API

Aop: 34

AOP Title

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LXR activation leading to hepatic steatosis

Short name:

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LXR Activation to Liver Steatosis

Authors

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Marina Goumenou

Point of Contact

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Marina Goumenou

Contributors

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  • Marina Goumenou

Status

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Author status OECD status OECD project SAAOP status
Under development: Not open for comment. Do not cite Under Development


This AOP was last modified on December 03, 2016 16:37

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Revision dates for related pages

Page Revision Date/Time
Activation, LXR September 16, 2017 10:14
Up Regulation, CD36 September 16, 2017 10:14
Increase, FA Influx September 16, 2017 10:14
Activation, ChREBP September 16, 2017 10:14
Activation, SREBP-1c September 16, 2017 10:14
Activation, FAS September 16, 2017 10:14
Activation, SCD-1 September 16, 2017 10:14
Synthesis, De Novo FA September 16, 2017 10:14
Accumulation, Triglyceride September 16, 2017 10:14
Damaging, Mitochondria September 16, 2017 10:14
N/A, Liver Steatosis September 16, 2017 10:14
peroxisome proliferator activated receptor promoter demethylation September 16, 2017 10:14
Activation, LXR leads to Up Regulation, CD36 December 03, 2016 16:37
Up Regulation, CD36 leads to Increase, FA Influx December 03, 2016 16:37
Activation, LXR leads to Activation, ChREBP December 03, 2016 16:37
Activation, LXR leads to Activation, SREBP-1c December 03, 2016 16:37
Activation, LXR leads to Activation, FAS December 03, 2016 16:37
Activation, LXR leads to Activation, SCD-1 December 03, 2016 16:37
Activation, SCD-1 leads to Synthesis, De Novo FA December 03, 2016 16:37
Activation, ChREBP leads to Synthesis, De Novo FA December 03, 2016 16:37
Activation, SREBP-1c leads to Synthesis, De Novo FA December 03, 2016 16:37
Activation, FAS leads to Synthesis, De Novo FA December 03, 2016 16:37
Synthesis, De Novo FA leads to Accumulation, Triglyceride December 03, 2016 16:37
Increase, FA Influx leads to Accumulation, Triglyceride December 03, 2016 16:37
Accumulation, Triglyceride leads to Damaging, Mitochondria December 03, 2016 16:37
demethylation, PPARg promoter leads to Up Regulation, CD36 November 29, 2016 20:10

Abstract

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Liver steatosis (fatty liver) is considered as one of the first manifestations of possible hepatotoxicity, however it is not regarded as an adverse effect per se and usually can be reversible. The importance of steatosis is highlighted from the fact that it is a prerequisite for the development of non-alcoholic fatty liver disease (NAFLD). NAFLD is a clinicopathological condition that comprises a wide spectrum of liver damage, ranging from steatosis alone to steatohepatitis, advanced fibrosis and cirrhosis. Non-alcoholic steatohepatitis (NASH) represents only a stage in the spectrum of NAFLD and is defined pathologically by the presence of steatosis together with necro-inflammatory activity. The clinical implications of NAFLD are derived mostly from its potential to progress to end-stage liver disease, whereas simple uncomplicated steatosis follows a relatively benign course in most patients. Steatosis is the output of the disturbance on the homeostasis of hepatic lipids which depends on the dynamic balance of several pathways including fatty acid (FA) uptake, de novo FA synthesis, β-oxidation, and very low-density lipoprotein (VLDL) secretion. It is characterized by the accumulation of lipid droplets (mainly triglycerides) in the hepatocytes. This AOP describes the linkage between hepatic steatosis triggered by nuclear receptors activation (PPAR gamma and LXR) through modulation of genes responsible for lipid homeostasis [the carbohydrate response element binding protein (ChREBP), the sterol response element binding protein 1c (SREBP-1c), the free fatty acid uptake transporter FAT/CD36, the fatty acid synthase (FAS), the stearoyl-CoA desaturase 1 (SCD1)] which subsequently leasds to in rease of de novo fatty acids/triglycerides synthesis and fat influx from the peripheral tissues to liver. The accumulation of lipid in the hepatocytes can cause cytoplasm displacement, nucleus distortion, mitochondrial toxicity and eventually necrosis and/or apoptosis. The progression of this condition can lead to tissue inflammation (steatohepatitis) and fibrosis with the involvement of other cells of the hepatic tissue like the Kupffer (inflammation) and the stellate (fibrosis) cells. This purely qualitative AOP description is plausible, the scientific data supporting the AOP are logic, coherent and consistent and there is temporal agreement between the individual KEs. Quantitative data on dose-response-relationships and temporal sequences between key events are still lacking; the provision of quantitative data will strengthen the weight of evidence and make the AOP applicable for chemical risk assessment purposes.


