This AOP is licensed under a Creative Commons Attribution 4.0 International License.

Aop: 263

Title

Each AOP should be given a descriptive title that takes the form “MIE leading to AO”. For example, “Aromatase inhibition [MIE] leading to reproductive dysfunction [AO]” or “Thyroperoxidase inhibition [MIE] leading to decreased cognitive function [AO]”. In cases where the MIE is unknown or undefined, the earliest known KE in the chain (i.e., furthest upstream) should be used in lieu of the MIE and it should be made clear that the stated event is a KE and not the MIE. More help

Uncoupling of oxidative phosphorylation leading to growth inhibition via reduced cell proliferation

Short name

A short name should also be provided that succinctly summarises the information from the title. This name should not exceed 90 characters. More help

Uncoupling of OXPHOS leading to growth inhibition 1

Graphical Representation

A graphical summary of the AOP listing all the KEs in sequence, including the MIE (if known) and AO, and the pair-wise relationships (links or KERs) between those KEs should be provided. This is easily achieved using the standard box and arrow AOP diagram (see this page for example). The graphical summary is prepared and uploaded by the user (templates are available) and is often included as part of the proposal when AOP development projects are submitted to the OECD AOP Development Workplan. The graphical representation or AOP diagram provides a useful and concise overview of the KEs that are included in the AOP, and the sequence in which they are linked together. This can aid both the process of development, as well as review and use of the AOP (for more information please see page 19 of the Users' Handbook).If you already have a graphical representation of your AOP in electronic format, simple save it in a standard image format (e.g. jpeg, png) then click ‘Choose File’ under the “Graphical Representation” heading, which is part of the Summary of the AOP section, to select the file that you have just edited. Files must be in jpeg, jpg, gif, png, or bmp format. Click ‘Upload’ to upload the file. You should see the AOP page with the image displayed under the “Graphical Representation” heading. To remove a graphical representation file, click 'Remove' and then click 'OK.' Your graphic should no longer be displayed on the AOP page. If you do not have a graphical representation of your AOP in electronic format, a template is available to assist you. Under “Summary of the AOP”, under the “Graphical Representation” heading click on the link “Click to download template for graphical representation.” A Powerpoint template file should download via the default download mechanism for your browser. Click to open this file; it contains a Powerpoint template for an AOP diagram and instructions for editing and saving the diagram. Be sure to save the diagram as jpeg, jpg, gif, png, or bmp format. Once the diagram is edited to its final state, upload the image file as described above. More help

Click to download graphical representation template

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Authors

List the name and affiliation information of the individual(s)/organisation(s) that created/developed the AOP. In the context of the OECD AOP Development Workplan, this would typically be the individuals and organisation that submitted an AOP development proposal to the EAGMST. Significant contributors to the AOP should also be listed. A corresponding author with contact information may be provided here. This author does not need an account on the AOP-KB and can be distinct from the point of contact below. The list of authors will be included in any snapshot made from an AOP. More help

You Song^a and Daniel L. Villeneuve^b

^a Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, NO-0349 Oslo, Norway

^b U.S. Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota 55804, USA

Acknowledgement

This project was funded by the Research Council of Norway (RCN), grant no. 301397 “RiskAOP - Quantitative Adverse Outcome Pathway assisted risk assessment of mitochondrial toxicants” (https://www.niva.no/en/projectweb/riskaop), and supported by the NIVA Computational Toxicology Program, NCTP (www.niva.no/nctp).

Point of Contact

Indicate the point of contact for the AOP-KB entry itself. This person is responsible for managing the AOP entry in the AOP-KB and controls write access to the page by defining the contributors as described below. Clicking on the name will allow any wiki user to correspond with the point of contact via the email address associated with their user profile in the AOP-KB. This person can be the same as the corresponding author listed in the authors section but isn’t required to be. In cases where the individuals are different, the corresponding author would be the appropriate person to contact for scientific issues whereas the point of contact would be the appropriate person to contact about technical issues with the AOP-KB entry itself. Corresponding authors and the point of contact are encouraged to monitor comments on their AOPs and develop or coordinate responses as appropriate. More help

You Song (email point of contact)

Contributors

List user names of all authors contributing to or revising pages in the AOP-KB that are linked to the AOP description. This information is mainly used to control write access to the AOP page and is controlled by the Point of Contact. More help

You Song
Dan Villeneuve

Status

The status section is used to provide AOP-KB users with information concerning how actively the AOP page is being developed, what type of use or input the authors feel comfortable with given the current level of development, and whether it is part of the OECD AOP Development Workplan and has been reviewed and/or endorsed. “Author Status” is an author defined field that is designated by selecting one of several options from a drop-down menu (Table 3). The “Author Status” field should be changed by the point of contact, as appropriate, as AOP development proceeds. See page 22 of the User Handbook for definitions of selection options. More help

