Kurt A. Gust, Mitchell S. Wilbanks, Zachary A. Collier, Edward J. Perkins.
1Army Engineer Research and Development Center, Vicksburg, MS, 39180, Kurt.A.Gust@usace.army.mil; Mitchell.S.Wilbanks@usace.army.mil;
Point of Contact: Kurt A. Gust, Kurt.A.Gust@usace.army.mil
Point of Contact
Kurt A. Gust
- Kurt A. Gust
|Author status||OECD status||OECD project||SAAOP status|
|Open for comment. Do not cite||EAGMST Under Review||2.3||Included in OECD Work Plan|
This AOP was last modified on June 01, 2017 11:53
|Decreased, PPARalpha transactivation of gene expression||September 16, 2017 10:14|
|Decreased, Mitochondrial Fatty Acid Beta Oxidation||September 16, 2017 10:14|
|Decreased, Ketogenesis (production of ketone bodies)||September 16, 2017 10:14|
|Not Increased, Circulating Ketone Bodies||September 16, 2017 10:14|
|Decreased, Body Weight||June 02, 2017 16:29|
|Decreased, Peroxisomal Fatty Acid Beta Oxidation of Fatty Acids||September 16, 2017 10:14|
|Increased, Catabolism of Muscle Protein||September 16, 2017 10:14|
|Binding of antagonist, PPAR alpha||September 16, 2017 10:14|
|stabilization, PPAR alpha co-repressor||September 16, 2017 10:14|
|Decreased, PPARalpha transactivation of gene expression leads to Decreased, Peroxisomal Fatty Acid Beta Oxidation of Fatty Acids||June 02, 2017 16:50|
|Decreased, PPARalpha transactivation of gene expression leads to Decreased, Mitochondrial Fatty Acid Beta Oxidation||June 02, 2017 16:45|
|Decreased, PPARalpha transactivation of gene expression leads to Decreased, Ketogenesis (production of ketone bodies)||June 02, 2017 16:50|
|Decreased, Peroxisomal Fatty Acid Beta Oxidation of Fatty Acids leads to Decreased, Mitochondrial Fatty Acid Beta Oxidation||November 29, 2016 20:41|
|Decreased, Mitochondrial Fatty Acid Beta Oxidation leads to Decreased, Ketogenesis (production of ketone bodies)||November 29, 2016 20:42|
|Decreased, Ketogenesis (production of ketone bodies) leads to Not Increased, Circulating Ketone Bodies||November 29, 2016 20:42|
|Not Increased, Circulating Ketone Bodies leads to Increased, Catabolism of Muscle Protein||November 29, 2016 20:42|
|Increased, Catabolism of Muscle Protein leads to Decreased, Body Weight||November 29, 2016 20:42|
|Binding of antagonist, PPAR alpha leads to stabilization, PPAR alpha co-repressor||November 29, 2016 20:51|
|stabilization, PPAR alpha co-repressor leads to Decreased, PPARalpha transactivation of gene expression||November 29, 2016 20:51|
|GW6471||January 30, 2017 16:02|
|Nitrotoluenes (hypothesized binding)||January 30, 2017 16:26|
The present AOP describes chemical binding and stabilization of a co-repressor to the peroxisome proliferator-activated receptor α (PPARα) signaling complex causing a chain of events that includes: antagonism of PPARα nuclear signaling, decreased transcriptional expression of PPARα-regulated genes that support energy metabolism, and inhibited metabolic energy production culminating with starvation-like weight loss. The MIE for this AOP involves antagonistic PPARα binding. The antagonist-binding to the PPARα regulatory complex causes the KE1, stabilization of co-repressor (SMRT or N-CoR) to PPARalpha ligand binding domain suppressing PPARα nuclear signaling (Nagy et al 1999, Xu et al 2002). 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). As a result of the MIE and then KE1, the KE2 occurs where PPARalpha transactivation is inhibited for genes involved in the next 3 key events of the AOP: (KE3) decreased peroxisomal fatty acid beta oxidation (Desvergne and Wahili 1999, Kersten 2014, Dreyer et al 1992, Lazarow 1978), (KE4) decreased mitochondrial fatty acid beta oxidation (Kersten 2014, Brandt et al 1998; Mascaro et al 1998, Aoyama et al 1998, Gulick et al 1994, Sanderson et al 2008), and (KE5) decreased ketogenesis (Cahil 2006, Kersten et al 2014, Sengupta et al 2010, Desvergne and Wahli 1999). The KE3 results in decreased catabolism of very long chain fatty acids