Background (optional)

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This optional section 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.

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Summary of the AOP

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Stressors

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Describes stressors known to trigger the MIE and provides evidence supporting that initiation. This will often be a list of prototypical compounds demonstrated to interact with the target molecule in the manner detailed in the MIE description to initiate a given pathway (e.g., 2,3,7,8-TCDD as a prototypical AhR agonist; 17α-ethynyl estradiol as a prototypical ER agonist). 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. The evidence supporting the stressor will typically consist of a brief description and citation of literature showing that particular stressors can trigger the MIE.

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Molecular Initiating Event

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Title Short name
Activation, LXR Activation, LXR
peroxisome proliferator activated receptor promoter demethylation demethylation, PPARg promoter

Key Events

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Title Short name
Up Regulation, CD36 Up Regulation, CD36
Increase, FA Influx Increase, FA Influx
Activation, ChREBP Activation, ChREBP
Activation, SREBP-1c Activation, SREBP-1c
Activation, FAS Activation, FAS
Activation, SCD-1 Activation, SCD-1
Synthesis, De Novo FA Synthesis, De Novo FA
Accumulation, Triglyceride Accumulation, Triglyceride
Damaging, Mitochondria Damaging, Mitochondria

Adverse Outcome

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Title Short name
N/A, Liver Steatosis N/A, Liver Steatosis

Relationships Between Two Key Events (Including MIEs and AOs)

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Title Directness Evidence Quantitative Understanding
Activation, LXR leads to Up Regulation, CD36 Directly leads to Not Specified
Up Regulation, CD36 leads to Increase, FA Influx Directly leads to Not Specified
Activation, LXR leads to Activation, ChREBP Directly leads to Not Specified
Activation, LXR leads to Activation, SREBP-1c Directly leads to Not Specified
Activation, LXR leads to Activation, FAS Directly leads to Not Specified
Activation, LXR leads to Activation, SCD-1 Directly leads to Not Specified
Activation, SCD-1 leads to Synthesis, De Novo FA Directly leads to Not Specified
Activation, ChREBP leads to Synthesis, De Novo FA Directly leads to Not Specified
Activation, SREBP-1c leads to Synthesis, De Novo FA Directly leads to Not Specified
Activation, FAS leads to Synthesis, De Novo FA Directly leads to Not Specified
Synthesis, De Novo FA leads to Accumulation, Triglyceride Directly leads to Not Specified
Increase, FA Influx leads to Accumulation, Triglyceride Directly leads to Not Specified
Accumulation, Triglyceride leads to Damaging, Mitochondria Directly leads to Not Specified
demethylation, PPARg promoter leads to Up Regulation, CD36 Directly leads to Moderate

Network View

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Life Stage Applicability

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Is the AOP specific to certain tissues, life stages / age classes? Indicate if there are critical life stages, where exposure must occur, to results in the final adverse effect. Or specify if there are key events along the pathway which are dependent on the life stage although the AOP is known to be initiated regardless of life stage. Indicate also if the AOP is associated also with age- or sex-dependence.

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Taxonomic Applicability

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Indicate the relevant domain of applicability in terms of taxa.

