AOP ID and Title:

Short Title: Oxidative stress and Developmental impairment in learning and memory

Authors

Florianne Tschudi-Monnet, Department of Biological Sciences, University of Lausanne, Switzerland, and Swiss Centre for Applied Human Toxicology (SCAHT), Florianne.Tschudi-Monnet@unil.ch

Marie-Gabrielle Zurich, Department of Biological Sciences, University of Lausanne and SCAHT, Switzerland, mzurich@unil.ch

Carolina Nunes, Department of Biological Sciences, University of Lausanne, Switzerland, carolina.nunes@unil.ch

Jenny Sandström, SCAHT, Switzerland, jsm.sandstrom@gmail.com

Rex FitzGerald, SCAHT, Switzerland, rex.fitzgerald@unibas.ch

Michael Aschner, Albert Einstein College of Medecine, New York, USA, michael.aschner@einstein.yu.edu

Joao Rocha, Department of Biochemistry and Molecular Biology, Federal University of Santa Maria, Santa Maria, Brazil, jbtrocha@gmail.com

 

The authors of KEs AOPwiki ID 1392 (oxidative stress), 55 (Cell injury/death), 386 (Decrease network function), of the AO (Learning and memory, impairment), and of KER 359 (decrease network function leads to impairment in learning and memory) are greatly acknowledged.

 

Status

Abstract

This Adverse Outcome Pathway (AOP) describes the linkage between binding to sulfhydryl(SH)-/seleno-proteins involved in protection against oxidative stress and impairment in learning and memory, the Adverse Outcome (AO). Binding to SH-/ seleno-proteins involved in protection against oxidative stress has been defined as the Molecular Initiating Event (MIE). Production, binding and degradation of Reactive Oxygen Radicals (ROS) are tightly regulated, and an imbalance between production and protection may cause oxidative stress, which is common to many toxicity pathways. Oxidative stress may lead to an imbalance in glutamate neurotransmission, which is involved in learning and memory. Oxidative stress may also cause cellular injury and death. During brain development and in particular during the establishment of neuronal connections and networks, such perturbations may lead to functional impairment in learning and memory. Neuroinflammation (Resident cell activation; Increased pro-inflammatory mediators) is triggered early in cell injury cascades and is considered as an exacerbating factor. The weight-of-evidence supporting the relationship between the described key events is based mainly on developmental effects observed after an exposure to the heavy metal, mercury, known for its strong affinity to many SH-/seleno-containing proteins, but in particular to those having anti-oxidant properties, such as glutathione (GSH). The overall assessment of this AOP is considered as strong, based on the biological plausibility, the empirical support and on the essentiality of the Key Events (KEs), which are moderate to strong, since blocking, preventing or attenuating an upstream KE is mitigating the downstream KE. The gap of knowledge is mainly due to limited quantitative evaluations, impeding thus the development of predictive models.

Background

This AOP was originally started in a workshop report entitled: Adverse Outcome Pathways (AOP) relevant to Neurotoxicity and published in Critical Review in Toxicol: Bal-Price, A., Crofton, K.M., Sachana, M., Shafer, T.J., Behl, M., Forsby, A., Hargreaves, A., Landesmann, B., Lein, P.J., Louisse, J., Monnet-Tschudi, F., Paini, A., Rolaki, A., Schrattenholz, A., Sunol, C., van Thriel, C., Whelan, M., Fritsche, E., 2015. Putative adverse outcome pathways relevant to neurotoxicity. Crit Rev Toxicol 45(1), 83-91.

The process of inflammation is common to many tissues and can be described by several KEs, as proposed in a dedicated workshop (Villeneuve et al., 2018). Brain inflammation called Neuroinflammation can be described by the two common KEs: Tissue resident cell, activation and pro-inflammatory mediators, increased. However, Neuroinflammation is a concept accepted by the regulators and is found in the whole literature describing brain inflammation. Therefore, in accord with the external reviewers, we decided to use the KE Neuroinflammation  for building the KERs of this AOP, but we introduced in the list of the KEs the two KEs common to the inflammatory process, as proposed in Villeneuve et al., 2018.

Summary of the AOP

Events

Molecular Initiating Events (MIE), Key Events (KE), Adverse Outcomes (AO)

Key Event Relationships

Stressors

Overall Assessment of the AOP

Experimental and epidemiological evidences indicate that compared to the adult central nervous system (CNS), the developing CNS is generally more susceptible to toxicant exposure (Costa et al., 2004; Grandjean and Landrigan, 2006). Pre-natal and post-natal exposure may have long-term consequences, i.e. not detected immediately at the end of the exposure period. Such effects on visuospatial memory for example have been described on child development in communities with chronic low level mercury exposure (Castoldi et al., 2008a; Debes et al., 2006; Grandjean et al., 2014; Lam et al., 2013).

The aim of this AOP is to capture the KEs and the KERs that occur after binding to thiol- and selenol groups of proteins involved in protection against oxidative stress, the MIE, and impairment in learning and memory, the AO, which is a neurotoxicity marker belonging to the OECD regulatory tool box. The chemical initiators used for the empirical support are methylmercury and mercury chloride, and acrylamide. Data are most extensive for mercury as stressor during development; data for acrylamide are much more limited and restricted to some KEs. Chronic, low-dose prenatal MeHg exposure from maternal consumption of fish has been associated with endpoints of neurotoxicity in children, including poor performance on neurobehavioral tests, particularly on tests of attention, fine-motor function, language, visual-spatial abilities (e.g., drawing), and verbal memory (NRC, 2000). However, it is important to note that some uncertainties remain about the effects of low dose of mercury during brain development (Grandjean et al., 1999). Epidemiological studies in Seychelles on prenatal exposure through fish consumption did not evidenced adverse effects on memory when analyses were performed at 22 and 24 years (Van Wyngaarden et al., 2017), whereas similar experiments made in the Faroe Islands revealed dysfunctions in language, attention and memory at 7 years (Grandjean et al., 1997). And a clear association was observed between mercury cord blood level and memory deficit (Grandjean et al., 1997; Debes et al., 2006). Castoldi and coworkers (2008) proposed that modulating factors, such as diet, nutrition, gender, pattern of exposure and co-exposure could explain the discrepancies of these epidemiological studies. Nevertheless, there are experimental evidences showing that the neurocognitive domain, in particular dentate gyrus, hippocampus and cortex are susceptible to the neurotoxicity of mercury in the developing brain (Sokolowski et al., 2011, 2013; Ceccatelli et al., 2013); therefore, we focus on impairment in learning and memory as the AO. Some –SH- or –SeH-containing proteins involved in protection against oxidative stress have been demonstrated to be inhibited by MeHg either in vitro or in vivo, but a causal relationship has not been established between these inhibitory effects and the final pathological events (Oliveira, 2017). However, the analysis of the essentiality of the KEs and of the weight of evidence for the KERs supports a plausible mechanistic link between the MIE and the AO.

Domain of Applicability

Life Stage Applicability Taxonomic Applicability Sex Applicability

This AOP is mainly focused on the developmental period, although it cannot be excluded that long-term exposure in adult may trigger a similar cascade of KEs leading also to impairment in learning and memory, as observed in neurodegenerative diseases such as Alzheimer's disease (Mutter et al., 2004). While no specific sex differences have been analyzed/described for most KEs, Curtis and coworkers (2010) observed a higher level of TNF-a in hippocampus of male prairie wolf than in female, both treated for 10 weeks with inorganic mercury, in the form of HgCl₂; whereas Zhang and coworkers (2013) found a higher neuroinflammatory response associated with altered social behavior in female mice offspring than in male, following gestational exposure to HgCl₂. However, after developmental methylmercury exposure, long-lasting behavioral alterations were more prominent in males (Ceccatelli et al., 2013; Castoldi et al., 2008b). These discrepancies may be due to sex differences in kinetics or susceptibility (Vahter et al., 2006).

Essentiality of the Key Events

KE	Defining Question	High (Strong)	Moderate	Low (Weak)
	Are downstream KEs and/or the AO prevented if an upstream KE is blocked?	Direct evidence from specifically designed experimental studies illustrating essentiality for at least one of the important KEs (e.g. stop/reversibility studies, antagonism, KO models, etc.)	Indirect evidence that sufficient modification of an expected modulating factor attenuates or augments a KE leading to increase in KE down or AO	No or contradictory experimental evidence on the essentiality of any of the KEs
KE1 Decreased protection against oxidative stress	HIGH	RATIONALE: The fact that a decrease in anti-oxidant properties causes oxidative stress is well accepted. In addition, experimental evidences of knocking out proteins involved in protection against oxidative stress incresed the susceptibilty to oxidative stress.
KE2 Oxidative stress	HIGH	RATIONALE: The deleterious consequences of oxidative stress are well accepted in various animal models. Oxygen radical scavengers, such as glutathione, catalase, selenium and cysteine can block the deleterious effects of oxidative stress.
KE3 Glutamate dyshomeostasis	HIGH	RATIONALE: Glutamate is the main excitatory transmitter, and is involved in memory processes, it is well accepted that perturbation of glutamate homeostasis has deleterious functional consequences. Disruption of glutamate signaling is thought to play a role, at least in part, in the etiology underlying several neurodevelopmental disorders, including memory dysfunction.
KE4 Cell Injury/death, increased	HIGH	RATIONALE: Cell injury/death is a highly converging node in AOPs. Decrease in synaptic connectivity or cell loss will in turn induce perturbations in the establishment of neuronal connections and trigger inflammatory responses, which through a feedback loop can exacerbate this KE. Therefore, prevention of cell injury/death by anti-oxidant or by inhibitors of NMDA receptors prevents the downstream KEs.
KE5 Neuroinflammation KE5' Tissue resident cell activation KE5'' Pro-inflammatory mediators, increased	MODERATE	RATIONALE: It is widely accepted in different experimental animal models that the use of minocycline, an antibiotic, which blocks microglial reactivity has protective effects, as have other interferences with any inflammatory mediators. However, we rate the essentiality of this KE as moderate given the complexity of the neuroinflammatory response, having either protective/reparative or aggravating consequences,
KE6 Decreased network formation and function	HIGH	RATIONALE: Glutamate neurotransmission is an important mechanism underlying memory function (for review: Featherstone, 2010). During brain development, glutamate has also trophic effects, by stimulating BDNF production or through the activation of the different glutamate receptors. The trophic effect of glutamate receptor activation is developmental stage-dependent and may play an important role in determining the selective survival of neurons that made proper connections (Balazs, 2006).
AO Impairment of learning and memory	HIGH	RATIONALE: Impairment in learning and memory is a converging KE in several AOPs related to brain development. Regarding this AOP and its chemical initiators, it was shown that the neurocognitive domain, in particular dentate gyrus, hippocampus and cortex are susceptible to the neurotoxicity of mercury in the developing brain (Sokolowski et al., 2011, 2013; Ceccatelli et al., 2013). Chronic, low-dose prenatal MeHg exposure from maternal consumption of fish has been associated with endpoints of neurotoxicity in children, including poor performance on neurobehavioral tests, particularly on tests of attention, fine-motor function, language, visual-spatial abilities (e.g., drawing), and verbal memory (NRC, 2000). Prenatal MeHg exposure is associated with childhood memory and learning deficits, particularly visual memory performance in school-aged children (Orenstein, 2014).

 

Weight of Evidence Summary

Dose-response and temporal concordance of KEs

There is no study where all KEs are measured simultaneously after exposure to several doses, impeding a dose-response and concordance analysis. In one single study (in blue in the table), three downstream KEs were measured following pre-natal exposure to methylmercury. Comparisons of all animal studies show that doses used are ranging from 0.5 - 5 mg/kg; but dose-response was seldom performed. In these studies, the time (pre-natal, post-natal, lactation,...) and duration of exposure are quite diverse and no analysis of brain mercury content was made, so it is not possible to compare doses between studies. Therefore, based on the present data, it is impossible to define whether KEs up occur at lower doses and earlier time points than KEs down.

For in vitro studies, KEs up are often measured after acute exposure to high concentrations.

The following table summarizes concentrations/doses, time, and duration of exposure for the various test systems and KEs.

