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Relationship: 2205

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

Decrease, Cell proliferation leads to Decrease, Growth

Upstream event

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

Decrease, Cell proliferation

Downstream event

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

Decrease, Growth

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 decreased cell proliferation	adjacent	Moderate	Moderate	Allie Always (send email)	Open for citation & comment	WPHA/WNT Endorsed
Mitochondrial ATP synthase antagonism leading to growth inhibition (1)	adjacent			Brendan Ferreri-Hanberry (send email)	Under development: Not open for comment. Do not cite
Mitochondrial complex III antagonism leading to growth inhibition (1)	adjacent			Agnes Aggy (send email)	Under development: Not open for comment. Do not cite
Uncoupling of oxidative phosphorylation leading to growth inhibition via glucose depletion	adjacent			Arthur Author (send email)	Under development: Not open for comment. Do not cite	Under Development

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
zebrafish	Danio rerio	High	NCBI

Sex Applicability

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

Sex	Evidence
Unspecific	High

Life Stage Applicability

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

Term	Evidence
Embryo	High

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 key event relationship describes reduced cell proliferation (cell growth, division or a combination of these) leading to reduced tissue, organ or individual 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 Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help

The overall evidence supporting Relationship 2205 is considered moderate.

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

The biological plausibility of Relationship 2205 is considered high.

Rationale: The biological structural and functional relationship between cell proliferation and growth is well established. It is commonly accepted that the size of an organism, organ or tissue is dependent on the total number and volume of the cells it contains, and the amount of extracellular matrix and fluids (Conlon 1999). Impairment to cell proliferation can logically affect tissue and organismal growth.

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

The empirical support of Relationship 2205 is considered low.

Rationale: Because cell proliferation is typically measured in vitro, while growth of an organism is measured in vivo, few studies have measured both in the same experiment. There is one zebrafish study reporting concordant relationship between reduced cell proliferation and embryo growth with some inconsistencies (Bestman 2015).

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

In zebrafish embryos exposed to 2,4-DNP, significant growth inhibition (AO), as indicated by whole embryo length, caudal primary (CaP) motor neuron axons and otic vesicle length (OVL) ratio after 21h, somite width and eye diameter after 45h exposure was identified, after 21h, whereas a non- significant reduction in cell proliferation was observed (Bestman 2015).

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

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

The quantitative understanding of Relationship 2205 is moderate.

Rationale: Multiple mathematical models describing the quantitative relationships between cell proliferation and tissue growth exist for both animals (Binder 2008) and plants (Mosca 2018). There are also numerous models that are specifically developed for predicting tumor growth based on the proliferation rate (Jarrett 2018).

Response-response Relationship

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

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

Taxonomic applicability

Relationship 2205 is considered applicable to all eukaryotes (both unicellular and multicellular), as growth (or population growth of alga) is well known to be achieved through cell proliferation in animals, plants and some microorganisms.

Sex applicability

Relationship 2205 is considered applicable to both all sexes, as cell proliferation leading to growth is a fundamental process and not sex-specific.

Life-stage applicability

Relationship 2205 is considered applicable to all life stages, as cell proliferation leading to growth is essential for maintaining basic biological processes throughout an organism’s life.

References

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

Bestman JE, Stackley KD, Rahn JJ, Williamson TJ, Chan SS. 2015. The cellular and molecular progression of mitochondrial dysfunction induced by 2,4-dinitrophenol in developing zebrafish embryos. Differentiation 89:51-69. DOI: 10.1016/j.diff.2015.01.001.

Binder BJ, Landman KA, Simpson MJ, Mariani M, Newgreen DF. 2008. Modeling proliferative tissue growth: a general approach and an avian case study. Phys Rev E Stat Nonlin Soft Matter Phys 78:031912. DOI: 10.1103/PhysRevE.78.031912.

Conlon I, Raff M. 1999. Size control in animal development. Cell 96:235-244. DOI: 10.1016/s0092-8674(00)80563-2.

Jarrett AM, Lima EABF, Hormuth DA, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE. 2018. Mathematical models of tumor cell proliferation: A review of the literature. Expert Review of Anticancer Therapy 18:1271-1286. DOI: 10.1080/14737140.2018.1527689.

Mosca G, Adibi, M., Strauss, S., Runions, A., Sapala, A., Smith, R.S. 2018. Modeling Plant Tissue Growth and Cell Division. In Morris R., ed, Mathematical Modelling in Plant Biology. Springer, Cham.