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Sex Applicability

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Graphical Representation

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Click to download graphical representation template

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Overall Assessment of the AOP

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Consider the following criteria (may include references to KE Relationship pages): 1. concordance of dose-response relationships; 2. temporal concordance among the key events and adverse effect; 3. strength, consistency, and specificity of association of adverse effect and initiating event; 4. biological plausibility, coherence, and consistency of the experimental evidence; 5. alternative mechanisms that logically present themselves and the extent to which they may distract from the postulated AOP. It should be noted that alternative mechanisms of action, if supported, require a separate AOP; 6. uncertainties, inconsistencies and data gaps.


Concordance of dose-response relationships

The existing studies do not provide dose-response curves. However it may be possible in some cases to construct curves from the given numerical data and to relate the dose response for LXR activation with the dose response for TG accumulation in vitro and in vivo in a second more quantitative iteration as the next step of this AOP development.


Temporal concordance among the key events and adverse outcome

According to the available information the sequence of the events is in strong agreement and consequently the presented MoA could be considered as qualitatively accurate.


Strength, consistency, and specificity of association of adverse outcome and initiating event

The scientific evidence is presented in Scientific Evidence in support of the MoA.


Biological plausibility, coherence, and consistency of the experimental evidence

The steatogenic effect of chemicals like LXR ligands is well established in the literature (Peet 1998, Schultz et al. 2000, Horton et al. 2002) and it is well correlated with the expression of the receptor (Moya et al. 2010) the binding to it. In addition it is believed that LXR acts as a cholesterol sensor. Consistent with this role, it has been proposed that LXR induces SREBP-1c in order to generate fatty acids needed for the formation of cholesterol esters, which buffer the free cholesterol concentration (Ferré & Foufelle 2007). Further analysis of the logic, coherence and consistency along with the experimental data has already been presented in Chapter Scientific Evidence in support of the MoA.


Alternative mechanism(s) that logically present themselves and the extent to which they may distract from the postulated AOP.

As already mentioned abovem there are many other possible MoAs of a chemical in the development of steatosis including MoAs involving the inhibition of β-oxidation, the inhibition of oxidative phosphorylation (leading to a decrease of ATP needed for β-oxidation) and the malfunction of the mechanisms of the excretion of TG from the cell. These pathways are not covered in the presented MoA as they are not directly linked with the activation of LXR. Furthermore, as already explained LXR is not the only receptor which has been identified to be involved in fatty acid metabolism and steatogenesis. Exogenous chemicals acting as ligands for any of the following Nuclear Receptors (AhR, PXR, PPARa, PPARγ and ER) may play a role in the development of steatosis (grey elements on the AOP flow diagram, Fig. 3). There also known interactions or cross-talk between the NRs. Examples of possible interactions are related with fact that LXR is also regulated by the PPARa, the FAT/36 up-regulation from the AhR, PXR and PPARγ, the inhibition of β-oxidation from PPARa and indirectly from the ER.

It may be possible from existing literature, or further experimental work to develop MoAs taking binding to each of these receptors as the molecular initiating event and describing the converging pathways leading to steatosis. In fact this work is in progress and indicated as the grey elements on the AOP flow diagram above.

The biology of LXR function has been studied using the high affinity synthetic ligands T0901317. According to the study of Mitro (2007), T0901317 binds and activates hPXR and hLXRβ with similar affinity, and can regulate multiple PXR target genes in human cells and mice (like CD36) with similar efficacy to established PXR ligands, but significantly greater potency (Mitro 2007). The author suggested that some of the effects observed with T0901317 such as the more deleterious increase in lipogenesis and hepatic lipid accumulation (in comparison to the LXR-selective GW3965) that have been ascribed to LXR activation maybe the result of simultaneous stimulation of PXR and LXR activity and that the assumption that T0901317 behaves as an LXR-selective agonist may have led to some inaccurate conclusions regarding the effects of LXR activation in vivo.