KEs	In vivo	In vitro
MIE Binding to SH-/seleno-proteins		Binding of Hg to thiol groups and to various selenium-containing proteins: Glutathione, thioredoxin reductase, thioredoxin, glutaredoxin, glutathione reductase was measured using purified proteins (Carvahlo et al., 2008, 2011; Wiederhold et al., 2010; Sugiura et al., 1978; Arnold et al., 1986; Han et al., 2001; Qiao et al., 2017)
KE1 Decreased protection against oxidative stress	Cytoplasmic and nuclear TrxR and Cytoplasmic Gpx were reduced in cerebral and cerebellar cortex of 22 days-old offspring (Ruszkiewicz, 2016) Male C57BL/6NJcl mice exposed to methylmercury (1.5 mg/kg/day for 6-weeks) (Fujimura, 2017) Adult male Sprague-Dawley rats exposed to methylmercury (1 mg/kg orally for 6 months) (Joshi, 2014) Zebra fish brain exposed to Hg2+, MeHg 1.8 molar (measured in brain tissue), for 28 days (Branco, 2012) Prenatal and postnatal exposure of mice to 40 ppm of HgCl2 decreased the activity of catalase, thioredoxin reductase, Gpx, superoxide dismutase (Malqui et al., 2017)	Mouse primary cortical cultures exposed to 5 mM of methylmercury for 24h (Rush, 2012) MeHg inhibits ex vivo rat thioredoxin reductase; IC50 0.158 μM (cerebral) (Wagner et al., 2010) Human neuroblastoma cells (SH-SY5Y)exposed to 1 µM of methylmercury for 6 or 24 h (Branco, 2017; Franco, 2009)
KE2 Oxidative stress	Male C57BL/6NJcl mice exposed to methylmercury (1.5 mg/kg/day for 6-weeks) (Fujimura, 2017) Adult male Sprague-Dawley rats exposed to methylmercury (1 mg/kg orally for 6 months) (Joshi, 2014) Adult male Sprague-Dawley rats exposed to methylmercury (1 mg/kg orally for 6 months) (Joshi, 2014) Zebra fish brain exposed to Hg2+, MeHg 1.8 molar (measured in brain tissue), for 28 days (Branco, 2012) Prenatal and postnatal exposure of mice to 40 ppm of HgCl2 caused oxidative stress evaluated by increased lipid peroxidation (Malqui et al., 2017)	Mouse primary cortical cultures exposed to 5 mM of methylmercury for 24h (Rush, 2012) Methylmercury (2-10 µM) in synaptic vesicles isolated from rat brain (with LD50 at 50 µM) (Porciuncula et al., 2003) Human neuroblastoma cells (SH-SY5Y)exposed to 1 µM of methylmercury for 6-24 h (Franco, 2009)
KE3 Glutamate dyshomeostasis	Rat Young (3-4 weeks) dosed with acrylamide by gavage (5, 15, 30 mg/kg, 5 applications per week during 4 weeks) (Tian, 2018) Microdialysis probe in adult Wistar rats showed that acute exposure to methylmercury (10, 100 mM) induced an increase release of extracellular glutamate (9.8 fold at 10 mM and 2.4 fold at 100 mM). This extracellular glutamate level remained elevated at least 90 min (Juarez et al., 2002)	Mouse astrocytes, neurons in mono- or co-cultures exposed to methylmercury 1-50 µM for 24h (Morken, 2005) Methylmercury (2-10 µM) in synaptic vesicles isolated from rat brain (with LD50 at 50 µM) (Porciuncula et al., 2003
KE4 Cell Injury/death, increased	Rat, perinatal exposure to methylmercury (GD7-PD21, i.e. 35 days) 0.5 mg/kg bw/day in drinking water (Roda et al., 2008) Rat Young (3-4 weeks) exposed to acrylamide by gavage (5, 15, 30 mg/kg, 5 applications per week during 4 weeks) (Tian, 2018) Pregnant rat exposed to methylmercury (1.5 mg/kg orally) from GD5 till parturition (Jacob, 2017)	Mouse astrocytes, neurons in mono- or co-cultures exposed to methylmercury 1-50 µM for 24h (Morken, 2005)
KE5 Neuroinflammation KE5' Tissue resident cell activation KE5'' Pro-inflammatory mediators, increased	Rat, perinatal exposure to methylmercury (GD7-PD21, i.e. 35 days) 0.5 mg/kg bw/day in drinking water (Roda et al., 2008) Monkeys, 6,12,18 months oral exposure 50 mg/kg bw (Charleston et al., 1996)	3D rat brain cell cultures 10 day treatmentHgCl2 10-9-10-6M MeHgCl 10-9-3x10-7M (Monnet-Tschudi et al., 1996; Eskes et al., 2002)
KE6 Decreased network formation and function	Mice dosed during postnatal week 1-3 with subcutaneous 2-5 mg mercury chloride/kg/once per week (Eddins et al., 2008) Pregnant rat dosed on GD 15 with 8 mg/kg of methylmercury by gavage. Offsprings were tested at day 16, 21 and 60. (Cagiano et al., 1990) Pregnant rat exposed to methylmercury (1.5 mg/kg orally) from GD5 till parturition (Jacob, 2017)
AO Impairment of learning and memory	Mice dosed during postnatal week 1-3 with subcutaneous 2-5 mg mercury chloride/kg/once per week (Eddins et al., 2008) Pregnant rat dosed on GD 15 with 8 mg/kg of methylmercury by gavage. Offsprings were tested at day 16, 21 and 60 (Cagiano et al., 1990) Pregnant rat exposed to methylmercury (1.5 mg/kg orally) from GD5 till parturition (Jacob, 2017) Pregnant mice received 0.5 mg methylmercury/kg/day in drinking water from gestational day 7 until day 7 after delivery. Offspring behavior was monitored at 5-15 and 26-36 weeks of age (Onishchenko et al., 2007) Balb mice exposed to methylmercury in diet (low dose: 1.5 mg/kg; high dose: 4.5 mg/kg) during 11 weeks (6 weeks prior mating, 3 weeks during gestation and 2 weeks post-partum). Offsprings tested at PD 15 showed an accumulation of Hg in brain (0.08 mg/kg for low dose and 0.25 mg/kg for the high dose) (Glover et al., 2009) Prenatal and postnatal exposure of mice to 40 ppm of HgCl2 caused impairment of memory (object recognition, Y maze) Malqui et al., 2017)	Maternal peripartum hair mercury level was measured to assess prenatal mercury exposure. The concentrations of mercury was found in the range of 0.3-5.1 µg/g, similar to fish-eating population in US. Statistical analyses revealed that each ug/g increase in hair Hg was associated with a decrement in visual memory, learning and verbal memory (Orenstein et al., 2014) Epidemiological studies in the Faroe Islands revealed that mercury exposure through fish consumption (maternal hair conc. 10 ug/g) dysfunctions in memory, language and attention at age 7 (Grandjean et al., 1997; Debes et al., 2006)

 

 

 

Biological Plausibility and Empirical Support of the KERs 

KERs	Defining Question Is there a mechanistic (i.e. structural or functional) relationship between KEup and KEdown consistent with established biological knowledge?	High (Strong) Extensive understanding of the KER based on extensive previous documentation and broad acceptance	Moderate The KER is plausible based on analogy to accept biological relationship but scientific understanding is not completely established	Low (Weak) There is empirical support for a statistical association between KEs but the structural or functional relationship between them is not understood
MIE to KE Decrease protection against oxidative stress	MODERATE	RATIONALE: Thiol- and selenol containing proteins, which mainly belong to the anti-oxidant protections, have a high affinity for binding soft metals such as mercury (Farina, 2011). Binding to these thiol/sulfhydryl/SH/SeH groups results in structural modifications affecting the catalytic capacity, and thereby reducing the capacity to neutralize ROS. However, binding to other SH/SeH groups of other proteins not involved in protection against oxidative stress can occur and trigger other neurotoxicity pathways. Alternatively, binding to SH groups of electrophilic compounds may also induce cyto-protective reactions (e.g. via Nrf2).
KE Decrease protection against oxidative stress to KE Oxidative stress	HIGH	RATIONALE: Oxidative stress is defined as an imbalance in the production of reactive oxygen species (ROS) and antioxidant defenses. Several studies have shown depletion of GSH, the main anti-oxidant, and an increase in oxidative stress following methylmercury or mercury chloride exposures (Meinerz, 2011; Rush, 2012; Agrawal, 2015). Protection against oxidative stress was observed by supplementation with diphenyl selenide (Meinerz, 2011) or by glutathione ester (Rush, 2012). Limited conflicting data.
KE Oxidative stress to KE Glutamate (Glu) dyshomeostasis	LOW	RATIONALE: Glutamate transport is driven by the Na+ ion gradient, which is dependent on the Na/K ATPase, which, in turn, requires energy. Glutamate enters the cells accompanied by 2 Na+ and one H+.  Perturbations of energy metabolism such as mitochondrial dysfunction and increased production of ROS will lead to glutamate dyshomeostasis, due to the indirect coupling of glutamate transporters with ATP level, and to the important role of glutamate transporters in glutamate homeostasis. (Boron and Boulpaep, 2003). Methylmercury was shown to inhibit both the H+-ATPase activity and vesicular glutamate uptake (Porciuncula et al., 2003). As, on one hand, ROS production can interfere with glutamate uptake, and on the other hand, glutamate accumulation leads to excitotoxicity and ROS production, the exact sequence of the KER is difficult to assess. But the fact that both KEs are involved in mercury-induced neurotoxicity is broadly accepted (Farina et al., 2011; Antunes dos Santos et al., 2016; Morris et al., 2017; Kern et al., 2016).
KE Glutamate dyshomeostasis to KE Cell injury/death	HIGH	RATIONALE: Glutamate dyshomeostasis, in particular excess of glutamate in the synaptic cleft, leads to overactivation of ionotropic glutamate receptors, referred to as excitotoxicity. This, in turn, will cause cell injury/death via ROS production. This KER is also inherent to the developing brain, where glutamate ionotropic receptors are expressed early in various neural cells and when NMDA receptors are expressed in neurons. There is empirical support for all three chemical initiators (mercury, acrylamide, acrolein). In addition, several experiments aiming at blocking glutamate excitotoxicity and the resulting ROS production are protective for cell injury/death. Limited conflicting data.
KE Cell injury/death to KE Neuroinflammation	MODERATE	RATIONALE: It is widely accepted that cell/neuronal injury and death lead to neuroinflammation (microglial and astrocyte reactivities) in adult brain, and in the developing brain, where neuroinflammation was observed after cell injury/death induced by excitotoxic lesions (Acarin et al., 1997; Dommergues et al., 2003). Empirical support is available for all three chemical initiators (mercury, acrylamide, acrolein).  Few experiments, showing a protection when blocking any feature of neuroinflammation have been described. There are some contradicting data showing an absence of neuroinflammatory response despite the occurrence of mercury-induced apotosis and slight behavioral alterations.
KE Neuroinflammation to KE Cell injury/death	MODERATE	RATIONALE: In vitro co-culture experiments have demonstrated that reactive glial cells (microglia and astrocytes) can kill neurons via the release of pro-inflammatory cytokines, such as TNF-a, IL-1b and IL-6 and/or ROS/RNS (Chao et al., 1995; Brown and Bal-Price, 2003; Kraft and Harry, 2011; Taetzsch and Block, 2013) and that interventions aiming at blocking these inflammatory biomolecules can rescue the neurons (Yadav et al., 2012; Brzozowski et al., 2015). Several reports showed that modulating mercury or acrylamide-induced neuroinflammation was protective for neurons. Because of the complexity of the neuroinflammatory response, that can have neuroprotective or neurodegenerative consequences depending on the duration, local environment or still unknown factors, the rating of this KER was kept as moderate. The vicious cycle between cell injury/death and neuroinflammation is well known and was described in other AOPs. Neuroinflammation could be considered as a modulating factor, but because of the numerous inhibiting experiments, it is considered as an essential KE. Some conflicting data due to the dual role of some inflammatory mediators have been reported.
KE Cell injury/death to KE Decreased network formation and function	HIGH	RATIONALE: Neuronal network formation and functional crosstalk are established via synaptogenesis. It was shown that under physiological conditions components of the apoptotic machinery in the developing brain regulate synapse formation and neuronal connectivity (Dekkers et al., 2013). The brain’s electrical activity dependence on synapse formation is critical for proper neuronal communication. Glial cells are also involved in the establishment and stabilization of the neuronal network. Extensive experimental support for the adverse effects of mercury on synaptogenesis exist, establishing a strong link between mercury-induced apoptosis and/or neuronal loss and perturbations in a number of neurotransmitter systems (Jacob, 2017; Bridges, 2017) and perturbations of functionality (Falluel-Morel, 2007; Ferraro, 2009; Teixera, 2014; Onishchenko, 2007). Limited protective experiments and conflicting data reported.
KE Decreased network formation and function to AO Impairment in learning and memory	HIGH	RATIONALE: A review on the Morris water maze (MWM) (Morris, 1981), as an investigative tool of spatial learning and memory in laboratory rats (Vorhees and Williams, 2006) pointed out that perturbed neuronal networks rather than neuronal death per se in certain regions is responsible for the impairment in MWM performance. Functional integrated neural networks that involve the coordination action of different brain regions are consequently important for spatial learning and memory performance (D'Hooge and De Deyn, 2001). Broad empirical support showing mercury-induced effects on learning and memory as consequence of network disruption (Sokolowski et al. 2013; Eddins et al., 2008; Glover et al., 2009). Similar observations were made in humans (Orenstein et al., 2014; Yorifuji et al., 2011).  Interestingly, behavioral alterations were detected long time after exposure (delayed effects). Few conflicting data have been reported, but other behavioral deficits, such as alterations in motor activity and increased anxiety suggest that systems other than hippocampus-related learning and memory are also affected.
KE oxidative stress to KE Cell injury/death	HIGH	RATIONALE: The central nervous system is especially vulnerable to free radical damage since it has a high oxygen consumption rate, an abundant lipid content and reduced levels of antioxidant enzymes (Coyle and Puttfarcken, 1993; Markesbery, 1997). The developing nervous system is particularly vulnerable to chemical insults (Grandjean and Landrigan, 2014). One reason for this higher vulnerability is the incapacity of immature neural cells to cope with oxidative stress by increasing glutathione (GSH) production (Sandström et al., 2017a). Broad empirical support for mercury and acrylamide showing an association between increased ROS production and/or decreased protection against oxidative stress and apoptosis and/or necrosis (Lu et al., 2011; Sarafian et al., 1994; Allam et al., 2011; Lakshmi et al., 2012). Anti-oxidant treatments proved to be protective. Few conflicting data, except a mercury-induced upregulation of GSH level and GR activity as an adaptive mechanism following lactational exposure to methylmercury (10 mg/L in drinking water) associated with motor deficit, suggesting neuronal impairment (Franco et al., 2006).

 

 

 

 

 

Quantitative Consideration

Some quantitative relationships have been described between the upstream early KEs (MIE, oxidative stress, Cell injury/death), although the diversity of test systems and posology (dosing/exposure amount and duration) hampers comparison between studies. It is more difficult to evaluate quantitative relationships between later downstream KEs, such as Neuroinflammation and Decreased Network Function. Neuroinflammation is a complex adaptive mechanism which is not yet completely understood; it can have neuroprotective or neurodegenerative consequences, depending on triggering signals, duration, microenvironment or other unknown influences, which may determine the outcome of the neuroinflammatory process. Decreased network function is currently difficult to quantify because quantitative technologies for mapping and understanding of brain networks (and their plasticity) are still under development.

Optimally, we would like data from a single type of test system showing that exposure to stressor, e.g. mercury, is correlated with changes in all KEs. Such models are emerging, using cells of human origin (Pamies et al., 2016; Sandström et al., 2017b; Fritsche et al., 2017) and/or non-mammalian models, such as zebrafish (Geier et al., 2018; Padilla et al., 2018) and will allow in the future generation of quantitative data which may be used for in silico hazard prediction.