Author status	OECD status	OECD project	SAAOP status
Open for comment. Do not cite	Under Development	1.92	Included in OECD Work Plan

This AOP was last modified on June 14, 2021 13:13

The date the AOP was last modified is automatically tracked by the AOP-KB. The date modified field can be used to evaluate how actively the page is under development and how recently the version within the AOP-Wiki has been updated compared to any snapshots that were generated. More help

Revision dates for related pages

Page	Revision Date/Time
Decrease, Coupling of oxidative phosphorylation	May 28, 2021 07:59
Decrease, Growth	November 28, 2020 15:07
Decrease, Adenosine triphosphate pool	June 14, 2021 13:40
Decrease, Cell proliferation	December 07, 2020 06:55
Decrease, Coupling of OXPHOS leads to Decrease, ATP pool	December 07, 2020 07:25
Decrease, ATP pool leads to Decrease, Cell proliferation	December 07, 2020 07:43
Decrease, Cell proliferation leads to Decrease, Growth	December 07, 2020 07:59
2,4-Dinitrophenol	November 29, 2016 18:42
Pentachlorophenol	November 12, 2020 17:59
Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone	November 12, 2020 17:59
Carbonyl cyanide m-chlorophenyl hydrazone	November 12, 2020 17:59
Triclosan	November 12, 2020 18:00
Dinoseb	November 12, 2020 18:00
3,5-Dichlorophenol	October 10, 2017 07:47
Emodin	November 20, 2020 13:48

Abstract

In the abstract section, authors should provide a concise and informative summation of the AOP under development that can stand-alone from the AOP page. Abstracts should typically be 200-400 words in length (similar to an abstract for a journal article). Suggested content for the abstract includes the following: The background/purpose for initiation of the AOP’s development (if there was a specific intent) A brief description of the MIE, AO, and/or major KEs that define the pathway A short summation of the overall WoE supporting the AOP and identification of major knowledge gaps (if any) If a brief statement about how the AOP may be applied (optional). The aim is to capture the highlights of the AOP and its potential scientific and regulatory relevance More help

Uncoupling of oxidative phosphorylation (OXPHOS) is a well-known mechanism of action of many chemicals. Mitochondrial uncoupler-mediated energetic dysfunction is known to affect growth, a critical process in most organisms and a chronic toxicity endpoint included in many OECD test guidelines. This adverse outcome pathway (AOP) causally links uncoupling of OXPHOS to growth inhibition, through ATP depletion and reduced cell proliferation as the intermediate key events (KEs), with strong weight of evidence support. The AOP is generalized to reflect its expected applicability to a broad range of taxa, ranging from microalga to human. Three out of four KEs included can be quantified using high-throughput methods, making this AOP particularly useful for screening, prioritization and hazard assessment of mitochondrial uncouplers as potential growth inhibiting chemicals. This AOP is therefore considered to be of regulatory and ecological relevance. The AOP also forms the core of a larger AOP network addressing uncoupling of OXPHOS mediated growth inhibition (AOP 263-268).

Background (optional)

This optional subsection should be used to provide background information for AOP reviewers and users that is considered helpful in understanding the biology underlying the AOP and the motivation for its development. The background should NOT provide an overview of the AOP, its KEs or KERs, which are captured in more detail below. Examples of potential uses of the optional background section are listed on pages 24-25 of the User Handbook. More help

The mitochondrial OXPHOS machinery is a key physiological process responsible for producing the primary cellular energy, adenosine triphosphate (ATP). During OXPHOS, a series of redox reactions (oxidation) are mediated by protein complexes in an electron transport chain to create a protonmotive force (PMF) across the inner mitochondrial membrane (Liberman 1969). The PMF acts as a driving force of ATP synthesis through phosphorylation of adenosine diphosphate (ADP). Mitochondrial oxidation and phosphorylation are coupled to ensure continuous ATP supply for various physiological processes. A number of chemicals can bind to the inner mitochondrial membrane and dissipate the PMF, thus leading to uncoupling of OXPHOS and reduction in ATP synthetic efficiency. Classical “uncouplers” are normally protonophores with major characteristics of bulky hydrophobic moiety, an acid dissociable group and a strong electron-withdrawing group (Terada 1990). With the rapid development of in silico (Russom 1997; Schultz 1997; Naven 2012; Dreier 2019; Troger 2020) and in vitro (Escher 2002; Attene-Ramos 2013; Attene-Ramos 2015; Xia 2018) approaches, more and more uncouplers have been identified. However, their hazards to biota remain to be assessed. Uncoupling of OXPHOS can affect many ATP-dependent biological functions. In particular, cell proliferation as a major process to achieve organismal growth is positively correlated with the cellular ATP level and highly susceptible to energy depletion (Ramaiah 1964; Bonora 2012). Therefore, a link between uncoupling of OXPHOS and growth inhibition can be established with ATP depletion and reduced cell proliferation as the intermediate steps.