which can reduce substrate availability for energy production (Evans et al 2004). Both KE2 and KE3 can drive KE4 decreasing conversion of short, medium and long chain fatty acids into substrates for use in ATP production (Evans et al 2004). KE2 (and also potentially KE4) can drive KE5 resulting in decreased potential to repackage energy substrates as ketone bodies to support systemic energy demands during periods where the systemic energy budget is negative (Badman et al 2007, Potthoff 2009). The KE6, no change or a decrease in circulating ketone bodies, occurs under cellular energy deficit conditions, a state where ketogenesis is typically induced thus increasing circulating ketone bodies as metabolic fuel to sustain energy homeostasis (Cahill 2006). Physiological studies of the progression of human starvation have demonstrated the critical importance of ketogenesis, especially production of β-hydroxybutyrate, for meeting systemic energy demands by supplementing glucose to sustain the energy requirements of the brain (Cahill 2006, Owen et al 2005). Sustained negative energy budgets lead to KE7, an increase in muscle protein catabolism, with glutamine and alanine recycled for gluconeogenesis (Felig et al 1970A, Kashiwaya et al 1994). Finally, the AO of body-weight loss occurs, which within the context of dynamic energy budget theory decreases energy allocations to organismal maturation and reproduction (Nisbet et al 2000) and has been demonstrated to negatively affect ecological fitness (Martin et al 1987).
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. Instructions To add background information, click Edit in the upper right hand menu on the AOP page. Under the “Background (optional)” field, a text editable form provides ability to edit the Background. Clicking ‘Update AOP’ will update these fields.
Summary of the AOP
Molecular Initiating Event
|Binding of antagonist, PPAR alpha||Binding of antagonist, PPAR alpha|
|Decreased, PPARalpha transactivation of gene expression||Decreased, PPARalpha transactivation of gene expression|
|Decreased, Mitochondrial Fatty Acid Beta Oxidation||Decreased, Mitochondrial Fatty Acid Beta Oxidation|
|Decreased, Ketogenesis (production of ketone bodies)||Decreased, Ketogenesis (production of ketone bodies)|
|Not Increased, Circulating Ketone Bodies||Not Increased, Circulating Ketone Bodies|
|Decreased, Peroxisomal Fatty Acid Beta Oxidation of Fatty Acids||Decreased, Peroxisomal Fatty Acid Beta Oxidation of Fatty Acids|
|Increased, Catabolism of Muscle Protein||Increased, Catabolism of Muscle Protein|
|stabilization, PPAR alpha co-repressor||stabilization, PPAR alpha co-repressor|
|Decreased, Body Weight||Decreased, Body Weight|
Relationships Between Two Key Events (Including MIEs and AOs)
Life Stage Applicability
|Mus musculus||Mus musculus||Strong||NCBI|
|Colinus virginianus||Colinus virginianus||Moderate||NCBI|
|Pimephales promelas||Pimephales promelas||Moderate||NCBI|
|Rattus norvegicus||Rattus norvegicus||Moderate||NCBI|
|Homo sapiens||Homo sapiens||Strong||NCBI|
Graphical RepresentationClick to download graphical representation template
Overall Assessment of the AOP
The domain of applicability, the essentiality of the key events and weight of evidence for the overall hypothesized AOP are provided in the following:
Domain of Applicability
1) rats and mice-females,. 2) Northern bobwhite quail-juvenile males. 3) fathead minnow-adult, sex not determined. 4) carp-juvenile, sex not determined. 5) human, male & female
Essentiality of the Key Events
Rationale for essentiality calls:
• MIE: PPAR alpha, Binding of antagonist: Regarding the present MIE, molecules can bind to the PPARα regulatory complex affecting the binding of co-activators and co-repressors. Specifically designed molecules such as the PPARα antagonists GW6471 can bind to PPARα selectively recruiting binding of co-repressors to the PPARα nuclear signaling complex (Xu et al 2002).