Summary table of Quantitative Evaluations

KEs	Methylmercury (MeHg, CH3Hg)
	5 µM mouse brain in vitro (Rush, 2012)		15–30 µM mouse brain, after 40 mg/L in drinking water for 21 days (Glaser, 2013)		1 µM mouse cerebral cortex ex vivo after oral dosing (Lu et al 2011; conc. from Huang et al 2008)		17-24, 75-µM (rat cerebral cortex ex vivo after 4w ip dosing) 4w (Xu, 2012; Liu 2013; Feng, 2014)
KE1 Decreased protection against oxidative stress	GSH reduced 80% of control 24h		Cortical mitochondrial GPx activity decreased (70% of control), GR increased (170% of control)		GSH decreased (ca 50% of control) 7 weeks		Antioxidants NPSH, SOD, GSH-Px decreased (ca 80% and 50% of control)
KE2 Oxidative stress	ROS increased 120-150% of control 24h		Cortical mitochondrial TBA-RS increased (ca 140% of control) and complex I, II-III, and IV activity decreased (ca 50% of control). Brain 8-OHdG content increased (ca 400% of control).		LPO increased (ca 200% of control) 7 weeks		ROS (DCF) increased (190 and 400% of control at 22,87 μM)
KE3 Glutamate dyshomeostasis							Glutamine synthetase decreased (80 and 50% of control at 24,89 μM) Glutamate content increased (100 and 120% of control at 24,89 µM) Glutamine content decreased (80 and 50% of control at 24,89 μM)
KE4 Cell Injury/death, increased					Apoptosis-related gene expression: Bcl-2 decreased, ca 50% of control; Bax, Bak, p53, caspase-3,-5,-7 increased, ca 200-350% of control 7 weeks		Apoptosis increased dose-dependently (300 and 853% of control at 24,89 µM). 8-OHdG expression increased (200 and 450% of control at 24,89 µM)
KE5 Neuroinflammation
KE6 Decreased network formation and function
AO Impairment of learning and memory

 

 

 

 

 

KEs	Mercuric chloride (HgCl2)
	6 µM rat brain, 1.13 µg Hg/g 6 mo (Agrawal, 2015)		0.1-100 µM cultured mouse cerebellar granule cells 10 min (Fonfria, 2005)
KE1 Decreased protection against oxidative stress	Blood GSH decreased (ca 90% of control)
KE2 Oxidative stress
KE3 Glutamate dyshomeostasis			Glutamate (3H-aspartate) uptake inhibited (IC50 3.5 uM). Glutamate release stimulated (47% of total endogenous glutamate at 10 µM)
KE4 Cell Injury/death, increased	Serum AST increased (ca 140% of control).		Cell viability (MTT) decreased (ca 10% of control at 10 µM)
KE5 Neuroinflammation
KE6 Decreased network formation and function	Brain noradrenaline and dopamine content decreased (ca 30% of control).
AO Impairment of learning and memory

Considerations for Potential Applications of the AOP (optional)

• Contribution to the network of KEs/AOPs on Developmental Neurotoxicity (DNT)
• Generating quantitative data by measuring all KEs in a single model after repeated/long term exposure to a wide concentration range of the chemical initiators to facilitate the development of computational predictive approaches

References

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Antunes Dos Santos, A., M. Appel Hort, M. Culbreth, C. Lopez-Granero, M. Farina, J. B. Rocha and M. Aschner (2016). "Methylmercury and brain development: A review of recent literature." J Trace Elem Med Biol 38: 99-107.

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Appendix 1

List of MIEs in this AOP

Event: 1487: Binding, Thiol/seleno-proteins involved in protection against oxidative stress

Short Name: Binding, SH/SeH proteins involved in protection against oxidative stress

AOPs Including This Key Event

Stressors

Biological Context

Organ term

Evidence for Perturbation by Stressor

Overview for Molecular Initiating Event

Mercury (Methylmercury, mercury chloride)

The binding of Methylmercury (MeHg) to redox sensitive thiol- or selenol-groups can disrupt the activity of enzymes or the biochemical role of non-enzymatic proteins. The stable or transitory interaction (binding) of MeHg with critical thiol and selenol groups in target enzymes can disrupt the biological function of different types of enzymes, particularly of the antioxidant selenoenzymes thioredoxin reductase (TrxR) and glutathione peroxidase isoforms. The dysregulation of cerebral glutathione (GSH and GSSG) and thioredoxin [Trx or Trx(SH)₂]  systems by MeHg (Farina et al. 2011; Branco et al. 2017) can impair the fine cellular redox balance via disruption of sensitive cysteinyl- or thiol-containing proteins (Go etal., 2013; Go et al. 2014; Jones 2015).

 

Figure 1 – Hypothetical Binding of MeHg to different types of target proteins. The binding of MeHg to proteins can cause either a transitory inhibition of the protein fucntion (first line, the yellow protein was reactivated by interacting with LMM-SH or R-SH). The pink protein is an example of protein that after the binding of MeHg suffered a change in the structure in such a way that it cannot be reactivated by LMM-SH or R-SH.  The third protein (blue) is an example of protein that was permanently denaturated after MeHg binding and even after the removal of MeHg the activity was not recovered. The same type of interactions can be applied to the selenol-containing proteins (i.e., the selenoproteins).

The affinity of Mercury chloride (Hg²⁺) for thiol and selenol groups is higher than that of MeHg (compare Table 2 with Table 1). The constants described in Table 1 and 2 indicate that MeHg and Hg²⁺ behave as  strong soft electrophiles, i.e., theyhave much higher affinity for the soft nucleophiles centers of thiol- and selenol-containing molecules (Rabenstein 1978a; Arnold et al. 1986; Sugiura et al., 1976).Furthermore, the rate constant for the reaction of MeHg with thiol/thiolate (R-SH/R-S^-) has been estimated to be about 6 x 10⁸ M^-1.sec^-1,  indicating that the reactions of electrophilic forms of Hg (EpHg⁺ ; here MeHg and Hg²⁺) with thiolate and selenolate groups are diffusion controlled reactions (Rabenstein  and Fairhurst, 1975). The constant indicates that the binding of EpHg⁺ to thiolate (-S^-) or selenolate (-Se^-) groups will occurr almost instaneously, when an EpHg⁺ collides with –S^- or -Se^- groups.

The studies of Rabenstein and others have also pointed out that the affinity of MeHg for –SeH groups is higher than for  –SH groups (Sugira et al. 1976; Arnold et al. 1986). Consequently, –SeH-containing molecules (i.e., selenoproteins) should be the preferential targets for MeHg (Farina et al. 2011). Accordingly, several studies have demonstrate that the selenoenzymes glutathione peroxidase (GPx) and thioredoxin reductase (TrxR) were inhibited after in vitro and in vivo exposure to MeHg  or Hg²⁺ (Carvalho et al., 2008a; 2011, Farina et al.,  2009; Franco et al., 2009; Wagner et al., 2010; Branco et al., 2011; 2012; 2014, 2017; Dalla Corte et al., 2013; Meinerz et al., 2017).

As corollary, the occurrence of free MeHg and Hg²⁺ or bound to other ligands such as carboxylates, amines, chloride or hydroxyl anions in the physiological media of living cells is insignificant or nonexistent (George et al. 2008). The binding of MeHg to abundant low molecular mass thiols or LMM-SH (e.g., cysteine and reduced glutathione-GSH) and high molecular mass thiol-containing proteins or HMM-SH (e.g., albumin, hemoglobin, etc) is critical for the MeHg distribution from non-target to target organs and cells (Farina et al. 2017). The coordination of MeHg with one –S^- group of a LMM-SH will determine MeHg distribution to its targets organs, including the brain. The coordination of Hg²⁺ with two –S^- of LMM-SH molecules (particularly, cysteine or Cys) will determine the distribution of Hg²⁺ to kidney (which is its main target) and to non-classical targets organs, such as the brain (Oliveira et al. 2017). The entrance of Hg²⁺ into the brain is proportionally small, but recent literature data have indicated the neurotoxicity of very low and environmentally relevant doses of Hg²⁺ in rodents (Mello-Carpes et al. 2013 ), which confirms data obtained with toxic doses in rodents (Peixoto et al. 2007 ;  Franciscato et al. 2009 ; Chehimi et al. 2012).

Table 1 - Affinity constants of methylmercury for important chemical groups found in biomolecules (adapted from ^aRabestein, 1978a, ^bRabestein and Bravo, 1987, using different thiol-containing molecules with the arylmercurialpara-mercurybenzenosulfonate,  and from ^cArnold et al. 1986 taking into consideration that the calculated formation constant of –Se-MeHg conjugates was 0.1 to 1.2 order greater than that of –S-MeHg). The values represent the Log of the constants.

Functional Group	Occurrence	Formation constant
Thiol/thiolate (-SH/-S-)	Cysteine, glutathione, proteins	≈14-18 a,b
Selenol/selenolate (-SeH/Se-)	Selenocysteinyl residues in selenoproteins	≈ 16-18c

Table 2. Formation constants of Hg²⁺ with some representative nucleophilic centers from biomolecules.

Functional group	Hg2+
R-S-R	≈ 6-12
R-SH	≈ 40-50
R-SeH	≈ 50-60

The approximate (≈) Log of the constants. The values were adapted  from Stricks and Kolthoff 1953; Mousavi 2011 and Liem-Nguyem et al. 2017.

We have to emphasize that what we call of binding to –SH or –SeH groups is, in fact, an exchange reaction of MeHg from MeHg-S conjugates (e.g., MeHg-cysteine or MeHg-Cys and MeHg-glutathione or MeHg-SG. conjugates) to  a free thiol/thiolate- or selenol/selenolate-group from non-target or target proteins. Thus, the interaction of MeHg with its target proteins in the brain usually involves the exchange of MeHg from low-molecular mass conjugates (LMM-S-conjugates) to a thiol or selenol group in different types of proteins (Rabenstein 1978b; Rabenstein and Fairhurst, 1975; Reid and Rabenstein et al.; 1982; Rabenstein and Reid, 1984; Arnold et al. 1986; Farina et al. 2011, 2017; Dórea et al. 2013).

Figure 2 – Binding of MeHg (CH3Hg+) to target thiol- (HMM-SH) or selenol-containing proteins (HMM-SeH). Note that, in fact, the binding of MeHg to their high molecular mass target proteins is mediated by exchange reactions of MeHg from low molecular mass thiol (LMM-SH) molecules to HMM-SH (represented by Prot-SH) or HMM-SeH (represented by Prot-SeH). The scheme also demonstrated that MeHg conjugated with one LMM-SH (here represented by either Cys₁-SHgCH₃ or G₁SHgCH₃) can exchange with others LMM-SH (here represented by Cys₂-SH or G₂SH). After one exchange reaction, the conjugated Cys₁-SHgCH₃ and G₁SHgCH₃ release the free LMM-SH molecules Cys₁-SH or G₁SH.

 

Table 3: References for the inhibition by MeHg and Hg²⁺ of SH-/seleno-proteins involved in protection against oxidative stress

Protein activity inhibited by MeHg	Exposure	Functional group likely involved in the inhibition	Organism-preparation
Glutathione peroxidase (total GPx)	in vivo	-SeH	Adult mice	Glasser et al. 2013
Total GPx	in vivo	-SeH	Adult mice	Glasser et al. 2010a
Mitochondrial total GPx	in vivo	-SeH	Adult mice	Franco et al. 2009
Total GPx	in vitro	-SeH	SH-SY5Y cells	Franco et al. 2009
GPx1 and GPx4	in vivo	-SeH	Adult mice	Zemolin et al. 2012
Total GPx	in vivo	-SeH	Adult male mice	Malagutti et al. 2009
Total GPx	in vitro	-SeH	PC12 cells	Li et al. 2008
Total GPx	in vivo	-SeH	Mice gestational exposure	Stringari et al. 2008
Total GPx	in vivo	-SeH	Adult rats	Cheng et al. 2005
Total GPx	in vitro	-SeH	Fetal Telencepalic cells from rats	Sorg et al. 1998
Total GPx	in vitro	-SeH	Mice neuroblastoma cells	Kromidas et al. 1990
Thioredoxin Reductase (TrxR)	in vivo	-SeH  and –SH	Adult mice	Zemolin et al. 2012
TrxR	in vitro	-SeH  and –SH	Adult mice	Wagner et al. 2010
TrxR	in vivo	-SeH-  and –SH	Adult rats	Dalla Corte et al. 2013
Mitochondrial total Gpx	In vivo	-SeH	Adult rat	Mori et al., 2007
Mitochondrial total Gpx	In vivo	-SeH	Adult Swiss male mice brain	Franco et al., 2009
Total brain TrxR	In vivo	-SeH and -SH	Juvenile fish (zebra-seabreams)	Branco et al. 2011 Branco et al. 2012a,b
Protein activity inhibited by Hg2+	exposure	Functional group likely involved in the inhibition	organism-preparation
Total brain TrxR	In vivo	-SeH and -SH	Juvenile fish (zebra-seabreams)	Branco et al. 2012a,b

 

Acrylamide

Acrylamide is an a,β-unsaturated (conjugated) reactive molecule, which can react with thiol (-SH) and amino (-NH2) groups in proteins  (LoPachin, 2004; LoPachin et al. 2007; 2009; 2011;  Friedman, 2003; Bent et al. 2016; Martyniuk et al.2011; LoPachin and Gavin, 2014 ). However, the rate constant for the reaction between acrylamide with thiol/thiolate groups is much lower than that for MeHg.  The rate of reaction of this compound with HMM-SH and LMM-SH is slow but can occur under physiological conditions (Tong et al. 2004; LoPachin, 2004). The inhibition of brain enzymes by acrylamide have been studied and the inhibition caused by acrylamide in some HMM-SH can be reversible  (Howland et al. 1980). Despite of this, we can infer that some targets of MeHg and acrylamide can overlap, in particular GSH,where the rate constant for MeHg and acrylamide are ≈6.0 x 10⁸ M^-1.sec^-1 and ≈0.15-2.1 x 10^-2 M^-1.sec^-1, respectively (Yousef and Demerdash, 2006; Lapadula et al. 1989; Kopańska et al. 2015). Acrylamide can also be metabolized to an epoxide intermediate (glycidamide), which can also form adducts with cysteinyl residues in HMM-SH target proteins (Bergmark et al. 1991).

 

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Due to the ubiquitous distribution of the SH-/ and seleno-proteins involved in protection against oxidative stress and inview of the strong affinity of MeHg and Hg²⁺ for thiolate and selenolate groups the binding of MeHg and Hg²⁺ to thiol and selenol groups is expected to occur in the living cells of all taxonomic groups found in the biosphere.The conservation of these effects across different vertebrate species indicates that thiol- and selenol-containing proteins (particularly, TrxR and GPx) can also be important targets of electrophilic forms of Hg(EpHg⁺ or MeHg and Hg²⁺) toxicity in fish and birds (Heinz, 1979; Carvalho et al. 2008b; Heinz et al. 2009; Xu et al.2012, 2016). The disruption of the Trx and GSH systems by MeHg and Hg²⁺have been demonstrated in zebra-sea breams  (Branco et al. 2011; 2012a,b) and salmon (Salmo salar, Bernstssen et al. 2003).  MeHg can also interfere with the Trx and GSH systems in zebrafish (Yang et al. 2007; Cambier et al. 2012).

Key Event Description

In the brain, thiol (SH)- and seleno-containing proteins involved in protection against oxidative stress are mainly located in mitochondria and in the cytoplasm of the different neural cell types (Comini, 2016; Hoppe et al. 2008; Barbosa et al. 2017; Zhu et al. 2017). The main SH-containing peptide involved in protection against oxidative stress is Glutathione (GSH), a tripeptide acting as a cofactor for the enzyme peroxidase and thus serving as an indirect antioxidant donating the electrons necessary for its decomposition of H₂O₂. The seleno-containing proteins of interest are: (i) the Glutathione Peroxidase (GPx) family, involved in detoxification of hydroperoxides; (ii) the Thioredoxin Reductase (TrxR) family, involved in the regeneration of reduced thioredoxin (Pillai et al., 2014; ), and the less studied SelH, K, S, R, W, and P selenoproteins (Pisoschi and Pop, 2015, Reeves and Hoffmann, 2009). Binding of chemicals to these proteins induces either their inactivation or favor their degradation (Farina et al. 2009; Zemolin et al. 2012). Of particular importance, the GSH/GPx and thioredoxin (Trx)/TrxR systems are the two main redox regulators of mammalian cells and the disruption of their activities can compromise cell viability (Ren et al. 2016).