Summary of the AOP

This section is for information that describes the overall AOP. The information described in section 1 is entered on the upper portion of an AOP page within the AOP-Wiki. This is where some background information may be provided, the structure of the AOP is described, and the KEs and KERs are listed. More help

Events:

Molecular Initiating Events (MIE)

An MIE is a specialised KE that represents the beginning (point of interaction between a stressor and the biological system) of an AOP. More help

Key Events (KE)

This table summarises all of the KEs of the AOP. This table is populated in the AOP-Wiki as KEs are added to the AOP. Each table entry acts as a link to the individual KE description page. More help

Adverse Outcomes (AO)

An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP. More help

Sequence	Type	Event ID	Title	Short name

MIE

1446

Decrease, Coupling of oxidative phosphorylation

Decrease, Coupling of OXPHOS

	KE	1771	Decrease, Adenosine triphosphate pool	Decrease, ATP pool
	KE	1821	Decrease, Cell proliferation	Decrease, Cell proliferation

1521

Decrease, Growth

Relationships Between Two Key Events (Including MIEs and AOs)

This table summarises all of the KERs of the AOP and is populated in the AOP-Wiki as KERs are added to the AOP. Each table entry acts as a link to the individual KER description page.To add a key event relationship click on either Add relationship: events adjacent in sequence or Add relationship: events non-adjacent in sequence.For example, if the intended sequence of KEs for the AOP is [KE1 > KE2 > KE3 > KE4]; relationships between KE1 and KE2; KE2 and KE3; and KE3 and KE4 would be defined using the add relationship: events adjacent in sequence button. Relationships between KE1 and KE3; KE2 and KE4; or KE1 and KE4, for example, should be created using the add relationship: events non-adjacent button. This helps to both organize the table with regard to which KERs define the main sequence of KEs and those that provide additional supporting evidence and aids computational analysis of AOP networks, where non-adjacent KERs can result in artifacts (see Villeneuve et al. 2018; DOI: 10.1002/etc.4124).After clicking either option, the user will be brought to a new page entitled ‘Add Relationship to AOP.’ To create a new relationship, select an upstream event and a downstream event from the drop down menus. The KER will automatically be designated as either adjacent or non-adjacent depending on the button selected. The fields “Evidence” and “Quantitative understanding” can be selected from the drop-down options at the time of creation of the relationship, or can be added later. See the Users Handbook, page 52 (Assess Evidence Supporting All KERs for guiding questions, etc.). Click ‘Create [adjacent/non-adjacent] relationship.’ The new relationship should be listed on the AOP page under the heading “Relationships Between Two Key Events (Including MIEs and AOs)”. To edit a key event relationship, click ‘Edit’ next to the name of the relationship you wish to edit. The user will be directed to an Editing Relationship page where they can edit the Evidence, and Quantitative Understanding fields using the drop down menus. Once finished editing, click ‘Update [adjacent/non-adjacent] relationship’ to update these fields and return to the AOP page.To remove a key event relationship to an AOP page, under Summary of the AOP, next to “Relationships Between Two Key Events (Including MIEs and AOs)” click ‘Remove’ The relationship should no longer be listed on the AOP page under the heading “Relationships Between Two Key Events (Including MIEs and AOs)”. More help

Title	Adjacency	Evidence	Quantitative Understanding

Decrease, Coupling of OXPHOS leads to Decrease, ATP pool	adjacent	High	High
Decrease, ATP pool leads to Decrease, Cell proliferation	adjacent	Moderate	Moderate
Decrease, Cell proliferation leads to Decrease, Growth	adjacent	Moderate	Moderate

Network View

The AOP-Wiki automatically generates a network view of the AOP. This network graphic is based on the information provided in the MIE, KEs, AO, KERs and WoE summary tables. The width of the edges representing the KERs is determined by its WoE confidence level, with thicker lines representing higher degrees of confidence. This network view also shows which KEs are shared with other AOPs. More help