From the data of this study it is evident that SREBP-1c, FAS and SCD-1, which are LXR but not PXR regulated genes, were significantly up-regulated by T0901317. In contrast GW3965 up-regulates less effectively the SREBP-1c, marginally the SCD-1 and not at all the FAS despite the fact that it is considered as a selective LXR agonist (Mitro 2007). The CD36 gene is considered also as a liver specific target of LXR activation (Zhou 2008). However, in the study of Mitro (2007), GW3965 did not up-regulate CD36. These findings could be explained by the lower affinity of this synthetic LXR agonist (EC50 of 0.19 and 0.03 μM for hLXRα and hLXRβ) in relation to the T0901317 (EC50 of 0.02-0.05 μM for both isoforms). Interestingly and despite the low up-regulating activity, GW3965 increases FA and TG accumulation in rat and primary human hepatocytes (Kotokorpi et al. 2007). Based on this information, it could be possible that T0901317 binding on PXR could enhance its steatogenic activity with the proposed MoA still being plausible. This plausibility, however, is clearly related with quantitative aspects.

In conclusion, the MoA described can be considered very well supported by the available scientific evidence and it is biologically plausible.


Uncertainties, inconsistencies and data gaps

The information used for the development of the present pathway is based on in-vitro and in-vivo studies. In the in-vitro studies several cell lines have been used. The expression of the LXR, the SREBP-1c and other elements on these cell lines is a key factor for the plausibility of the pathway in human. According to the study from Moya et al 2010, LXR expression (as measured from mRNA using RT-PCR) in human hepatocytes, HepG2 and HeLa cells was approximately 70%, 70% and 50% in relation to the level of expression in human liver. In addition the expression of SREBP-1c was significantly down-regulated (to less than 25% of normal levels of expression in the liver) in all 3 cell lines. Consequently positive results in relation to fat accumulation after LXR activation from studies using these cell lines may under-estimate the magnitude of effect on human liver while negative results could be interpreted as inconclusive. The assessment of the relative expression of these receptors in other cell lines would be of great importance in order to evaluate the relevance of each in vitro study result.

In relation to the in vivo studies which have be made mainly (if not exclusively) in rodents the relevance for humans should be addressed. LXR expression is considered adequately conserved from rodents to humans. In addition it is well known that all the other elements of the pathway are present in human liver. A good example of this is that the well-defined pathogenic role of FAT/CD36 in hepatic steatosis in rodents is also confirmed by the up-regulation in humans of the FAT/CD36 in cases of NASH, NAFLD, insulin resistance, hyperinsulinaemia, HCV and morbidly obese patients (Zhu et al. 2011, Love-Gregory & Abmurad 2011, Miquilena-Colina et al. 2011, Bechmann et al. 2010). However, there is some speculation in relation to the extent that adverse side effects observed in rodents will occur in higher species, including humans. These speculations are raised due to the different behaviour of the LXR agonist GW3965 in in vitro systems which although markedly stimulating lipogenic gene expression in primary human hepatocytes leading to significant TG accumulation at all 3 dose levels after 48 hr, produced only a very modest increase in the triglyceride content in rat cells (Kotokorpi et al. 2007), demonstrating that the use of this rat cell line could underestimate the effect in humans. FA increase was reported in both cell lines.

Another interesting finding is that in humans, total CD36 deficiency is relatively common (3–5%) in persons of African and Asian descents (Su & Abmurad 2009). Consequently the presented MoA could be affected mainly quantitatively among humans of different origin.

Induction of lipogenic enzymes from the SREBP-1c is evidenced in adult mice but not during the fetal life indicating a different role of the SREBP-1c between these two stages (Liang et al. 2002). This finding gives a strong indication that the presented pathway may be altered in other than adult life stage.

Another finding is that of the study of Hu et al. 2005 according to which administration of T0901317 in PPAR-null mice promoted a dose-dependent increase in the rate of peroxisomal β-oxidation in the liver and in relation only to the LXRα. The author suggests that this induction may serve as a counter regulatory mechanism for responding to the hypertriglyceridemia and liver steatosis that is promoted by potent LXR agonists in vivo.

T090137 was shown to up-regulate hepatic expression and plasma activity of PLTP in mice in addition to angiopoietin-like protein 3 (Angptl3), playing a critical role in LXR-induced hypertriglyceridemia. However it should be noted that hypertriglyceridemic effect of LXR agonists is usually transient and limited to the first few days of the treatment likely due to enhanced VLDL-triglyceride hydrolysis resulting from increased expression of hepatic LPL (Baranowski 2008).