How it is Measured or Detected

• Binding of Hg to thiol groups was analyzed by multiple collector inductively coupled plasma mass spectometry (Wiederhold et al., 2010).
• The binding affinity of methylmercury by various selenium-containing lingands was investigated by proton magnetic resonance spectometry (Sugiura et al., 1978; Arnold et al., 1986).
• A methylene blue-mediated enzyme biosensor was developed for the detection of mercury-glutathione complex. The biosensor was the enzyme horseradish peroxidase. The binding site of HgCl₂ with the enzyme was a cysteine residue-SH (Han et al., 2001).
• A photometric method to quantify GSH loss after reactio with organic electrophiles has also been reported (Böhme et al., 2009).
• Binding of mercuric chloride to GSH was measured by high performance liquid chromatography (HPLC)-ultraviolet (UV) detection, HPLC-inductively coupled mass spectometry and HPLC-electrospray ionization mass spectometry (Qiao et al., 2017).
•  Carvalho et al. (2011) determined the binding of MeHg or Hg2+ with purified Thioredoxin Reductase using mass spectrometry. The liquid chromatography was not applied because they have used a pure chemical system, i.e, without living cells.
• Mass spectra analysis allowed to measure the binding of mercury chloride and methylmercury to proteins of the mamallian thioredixin system, thioredoxin reductase (Trx) and thioredoxin (Trx), and of the glutaredoxin system, glutathione reductase (GR) and glutaredoxin (Grx) (Carvahlo et al., 2008)
• The methodology to detect acrylamide-cysteine adducts has been performed by liquid chromatography coupled to tandem mass spectrometry  (Martyniuk et al. 2013). In this paper the authors dected by using  a shotgun proteomic approach a total of 15,243 peptides in ACR-exposed N27 cells. And from those 15,243 peptides, 103 peptides (from 100 different proteins) contained acrylamide-cysteine adducts.

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List of Key Events in the AOP

Event: 1538: Decreased protection against oxidative stress

Short Name: Protection against oxidative stress, decreased

AOPs Including This Key Event

Stressors

Biological Context

Organ term

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Glutathione, GPx and TrxR are present in bacteria, archea, algae, and in the majority of animals, including humans.

Key Event Description

High levels reactive oxygen species (ROS) can be very damaging to cells and molecules within the cell.  As a result, the cell has important defense mechanisms to protect itself from ROS, such as glutathione and selenoenzymes.

 

Glutathione (GSH) is the most abundant low molecular mass thiol compound synthesized in cells, reaching intracellular concentrations of 1–10 mM, and is the major antioxidant and redox buffer in human cells. In fact, GSH serves as a reducing agent for ROS and other unstable molecules generated by catalytic systems, including glutathione peroxidase (GPx)(Forman, 2009).

 

Selenium plays a crucial role in antioxidant defense, as one Se atom is absolutely required at the active site of all selenoenzymes, such as GPx and thioredoxin reductase (TrxR), in the form of selenocystein (Rayman, 2000). GPx is an antioxidant enzyme that, in the presence of tripeptide GSH, adds two electrons to reduce H₂O₂ and lipid peroxides to water and lipid alcohols, respectively, while simultaneously oxidizing GSH to glutathione disulfide. The GPx/GSH system is thought to be a major defense in low-level oxidative stress, and decreased GPx activity or GSH levels may lead to the absence of adequate H₂O₂ and lipid peroxides detoxification, which may be converted to OH-radicals and lipid peroxyl radicals, respectively, by transition metals (Fe²⁺) (Brigelius-Flohe, 2013). Thioredoxin reductase (TrxR) is essential for maintaining intracellular redox status. The expression of this small (12 kDa) ubiquitous thiol-active protein is induced by ROS and an elevated serum level may indicate a state of oxidative stress. In this regard, TrxR, a NADPH-dependent lipid hydroperoxide reductase, uses NADPH to maintain the levels of reduced Trx via a mechanism similar to that used by GR to maintain GSH levels, contributing to the maintenance of thiol redox homeostasis in proteins. Importantly, the inhibition of TrxR impairs the cyclical regeneration of Trx activity, as Trx remains in the oxidized state (Bjornstedt, 1995, Zhong, 2002). Other, less studied selenoproteins, such as SelP, H, K, S, R, and W selenoproteins, play a role in antioxidant defense (Pisoschi, 2015, Reeves, 2009)

How it is Measured or Detected

• Glutathione (GSH) depletion. GSH can be measured by assaying the ratio of reduced to oxidized glutathione (GSH:GSSG) using a commercially available kit (e.g., http://www.abcam.com/gshgssg-ratio-detection-assay-kit-fluorometric-green-ab138881.html)
• Reduction of GPx activity. The activity of GPx can be measured by a colorimetric assay, using a commercially available kit (e.g., Abcam ab102530)
• Reduction of TrxR activity. The activity of TrxR can be measured by a colorimetric assay, using a commercially available kit (e.g., Abcam ab83463)
• Reduction of Selenoprotein R activity. The methionine sulfoxide reductase activity of SelR can be measured by HPLC (Chen, 2013)
• Selenoprotein P depletion. The depletion in SelP can be measured using an ELISA (e.g., MyBiosource #MBS9301054)
• Selenoprotein W depletion. The depletion in SelW can be measured using an ELISA (e.g., MyBiosource #MBS9312544)
• Selenoprotein S depletion. The depletion in SelS can be measured using an ELISA (e.g., MyBiosource #MBS9306607)
• Selenoprotein H and K depletion. The depletion in SelH and K can be measured by western blotting (Lee, 2015, Novoselov, 2007)

References

Bjornstedt, M., Hamberg, M., Kumar, S., Xue, J., Holmgre, A. (1995) Human thioredoxin reductase directly reduces lipid hydroperoxides by nadph and selenocysteine strongly stimulates the reaction via catalytically generated selenols. J Biol Chem 270, 11761-11764.

Brigelius-Flohe, R., Maiorino, M. (2013) Glutathione peroxidases. Biochim Biophys Acta 1830, 3289-3303.

Chen, P. et al. (2013) Direct Interaction of Selenoprotein R with Clusterin and Its Possible Role in Alzheimer's Disease. PLoS One 8, e66384.

Forman, H.J., Zhang, H., Rinna, A. (2009) Glutathione: overview of its protective roles, measurement, and biosynthesis. Mol Aspects Med 30, 1-12.

Lee, J.H. et al. (2015) Selenoprotein S-dependent Selenoprotein K Binding to p97(VCP) Protein Is Essential for Endoplasmic Reticulum-associated Degradation. J Biol Chem 290, 29941-29952.

Novoselov, S.V. et al. (2007) Selenoprotein H is a nucleolar thioredoxin-like protein with a unique expression pattern. J Biol Chem 282, 11960-11968.

Pisoschi, A.M., Pop, A. (2015) The role of antioxidants in the chemistry of oxidative stress: A review. Eur J Med Chem 97, 55-74.

Rayman, M.P. (2000) The importance of selenium to human health. Lancet 356, 233-241.

Reeves, M.A., Hoffmann, P.R. (2009) The human selenoproteome: recent insights into functions and regulation. Cell Mol Life Sci 66, 2457-2478.

Zhong, L., Holmgren, A. (2002) Mammalian thioredoxin reductases as hydroperoxide reductases. Methods Enzymol 347, 236-243.

Event: 1392: Oxidative Stress

Short Name: Oxidative Stress

AOPs Including This Key Event

Stressors

Biological Context

Evidence for Perturbation by Stressor

Platinum

Kruidering et al. (1997) examined the effect of platinum on pig kidneys and found that it was able to induce significant dose-dependant ROS formation within 20 minutes of treatment administration.

Aluminum

In a study of the effects of aluminum treatment on rat kidneys, Al Dera (2016) found that renal GSH, SOD, and GPx levels were significantly lower in the treated groups, while lipid peroxidation levels were significantly increased.

Cadmium

Belyaeva et al. (2012) investigated the effect of cadmium treatment on human kidney cells. They found that cadmium was the most toxic when the sample was treated with 500 μM for 3 hours (Belyaeva et al., 2012). As this study also looked at mercury, it is worth noting that mercury was more toxic than cadmium in both 30-minute and 3-hour exposures at low concentrations (10-100 μM) (Belyaeva et al., 2012).

Wang et al. (2009) conducted a study evaluating the effects of cadmium treatment on rats and found that the treated group showed a significant increase in lipid peroxidation. They also assessed the effects of lead in this study, and found that cadmium can achieve a very similar level of lipid peroxidation at a much lower concentration than lead can, implying that cadmium is a much more toxic metal to the kidney mitochondria than lead is (Wang et al., 2009). They also found that when lead and cadmium were applied together they had an additive effect in increasing lipid peroxidation content in the renal cortex of rats (Wang et al., 2009).

Jozefczak et al. (2015) treated Arabidopsis thaliana wildtype, cad2-1 mutant, and vtc1-1 mutant plants with cadmium to determine the effects of heavy metal exposure to plant mitochondria in the roots and leaves. They found that total GSH/GSG ratios were significantly increased after cadmium exposure in the leaves of all sample varieties and that GSH content was most significantly decreased for the wildtype plant roots (Jozefczak et al., 2015).

Andjelkovic et al. (2019) also found that renal lipid peroxidation was significantly increased in rats treated with 30 mg/kg of cadmium.

Mercury

Belyaeva et al. (2012) conducted a study which looked at the effects of mercury on human kidney cells, they found that mercury was the most toxic when the sample was treated with 100 μM for 30 minutes.

Buelna-Chontal et al. (2017) investigated the effects of mercury on rat kidneys and found that treated rats had higher lipid peroxidation content and reduced cytochrome c content in their kidneys.

Uranium

In Shaki et al.’s article (2012), they found rat kidney mitochondria treated with uranyl acetate caused increased formation of ROS, increased lipid peroxidation, and decreased GSH content when exposed to 100 μM or more for an hour.

Hao et al. (2014), found that human kidney proximal tubular cells (HK-2 cells) treated with uranyl nitrate for 24 hours with 500 μM showed a 3.5 times increase in ROS production compared to the control. They also found that GSH content was decreased by 50% of the control when the cells were treated with uranyl nitrate (Hao et al., 2014).

Arsenic

Bhadauria and Flora (2007) studied the effects of arsenic treatment on rat kidneys. They found that lipid peroxidation levels were increased by 1.5 times and the GSH/GSSG ratio was decreased significantly (Bhadauria and Flora, 2007).

Kharroubi et al. (2014) also investigated the effect of arsenic treatment on rat kidneys and found that lipid peroxidation was significantly increased, while GSH content was significantly decreased.

In their study of the effects of arsenic treatment on rat kidneys, Turk et al. (2019) found that lipid peroxidation was significantly increased while GSH and GPx renal content were decreased.

Silver

Miyayama et al. (2013) investigated the effects of silver treatment on human bronchial epithelial cells and found that intracellular ROS generation was increased significantly in a dose-dependant manner when treated with 0.01 to 1.0 μM of silver nitrate.

Manganese

Chtourou et al. (2012) investigated the effects of manganese treatment on rat kidneys. They found that manganese treatment caused significant increases in ROS production, lipid peroxidation, urinary H₂O₂ levels, and PCO production. They also found that intracellular GSH content was depleted in the treated group (Chtourou et al., 2012).

Nickel

Tyagi et al. (2011) conducted a study of the effects of nickel treatment on rat kidneys. They found that the treated rats showed a significant increase in kidney lipid peroxidation and a significant decrease in GSH content in the kidney tissue (Tyagi et al., 2011).

Zinc

Yeh et al. (2011) investigated the effects of zinc treatment on rat kidneys and found that treatment with 150 μM or more for 2 weeks or more caused a time- and dose-dependant increase in lipid peroxidation. They also found that renal GSH content was decreased in the rats treated with 150 μM or more for 8 weeks (Yeh et al., 2011).

It should be noted that Hao et al. (2014) found that rat kidneys exposed to lower concentrations of zinc (such as 100 μM) for short time periods (such as 1 day), showed a protective effect against toxicity induced by other heavy metals, including uranium. Soussi, Gargouri, and El Feki (2018) also found that pre-treatment with a low concentration of zinc (10 mg/kg treatment for 15 days) protected the renal cells of rats were from changes in varying oxidative stress markers, such as lipid peroxidation, protein carbonyl, and GPx levels.

nanoparticles

Huerta-García et al. (2014) conducted a study of the effects of titanium nanoparticles on human and rat brain cells. They found that both the human and rat cells showed time-dependant increases in ROS when treated with titanium nanoparticles for 2 to 6 hours (Huerta-García et al., 2014). They also found elevated lipid peroxidation that was induced by the titanium nanoparticle treatment of human and rat cell lines in a time-dependant manner (Huerta-García et al., 2014).

Liu et al. (2010) also investigated the effects of titanium nanoparticles, however they conducted their trials on rat kidney cells. They found that ROS production was significantly increased in a dose dependant manner when treated with 10 to 100 μg/mL of titanium nanoparticles (Liu et al., 2010).

Pan et al. (2009) treated human cervix carcinoma cells with gold nanoparticles (Au1.4MS) and found that intracellular ROS content in the treated cells increased in a time-dependant manner when treated with 100 μM for 6 to 48 hours. They also compared the treatment with Au1.4MS gold nanoparticles to treatment with Au15MS treatment, which are another size of gold nanoparticle (Pan et al., 2009). The Au15MS nanoparticles were much less toxic than the Au1.4MS gold nanoparticles, even when the Au15MS nanoparticles were applied at a concentration of 1000 μM (Pan et al., 2009). When investigating further markers of oxidative stress, Pan et al. (2009) found that GSH content was greatly decreased in cells treated with gold nanoparticles.

Ferreira et al. (2015) also studied the effects of gold nanoparticles. They exposed rat kidneys to GNPs-10 (10 nm particles) and GNPs-30 (30 nm particles), and found that lipid peroxidation and protein carbonyl content in the rat kidneys treated with GNPs-30 and GNPs-10, respectively, were significantly elevated.

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Oxidative stress is produced in, and can occur in, any species from bacteria through to humans.

Key Event Description

Oxidative stress is defined as an imbalance in the production of reactive oxygen species (ROS) and antioxidant defenses. High levels of oxidizing free radicals can be very damaging to cells and molecules within the cell.  As a result, the cell has important defense mechanisms to protect itself from ROS. For example, Nrf2 is a transcription factor and master regulator of the oxidative stress response. During periods of oxidative stress, Nrf2-dependent changes in gene expression are important in regaining cellular homeostasis (Nguyen, et al. 2009) and can be used as indicators of the presence of oxidative stress in the cell.