Stressors

The stressor field is a structured data field that can be used to annotate an AOP with standardised terms identifying stressors known to trigger the MIE/AOP. Most often these are chemical names selected from established chemical ontologies. However, depending on the information available, this could also refer to chemical categories (i.e., groups of chemicals with defined structural features known to trigger the MIE). It can also include non-chemical stressors such as genetic or environmental factors. Although AOPs themselves are not chemical or stressor-specific, linking to stressor terms known to be relevant to different AOPs can aid users in searching for AOPs that may be relevant to a given stressor. More help

Name	Evidence Term
2,4-Dinitrophenol	High
Pentachlorophenol	High
Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone	High
Carbonyl cyanide m-chlorophenyl hydrazone	High
Triclosan	High
Dinoseb	Moderate
3,5-Dichlorophenol	Moderate
Emodin	High

Life Stage Applicability

Identify the life stage for which the KE is known to be applicable. More help

Life stage	Evidence
Embryo	High
Juvenile	High

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help

Term	Scientific Term	Evidence	Link
zebrafish	Danio rerio	High	NCBI
Lemna minor	Lemna minor	Moderate	NCBI
human	Homo sapiens	Moderate	NCBI
mouse	Mus musculus	High	NCBI
rat	Rattus norvegicus	High	NCBI

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help

Sex	Evidence
Unspecific	High

Overall Assessment of the AOP

This section addresses the relevant biological domain of applicability (i.e., in terms of taxa, sex, life stage, etc.) and WoE for the overall AOP as a basis to consider appropriate regulatory application (e.g., priority setting, testing strategies or risk assessment). The goal of the overall assessment is to provide a high level synthesis and overview of the relative confidence in the AOP and where the significant gaps or weaknesses are (if they exist). Users or readers can drill down into the finer details captured in the KE and KER descriptions, and/or associated summary tables, as appropriate to their needs.Assessment of the AOP is organised into a number of steps. Guidance on pages 59-62 of the User Handbook is available to facilitate assignment of categories of high, moderate, or low confidence for each consideration. While it is not necessary to repeat lengthy text that appears elsewhere in the AOP description (or related KE and KER descriptions), a brief explanation or rationale for the selection of high, moderate, or low confidence should be made. More help

The weight of evidence (WoE) assessment of the AOP was conducted based on the evolved Bradford-Hill considerations (Becker 2015) and according to the criteria in OECD’s Guidance Document for Developing and Assessing AOPs (OECD 2018). In terms of evidence for the essentiality of the key events, the MIE (Event 1446) and KE1 (Event 1771) were scored as high, whereas KE2 (Event 1821) was scored as moderate due to a lack of solid evidence to support its essentiality. The overall WoE of KER1 (Relationship 2203) is considered high, as strong biological plausibility, empirical evidence and fairly good quantitative understanding were evidenced from multiple studies. The overall WoE of KER2 (Relationship 2204) is considered moderate, due to high biological plausibility, acceptable empirical concordance and some biological understanding. The overall WoE of KER3 (Relationship 2205) is scored as moderate, mainly due to biological plausibility, but there is presently a lack of empirical evidence and quantitative understanding to further support causality. The AOP is considered applicable to a wide range of species as well as a broad domain of chemicals. The rationales for making these judgements will be discussed in detail in the following sections.

Domain of Applicability

The relevant biological domain(s) of applicability in terms of sex, life-stage, taxa, and other aspects of biological context are defined in this section. Biological domain of applicability is informed by the “Description” and “Biological Domain of Applicability” sections of each KE and KER description (see sections 2G and 3E for details). In essence the taxa/life-stage/sex applicability is defined based on the groups of organisms for which the measurements represented by the KEs can feasibly be measured and the functional and regulatory relationships represented by the KERs are operative.The relevant biological domain of applicability of the AOP as a whole will nearly always be defined based on the most narrowly restricted of its KEs and KERs. For example, if most of the KEs apply to either sex, but one is relevant to females only, the biological domain of applicability of the AOP as a whole would be limited to females. While much of the detail defining the domain of applicability may be found in the individual KE and KER descriptions, the rationale for defining the relevant biological domain of applicability of the overall AOP should be briefly summarised on the AOP page. More help

The taxonomic application domain of the AOP potential covers all animals, plants and some microorganisms such as fungus and protists, as mitochondrial OXPHOS is highly conserved in eukaryotes (Roger 2017).

The life stage applicability domain of the AOP mainly contains embryos and juveniles, as growth is more relevant to developing organisms. It should be noted that fully grown adults are also susceptible to uncouplers, as tissue/organ (e.g., adipose tissue) growth and regeneration still occur in adults (Yun 2015; Demine 2019). Classical uncouplers such as 2,4-DNP have been reported to cause weight loss in adult humans (Grundlingh 2011). In fact, 2,4-DNP was sold for weight loss until its legal sale was banned over toxicity and abuse concerns (Baker 2020). These suggest that adults are in the applicability domain of this AOP.