Some studies have demonstrated absence of triglyceride accumulation on SREBP-1c (-/-) mice suggesting that SREBP-1c is a crucial element of the present MoA (Liang et al. 2002, Schultz et al. 2000, Horton et al. 2002, Shimano et al. 1999). In another study in FAT/CD36 knockout mice the effect of LXR agonists on increasing hepatic and circulating levels of triglycerides and free fatty acids (FFAs) was largely abolished suggesting that intact expression and/or activation of FAT/CD36 is required for the steatotic effect of LXR agonists (Febbraio et al. 1999). These two findings together and considering that they are constant and not related with specific experimental conditions could lead one to the hypothesis that both SREBP-1c and CD36 are imperative elements for the cause of steatosis. This hypothesis could be further examined.

The present MoA could also be affected by factors related to the formation of steatosis such as trends in adipose tissue (AT) deposition, the total body fat, the visceral AT and the subcutaneous AT which vary among different life stages such as childhood, puberty and adolescence, between sexes and among humans of different origin (Staiano 2012).

Assessment of the quantitative understanding of the AOP

In the present study only qualitative assessment of the proposed MoA was performed. In the studies used there are numerical data mainly to support the expression and up-regulation of the different elements of the pathway. However, further analysis of these numerical data is suggested in following steps.

Interestingly, the existence of many network motifs along the pathways was noted during the analysis of the literature, e.g. the positive feed forward LXR up-regulation. This information could be used in the future for the quantitative interpretation of dose response curves and the development of quantitative prediction models of the adverse outcome following the activation of the LXR.


Domain of Applicability

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The relevant domain(s) of applicability in terms of sex, life-stage, taxa, and other aspects of biological context are defined in this section. Domain of applicability is informed by the “Description” and “Taxonomic Relevance” section of each KE description and the “Description of the KER” section of each KER description. The relevant domain of applicability of the AOP as a whole will most often be defined based on the most narrowly restricted of its KEs. For example, if most of the KEs apply to either sex, but one is relevant to females only, the domain of applicability of the AOP as a whole would generally be limited to females. While much of the detail defining the domain of applicability may be found in the individual KE descriptions, the rationale for defining the relevant domain of applicability of the overall AOP should be briefly summarised on the AOP page.

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Essentiality of the Key Events

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The essentiality of various of the KEs is influential in considering confidence in an overall hypothesised AOP for potential regulatory application being secondary only to biological plausibility of KERs (Meek et al., 2014; 2014a). The defining question for determining essentiality (included in Annex 1) relates to whether or not downstream KEs and/or the AO is prevented if an upstream event is experimentally blocked. It is assessed, generally, then, on the basis of direct experimental evidence of the absence/reduction of downstream KEs when an upstream KE is blocked or diminished (e.g., in null animal models or reversibility studies). Weight of evidence for essentiality of KEs would be considered high if there is direct evidence from specifically designed experimental studies illustrating essentiality for at least one of the important key events [e.g., stop/reversibility studies, antagonism, knock out models, etc.) moderate if there is indirect 25 evidence that experimentally induced change of an expected modulating factor attenuates or augments a key event (e.g., augmentation of proliferative response (KEupstream) leading to increase in tumour formation (KEdownstream or AO)) and weak if there is no or contradictory experimental evidence of the essentiality of any of the KEs (Annex 1).