In addition to the directly damaging actions of ROS, cellular oxidative stress also changes cellular activities on a molecular level. Redox sensitive proteins have altered physiology in the presence and absence of ROS, which is caused by the oxidation of sulfhydryls to disulfides (2SH àSS) on neighboring amino acids (Antelmann and Helmann 2011). Importantly Keap1, the negative regulator of Nrf2, is regulated in this manner (Itoh, et al. 2010).

Protection against oxidative stress is relevant for all tissues and organs, although some tissues may be more susceptible. For example, the brain possesses several key physiological features, such as high O2 utilization, high polyunsaturated fatty acids content, presence of autooxidable neurotransmitters, and low antioxidant defenses as compared to other organs, that make it highly susceptible to oxidative stress (Halliwell, 2006; Emerit and al., 2004; Frauenberger et al., 2016).

How it is Measured or Detected

Oxidative Stress. Direct measurement of ROS is difficult because ROS are unstable. The presence of ROS can be assayed indirectly by measurement of cellular antioxidants, or by ROS-dependent cellular damage:

• Detection of ROS by chemiluminescence (https://www.sciencedirect.com/science/article/abs/pii/S0165993606001683)
• Detection of ROS by chemiluminescence is also described in OECD TG 495 to assess phototoxic potential.
• Glutathione (GSH) depletion. GSH can be measured by assaying the ratio of reduced to oxidized glutathione (GSH:GSSG) using a commercially available kit (e.g., http://www.abcam.com/gshgssg-ratio-detection-assay-kit-fluorometric-green-ab138881.html). 
• TBARS. Oxidative damage to lipids can be measured by assaying for lipid peroxidation using TBARS (thiobarbituric acid reactive substances) using a commercially available kit. 
• 8-oxo-dG. Oxidative damage to nucleic acids can be assayed by measuring 8-oxo-dG adducts (for which there are a number of ELISA based commercially available kits),or  HPLC, described in Chepelev et al. (Chepelev, et al. 2015).

Molecular Biology: Nrf2. Nrf2’s transcriptional activity is controlled post-translationally by oxidation of Keap1. Assay for Nrf2 activity include:

• Immunohistochemistry for increases in Nrf2 protein levels and translocation into the nucleus
• Western blot for increased Nrf2 protein levels
• Western blot of cytoplasmic and nuclear fractions to observe translocation of Nrf2 protein from the cytoplasm to the nucleus
• qPCR of Nrf2 target genes (e.g., Nqo1, Hmox-1, Gcl, Gst, Prx, TrxR, Srxn), or by commercially available pathway-based qPCR array (e.g., oxidative stress array from SABiosciences)
• Whole transcriptome profiling by microarray or RNA-seq followed by pathway analysis (in IPA, DAVID, metacore, etc.) for enrichment of the Nrf2 oxidative stress response pathway (e.g., Jackson et al. 2014)
• OECD TG422D describes an ARE-Nrf2 Luciferase test method
• In general, there are a variety of commercially available colorimetric or fluorescent kits for detecting Nrf2 activation

 

Assay Type & Measured Content	Description	Dose Range Studied	Assay Characteristics (Length / Ease of use/Accuracy)
ROS Formation in the Mitochondria assay (Shaki et al., 2012)	“The mitochondrial ROS measurement was performed flow cytometry using DCFH-DA. Briefly, isolated kidney mitochondria were incubated with UA (0, 50, 100 and 200 μM) in respiration buffer containing (0.32 mM sucrose, 10 mM Tris, 20 mM Mops, 50 μM EGTA, 0.5 mM MgCl2, 0.1 mM KH2PO4 and 5 mM sodium succinate) [32]. In the interval times of 5, 30 and 60 min following the UA addition, a sample was taken and DCFH-DA was added (final concentration, 10 μM) to mitochondria and was then incubated for 10 min. Uranyl acetate-induced ROS generation in isolated kidney mitochondria were determined through the flow cytometry (Partec, Deutschland) equipped with a 488-nm argon ion laser and supplied with the Flomax software and the signals were obtained using a 530-nm bandpass filter (FL-1 channel). Each determination is based on the mean fluorescence intensity of 15,000 counts.”	0, 50, 100 and 200 μM of Uranyl Acetate	Long/ Easy High accuracy
Mitochondrial Antioxidant Content Assay Measuring GSH content (Shaki et al., 2012)	“GSH content was determined using DTNB as the indicator and spectrophotometer method for the isolated mitochondria. The mitochondrial fractions (0.5 mg protein/ml) were incubated with various concentrations of uranyl acetate for 1 h at 30 °C and then 0.1 ml of mitochondrial fractions was added into 0.1 mol/l of phosphate buffers and 0.04% DTNB in a total volume of 3.0 ml (pH 7.4). The developed yellow color was read at 412 nm on a spectrophotometer (UV-1601 PC, Shimadzu, Japan). GSH content was expressed as μg/mg protein.”	0, 50, 100, or 200 μM Uranyl Acetate
H2O2 Production Assay Measuring H2O2 Production in isolated mitochondria (Heyno et al., 2008)	“Effect of CdCl2 and antimycin A (AA) on H2O2 production in isolated mitochondria from potato. H2O2 production was measured as scopoletin oxidation. Mitochondria were incubated for 30 min in the measuring buffer (see the Materials and Methods) containing 0.5 mM succinate as an electron donor and 0.2 µM mesoxalonitrile 3‐chlorophenylhydrazone (CCCP) as an uncoupler, 10 U horseradish peroxidase and 5 µM scopoletin.” (	0, 10, 30  μM Cd2+ 2  μM antimycin A
Flow Cytometry ROS & Cell Viability (Kruiderig et al., 1997)	“For determination of ROS, samples taken at the indicated time points were directly transferred to FACScan tubes. Dih123 (10 mM, final concentration) was added and cells were incubated at 37°C in a humidified atmosphere (95% air/5% CO2) for 10 min. At t 5 9, propidium iodide (10 mM, final concentration) was added, and cells were analyzed by flow cytometry at 60 ml/min. Nonfluorescent Dih123 is cleaved by ROS to fluorescent R123 and detected by the FL1 detector as described above for Dc (Van de Water 1995)”		Strong/easy medium
DCFH-DA Assay Detection of hydrogen peroxide production (Yuan et al., 2016)	Intracellular ROS production was measured using DCFH-DA as a probe. Hydrogen peroxide oxidizes DCFH to DCF. The probe is hydrolyzed intracellularly to DCFH carboxylate anion. No direct reaction with H2O2 to form fluorescent production.	0-400 µM	Long/ Easy High accuracy
H2-DCF-DA Assay Detection of superoxide production (Thiebault et al., 2007)	This dye is a stable nonpolar compound which diffuses readily into the cells and yields H2-DCF. Intracellular OH or ONOO- react with H2-DCF when cells contain peroxides, to form the highly fluorescent compound DCF, which effluxes the cell. Fluorescence intensity of DCF is measured using a fluorescence spectrophotometer.	0–600 µM	Long/ Easy High accuracy
CM-H2DCFDA Assay	**Come back and explain the flow cytometry determination of oxidative stress from Pan et al. (2009)**

References

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Andjelkovic, M., Djordjevic, A. B., Antonijevic, E., Antonijevic, B., Stanic, M., Kotur-Stevuljevic, J., . . . Bulat, Z. (2019). Toxic effect of acute cadmium and lead exposure in rat blood, liver, and kidney. International Journal of Environmental Research and Public Health, 16, 247. doi:10.3390/ijerph16020274

Antelmann, H., Helmann, J.D., 2011. Thiol-based redox switches and gene regulation. Antioxid. Redox Signal. 14, 1049-1063.

Belyaeva, E. A., Sokolova, T. V., Emelyanova, L. V., & Zakharova, I. O. (2012). Mitochondrial electron transport chain in heavy metal-induced neurotoxicity : Effects of cadmium , mercury , and copper. Thescientificworld, 2012, 1-14. doi:10.1100/2012/136063

Bhadauria, S., & Flora, S. J. S. (2007). Response of arsenic-induced oxidative stress, DNA damage, and metal imbalance to combined administration of DMSA and monoisoamyl-DMSA during chronic arsenic poisoning in rats. Cell Biol Toxicol, 23, 91-104. doi:10.1007/s10565-006-0135-8

Buelna-Chontal, M., Franco, M., Hernandez-Esquivel, L., Pavon, N., Rodriguez-Zalvala, J. S., Correa, F., . . . Chavez, E. (2017). CDP-choline circumvents mercury-induced mitochondrial damage and renal dysfunction. Cell Biology International, 41, 1356-1366. doi:10.1002/cbin.10871

Chepelev, N.L., Kennedy, D.A., Gagne, R., White, T., Long, A.S., Yauk, C.L., White, P.A., 2015. HPLC Measurement of the DNA Oxidation Biomarker, 8-oxo-7,8-dihydro-2'-deoxyguanosine, in Cultured Cells and Animal Tissues. J. Vis. Exp. (102):e52697. doi, e52697.

Chtourou, Y., Garoui, E. m., Boudawara, T., & Zeghal, N. (2012). Protective role of silymarin against manganese-induced nephrotoxicity and oxidative stress in rat. Environ Toxicol, 29, 1147-1154. doi:10.1002/tox.21845

Emerit, J., Edeas, M., Bricaire, F., 2004. Neurodegenerative diseases and oxidative stress. Biomed. Pharmacotherapy. 58(1): 39-46.

Ferreira, G. K., Cardoso, E., Vuolo, F. S., Michels, M., Zanoni, E. T., Carvalho-Silva, M., . . . Paula, M. M. S. (2015). Gold nanoparticles alter parameters of oxidative stress and
energy metabolism in organs of adult rats. Biochem. Cell Biol., 93, 548-557. doi:10.1139/bcb-2015-0030

Frauenberger, E.A., Scola, G., Laliberté, V.L.M., Duong, A., Andreazza, A.C., 2015. Redox modulations, Antioxidants, and Neuropsychitrica Disorders. Ox. Med. Cell. Longevity. Vol. 2016, Article ID 4729192.

Halliwell, B., 2006. Oxidative stress and neurodegeneration: where are we now? J. Neurochem. 97(6):1634-1658.

Heyno, E., Klose, C., & Krieger-Liszkay, A. (2008). Origin of cadmium-induced reactive oxygen species production: Mitochondrial electron transfer versus plasma membrane NADPH oxidase. New Phytologist, 179, 687-699. doi:10.1111/j.1469-8137.2008.02512.x

Hao Y, Ren J, Liu C, Li H, Liu J, Yang Z, Li R, Su Y. (2014). Zinc Protects Human Kidney Cells from Depleted Uraniuminduced Apoptosis. Basic Clin Pharmacol Toxicol. 114(3):271-80. doi: 10.1111/bcpt.12167.

Huerta-García, E., Perez-Arizti, J. A., Marquez-Ramirez, S. G., Delgado-Buenrostro, N. L., Chirino, Y. I., Iglesias, G. G., & Lopez-Marure, R. (2014). Titanium dioxide nanoparticles induce strong oxidative stress and mitochondrial damage in glial cells. Free Radical Biology and Medicine, 73, 84-94. doi:10.1016/j.freeradbiomed.2014.04.026

Itoh, K., Mimura, J., Yamamoto, M., 2010. Discovery of the negative regulator of Nrf2, Keap1: a historical overview. Antioxid. Redox Signal. 13, 1665-1678.

Jackson, A.F., Williams, A., Recio, L., Waters, M.D., Lambert, I.B., Yauk, C.L., 2014. Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan. Toxicol. Applied Pharmacol.274, 63-77.

Jozefczak, M., Bohler, S., Schat, H., Horemans, N., Guisez, Y., Remans, T., . . . Cuypers, A. (2015). Both the concentration and redox state of glutathione and ascorbate influence the sensitivity of arabidopsis to cadmium. Annals of Botany, 116(4), 601-612. doi:10.1093/aob/mcv075

Kharroubi, W., Dhibi, M., Mekni, M., Haouas, Z., Chreif, I., Neffati, F., . . . Sakly, R. (2014). Sodium arsenate induce changes in fatty acids profiles and oxidative damage in kidney of rats. Environ Sci Pollut Res, 21, 12040-12049. doi:10.1007/s11356-014-3142-y

Kruidering, M., Van De Water, B., De Heer, E., Mulder, G. J., & Nagelkerke, J. F. (1997). Cisplatin-induced nephrotoxicity in porcine proximal tubular cells: Mitochondrial dysfunction by inhibition of complexes I to IV of the respiratory chain. The Journal of Pharmacology and Experimental Therapeutics, 280(2), 638-649.

Liu, S., Xu, L., Zhang, T., Ren, G., & Yang, Z. (2010). Oxidative stress and apoptosis induced by nanosized titanium dioxide in PC12 cells. Toxicology, 267, 172-177. doi:10.1016/j.tox.2009.11.012

Miyayama, T., Arai, Y., Suzuki, N., & Hirano, S. (2013). Mitochondrial electron transport is inhibited by disappearance of metallothionein in human bronchial epithelial cells follwoing exposure to silver nitrate. Toxicology, 305, 20-29. doi:10.1016/j.tox.2013.01.004

Nguyen, T., Nioi, P., Pickett, C.B., 2009. The Nrf2-antioxidant response element signaling pathway and its activation by oxidative stress. J. Biol. Chem. 284, 13291-13295.

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OECD (2019), Test No. 495: Ros (Reactive Oxygen Species) Assay for Photoreactivity, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris, https://doi.org/10.1787/915e00ac-en.

Pan, Y., Leifer, A., Ruau, D., Neuss, S., Bonrnemann, J., Schmid, G., . . . Jahnen-Dechent, W. (2009). Gold nanoparticles of diameter 1.4 nm trigger necrosis by oxidative stress and mitochondrial damage. Small, 5(8), 2067-2076. doi:10.1002/smll.200900466

Shaki, F., Hosseini, M. J., Ghazi-Khansari, M., & Pourahmad, J. (2012). Toxicity of depleted uranium on isolated rat kidney mitochondria. Biochimica Et Biophysica Acta - General Subjects, 1820(12), 1940-1950. doi:10.1016/j.bbagen.2012.08.015

Soussi A, Gargouri M, El Feki A. (2018). Effects of co-exposure to lead and zinc on redox status, kidney variables, and histopathology in adult albino rats. Toxicol Ind Health. 34(7):469-480. doi: 10.1177/0748233718770293.