The sex applicability domain of the AOP is unspecific, as the AOP is mainly targeting growth effects in sexually immature organisms and the KEs are therefore harmonized between male and females. However, male and females may have different sensitivities to OXPHOS uncoupling, as strategies for allocating energy for developmental processes may be gender specific (Demarest 2015).

The chemical applicability domain of the AOP mainly includes weak acids, such as phenols, benzimidazoles, N-phenylanthranilates, salicylanilides, phenylhydrazones, salicylic acids, acyldithiocarbazates, cumarines, and aromatic amines, which are well-known protonophoric uncouplers. Uncouplers typically have properties as both weak acids and hydrophobic substances. As weak acids, they are capable of gaining and losing an electron. As hydrophobic substances, they are capable of distributing a negative charge over a number of atoms (often by π-orbitals which delocalize a proton's charge when it attaches to the molecule), so that they can diffuse back and forth across the inner mitochondrial membrane in either the charged or uncharged state, thus moving protons back across the concentration gradient generated by the electron transport chain. Classical uncouplers, such as 2,4-dinitrophenol (2,4-DNP), carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP), carbonyl cyanide m-chlorophenyl hydrazone (CCCP), pentachlorophenol (PCP), 3,5-dichlorophenol (3,5-DCP), 6-sec-butyl-2,4-dinitrophenol (dinoseb), SF 6847 (3,5-di-t-butyl-4-hydroxybenzylidinemalononitrile) have been widely used as positive controls in (eco)toxicological tests, whereas the hazards of “new” uncouplers, such as triclosan, emodin and metabolites of polybrominated diphenyl ethers (PBDEs) are also under extensive assessments. Other types of uncouplers that are SH-reactive chemicals or hydrophobic ions may also be in the applicability domain of this AOP. A number of potential uncouplers have been identified by in silico (Russom 1997; Schultz 1997; Naven 2012; Dreier 2019; Troger 2020) and in vitro (Escher 2002; Attene-Ramos 2013; Attene-Ramos 2015; Xia 2018) approaches, and are considered in the chemical applicability domain of the AOP.

Essentiality of the Key Events

An important aspect of assessing an AOP is evaluating the essentiality of its KEs. The essentiality of KEs can only be assessed relative to the impact of manipulation of a given KE (e.g., experimentally blocking or exacerbating the event) on the downstream sequence of KEs defined for the AOP. Consequently evidence supporting essentiality is assembled on the AOP page, rather than on the independent KE pages that are meant to stand-alone as modular units without reference to other KEs in the sequence.The nature of experimental evidence that is relevant to assessing essentiality relates to the impact on downstream KEs and the AO if upstream KEs are prevented or modified. This includes: Direct evidence: directly measured experimental support that blocking or preventing a KE prevents or impacts downstream KEs in the pathway in the expected fashion. Indirect evidence: evidence that modulation or attenuation in the magnitude of impact on a specific KE (increased effect or decreased effect) is associated with corresponding changes (increases or decreases) in the magnitude or frequency of one or more downstream KEs.When assembling the support for essentiality of the KEs, authors should organise relevant data in a tabular format. The objective is to summarise briefly the nature and numbers of investigations in which the essentiality of KEs has been experimentally explored either directly or indirectly. See pages 50-51 in the User Handbook for further definitions and clarifications. More help