•Key Event 1:PPAR alpha co-repressor, Increased - The binding of co-repressors to the PPARα signaling complex suppresses nuclear signaling and thus downstream transcription of PPARα-regulated genes. GW6471 binding to the co-repressor is reversible thus allowing the co-repressor to leave the ligand binding domain of PPARα, restoring normal function (Xu et al 2002).
•Key Event 2: PPARalpha transactivation of gene expression, Decreased - As described in a variety of reviews, PPARalpha represents a master regulator of energy metabolism which specifically promotes fatty oxidation for energy production & distribution (Evans et al 2004, Kersten 2014, Lefebvre et al 2006, Desvergne and Wahili 1999). Both PPARalpha knock outs and PPARalpha antagonism decreased transcriptional expression of gene targets involved in peroxisomal fatty acid beta oxidation (Kersten et al 1999, Desvergne and Wahili 1999), mitochondrial fatty acid beta oxidation (Brandt et al 1998; Mascaro et al 1998, Kersten 2014), and ketogenesis (Sengupta et al 2010, Desvergne and Wahli 1999, Kersten 2014).
•Key Event 3: Peroxisomal Fatty Acid Beta Oxidation of Fatty Acids, Decreased – The essentiality of peroxisomal fatty acid beta oxidation to maintaining the systems-level energy demands of the organism is not well known. This key event serves to metabolize very long chain fatty acids that are accumulated from the diet and to some degree acts as a supporting pathway to the mitochondrial fatty acid beta oxidation pathway (Kersten 2014). Given that both pathways are inhibited during PPARalpha antagonism, it is difficult to separate the contribution to systemic energy sustainment supported by peroxisomal fatty acid beta oxidation alone.
•Key Event 4: Mitochondrial Fatty Acid Beta Oxidation, Decreased – This key event is essential for deriving metabolic energy from fatty acid substrates thus supporting a large component of overall organismal energy demands (Evans et al 2004, Kersten 2014, Desvergne and Wahili 1999). Short, medium and long chain fatty acids (<C20) are catabolized by mitochondrial beta-oxidation. PPARalpha regulates nearly every enzymatic step in the uptake as well as the oxidative breakdown of acyl-CoAs to acetyl-CoA (Kersten 2014). The acetyl-CoA monomers serve as fundamental units for metabolic energy production (ATP) via the citric acid cycle followed by electron-transport chain mediated oxidative phosphorylation (Nelson and Cox, 2000A) as well as serve as the fundamental units for energy storage via gluconeogenesis (Nelson and Cox, 2000B) and lipogenesis (Nelson and Cox, 2000C). PPARalpha knockout studies have demonstrated impaired mitochondrial fatty acid oxidation leading to fatty acid accumulation in the liver (Badmann et al 2007) as well as an inability to meet systemic energy demands (Kersten et al, 1999).
•Key Event 5: Ketogenesis (production of ketone bodies), decreased - The liver represents a key organ involved in systemic energy distribution given its ability to synthesize glucose (an ability shared only with the kidney, Gerich et al 2001) as well as its exclusive role in the generation of ketone bodies (Cahill 2006, Sengupta et al 2010, Kersten 2014). This is especially important for the metabolic energy needs of the brain which can only use glucose and the ketone body, β-hydroxybutyrate for cellular energy production (Cahill 2006, Owen 2005, Kersten 2014). Therefore, ketogenesis is critical to supporting general systemic energy homeostasis in fasting events (Cahill 2006, Evans et al 2004, Sengupta et al 2010). Interference with ketogenesis, for example by PPARα inhibition, has been demonstrated to inhibit β-hydroxybutyrate production (measured in serum) during fasting events in mice (Badman et al 2007, Potthoff 2009, Sengupta et al 2010). The Badman et al (2007) study indicated that metabolism of fatty acid substrates (measured as liver triglycerides) that would otherwise contribute to β-hydroxybutyrate production was additionally inhibited under PPARα knockout.
