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Relationship: 880

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Decreased, PPARalpha transactivation of gene expression leads to Decreased, Ketogenesis (production of ketone bodies)

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. 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

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Antagonist binding to PPARα leading to body-weight loss adjacent Moderate Moderate Kurt A. Gust (send email) Open for citation & comment TFHA/WNT Endorsed

Taxonomic Applicability

Select one or more structured terms 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. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
Mus musculus Mus musculus High NCBI
Homo sapiens Homo sapiens High NCBI
Rattus rattus Rattus rattus High NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence
Male Moderate
Female Moderate

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
Adults Moderate

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

PPARα is a transcriptional regulator for a variety of genes that facilitate systemic energy homeostasis (Kersten 2014, Evans et al 2004, Desvergne and Wahli 1999).  The KE, “decreased PPARα transactivation of gene expression” results in the KE, “decreased transcriptional expression for genes that catalyze ketogenesis” (Cahil 2006, Kersten et al. 2014, Sengupta et al. 2010, Desvergne and Wahli 1999) by inhibiting expression of the enzymes involved in ketogenesis.  Enzyme description (Kersten 2014, Sengupta et al. 2010) and metabolic flux examinations (Sengupta et al. 2010) additionally providing fairly robust characterization in support of the KER.  Ketogenesis is critical to supporting general systemic energy homeostasis in fasting events (Cahill 2006, Evans et al 2004, Sengupta et al 2010), thus KE4 becomes important after short term energy stores (glycogen) become limited (Muoio et al 2002).  Le May, et al (2000) have shown decreased ketogenesis  in livers of PPAR null mice linked to impaired mitochondrial hydroxymethylglutaryl-CoA synthase (Hmgcs) gene expression.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

Inhibition of PPARα signaling has been demonstrated to inhibit transcriptional expression of genes that catalyze ketogenesis as well as ketone body production (Badman et al 2007, Potthoff 2009, Sengupta 2010) affecting circulating levels of ketone bodies for systemic use.  Kersten et al (1999) demonstrated that PPARα is induced in fasted mice mobilizing the oxidation of fatty acids for energy production. In that study, PPARα-null mice did not actively induce fatty acid oxidation or ketogenesis leaving the mice unable to meet energy demands during fasting and leading to hypoglycemia, hyperlipidemia, hypoketonemia and fatty liver.  Upstream metabolic events, such as the KEs “peroxisomal fatty acid beta oxidation” and “mitochondrial fatty acid beta oxidation” play a key role in producing substrates for ketogenesis.  Although the connection between the KE, “decreased PPARα transactivation of gene expression” -> the KE, “decreased ketogenesis” is well established given the literature cited above, the dependency on the KEs “peroxisomal fatty acid beta oxidation” and “mitochondrial fatty acid beta oxidation” for substrate availability can affect the KE, “decreased ketogenesis” in addition to the influence of the up-stream KE, “decreased PPARα transactivation of gene expression, therefore we scored the KER for the KE, “decreased PPARα transactivation of gene expression” -> the KE, “Ketogenesis (production of ketone bodies)” as “moderate”.

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility 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 (see page 40 of the User Handbook for further information).   More help

Biological plausibility of this KER is strong given the supporting relationships cited in the literature described in the description above.

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

A critical data gap regarding this AOP is an absence of studies that have investigated the effects null mutants for ketogenesis on the physiology and individual performance during long term starvation relative to wild type individuals.

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help

Ketogenesis is more prevalent in fasted state.

Time-scale
This sub-section should be used to provide 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?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

A period of fasting such that available glucose is consumed is usually a pre-requisite for increased ketogenesis.

Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

Availability of alternative energy substrates may chance the dynamics of this KER.

Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Ketogenesis diminishes after transition from a fasted state to a fed state.

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

Evidence provided for human in Cahill (2006), Owen et al (2005) and Williamson et al (1962).  Evidence for mouse provided in Kersten et al (1999).  Comparative investigations of ketone body formation comparing human and mouse is not well established relative to fatty-acid oxidation comparisons.

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Badman MK, Pissios P, Kennedy AR, Koukos G, Flier JS, Maratos-Flier E: Hepatic fibroblast growth factor 21 is regulated by PPARalpha and is a key mediator of hepatic lipid metabolism in ketotic states. Cell metabolism 2007, 5(6):426-437.

Cahill GF, Jr. Fuel metabolism in starvation. Annu Rev Nutr 2006, 26:1-22.

Desvergne B, Wahli W (1999) Peroxisome proliferator-activated receptors: nuclear control of metabolism. Endocrine Reviews 20(5): 649-688.

Evans RM, Barish GD, Wang YX: PPARs and the complex journey to obesity. Nat Med 2004, 10(4):355-361.

Gerich JE, Meyer C, Woerle HJ, Stumvoll M: Renal gluconeogenesis: its importance in human glucose homeostasis. Diabetes Care 2001, 24(2):382-391.

Kersten S.  2014. Integrated physiology and systems biology of PPARalpha. Molecular Metabolism 2014, 3(4):354-371.

Lazarow, P.B., 1978. Rat liver peroxisomes catalyze the beta oxidation of fatty acids. J. Biol. Chem. 253, 1522-1528.

Le May et al., 2000. Reduced hepatic fatty acid oxidation in fasting PPARK null mice is due to impaired mitochondrial hydroxymethylglutaryl-CoA synthase gene expression. FEBS Lett. 475: 163-166.

Mannaerts GP, Van Veldhoven PP 1993 Metabolic role of mammalian peroxisomes. In: Gibson G, Lake B (eds) Peroxisomes: Biology and Importance in Toxicology and Medicine. Taylor & Francis, London, pp 19–62.

Nelson DL, Cox MM 2000A.  The Citric Acid Cycle. Lehninger Principles of Biochemistry. 3rd Edition.  Worth Publishers.  New York, NY. p567-592.

Nelson DL, Cox MM 2000B.  Carbohydrate Biosynthesis. Lehninger Principles of Biochemistry. 3rd Edition.  Worth Publishers.  New York, NY. p722-764.

Nelson DL, Cox MM 2000C.  Lipid Biosynthesis. Lehninger Principles of Biochemistry. 3rd Edition.  Worth Publishers.  New York, NY. p770-814.

Owen OE: Ketone bodies as a fuel for the brain during starvation. Biochem Mol Biol Educ 2005, 33(4):246-251.

Potthoff MJ, Inagaki T, Satapati S, Ding X, He T, Goetz R, Mohammadi M, Finck BN, Mangelsdorf DJ, Kliewer SA et al: FGF21 induces PGC-1α and regulates carbohydrate and fatty acid metabolism during the adaptive starvation response. Proceedings of the National Academy of Sciences 2009, 106(26):10853-10858.

Sengupta S, Peterson TR, Laplante M, Oh S, Sabatini DM: mTORC1 controls fasting-induced ketogenesis and its modulation by ageing. Nature 2010, 468(7327):1100-1104.

Williamson DH, Mellanby J, Krebs HA: Enzymic determination of d(−)-β-hydroxybutyric acid and acetoacetic acid in blood. Biochem J 1962, 82(1):90-96.