Support for Essentiality of KEs	Defining Question	What is the impact on downstream KEs and/or the AO if an upstream KE is modified or prevented?
	High	Direct evidence from specifically designed experimental studies illustrating prevention or impact on downstream KEs and/or the AO if upstream KEs are blocked or modified.
	Moderate	Indirect evidence that modification of one or more upstream KEs is associated with a corresponding (increase or decrease) in the magnitude or frequency of downstream KEs.
	Low	No or contradictory experimental evidence of the essentiality of any of the KEs.
Event 1446: (Decrease, Coupling of OXPHOS)	Essentiality of Event 1446 is high. Rationale: There is direct evidence from several specifically designed studies showing that removal of an uncoupler from exposure, or addition of a “recoupler” can lead to recovery of the mitochondrial membrane potential (MMP) and total ATP caused by the uncoupler. Evidence: Removal of the classical uncoupler carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) led to recovery of both MMP and ATP in rat cerebellar granule cells (Weisová 2012). In the red abalone (Haliotis rufescens) larvae, removal of the uncoupler pentachlorophenol also led to recovery of the ATP level (Shofer 2002). Addition of the recoupler GDP led to a rapid increase in ATP/ADP ratio in isolated guinea pig brown-adipose-tissue mitochondria where high activities of natural coupling by the UCPs were expected (Rafael 1976). Addition of octanoate to 2,4-DNP exposed rat hepatocytes mitigated the uncoupling effect and partial restored the ATP/ADP ratio (Sibille 1995). Removal of FCCP led to recovery from FCCP-mediated MMP and ATP reduction in Swiss mouse embryos (Zander-Fox 2015).
Event 1771: (Decrease, ATP pool)	Essentiality of Event 1771 is moderate. Rationale: There is limited direct evidence from specifically designed studies. However, multiple lines of indirect evidence show that modulation of ATP levels by uncouplers can also lead to corresponding changes in cell proliferation. Evidence: Addition of emodin blunted ATP-induced cell proliferation in a concentration-dependent manner in human lung adenocarconoma (A549) cells (Wang 2017), hence providing direct evidence to support the essentiality of this KE. Positive relationships between uncoupler-mediated ATP depletion and reduced cell proliferation have been documented by multiple studies(Sweet 1999; Fine 2009; Guimarães 2012; Sugiyama 2019).
Event 1821: (Decrease, Cell proliferation)	Essentiality of Event 1821 is moderate. Rationale: There is no direct evidence from specifically designed studies to support this KE. However, there are multiple lines of indirect evidence showing positive relationships between cell proliferation and growth. Evidence: Indirect evidence can be obtained from a limited number of relevant studies showing a positive role of cell proliferation in mammalian tumor (Figarola 2018) zebrafish embryo growth(Bestman 2015).
Inconsistencies & uncertainties	There is an uncertainty related to KE1446 that mild uncoupling of OXPHOS may also increase the ATP pool in some cases (Desquiret 2006), possibly as a compensatory response. The underlying mechanism remains to be further elucidated.

Evidence Assessment

The biological plausibility, empirical support, and quantitative understanding from each KER in an AOP are assessed together. Biological plausibility of each of the KERs in the AOP is the most influential consideration in assessing WoE or degree of confidence in an overall hypothesised AOP for potential regulatory application (Meek et al., 2014; 2014a). Empirical support entails consideration of experimental data in terms of the associations between KEs – namely dose-response concordance and temporal relationships between and across multiple KEs. It is examined most often in studies of dose-response/incidence and temporal relationships for stressors that impact the pathway. While less influential than biological plausibility of the KERs and essentiality of the KEs, empirical support can increase confidence in the relationships included in an AOP. For clarification on how to rate the given empirical support for a KER, as well as examples, see pages 53- 55 of the User Handbook. More help

Biological plausibility

Support for Biological Plausibility of KERs	Defining Question	Is there a mechanistic (i.e., structural or functional) relationship between KE_up and KE_down consistent with established biological knowledge?
	High	Extensive understanding based on extensive previous documentation and broad acceptance -Established mechanistic basis.
	Moderate	The KER is plausible based on analogy to accepted biological relationships but scientific understanding is not completely established.
	Low	There is empirical support for a statistical association between KEs, but the structural or functional relationship between them is not understood.
Relationship 2203: (Decrease, Coupling of OXPHOS leads to Decrease, ATP pool)	Biological Plausibility of Relationship 2203 is high. Rationale: In eukaryotic cells, the major metabolic pathways responsible for ATP production are OXPHOS, citric acid (TCA) cycle, glycolysis and photosynthesis. Oxidative phosphorylation is much (theoretically 15-18 times) more efficient than the rest due to high energy derived from oxygen during aerobic respiration (Schmidt-Rohr 2020). As the ATP level is relatively balanced between production and consumption (Bonora 2012), ATP depletion is a plausible consequence of reduced ATP synthetic efficiency following uncoupling of OXPHOS.
Relationship 2204: (Decrease, ATP pool leads to Decrease, Cell proliferation)	Biological Plausibility of Relationship 2204 is high. Rationale: Cell proliferation is a well-known ATP-dependent process. Cell division processes, such as the mitotic cell cycle uses ATP for chromosome movements and DNA replication (Kingston 1999). The synthetic processes of major cellular components that are necessary for cell structure and growth, such as proteins and lipids, also require sufficient ATP supply (Bonora 2012). Depletion of ATP therefore has a negative impact on these processes.
Relationship 2205: (Decrease, Cell proliferation leads to Decrease, Growth)	Biological Plausibility of Relationship 2205 is high. Rationale: The biological causality between cell proliferation and growth has also been 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.
Inconsistencies & uncertainties	There are currently no inconsistencies and uncertainties identified by the authors.