•Key Event 6: Circulating Ketone Bodies, Not Increased - Physiological studies of the progression of human starvation have identified that the preferred metabolic fuel is glucose in the fed state and progressing through two days of fasting, afterward ketone bodies become increasingly important for meeting energy demands (Cahill 2006, Owen et al 2005). Substrates derived from carbohydrates, fats and protein can contribute to gluconeogenesis (Cahill 2006, Gerich et al 2001) whereas substrates derived from fatty acids are the primary contributors to ketogenesis (Desvergne and Wahli 1999). Cahill (2006) and colleagues have demonstrated the importance of ketone body production, especially β-hydroxybutyrate, for maintaining energy homeostasis during starvation by serving as an alternative substrate to glucose for providing energy to the brain in the starvation state (Cahill 2006). Interference with ketogenesis, for example by PPARα inhibition, has been demonstrated to inhibit β-hydroxybutyrate production (measured in serum) during fasting events in mice (Badman et al 2007, Potthoff 2009). Under normal conditions, activated ketogenesis occurring during fasting events is rapidly deactivated when blood glucose concentrations increase to normal levels and resultant elevated circulating ketone bodies are reduced correspondingly (Cahill 2006).
•Key Event 7: Catabolism of Muscle Protein, Increased - After two to three days of fasting in humans, dietary glucose has been long-since expended and contribution to blood glucose from glycogen metabolism is reduced to zero (Cahill 2006). At this point, about two fifths of fatty acid metabolism in the whole body is dedicated to hepatic ketogenesis, largely in support of the energy demands of the brain, however the brain is still significantly supported by glucose derived from gluconeogenesis (Cahill 2006). As fatty acid stores are depleted, gluconeogenesis from other substrates becomes increasingly important including muscle protein catabolism in situ for supporting muscle function as well as releasing glutamine (Marliss et al 1971) and alanine (Felig et al 1970A) which can be recycled to glucose by gluconeogenesis in the kidney (Goodman et al 1966, Kashiwaya et al 1994, Cahill 2006). In prolonged starvation events, the catabolism of muscle protein for gluconeogenesis in order to support systemic energy needs results in loss of muscle mass which contributes to loss of overall body weight. This loss is rapidly reversible upon input of alternative metabolic fuel for example by nutrient assimilation from feeding.
•Adverse Outcome: Loss of body weight - If caloric intake is less than caloric use over time, an individual will lose body weight. Dynamic energy budget theory has provided useful insights on how organisms take up, assimilate and then allocate energy to various fundamental biological processes including maintenance, growth, development and reproduction (Nisbet et al 2000). Regarding energy allocation, somatic maintenance must first be met before then growth may occur, followed by maturation and then finally, surplus energy is dedicated to reproduction (Nisbet et al 2000). The influence of PPARalpha on systemic energy metabolism and energy homeostasis has been broadly established (see reviews by Kersten 2014, Evans et al 2004, Desvergne and Wahli 1999). PPARalpha has been demonstrated to play a critical role in stimulating fatty acid oxidation and ketogenesis during fasting resulting in increased ketone body levels in plasma (Badman et al 2007, Kersten 2014) a response that is eliminated in PPARalpha knockout mice (Badman et al 2007, Sanderson et al 2010). Kersten et al (1999) and Badman et al (2007) demonstrated that PPARalpha-null mice were unable to actively mobilize fatty acid oxidation, and further, Kersten et al (1999) demonstrated that these mice were unable to meet energy demands during fasting and leading to hypoglycemia, hyperlipidemia, hypoketonemia and fatty liver. Observations from toxicological and toxicogenomic research have implicated nitrotoluenes as potential PPAR antagonists in birds (Rawat et al 2010), rats (Deng et al 2011) and mice (Wilbanks et al 2014), an effect that additionally corresponded with weight loss in rats (Wilbanks et al 2014) and body weight loss, loss of muscle mass and emaciation in birds (Quinn et al 2007). These combined results indicate that inhibition of PPARalpha signaling and the resultant decrease in fatty acid oxidation and ketogenesis can detrimentally impair systemic energy budgets leading to starvation-like effects and resultant weight loss. In the absence of PPARalpha knockout, and upon removal of PPARalpha antagonist dosing, normal bioenergetic physiology can potentially be attained.
