho, J., & Nagamachi, C. Y. (2015). Short-term exposure to low doses of rotenone induces developmental, biochemical, behavioral, and histological changes in fish. Environmental Science and Pollution Research, 22(18), 13926–13938.

Nestler, H., Groh, K. J., Schönenberger, R., Eggen, R. I. L., & Suter, M. J.-F. (2012). Multiple-endpoint assay provides a detailed mechanistic view of responses to herbicide exposure in Chlamydomonas reinhardtii. Aquatic Toxicology, 110–111, 214–224.

Organisation for Economic Co-operation and Development (OECD). (2018). Users’ handbook supplement to the guidance document for developing and assessing adverse outcome pathways. OECD Series on Adverse Outcome Pathways No. 1. OECD Publishing, Paris.

Organisation for Economic Co-operation and Development (OECD). (2021). Guidance document for the scientific review of adverse outcome pathways. OECD Series on Testing and Assessment No. 344. OECD Publishing, Paris.

Sokolova, I. M. (2013). Energy-limited tolerance to stress as a conceptual framework to integrate the effects of multiple stressors. Integrative and Comparative Biology, 53(4), 597–608.

Sokolova, I. M., Sokolov, E. P., & Ponnappa, K. M. (2005). Cadmium exposure affects mitochondrial bioenergetics and gene expression of key mitochondrial proteins in the eastern oyster Crassostrea virginica Gmelin (Bivalvia: Ostreidae). Aquatic Toxicology, 73(3), 242–255.

Won, E. J., & Lee, J. S. (2014). Gamma radiation induces growth retardation, impaired egg production, and oxidative stress in the marine copepod Paracyclopina nana. Aquatic Toxicology, 150, 17–26.