Thiébault, C., Carrière, M., Milgram, S., Simon, A., Avoscan, L., & Gouget, B. (2007). Uranium induces apoptosis and is genotoxic to normal rat kidney (NRK-52E) proximal cells. Toxicological Sciences : An Official Journal of the Society of Toxicology, 98(2), 479-487. doi:kfm130 [pii]

Turk, E., Kandemir, F. M., Yildirim, S., Caglayan, C., Kucukler, S., & Kuzu, M. (2019). Protective effect of hesperidin on sodium arsenite-induced nephrotoxicity and hepatotoxicity in rats. Biological Trace Element Research, 189, 95-108. doi:10.1007/s12011-018-1443-6

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Yuan, Y., Zheng, J., Zhao, T., Tang, X., & Hu, N. (2016). Uranium-induced rat kidney cell cytotoxicity is mediated by decreased endogenous hydrogen sulfide (H2S) generation involved in reduced Nrf2 levels. Toxicology Research, 5(2), 660-673. doi:10.1039/C5TX00432B

Event: 1488: Glutamate dyshomeostasis

Short Name: Glutamate dyshomeostasis

AOPs Including This Key Event

Biological Context

Cell term

Organ term

Domain of Applicability

The involvement of glutamate in learning and memory processes is well conserved in all taxa, from invertebrates (ex. Drosophila) to vertebrates (Fagnou and Tuchek, 1995).

Key Event Description

Glutamate (Glu) is the major excitatory neurotransmitter in the mammalian central nervous system (CNS), where it plays major roles in multiple aspects, such as development, learning, memory and response to injury (Featherstone, 2010). However, it is well recognized that Glu at high concentrations at the synaptic cleft acts as a toxin, inducing neuronal injury and death (Meldrum, 2000; Ozawa et al., 1998) secondary to activation of glumatergic N-methyl D-aspartate (NMDA) receptors and Ca²⁺ influx. Glu dyshomeostasis is a consequence of perturbation of  astrocyte/neuron interactions and the transport of this amino acid, as will be discussed below.

Astrocytes are critically involved in neuronal function and survival, as they produce neurotrophic factors, such as brain-derived neurotrophic factor (BDNF) and glia-derived neurotrophic factor (GDNF), as well as express two main glutamate transporters responsible for the removal of excessive Glu from the synaptic clefts (Chai et al., 2013; Sheldon et al., 2007). Glutamate is the major excitatory neurotransmitter in the CNS, playing a major role in memory and cognitive function (Platt, 1997), and Glu transporters as such prevent the overstimulation of post-synaptic glutamate receptors that lead to excitotoxic neuronal injury (Sattler et al., 2001; Dobble, 1999). Among the five subtypes of Glu transporters identified, glutamate aspartate transporter (GLAST) and Glu transporter-1 (GLT-1) [excitatory amino acid transporter (EAAT) 1 and 2 in humans, respectively], are predominantly expressed in astrocytes. They are responsible for the uptake of excess glutamate from the extracellular space (Furuta et al., 1997; Lehre et al., 1995; Tanaka, 2000), supported by the fact that knockdown of either GLT-1 or GLAST in mice increases extracellular glutamate levels, leading to excitotoxicity related neurodegeneration and progressive paralysis (Bristol and Rothstein, 1996). In the adult brain, EAAT2 accounts for >90% of extracellular glutamate clearance (Danbolt, 2001; Kim et al., 2011; Rothstein et al., 1995), and genetic deletion of both alleles of GLT-1 in mice leads to the development of lethal seizures (Rothstein et al., 1996). On the other hand, EAAT1-3 play a major role during human brain development, in particular in corticogenesis, where they are expressed in proliferative zones and in radial glia, and alterations of Glu transporters contributes to disorganized cortex seen in migration disorders (Furuta et al., 2005;Regan et al., 2007). Indeed, disruption of glutamate signaling is thought to be part of the etiology underlying some neurodevelopmental disorders such as autism and schizophrenia (Chiocchetti et al., 2014; Schwartz et al., 2012). Genes involved in glutamatergic pathways, affecting receptor signalling, metabolism and transport, were enriched in genetic variants associated with autism spectrum disorders (Chiocchetti et al, 2014).

Extracellular Glu released by neurons is taken up by astrocytes, which is converted into glutamine (Gln) by glutamine synthetase (GS), a thiol-containing enzyme (cf MIE, Binding to SH-/seleno containing proteins). Intercellular compartmentation of Gln and Glu, the so-called Gln/Glu-GABA cycle (GGC), is critical for optimal CNS function.13C NMR studies have demonstrated that the ratio of Gln/Glu is extremely high and increases with brain activity (Shen et al., 1999). Thus the GGC gives rise to the amino acid neurotransmitters Glu and GABA via dynamic astrocyte neuron interactions. Glu released at synaptic terminals is taken up by surrounding astrocytes via GLT-1 and GLAST (Rothstein et al., 1994; 1996). A small proportion of the astrocytic formed Gln via a reaction mediated by GS is transported into the extracellular space by Gln carriers, with a predominant role for System N/A transporter (SNAT3), which belongs to the bidirectional transporter System N (Chaudhry et al., 2002).

In addition to System N, release of Gln from astrocytes is mediated by other transport systems, including Systems L (LAT2) and ASC (ASCT2). Extracellular Gln is taken up into GABAergic and Glu-ergic neurons by the unidirectional System A transporters SNAT1 (Melone et al., 2004) and SNAT2 (Grewal et al., 2009). Once in neurons, Gln is converted to Glu by the mitochondrial enzyme phosphate-activated glutaminase (Kvamme et al., 2001). Additionally, Glu is packaged into synaptic vesicles by the vesicular VGLUT transporter (Bellocchio et al., 1998), released into the extracellular space and taken up by astrocytes where it is converted back to Gln by GS, thus completing the GGC (Fig. 1).

Figure 1: Schematic representation of Glu and Gln transport systems related to the GGC. From Sidorik-Wegrzynowicz and Aschner, 2013)

How it is Measured or Detected

 L-Glu transporter activities can be quantified by direct or indirect methods:

• Direct quantification, L-Glu transporter activities are determined by the amount of ³H-labeled ligand (L-Glu or D-aspartate) taken up by the cells (Primary mixed astrocyte and neuron cultures [Perez-Dominguez et al., 2014]; primary astrocyte cultures [Matos et al., 2008; Li et al., 2006; Hazell et al., 2003]; Xenopus laevis oocyte overexpressing the L-Glu transporter subtype of interest [Sogaard et al., 2013; Trotti et al., 2001]; transfected HeLa cells [Zhang and Qu, 2012]) or tissues (ex. Hippocampal tissue [Selkirk et al., 2005])
• Indirect quantification, L-Glu transporter activities are determined by the L-Glu residue in the medium or buffer after incubation with cells expressing the different L-Glu transporters (Brison et al., 2014; Xin et al., 2019; Jin et al., 2015; Gu et al., 2014; Abe et alt.2000], .).
• The transport activity of the different L-Glu transporter subtypes should be determined in the presence of the appropriate inhibitors as shown in the table 1. Ex: The glutamate uptake activity via EAAT1 can be determined in the presence of dihydrokainic acid (DHK), a specific inhibitor for GLT-1, as described in Mutkus et al. (2005). Expression level of L-Glu transporter subtypes should be confirmed using Western blotting or immunocytochemistry.  It is interesting to note that pure astrocyte culture express only GLAST (EAAT1) (Danbolt et al., 2016); whereas  In mixed astrocyte and neuron cultures, GLAST (EAAT1) and GLT-1(EAAT2) are expressed (Danbolt et al., 2016). The expression of GLT1 (EAAT2) is suggested to be induced by soluble factors (Gegelashvili et al., 1997, 2000; Plachez et al., 2000; Martinez-Lozada et al., 2016). 
• The L-Glu concentrations in medium or in incubation buffer can be quantified by commercially-available kits quantifying the final products of the redox reaction in which L-Glu is a substrate.   

The kits using colorimetric final products (OD=450 nm):

• Glutamate Assay Kit from Abcam (ab83389)
• Glutamate Colorimetric Assay Kit from BioVision (K629)
• Glutamate Assay Kit from Merck (MAK004)  

The kit using the bioluminescent metabolite:

• Glutamate-Glo™ Assay from Promega (J7021)

Table 1: Summary based on the reviews of Murphy-Royal et al., 2017 and Pajarillo et al., 2019, with some modification.  Concerning the physiological functions of each EAAT subtype, see the review by Danbolt (Danbolt, 2001).

Human	Rodent	Distribution	Non-specific inhibitors	Specific inhibitors
EAAT1	GLAST	High expression in astrocytes at developmental stage	DL-threo-b-benzyloxyaspartate (TBOA) and its variants (e.g. PMBTBOA and TFB-TBOA) (Bridges et al.,1999; Lebrun et al., 1997; Shimamoto et al., 1998; Shimamoto, 2008)	UCPH101 (Erichsen et al., 2010)
EAAT2	GLT-1	Astrocytes (>90% adult CNS L-Glu uptake) Neuronal terminals (hippocampus, cortex, still controversial)		WAY213613 (Dunlop et al., 2005) DHK (Arriza et al., 1994; Bridges et al., 1999
EAAT3	EAAC1	Neurons. Especially high in hippocampal neurons. Also function as Cys transporter (Watts et al., 2014)
EAAT4	EAAT4	Purkinje cells in the cerebellum
EAAT5	EAAT5	Retina. Very weak in the CNS

 

 

References

Abe K, Abe Y, Saito H. Evaluation of L-glutamate clearance capacity of cultured rat cortical astrocytes. Biol Pharm Bull. 2000 Feb;23(2):204-7.

Arriza JL, Fairman WA, Wadiche JI, Murdoch GH, Kavanaugh MP, Amara SG (1994) Functional comparisons of three glutamate transporter subtypes cloned from human motor cortex. J Neurosci 14(9):5559–5569.

Bellocchio EE, Hu H, Pohorille A, Chan J, Pickel VM, Edwards RH. The localization of the brain-specific inorganic phosphate transporter suggests a specific presynaptic role in glutamatergic transmission. J Neurosci. 1998;18:8648–59.

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Grewal S, Defamie N, Zhang X, et al. SNAT2 amino acid transporter is regulated by amino acids of the SLC6 gamma-aminobutyric acid transporter subfamily in neocortical neurons and may play no role in delivering glutamine for glutamatergic transmission. J Biol Chem. 2009;284:11224–36.

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Hazell AS, Pannunzio P, Rama Rao KV, Pow DV, Rambaldi A. Thiamine deficiency results in downregulation of the GLAST glutamate transporter in cultured astrocytes. Glia. 2003 Aug;43(2):175-84.

Jin LW, Horiuchi M, Wulff H, Liu XB, Cortopassi GA, Erickson JD, Maezawa I., Rett Syndrome Microglia: A Mechanism for Mitochondrial Dysfunction and Neurotoxicity. J. Neurosci., Feb 2015; 35: 2516 – 2529

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Kvamme E, Torgner IA, Roberg B. Kinetics and localization of brain phosphate activated glutaminase. J Neurosci Res. 2001;66:951–8.

Lebrun B, Sakaitani M, Shimamoto K, Yasudakamatani Y, Nakajima T, New beta-hydroxyaspartate derivatives are competitive blockers for the bovine glutamate/aspartate transporter. 1997; J. Biol. Chem. 272, 20336e20339.

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Martinez-Lozada Z, Guillem AM, Robinson MB. Transcriptional Regulation of Glutamate Transporters: From Extracellular Signals to Transcription Factors. Adv Pharmacol. 2016;76:103-45.

Meldrum, B. S. (2000). "Glutamate as a neurotransmitter in the brain: review of physiology and pathology." J Nutr 130(4S Suppl): 1007S-1015S.

Melone M, Quagliano F, Barbaresi P, Varoqui H, Erickson JD, Conti F. Localization of the glutamine transporter SNAT1 in rat cerebral cortex and neighboring structures, with a note on its localization in human cortex. Cereb Cortex. 2004;14:562–74.

Matos M, Augusto E, Oliveira CR, Agostinho P. Amyloid-beta peptide decreases glutamate uptake in cultured astrocytes: involvement of oxidative stress and mitogen-activated protein kinase cascades. Neuroscience. 2008 Oct 28;156(4):898-910.

Murphy-Royal C, Dupuis J, Groc L, Oliet SHR. Astroglial glutamate transporters in the brain: Regulating neurotransmitter homeostasis and synaptic transmission. J Neurosci Res. 2017 Nov;95(11):2140-2151.

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Plachez C, Danbolt NC, Recasens M. Transient expression of the glial glutamate transporters GLAST and GLT in hippocampal neurons in primary culture. 2000; J. Neurosci. Res. 59, 587e593.

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Event: 55: Cell injury/death

Short Name: Cell injury/death

Key Event Component

AOPs Including This Key Event

Biological Context

Cell term

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Cell death is an universal event occurring in cells of any species (Fink and Cookson,2005).

Key Event Description

Two types of cell death can be distinguished by morphological features, although it is likely that these are two ends of a spectrum with possible intermediate forms. Apoptosis involves shrinkage, nuclear disassembly, and fragmentation of the cell into discrete bodies with intact plasma membranes. These are rapidly phagocytosed by neighbouring cells. An important feature of apoptosis is the requirement for adenosine triphosphate (ATP) to initiate the execution phase. In contrast, necrotic cell death is characterized by cell swelling and lysis. This is usually a consequence of profound loss of mitochondrial function and resultant ATP depletion, leading to loss of ion homeostasis, including volume regulation, and increased intracellular Ca2+. The latter activates a number of nonspecific hydrolases (i.e., proteases, nucleases, and phospholipases) as well as calcium dependent kinases. Activation of calpain I, the Ca2+-dependent cysteine protease cleaves the death-promoting Bcl-2 family members Bid and Bax which translocate to mitochondrial membranes, resulting in release of truncated apoptosis-inducing factor (tAIF), cytochrome c and endonuclease in the case of Bid and cytocrome c in the case of Bax. tAIF translocates to cell nuclei, and together with cyclophilin A and phosphorylated histone H2AX (γH2AX) is responsible for DNA cleavage, a feature of programmed necrosis. Activated calpain I has also been shown to cleave the plasma membrane Na+–Ca2+ exchanger, which leads to build-up of intracellular Ca2+, which is the source of additional increased intracellular Ca2+. Cytochrome c in cellular apoptosis is a component of the apoptosome.

DNA damage activates nuclear poly(ADP-ribose) polymerase-1(PARP-1), a DNA repair enzyme. PARP-1 forms poly(ADP-ribose) polymers, to repair DNA, but when DNA damage is extensive, PAR accumulates, exits cell nuclei and travels to mitochondrial membranes, where it, like calpain I, is involved in AIF release from mitochondria. A fundamental distinction between necrosis and apoptosis is the loss of plasma membrane integrity; this is integral to the former but not the latter. As a consequence, lytic release of cellular constituents promotes a local inflammatory reaction, whereas the rapid removal of apoptotic bodies minimizes such a reaction. The distinction between the two modes of death is easily accomplished in vitro but not in vivo. Thus, although claims that certain drugs induce apoptosis have been made, these are relatively unconvincing. DNA fragmentation can occur in necrosis, leading to positive TUNEL staining (see explanation below). Conversely, when apoptosis is massive, it can exceed the capacity for rapid phagocytosis, resulting in the eventual appearance of secondary necrosis.