Weight of Evidence Summary
Binding of molecules to peroxisome proliferator-activated receptor α (PPARα) can cause either agonistic or antagonistic signaling depending on molecular structure (Xu et al 2001, Xu et al 2002). Certain molecules that can bind to the PPARα ligand binding domain have been observed to cause conformational changes that induce increased affinity to co-repressors which decrease PPARα nuclear signaling (Xu et al 2002) representing the MIE for this AOP.
The transcription co-repressors, silencing mediator for retinoid and thyroid hormone receptors (SMRT) and nuclear receptor co-repressor (N-CoR) have been observed to compete with transcriptional co-activators for binding to nuclear receptors (including PPARα) thus suppressing basal transcriptional activity (Nagy et al 1999, Xu et al 2002). Regarding the KE1, the binding of co-repressors such as the silencing mediator for retinoid and thyroid hormone receptors (SMRT) and nuclear receptor co-repressor (N-CoR) to PPARα is reinforced by the MIE, which blocks the AF-2 helix from adopting the active conformation, as demonstrated in x-ray crystallography results presented in Xu et al (2002). Thus, molecules that bind to PPARα that can enhance co-repressor binding act as PPARα antagonists.
Given that PPARα trans-activation induces catabolism of fatty acids, this signaling pathway has been broadly demonstrated to play a key role in energy homeostasis (Kersten 2014, Evans et al 2004, Desvergne and Wahli 1999). In fact, PPARα regulates expression of genes encoding nearly every enzymatic step of fatty acid catabolism including fatty acid uptake into cells, fatty acid activation to acyl-CoAs, the release of cellular energy from fatty acids through the oxidative breakdown of acyl-CoAs to acetyl-CoA , and in starvation conditions, the repackaging of Acetyl-CoA substrates into ketone bodies (Kersten 2014, Desvergne and Wahli 1999, Evans et al 2004, Sengupta et al 2010).
A large body of research demonstrated that PPARα nuclear signaling directly controls transcriptional expression for genes catalyzing peroxisomal beta-oxidation of very long chain fatty acids (>20C), mitochondrial beta-oxidation of short, medium and long chain fatty acids (<20C), and ketogenesis (as reviewed in Kersten 2014, Evans et al 2004, Desvergne and Wahli 1999, Sanderson et al 2010). Peroxisomal beta-oxidation reactions shorten very long chain fatty acids from dietary sources releasing acetyl-CoA subunits (a primary metabolic fuel source) and shortened-chain fatty acids that can subsequently be catabolized by mitochondrial fatty acid beta oxidation reaction (as reviewed in Kersten et al 2014 and Desvergne and Wahli 1999).
Fatty acids shortened via peroxisomal beta-oxidation as well as fatty acids released from adipose tissue stores can be catabolized in mitochondrial beta-oxidation reactions to acetyl-CoA, NADH and ATP (Aoyama et al 1998). Within the mitochondria, the acetyl-CoA substrates can be used to maximize ATP production through full substrate oxidation via the citric acid cycle followed by oxidative phosphorylation by the electron transport chain (Nelson and Cox 2000A, Desvergne and Wahli 1999). This demonstrates importance of PPARα signaling for inducing cellular energy release from fatty acids.
Blocking PPARα signaling has been shown to inhibit expression of transcripts / enzymes involved in both peroxisomal and mitochondrial beta-oxidation causing impaired fatty acid catabolism, fatty acid accumulation in the liver and impaired cellular energy state during fasting events (Badman et al 2007, Kersten et al 1999). During periods of fasting, acetyl-CoA generated during either peroxisomal or mitochondrial beta-oxidation of fatty acids in the liver can each contribute to ketogenesis (Kersten 2014, Sengupta 2010). The liver represents a key organ involved in systemic energy distribution given its ability to synthesize glucose and catalyze the formation of ketone bodies , especially β-hydroxybutyrate, via ketogenesis (Cahil 2006, Kersten 2014). β-hydroxybutyrate is especially important for the metabolic energy needs of the brain which is unable to utilize fatty acids for cellular energy production (Owen 2005, Kersten 2014) as well as supporting general systemic energy homeostasis in fasting events (Cahil 2006, Evans et al 2004).