Empirical support

Empirical Support for KERs	Defining Question	Does KE_up occur at lower doses and earlier time points than KE down and at the same dose of stressor, is the incidence of KE_up >than that for KE_down? Are there inconsistencies in empirical support across taxa, species and stressors that don’t align with expected pattern for hypothesized AOP?
	High	Multiple studies showing dependent change in both events following exposure to a wide range of specific stressors. (Extensive evidence for temporal, dose- response and incidence concordance) and no or few critical data gaps or conflicting data.
	Moderate	Demonstrated dependent change in both events following exposure to a small number of specific stressors and some evidence inconsistent with expected pattern that can be explained by factors such as experimental design, technical considerations, differences among laboratories, etc.
	Low	Limited or no studies reporting dependent change in both events following exposure to a specific stressor (i.e., endpoints never measured in the same study or not at all); and/or significant inconsistencies in empirical support across taxa and species that don’t align with expected pattern for hypothesised AOP.
Relationship 2203: (Decrease, Coupling of OXPHOS leads to Decrease, ATP pool)	Empirical support of Relationship 2203 is high. Rationale: The majority of relevant studies show good incidence, temporal and/or dose concordance in different organisms and cell types after exposure to known uncouplers, with relatively few exceptions (see the Relationship 2203 page and concordance table for detailed evidence).
Relationship 2204: (Decrease, ATP pool leads to Decrease, Cell proliferation)	Empirical support of Relationship 2204 is moderate. Rationale: Although only a few studies were found to be relevant, incidence concordance was found for mammalian cells (see the Relationship 2204 page and concordance table for detailed evidence).
Relationship 2205: (Decrease, Cell proliferation leads to Decrease, Growth)	Empirical support of Relationship 2205 is low. Rationale: This KER was included in a very limited number of studies, as it addresses effects occurring at the apical level that in vitro studies cannot cover. There is one zebrafish study reporting concordant relationship between reduced cell proliferation and embryo growth with some inconsistencies (see the Relationship 2205 page and concordance table for detailed evidence).
Inconsistencies & uncertainties	There are some inconsistencies regarding temporal and dose concordance: A significant decrease followed by a significant increase of total ATP (KE1) was observed in human RD cells during a 48h exposure to the uncoupler FCCP(Kuruvilla 2003), possibly due to the enhancement of other ATP synthetic pathways (e.g., glycolysis) as a compensatory action to impaired OXPHOS (Jose 2011). In zebrafish embryos exposed to 2,4-DNP, significant growth inhibition (AO) was identified after 21h, whereas non-significant reductions in ATP (KE1) and cell proliferation (KE2) were reported(Bestman 2015).

Quantitative Understanding

Some proof of concept examples to address the WoE considerations for AOPs quantitatively have recently been developed, based on the rank ordering of the relevant Bradford Hill considerations (i.e., biological plausibility, essentiality and empirical support) (Becker et al., 2017; Becker et al, 2015; Collier et al., 2016). Suggested quantitation of the various elements is expert derived, without collective consideration currently of appropriate reporting templates or formal expert engagement. Though not essential, developers may wish to assign comparative quantitative values to the extent of the supporting data based on the three critical Bradford Hill considerations for AOPs, as a basis to contribute to collective experience.Specific attention is also given to how precisely and accurately one can potentially predict an impact on KEdownstream based on some measurement of KEupstream. This is captured in the form of quantitative understanding calls for each KER. See pages 55-56 of the User Handbook for a review of quantitative understanding for KER's. More help