Two alternative pathways - either extrinsic (receptor-mediated) or intrinsic (mitochondria-mediated) - lead to apoptotic cell death. The initiation of cell death begins either at the plasma membrane with the binding of TNF or FasL to their cognate receptors or within the cell. The latter is due to the occurrence of intracellular stress in the form of biochemical events such as oxidative stress, redox changes, covalent binding, lipid peroxidation, and consequent functional effects on mitochondria, endoplasmic reticulum, microtubules, cytoskeleton, or DNA. The intrinsic mitochondrial pathway involves the initiator, caspase-9, which, when activated, forms an “apoptosome” in the cytosol, together with cytochrome c, which translocates from mitochondria, Apaf-1 and dATP. The apoptosome activates caspase-3, the central effector caspase, which in turn activates downstream factors that are responsible for the apoptotic death of a cell (Fujikawa, 2015). Intracellular stress either directly affects mitochondria or can lead to effects on other organelles, which then send signals to the mitochondria to recruit participation in the death process (Fujikawa, 2015; Malhi et al., 2010). Constitutively expressed nitric oxide synthase (nNOS) is a Ca2+-dependent cytosolic enzyme that forms nitric oxide (NO) from L-arginine, and NO reacts with the free radical such as superoxide (O2−) to form the very toxic free radical peroxynitrite (ONOO−). Free radicals such as ONOO−, O2 − and hydroxyl radical (OH−) damage cellular membranes and intracellular proteins, enzymes and DNA (Fujikawa, 2015; Malhi et al., 2010; Kaplowitz, 2002; Kroemer et al., 2009).  

How it is Measured or Detected

 

Necrosis:

Lactate dehydrogenase (LDH) is a soluble cytoplasmic enzyme that is present in almost all cells and is released into extracellular space when the plasma membrane is damaged. To detect the leakage of LDH into cell culture medium, a tetrazolium salt is used in this assay. In the first step, LDH produces reduced nicotinamide adenine dinucleotide (NADH) when it catalyzes the oxidation of lactate to pyruvate. In the second step, a tetrazolium salt is converted to a colored formazan product using newly synthesized NADH in the presence of an electron acceptor. The amount of formazan product can be colorimetrically quantified by standard spectroscopy. Because of the linearity of the assay, it can be used to enumerate the percentage of necrotic cells in a sample (Chan et al., 2013). 

The MTT assay is a colorimetric assay for assessing cell viability. NAD(P)H-dependent cellular oxidoreductase enzymes may reflect the number of viable cells present. These enzymes are capable of reducing the tetrazolium dye MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide to its insoluble formazan, which has a purple color. Other closely related tetrazolium dyes include XTT, MTS and the WSTs. Tetrazolium dye assays can also be used to measure cytotoxicity (loss of viable cells) or cytostatic activity (shift from proliferation to quiescence) of potential medicinal agents and toxic materials. MTT assays are usually done in the dark since the MTT reagent is sensitive to light (Berridgeet al.,2005).

Propidium iodide (PI) is an intercalating agent and a fluorescent molecule used to stain necrotic cells. It is cell membrane impermeant so it stains only those cells where the cell membrane is destroyed. When PI is bound to nucleic acids, the fluorescence excitation maximum is 535 nm and the emission maximum is 617 nm (Moore et al.,1998)

Alamar Blue (resazurin) is a fluorescent dye. The oxidized blue non fluorescent Alamar blue is reduced to a pink fluorescent dye in the medium by cell activity (O'Brien et al., 2000) (12).

Neutral red uptake, which is based on the ability of viable cells to incorporate and bind the supravital dye neutral red in lysosomes (Repetto et al., 2008)(13). Moreover, quantification of ATP, signaling the presence of metabolically active cells, can be performed (CellTiter-Glo; Promega).

ATP assay: Quantification of ATP, signaling the presence of metabolically active cells (CellTiter-Glo; Promega).

Apoptosis:

TUNEL is a common method for detecting DNA fragmentation that results from apoptotic signalling cascades. The assay relies on the presence of nicks in the DNA which can be identified by terminal deoxynucleotidyl transferase or TdT, an enzyme that will catalyze the addition of dUTPs that are secondarily labeled with a marker. It may also label cells that have suffered severe DNA damage.

Caspase activity assays measured by fluorescence. During apoptosis, mainly caspase-3 and -7 cleave PARP to yield an 85 kDa and a 25 kDa fragment. PARP cleavage is considered to be one of the classical characteristics of apoptosis. Antibodies to the 85 kDa fragment of cleaved PARP or to caspase-3 both serve as markers for apoptotic cells that can be monitored using immunofluorescence (Li, Peng et al., 2004).

Hoechst 33342 staining: Hoechst dyes are cell-permeable and bind to DNA in live or fixed cells. Therefore, these stains are often called supravital, which means that cells survive a treatment with these compounds. The stained, condensed or fragmented DNA is a marker of apoptosis (Loo, 2002; Kubbies and Rabinovitch, 1983). 

Acridine Orange/Ethidium Bromide staining is used to visualize nuclear changes and apoptotic body formation that are characteristic of apoptosis. Cells are viewed under a fluorescence microscope and counted to quantify apoptosis.

References

• Fujikawa, D.G. (2015), The role of excitotoxic programmed necrosis in acute brain injury, Comput Struct Biotechnol J, vol. 13, pp. 212-221.
• Malhi, H. et al. (2010), Hepatocyte death: a clear and present danger, Physiol Rev, vol. 90, no. 3, pp. 1165-1194.
• Kaplowitz, N. (2002), Biochemical and Cellular Mechanisms of Toxic Liver Injury, Semin Liver Dis, vol. 22, no. 2, http://www.medscape.com/viewarticle/433631 (accessed on 20 January 2016).
• Kroemer, G. et al., (2009), Classification of cell death: recommendations of the Nomenclature Committee on Cell Death, Cell Death Differ, vol. 16, no. 1, pp. 3-11.
• Chan, F.K., K. Moriwaki and M.J. De Rosa (2013), Detection of necrosis by release of lactate dehydrogenase (LDH) activity, Methods Mol Biol, vol. 979, pp. 65–70.
• Berridge, M.V., P.M. Herst and A.S. Tan (2005), Tetrazolium dyes as tools in cell biology: new insights into their cellular reduction. Biotechnology Annual Review, vol. 11, pp 127-152.
• Moore, A, et al.(1998), Simultaneous measurement of cell cycle and apoptotic cell death,Methods Cell Biol, vol. 57, pp. 265–278.
• Li, Peng et al. (2004), Mitochondrial activation of apoptosis, Cell, vol. 116, no. 2 Suppl,pp. S57-59, 2 p following S59.
• Loo, D.T. (2002), TUNEL Assay an overview of techniques, Methods in Molecular Biology, vol. 203: In Situ Detection of DNA Damage, chapter 2, Didenko VV (ed.), Humana Press Inc.
• Kubbies, M. and P.S. Rabinovitch (1983), Flow cytometric analysis of factors which influence the BrdUrd-Hoechst quenching effect in cultivated human fibroblasts and lymphocytes, Cytometry, vol. 3, no. 4, pp. 276–281.
• Fink, S.L. and B.T. Cookson (2005), Apoptosis, pyroptosis, and necrosis: mechanistic description of dead and dying eukaryotic cells, Infect Immun, vol. 73, no. 4, pp.1907-1916.
• O'Brien J, Wilson I, Orton T, Pognan F. 2000. Investigation of the Alamar Blue (resazurin) fluorescent dye for the assessment of mammalian cell cytotoxicity. European journal of biochemistry / FEBS 267(17): 5421-5426.
• Repetto G, del Peso A, Zurita JL. 2008. Neutral red uptake assay for the estimation of cell viability/cytotoxicity. Nature protocols 3(7): 1125-1131.

Event: 188: Neuroinflammation

Short Name: Neuroinflammation

Key Event Component

AOPs Including This Key Event

Stressors

Biological Context

Organ term

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Neuroinflammation is observed in human, monkey, rat, mouse, and zebrafish, in association with neurodegeneration or following toxicant exposure, or SARS-CoV-2 and other coronavirus infection. Some references (non-exhaustive list) are given below for illustration:

Human: Vennetti et al., 2006

Monkey (Macaca fascicularis): Charleston et al., 1994, 1996

Rat: Little et al., 2012; Zurich et al., 2002; Eskes et al., 2002

Mouse: Liu et al., 2012

Zebrafish: Xu et al., 2014.

Key Event Description

Neuroinflammation or brain inflammation differs from peripheral inflammation in that the vascular response and the role of peripheral bone marrow-derived cells are less conspicuous. The most easily detectable feature of neuroinflammation is activation of microglial cells and astrocytes. It is evidenced by changes in shape, increased expression of certain antigens, and accumulation and proliferation of the glial cells in affected regions (Aschner, 1998; Graeber & Streit, 1990; Monnet-Tschudi et al, 2007; Streit et al, 1999; Kraft and Harry, 2011; Claycomb et al., 2013). Upon stimulation by cytokines or inflammogens (e.g. from pathogens or from damaged neurons), both glial cell types activate inflammatory signalling pathways, which result in increased expression and/or release of inflammatory mediators such as cytokines, eicosanoids, and metalloproteinases (Dong & Benveniste, 2001), as well as in the production of reactive oxygen (ROS) and nitrogen species (RNS) (Brown & Bal-Price, 2003). Different types of activation states are possible for microglia and astrocytes, resulting in pro-inflammatory or anti-inflammatory signalling and other cellular functions (such as phagocytosis) (Streit et al., 1999; Nakajima and Kohsaka, 2004).

Therefore, neuroinflammation can have both neuroprotective/neuroreparative and neurodegenerative consequences (Carson et al., 2006 ; Monnet-Tschudi et al, 2007; Aguzzi et al., 2013 ; Glass et al., 2010). Under normal physiological conditions, microglial cells scan the nervous system for neuronal integrity (Nimmerjahn et al, 2005) and for invading pathogens (Aloisi, 2001; Kreutzberg, 1995; Kreutzberg, 1996; Rivest, 2009). They are the first type of cell activated (first line of defence), and can subsequently induce astrocyte activation (Falsig, 2008). Two distinct states of microglial activation have been described (Gordon, 2003; Kigerl et al, 2009; Maresz et al, 2008; Mosser & Edwards, 2008; Perego et al; Ponomarev et al, 2005; Moehle and West, 2015): The M1 state is classically triggered by interferon-gamma and/or other pro-inflammatory cytokines, and this state is characterized by increased expression of integrin alpha M (Itgam) and CD86, as well as the release of pro-inflammatory cytokines (TNF-alpha, IL-1beta, IL-6), and it is mostly associated with neurodegeneration. The M2 state is triggered by IL-4 and IL-13 (Maresz et al, 2008; Perego et al, 2011; Ponomarev et al, 2007) and induces the expression of mannose receptor 1 (MRC1), arginase1 (Arg 1) and Ym1/2; it is involved in repair processes. The activation of astrocytes by microglia-derived cytokines or TLR agonists resembles the microglial M1 state (Falsig 2006). Although classification of the M1/M2 polarization of microglial cells may be considered as a simplification of authentic microglial reaction states (Ransohoff, 2016), a similar polarization of reactive astrocytes has been descibed recently Liddlelow et al., 2017): Interleukin-1 alpha (IL-1alpha), TNF and subcomponent q (C1q) released by activated microglial cells induce A1-reactive astrocytes, which lose the ability to promote neuronal survival, outgrowth, synaptogenesis and phagocytosis and induce the death of neurons and oligodendrocytes.

 

Neuroinflammation and Brain development

During brain development, microglia are known to play a critical role as shapers of neural circuits, by providing trophic factors and by remodeling and pruning synapses (Rajendran and Paolicelli, 2018). In addition to playing a role in synaptic management, microglia are important for the pruning of dying neurons and in the clearance of debris (Harry, 2013). Microglia seem to affect also processes associated with neuronal proliferation and differentiation (Harry and Kraft, 2012). Similarly to microglia, astrocytes have instructive roles in neurogenesis, gliogenesis, angiogenesis, axonal outgrowth, synaptogenesis, and synaptic pruning (Reemst et al., 2016).

The development-dependent reactivity of microglial cells and astrocytes is not well known. Ischemic insult appears to elicit similar microglial reactivity both during brain development and in adulthood (Baburamani et al, 2014; Leonardo & Pennypacker, 2009). In contrast, treatment with lead acetate was previously shown to result in a more pronounced microglial and astrocyte reactivity in immature 3D rat brain cell cultures as compared to mature ones (Zurich et al, 2002). Astrocyte reactivity was also more pronounced in immature 3D rat brain cell cultures following paraquat exposure, whereas development-dependent differences in the phenotype of reactive microglia were observed (Sandström et al., 2017). This suggests that neuroinflammation is occurring during brain development and may express a different phenotype than in adulthood, and that dysfunction of microglia and astrocyte during brain development could contribute to neurodevelopmental disorders and potentially to late-onset neuropathology (Reemst et al., 2016).

 

Neuroinflammation in relation to COVID19

SARS-CoV-2 patients with moderate and severe COVID-19 presented an elevated plasma levels of glial fibrillary acidic protein (GFAP), which is known as biochemical indicator of glial activation (Kanberg et al., 2020).