Not only does PPARα signaling stimulate the release of cellular energy from fatty acids, it also regulates the transcription of enzymes that catalyze the repackaging of that cellular energy to ketone bodies via ketogenesis (Sengupta et al 2010, Desvergne and Wahli 1999). 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 PPARalpha is induced in fasted mice mobilizing the oxidation of fatty acids for energy production. In that study, PPARalpha-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. In such energy deficits, muscle protein catabolism is induced to where the amino acids glutamine and alanine serve as substrates for gluconeogenesis in the kidney to supplement cellular energy production / distribution (Cahill 2006, Marliss et al 1971, Felig et al 1970A, Goodman et al 1966, Kashiwaya et al 1994). Specifically, organism level responses associated with exposure to PPARα antagonists include effects characteristic of a starvation response including decreased exercise endurance (Wilbanks et al 2014) body weight loss (Wilbanks et al 2014, Quinn et al 2007), loss of muscle mass and emaciation (Quinn et al 2007).
In general, if caloric intake is less than caloric use over time, an individual will lose body weight. This is a basic principle in human dieting as well as an important principle related to individual health and ecological fitness of animal populations. Dynamic energy budget theory has provided useful insights on how organisms take up, assimilate and then allocate energy to various fundamental biological processes including maintenance, growth, development and reproduction (Nisbet et al 2000). Regarding energy allocation, somatic maintenance must first be met before then growth may occur, followed by maturation and then finally, surplus energy is dedicated to reproduction (Nisbet et al 2000).
As an example of the importance of energy allocation to ecological fitness, a review by Martin et al (1987) demonstrated that energy availability (availability of food) was the predominant limiting factor in reproductive success and survival for both young and parents in a broad life history review for bird species. This is a likely scenario for many organisms.
"'Concordance of dose-response relationships:"'
Dose-response relationships have been developed for GW6471 and the relative binding of PPARα co-repressors and co-activators to the PPARα nuclear signaling complex where the proportion of co-repressors increases dramatically with increasing GW6471 concentration (Xu et al 2002). Correspondingly, the relative activity of PPARα decreased to zero with increasing GW6471 concentrations (Xu et al 2002). Additionally, recent observations of PPARalpha antagonism by nitrotoluenes have demonstrated dose-response relationships for PPARalpha nuclear signaling inhibition in in vitro investigations which corresponded with dose-responsive decreases in transcriptional expression of genes involved in lipid metabolism pathways (Wilbanks et al 2014, Gust et al 2015). These results corresponded with an dose-responsive relationship where increasing nitrotoluene dose caused decreased muscle mass, decreased body weight and increased emaciation in chronic dosing studies (Quinn et al 2007).
"'Temporal concordance among the key events and adverse effect:"'
Co-repressor binding was observed prior to inhibition of PPARα signaling (Xu et al 2002). PPARα knock out nullifies downstream expression of transcripts for genes involved in peroxisomal beta-oxidation of fatty acids, mitochondrial beta-oxidation of fatty acids, and ketogenesis pathways relative to wild types (Kersten et al 2014). Peroxisomal beta-oxidation of very long chain fatty acids into long chain fatty acids occurs prior to import into mitochondria and progression of mitochondrial beta-oxidation (Lazarow 1978, Kersten 2014). Mitochondrial beta-oxidation of long chain fatty acids occurs prior to generation of ketone bodies via ketogenesis (Sengupta et al 2010, Badman et al 2007). Ketogenesis occurs prior to increases in circulating ketone bodies (Sengupta et al 2010, Badman et al 2007, Cahill 2006). Increases in circulating ketone bodies can be observed prior to loss of muscle mass to muscle-protein catabolism given that this linkage is not directly connected. Muscle protein catabolism derives amino acids that are recycled to glucose via renal gluconeogenesis (Goodman et al 1966, Kashiwaya et al 1994, Cahill 2006). Catabolism of muscle protein occurs prior to body weight loss (Quinn et al 2007).