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Weight of Evidence Summary

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This involves evaluation of the Overall AOP based on Relative Level of Confidence in the KERs, Essentiality of the KEs and Degree of Quantitative Understanding based on Annexes 1 and 2. Annex 1 (“Guidance for assessing relative level of confidence in the Overall AOP”) guides consideration of the weight of evidence or degree of confidence in the predictive relationship between pairs of KEs based on KER descriptions and support for essentiality of KEs. It is designed to facilitate assignment of categories of high, moderate or low against specific considerations for each a series of defined element based on current experience in assessing MOAs/AOPs. In addition to increasing consistency through delineation of defining questions for the elements and the nature of evidence associated with assignment to each of the categories, importantly, the objective of completion of Annex 1 is to transparently delineate the rationales for the assignment based on the specified considerations. While it is not necessary to repeat lengthy text which appears in earlier parts of the document, the entries for the rationales should explicitly express the reasoning for assignment to the categories, based on the considerations for high, moderate or low weight of evidence included in the columns for each of the relevant elements. 24 While the elements can be addressed separately for each of the KERs, the essentiality of the KEs within the AOP is considered collectively since their interdependence is often illustrated through prevention or augmentation of an earlier or later key event. Where it is not possible to experimentally assess the essentiality of the KEs within the AOP (i.e., there is no experimental model to prevent or augment the key events in the pathway), this should be noted. Identified limitations of the database to address the biological plausibility of the KERs, the essentiality of the KEs and empirical support for the KERs are influential in assigning the categories for degree of confidence (i.e., high, moderate or low). Consideration of the confidence in the overall AOP is based, then, on the extent of available experimental data on the essentiality of KEs and the collective consideration of the qualitative weight of evidence for each of the KERs, in the context of their interdependence leading to adverse effect in the overall AOP. Assessment of the overall AOP is summarized in the Network View, which represents the degree of confidence in the weight of evidence both for the rank ordered elements of essentiality of the key events and biological plausibility and empirical support for the interrelationships between KEs. The AOP-Wiki provides such a network graphic based on the information provided in the MIE, KE, AO, and KER tables. The Key Event Essentiality calls are used to determine the size of each key event node with larger sizes representing higher confidence for essentiality. The Weight of Evidence summary in the KER table is used to determine the width of the lines connecting the key events with thicker lines representing higher confidence.

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Quantitative Considerations

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The extent of quantitative understanding of the various KERs in the overall hypothesised AOP is also critical in consideration of potential regulatory application. For some applications (e.g. doseresponse analysis in in depth risk assessment), quantitative characterisation of downstream KERs may be essential while for others, quantitative understanding of upstream KERs may be important (e.g., QSAR modelling for category formation for testing). Because evidence that contributes to quantitative understanding of the KER is generally not mutually exclusive with the empirical support for the KER, evidence that contributes to quantitative understanding should generally be considered as part of the evaluation of the weight of evidence supporting the KER (see Annex 1, footnote b). General guidance on the degree of quantitative understanding that would be characterised as weak, moderate, or strong is provided in Annex 2.

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Considerations for Potential Applications of the AOP (optional)

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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. Detailing such considerations can aid the process of transforming narrative descriptions of AOPs into practical tools. In this context, it is necessarily beneficial to involve members of the regulatory risk assessment community on the development and assessment team. The Network view which is generated based on assessment of weight of evidence/degree of confidence in the hypothesized AOP taking into account the elements described in Section 7 provides a useful summary of relevant information as a basis to consider appropriate application in a regulatory context. Consideration of application needs then, to take into consideration the following rank ordered qualitative elements: Confidence in biological plausibility for each of the KERs Confidence in essentiality of the KEs Empirical support for each of the KERs and overall AOP The extent of weight of evidence/confidence in both these qualitative elements and that of the quantitative understanding for each of the KERs (e.g., is the MIE known, is quantitative understanding restricted to early or late key events) is also critical in determining appropriate application. For example, if the confidence and quantitative understanding of each KER in a hypothesised AOP are low and or low/moderate and the evidence for essentiality of KEs weak (Section 7), it might be considered as appropriate only for applications with less potential for impact (e.g., prioritisation, category formation for testing) versus those that have immediate implications potentially for risk management (e.g., in depth assessment). If confidence in quantitative understanding of late key events is high, this might be sufficient for an in depth assessment. The analysis supporting the Network view is also essential in identifying critical data gaps based on envisaged regulatory application.

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References

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Confidence in the AOP

Information from this section should be moved to the Key Event Relationship pages!

Elaborate on the domains of applicability listed in the summary section above. Specifically, provide the literature supporting, or excluding, certain domains.