Quantitative understanding of the KERs	High	Change in KE_downstream can be precisely predicted based on a relevant measure of KE_upstream. Uncertainty in the quantitative prediction can be precisely estimated from the variability in the relevant measure of KE_upstream. Known modulating factors and feedback/feedforward mechanisms are accounted for in the quantitative description. There is evidence that the quantitative relationship between the KEs generalises across the relevant applicability domain of the KER.
	Moderate	Change in KE_downstream can be precisely predicted based on a relevant measure of KE_upstream. Uncertainty in the quantitative prediction is influenced by factors other than the variability in the relevant measure of KE_upstream. Quantitative description does not account for all known modulating factors and/or known feedback/feedforward mechanisms. The quantitative relationship has only been demonstrated for a subset of the overall applicability domain of the KER (e.g., based on a single species).
	Low	Only a qualitative or semi-quantitative prediction of the change in KE_downstream can be determined from a measure of KE_upstream. Known modulating factors and/or known feedback/feedforward mechanisms are not accounted for. The quantitative relationship has only been demonstrated for a narrow subset of the overall applicability domain of the KER (e.g., based on a single species).
Relationship 2203: (Decrease, Coupling of OXPHOS leads to Decrease, ATP pool)	Quantitative understanding of Relationship 2203 is high. Rationale: The theoretical quantitative relationship between OXPHOS and ATP yield has been well established. There are also published computational/mathematical models in which modulating factors known to affect OXPHOS and ATP synthesis are considered. Evidence: A biophysical computational model developed for mitochondrial respiration and OXPHOS (Beard 2005). Continuous development of the mitochondrial energy transduction models since 1967 (Schmitz 2011). A comprehensive mathematical model developed for OXPHOS and ATP production under different physiological and pathological conditions (Heiske 2017). A comprehensive analysis of the quantitative relationships between protonmotive force, ATP synthase rotation, ATP synthesis and hydrolysis (Kubo 2020). A regression based response-response relationship for uncoupling of OXPHOS and ATP depletion (Song 2020).
Relationship 2204: (Decrease, ATP pool leads to Decrease, Cell proliferation)	Quantitative understanding of Relationship 2204 is moderate. Rationale: The total ATP level has been used as an indicator of cell proliferation. Several studies have reported the quantitative relationships between the two events, as well as a threshold value for KE1 to trigger KE2. However, not all modulating factors have been accounted and no well-established computational/mathematical models are found. Evidence: Quantitative understanding of ATP level, cell viability and colony growth (Ahmann 1987). Quantitative relationship between ATP level and cell proliferation (Crouch 1993). Thresholds for ATP depletion (85-90% reduction) to determine cell cycle arrest (<85-90%) or cell death (>85-90%) (Nieminen 1994).
Relationship 2205: (Decrease, Cell proliferation leads to Decrease, Growth)	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). However, there is currently a lack of quantitative model to link cell proliferation and individual growth in the presence of uncouplers. Evidence: A mathematical model developed for describing the quantitative relationship between cell proliferation and tissue growth (Binder 2008). A mathematical model developed for cell division and plant tissue growth (Mosca 2018). Multiple mathematical models developed for cell proliferation and tumor growth (Jarrett 2018).

Considerations for Potential Applications of the AOP (optional)

At their discretion, the developer may include in this section discussion of the potential applications of an AOP to support regulatory decision-making. This may include, for example, possible utility for test guideline development or refinement, development of integrated testing and assessment approaches, development of (Q)SARs / or chemical profilers to facilitate the grouping of chemicals for subsequent read-across, screening level hazard assessments or even risk assessment. While it is challenging to foresee all potential regulatory application of AOPs and any application will ultimately lie within the purview of regulatory agencies, potential applications may be apparent as the AOP is being developed, particularly if it was initiated with a particular application in mind. This optional section is intended to provide the developer with an opportunity to suggest potential regulatory applications and describe his or her rationale.To edit the “Considerations for Potential Applications of the AOP” section, on an AOP page, in the upper right hand menu, click ‘Edit.’ This brings you to a page entitled, “Editing AOP.” Scroll down to the “Considerations for Potential Applications of the AOP” section, where a text entry box allows you to submit text. In the upper right hand menu, click ‘Update AOP’ to save your changes and return to the AOP page or 'Update and continue' to continue editing AOP text sections. The new text should appear under the “Considerations for Potential Applications of the AOP” section on the AOP page. More help

The present AOP has several potential applications. First, the AOP anchors a recognized endpoint of regulatory concern (i.e., growth), at least in OECD member countries, and is directly relevant for a number of OECD test guidelines (e.g., TG206, 208, 201, 210, 211, 212, ,215, 221, 228 and 241). These guidelines cover a diversity of taxonomic groups including birds, fish, amphibians, terrestrial plants, aquatic plants and algae, and various invertebrates. Second, the AOP anchors an important molecular initiating event (e.g., uncoupling of oxidative phosphorylation) and can be used to support several initiatives (e.g., Tox21 and ToxCast) for identification of mitochondrial toxicants. The present AOP helps establish the utility of such assays for identifying chemicals with potential to cause growth impacts. Third, three out of four key events in this AOP can be measured using high-throughput in vitro assays, hence offering a tiered testing strategy (i.e., in silico→in vitro→in vivo) or integrated approaches to testing and assessment (IATA) for efficient screening, classification and assessment of potential mitochondrial uncouplers and growth-regulating chemicals. Finally, the quantitative relationships of the key events in this AOP have been relatively well defined, allowing it to be further developed into quantitative prediction models for higher tier assessments. This is a range of potential applications that were conceived during the development of the present AOP. However, it is neither an exhaustive list of potential applications, nor can explicit examples of these applications in practice be cited at this time.

We invite users of this AOP to share their applications of this AOP via the Discussion so that practical examples of use can be added.

References

List the bibliographic references to original papers, books or other documents used to support the AOP. More help

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