The transcription co-repressors, silencing mediator for retinoid and thyroid hormone receptors (SMRT) and nuclear receptor co-repressor (N-CoR) competion with transcriptional co-activators for binding to nuclear receptors (including PPARα) has been observed in humans as well as yeast (Nagy et al 1999) suggest broad taxonomic applicability for this MIE. The evidence of PPARalpha as a regulator of fatty acid metabolism is well described in the literature (for example, Kersten 2014, Evans 2004, Desvergne and Wahili 1999), and is consistent across many species including human, mouse, rat, Northern bobwhite, fathead minnow and carp (Kersten et al 1999, Kersten 2014, Wintz et al 2006, Gust et al 2015, Deng et al 2011, Wilbanks et al 2014, Xu and Jing, 2012). Inhibition of PPARalpha via gene knockout or treatment with PPARalpha antagonist consistently results in deceased fatty acid metabolism with indicators of increased serum triglycerides, fatty livers and steatosis (Kersten 2014, Evans 2004, Desvergne and Wahili 1999, Kersten et al 1999, Wintz et al 2006, Deng et al 2011). Given PPARalpha’s central role in systemic energy metabolism,studies of PPARalpha antagonism have shown decreased potential for sustaining energy needs of the organism (Kersten et al 1999) leading to decreased burst exercise performance (Wilbanks et al 2014) and weight loss (Wilbanks et al 2014, Quinn et al 2007). Research thus far suggest that PPARalpha transcriptional regulation pathway as well as the metabolic pathways for which PPARalpha acts as a regulator indicates that the progression of key events through to the adverse outcome will tend to be evolutionarily conserved for within mammals and likely across animal phyla.
"'Uncertainties, inconsistencies, and data gaps:"'
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. Additionally, knowledge about feedback mechanisms between ketogenesis vs gluconeogenesis would be beneficial for interpreting systemic energy metabolism. Regarding the antagonistic action of nitrotoluenes on PPARalpha nuclear signaling (Wilbanks et al 2014, Gust et al 2015), receptor-binding assays would be beneficial to determine if this class of chemicals is binding the SMRT and N-CoR co-repressors, similar to the antagonistic action of GW6471 (Xu et al 2002).
The weight of evidence scoring for this AOP is fully described in Collier et al (2015)
Given the complex nature of PPARalpha’s functioning within a multi-subunit transcription factor regulating the transcriptional expression of a multitude of genes that facilitate lipid metabolism, to our knowledge, the relationship between PPARalpha signaling and individual gene expression has not yet been quantitatively modeled. However, the gene regulatory networks structure is well established (KEGG Pathway, map03320) and numerous empirical observations of the positive relationship between PPARalpha signaling with transcript expression and downstream metabolic pathways (Kersten 2014, Desvergne and Wahli 1999), there is opportunity to develop a quantitative gene signaling model for this system. For peroxisomal and mitochondrial fatty acid beta-oxidation pathways and ketogenesis, a variety of enzyme kinetics information is available for modeling (see reviews by Kersten 2014, Desvergne and Wahli 1999) as well as basic knowledge of the reaction stoichiometry of each metabolic reactions that can contribute to metabolic energy substrates for systemic use. Resultant models should be integrated with the work of Kashiwaya et al (1994) who have developed a detailed quantitative model for the metabolic flux of glucose including the influence of ketone bodies and insulin action on the dynamics of glycolysis versus gluconeogenesis. Dynamic energy budget (DEB) models (Nisbet et al 2000) have strong utility for integrating the dynamics of energy input and allocation to organismal processes of importance for characterizing/predicting the condition of the individual (ie. growth and maturation) as well as population-level responses (ie. allocation of energy to reproduction). DEB modeling has great potential for integrating suborganismal processes into individual and population level outcomes (Ananthasubramaniam et al 2015) and could serve to integrate data from dose-responsive relationships among PPARalpha antagonistic nitrotoluenes and fatty acid metabolism, muscle loss and body weight loss (Rawat et al 2010, Deng et al 2011, Wilbanks et al 2014, Quinn et al 2007, Xu and Jin 2012) thus supporting development of a semi-quantitative or quantitative AOP.
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. 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. Instructions 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. The new text should appear under the “Considerations for Potential Applications of the AOP” section on the AOP page.
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