How well characterised is the AOP?

Liver steatosis is a well understood adverse outcome. A great number of publications from in vitro, in vivo, mechanistic, clinical and epidemiological studies exist for the qualitative assessment of steatosis. However, the quantitative analysis of the role of a specific exogenous chemical in an adverse outcome in human is a very challenging task due to the involvement of a large number of inter-related factors following the MIE. In fact one chemical may bind to more than one receptor and consequently have different impacts either quantitatively or qualitatively on the downstream events.


How well are the initiating and other key events causally linked to the outcome?

LXR agonists such T0901317 have been shown to produce LXR activation, as well as triglyceride accumulation, which has been demonstrated in rodent (mouse and rat) and human liver cell lines in vitro. The same chemicals shown to be LXR agonists in the in vitro assays have shown triglyceride accumulation in the liver leading to steatosis in animals and humans through steps of the reported MoA.


What are the limitations in the evidence in support of the AOP?

Disagreement in the scientific evidence supporting the presented AOP was not found. In relation to data gaps in addition to lack of quantitative information as discussed above there is also lack of specific information in relation to the role of other target genes expressed after the LXR activation.


Is the AOP specific to certain tissues, life stages / age classes?

There is evidence of different levels of expression of CD36 in different ethnic groups which may be expected to alter the sensitivity to development of steatosis. There may also be differences in expression and role of the same proteins/enzymes in foetal life but this has not been fully elucidated. Further information can be found in the Chapter Uncertainties, inconsistencies and data gaps.


Are the initiating and key events expected to be conserved across taxa?

From the analysis of the available information from experimental studies using rodents the elements of the MoA appeared to be well conserved between mice and rats. Some concerns in relation to the relevance of the in vivo studies to human are raised mainly due to the different behaviour of the LXR agonist GW3965 which while stimulating lipogenic gene expression in human hepatocytes, causes only a slight increase in TGs in rats (Kotokorpi et al. 2007). Some more differences were also reported between hamsters and monkeys in relation to hypertriglyceridemia (Groot et al. 2005).

Scientific Evidence to Support AOP

Information from this section should be moved to the Key Event Relationship pages!

Detailed Description

Old format, potemtially to be migrated to table above and underlying articles

Events Scientific Support Strength of Evidence
LXR binding and activation

(Molecular initiating event) receptor

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

Very Strong
Binding in the LXREs [11] Very Strong
Target genes transcription [12] Very Strong
Auto-regulation of the LXRa

[13] [14] [15] [16]

Very Strong
Increase in expression and activity of the carbohydrate response element-binding protein (ChREBP)

[17] [18]

Strong

According to the study of Denechaud increase in the glucose flux in the cell is a prerequisite for ChREBP activation from T0901317 in mice

Increase in expression of the SREBP-1c from LXR

[19] [3] [20] [21] [22]

Very Strong

However, there are many studies supporting a different behaviour between LXRα and LXRβ, suggesting that SREBP-1c up-regulation is only due to LXRα

Increase in expression of the SREBP-1c from the ChREBP [23] Well established
Induction of lipogenic enzymes from the SREBP-1c (FAS, ACC, GK)

[24] [25] [26] [27] [3] [28] [29] [30] [31]

Very Strong

However there is evidence that this effect is not induced in the embryonic state indicating a different role of the SREBP-1c between embryonic and adult life (Liang et al. 2002). It is also suggested that for lipogenic genes, SREBP-1c acts together with ChREBP (Ishii et al. 2004).

Direct induction of the fatty acid synthase (FAS)

[3] [32] [33]

Very Strong
Direct induction of the stearoyl-CoA desaturase 1 (SCD1)

[34] [35] [3] [36]

Very Strong
Up-regulation of the free fatty acid uptake transporter FAT/CD36

[37] [38] [39] [40] [41] [42]

Very Strong
De novo fatty acids and triglyceride synthesis [43] Very Strong
Fat influx from the peripheral tissues

[11] [44] [45]

Very Strong
Steatosis

[45] [46]

Very Strong


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