How it is Measured or Detected

Neuroinflammation, i.e. the activation of glial cells can be measured by quantification of cellular markers (most commonly), or of released mediators (less common). As multiple activation states exist for the two main cell types involved, it is necessary to measure several markers of neuroinflammation:

• Microglial activation can be detected based on the increased numbers of labeled microglia per volume element of brain tissue (due to increase of binding sites, proliferation, and immigration of cells) or on morphological changes. A specific microglial marker, used across different species, is CD11b. Alternatively various specific carbohydrate structures can be stained by lectins (e.g. IB4). Beyond that, various well-established antibodies are available to detect microglia in mouse tissue (F4/80), phagocytic microglia in rat tissue (ED1) or more generally microglia across species (Iba1). Transgenic mice are available with fluorescent proteins under the control of the CD11b promoter to easily quantify microglia without the need for specific stains.
• The most frequently used astrocyte marker is GFAP (99% of all studies) (Eng et al., 2000). This protein is highly specific for astrocytes in the brain, and antibodies are available for immunocytochemical detection. In neuroinflammatory brain regions, the stain becomes more prominent, due to an upregulation of the protein, a shape change/proliferation of the cells, and/or better accessibility of the antibody. Various histological quantification approaches can be used. Occasionally, alternative astrocytic markers, such as vimentin of the S100beta protein, have been used for staining of astrocytes (Struzynska et al., 2007). Antibodies for complement component 3 (C3), the most characteristic and highly upregulated marker of A1 neurotoxic reactive astrocytes are commercially available.
• All immunocytochemical methods can also be applied to cell culture models.
• In patients, microglial accumulation can be monitored by PET imaging, using [11C]-PK 11195 as a microglial marker (Banati et al., 2002).
• Activation of glial cells can be assessed in tissue or cell culture models also by quantification of sets of activation markers. This can for instance be done by PCR quantification of inflammatory factors, by measurement of the respective mediators, e.g. by ELISA-related immuno-quantification. Such markers include:
• Pro- and anti-inflammatory cytokine expression (IL-1β; TNF-α, Il-6, IL-4); or expression of immunostimmulatory proteins (e.g. MHC-II)
• Itgam, CD86 expression as markers of M1 microglial phenotype
• Arg1, MRC1, as markers of M2 microglial phenotype

For descriptions of techniques, see Falsig 2004; Lund 2006 ; Kuegler 2010; Monnet-Tschudi et al., 2011; Sandström et al., 2014; von Tobel et al.,  2014

 

Regulatory example using the KE

Measurement of glial fibrillary acidic protein (GFAP) in brain tissue, whose increase is a marker of astrocyte reactivity, is required by the US EPA in rodent toxicity studies for fuel additives (40 CFR 79.67). It has been used on rare occasions for other toxicant evaluations.

References

Aguzzi, A., Barres, B.A., Bennett, M.L., 2013. Microglia: scapegoat, saboteur, or something else? Science 339(6116), 156-161.

Aloisi, F., 2001. Immune function of microglia. Glia 36, 165-179.

Aschner M (1998) Immune and inflammatory responses in the CNS: modulation by astrocytes. ToxicolLett 103: 283-287

Banati, R. B. (2002). "Visualising microglial activation in vivo." Glia 40: 206-217.   

Baburamani AA, Supramaniam VG, Hagberg H, Mallard C (2014) Microglia toxicity in preterm brain injury. Reprod Toxicol 48: 106-112

Brown GC, Bal-Price A (2003) Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria. Mol Neurobiol 27: 325-355

Carson, M.J., Thrash, J.C., Walter, B., 2006. The cellular response in neuroinflammation: The role of leukocytes, microglia and astrocytes in neuronal death and survival. Clin Neurosci Res 6(5), 237-245.

Charleston JS, Body RL, Bolender RP, Mottet NK, Vahter ME, Burbacher TM. 1996. Changes in the number of astrocytes and microglia in the thalamus of the monkey Macaca fascicularis following long-term subclinical methylmercury exposure. NeuroToxicology 17: 127-138.

Charleston JS, Bolender RP, Mottet NK, Body RL, Vahter ME, Burbacher TM. 1994. Increases in the number of reactive glia in the visual cortex of Macaca fascicularis following subclinical long-term methyl mercury exposure. ToxicolApplPharmacol 129: 196-206.

Claycomb, K.I., Johnson, K.M., Winokur, P.N., Sacino, A.V., Crocker, S.J., 2013. Astrocyte regulation of CNS inflammation and remyelination. Brain Sci 3(3), 1109-1127.

Dong Y, Benveniste EN (2001) Immune Function of Astrocytes. Glia 36: 180-190

Eng LF, Ghirnikar RS, Lee YL (2000) Glial Fibrillary Acidic Protein: GFAP-Thirty-One Years (1969-2000). NeurochemRes 25: 1439-1451

Eskes C, Honegger P, Juillerat-Jeanneret L, Monnet-Tschudi F. 2002. Microglial reaction induced by noncytotoxic methylmercury treatment leads to neuroprotection via interactions with astrocytes and IL-6 release. Glia 37(1): 43-52.

Falsig J, Latta M, Leist M. Defined inflammatory states in astrocyte cultures correlation with susceptibility towards CD95-driven apoptosis. J Neurochem. 2004  Jan;88(1):181-93.

Falsig J, Pörzgen P, Lund S, Schrattenholz A, Leist M. The inflammatory transcriptome of reactive murine astrocytes and implications for their innate immune function. J Neurochem. 2006 Feb;96(3):893-907.

Falsig J, van Beek J, Hermann C, Leist M. Molecular basis for detection of invading pathogens in the brain. J Neurosci Res. 2008 May 15;86(7):1434-47.

Glass CK, Saijo K, Winner B, Marchetto MC, Gage FH (2010). Mechanisms underlying inflammation in neurodegeneration. Cell. 2010 Mar 19;140(6):918-34.

Gordon S (2003) Alternative activation of macrophages. Nat Rev Immunol 3: 23-35

Graeber MB, Streit WJ (1990) Microglia: immune network in the CNS. Brain Pathol 1: 2-5

Harry GJ and Kraft AD (2012) Microglia in the developing brain: apotential target with lifetime effects. Neurotoxicology. 33(2):191-206.

Harry GJ (2013) Microglia during development and aging. Pharmacology & therapeutics 139: 313-326

Kanberg N, et al. Neurochemical evidence of astrocytic and neuronal injury commonly found in COVID-19. Neurology. 2020 Sep 22;95(12):e1754-e1759

Kigerl KA, Gensel JC, Ankeny DP, Alexander JK, Donnelly DJ, Popovich PG (2009) Identification of two distinct macrophage subsets with divergent effects causing either neurotoxicity or regeneration in the injured mouse spinal cord. J Neurosci 29: 13435-13444

Kraft AD, Harry GJ., Features of microglia and neuroinflammation relevant to environmental exposure and neurotoxicity. International Journal of Environmental research and Public Health., 2011, 8(7): 2980-3018.

Kreutzberg GW (1995) Microglia, the first line of defence in brain pathologies. Arzneimttelforsch 45: 357-360

Kreutzberg GW (1996) Microglia : a sensor for pathological events in the CNS. Trends Neurosci 19: 312-318

Kuegler PB, Zimmer B, Waldmann T, Baudis B, Ilmjärv S, Hescheler J, Gaughwin P, Brundin P, Mundy W, Bal-Price AK, Schrattenholz A, Krause KH, van Thriel C, Rao MS, Kadereit S, Leist M. Markers of murine embryonic and neural stem cells, neurons and astrocytes: reference points for developmental neurotoxicity testing. ALTEX. 2010;27(1):17-42

Leonardo CC, Pennypacker KR (2009) Neuroinflammation and MMPs: potential therapeutic targets in neonatal hypoxic-ischemic injury. J Neuroinflammation 6: 13

Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, et al. 2017. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541(7638): 481-487.

Little AR, Miller DB, Li S, Kashon ML, O'Callaghan JP. 2012. Trimethyltin-induced neurotoxicity: gene expression pathway analysis, q-RT-PCR and immunoblotting reveal early effects associated with hippocampal damage and gliosis. Neurotoxicol Teratol 34(1): 72-82.

Liu Y, Hu J, Wu J, Zhu C, Hui Y, Han Y, et al. 2012. alpha7 nicotinic acetylcholine receptor-mediated neuroprotection against dopaminergic neuron loss in an MPTP mouse model via inhibition of astrocyte activation. J Neuroinflammation 9: 98.

Lund S, Christensen KV, Hedtjärn M, Mortensen AL, Hagberg H, Falsig J, Hasseldam H, Schrattenholz A, Pörzgen P, Leist M. The dynamics of the LPS triggered inflammatory response of murine microglia under different culture and in vivo conditions. J Neuroimmunol. 2006 Nov;180(1-2):71-87.

Maresz K, Ponomarev ED, Barteneva N, Tan Y, Mann MK, Dittel BN (2008) IL-13 induces the expression of the alternative activation marker Ym1 in a subset of testicular macrophages. J Reprod Immunol 78: 140-148

Moehle MS, West AB (2015) M1 and M2 immune activation in Parkinson's Disease: Foe and ally? Neuroscience 302:59-73 doi:10.1016/j.neuroscience.2014.11.018

Monnet-Tschudi F, Zurich MG, Honegger P (2007) Neurotoxicant-induced inflammatory response in three-dimensional brain cell cultures. Hum Exp Toxicol 26: 339-346

Monnet-Tschudi, F., A. Defaux, et al. (2011). "Methods to assess neuroinflammation." Curr Protoc Toxicol Chapter 12: Unit12 19.         

Mosser DM, Edwards JP (2008) Exploring the full spectrum of macrophage activation. Nat Rev Immunol 8: 958-969

Nakajima K, Kohsaka S. 2004. Microglia: Neuroprotective and neurotrophic cells in the central nervous system. Current Drug Targets-Cardiovasc & Haematol Disorders 4: 65-84.

Perego C, Fumagalli S, De Simoni MG (2011) Temporal pattern of expression and colocalization of microglia/macrophage phenotype markers following brain ischemic injury in mice. J Neuroinflammation 8: 174

Ponomarev ED, Maresz K, Tan Y, Dittel BN (2007) CNS-derived interleukin-4 is essential for the regulation of autoimmune inflammation and induces a state of alternative activation in microglial cells. J Neurosci 27: 10714-10721

Ponomarev ED, Shriver LP, Maresz K, Dittel BN (2005) Microglial cell activation and proliferation precedes the onset of CNS autoimmunity. J Neurosci Res 81: 374-389

Rajendran L¹, Paolicelli RC (2018). Microglia-Mediated Synapse Loss in Alzheimer's Disease. J Neurosci.  38:2911-2919.

Ransohoff RM. 2016. A polarizing question: do M1 and M2 microglia exist? Nat Neurosci 19(8): 987-991.

Reemst K, Noctor SC, Lucassen PJ, Hol EM. (2016) The Indispensable Roles of Microglia and Astrocytes during Brain Development. Front Hum Neurosci.  10:566. DOI:10.3389/fnhum.2016.00566

Rivest, S., 2009. Regulation of innate immune responses in the brain. Nat Rev Immunol 9(6), 429-439.

Sandstrom von Tobel, J., D. Zoia, et al. (2014). "Immediate and delayed effects of subchronic Paraquat exposure during an early differentiation stage in 3D-rat brain cell cultures." Toxicol Lett. DOI : 10.1016/j.toxlet.2014.02.001

Sandstrom J, Broyer A, Zoia D, et al. (2017a) Potential mechanisms of development-dependent adverse effects of the herbicide paraquat in 3D rat brain cell cultures. Neurotoxicology 60:116-124 doi:10.1016/j.neuro.2017.04.010

Sandstrom J, Eggermann E, Charvet I, et al. (2017b) Development and characterization of a human embryonic stem cell-derived 3D neural tissue model for neurotoxicity testing. Toxicol In Vitro 38:124-135 doi:10.1016/j.tiv.2016.10.001

Streit, W.J., Walter, S.A., Pennell, N.A., 1999. Reactive microgliosis. Progress in Neurobiol. 57, 563-581.

Struzynska L, Dabrowska-Bouta B, Koza K, Sulkowski G (2007) Inflammation-Like Glial Response in Lead-Exposed Immature Rat Brain. Toxicol Sc 95:156-162

Venneti S, Lopresti BJ, Wiley CA. 2006. The peripheral benzodiazepine receptor (Translocator protein 18kDa) in microglia: from pathology to imaging. Prog Neurobiol 80(6): 308-322.

von Tobel, J. S., P. Antinori, et al. (2014). "Repeated exposure to Ochratoxin A generates a neuroinflammatory response, characterized by neurodegenerative M1 microglial phenotype." Neurotoxicology 44C: 61-70.

Xu DP, Zhang K, Zhang ZJ, Sun YW, Guo BJ, Wang YQ, et al. 2014. A novel tetramethylpyrazine bis-nitrone (TN-2) protects against 6-hydroxyldopamine-induced neurotoxicity via modulation of the NF-kappaB and the PKCalpha/PI3-K/Akt pathways. Neurochem Int 78: 76-85.

Zurich M-G, Eskes C, Honegger P, Bérode M, Monnet-Tschudi F. 2002. Maturation-dependent neurotoxicity of lead aceate in vitro: Implication of glial reactions. J Neurosc Res 70: 108-116.

Event: 1492: Tissue resident cell activation

Short Name: Tissue resident cell activation

Key Event Component

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability

Extend to at least invertebrates

Not to plants and not to single-celled organisms

BRAIN:

Tissue resident activation is observed in human, monkey, rat, mouse, and zebrafish, in association with neurodegeneration or following toxicant exposure. Some references (non-exhaustive list) are given below for illustration:

Human: Vennetti et al., 2006

Monkey (Macaca fascicularis): Charleston et al., 1994, 1996

Rat: Little et al., 2012; Zurich et al., 2002; Eskes et al., 2002

Mouse: Liu et al., 2012

Zebrafish: Xu et al., 2014.

LIVER:

Human: Su et al., 2002; Kegel et al., 2015; Boltjes et al.,2014

Rat: Luckey and Peterson,2001

Mouse: Dalton t al., 2009

Key Event Description

Tissue resident cell activation is considered as a hallmark of inflammation irrespective of the tissue type. Strategically placed cells within tissues respond to noxious stimuli, thus regulating the recruitment of neutrophil and the initiation and resolution of inflammation (Kim and Luster, 2015).  Examples for these cells are resident immune cells, parenchymal cells, vascular cells, stromal cells, or smooth muscle cells.  These cells may be specific for a certain tissue, but they have a common tissue-independent role.

Under healthy conditions there is a homeostatic state, characterized as a generally quiescent cellular milieu. Various danger signals or alarmins that are involved in induction of inflammation like pathogen-associated molecular pattern molecules (PAMPs) and damage-associated molecular pattern molecules (DAMPs) activate these resident cells in affected tissues.  

Examples of well-characterized DAMPs (danger signals or alarmins) (Saïd-Sadier  and Ojcius, 2012)

DAMPs	Receptors	Outcome of receptor ligation
Extracellular nucleotides (ATP, ADP, adenosine)	PI, P2X and P2Y receptors (ATP, ADP); Al, A2A, A2B and A3 receptors (adenosine)	Dendritic cell (DC) maturation, chemotaxis, secretion of cytokines (IL-1β, IL-18), inflammation
Extracellular heat shock proteins	CD14, CD91, scavenger receptors, TLR4, TLR2, CD40	DC maturation, cytokine induction, DC, migration to lymph nodes
Extracellular HMGB1	RAGE, TLR2, TLR4	Chemotaxis, cytokine induction, DC activation, neutrophil recruitment, inflammation, activation of immune cells
Uric acid crystals	CD14, TLR2, TLR4	DC activation, cytokine induction, neutrophil recruitment, gout induction
Oxidative stress	Intracellular redox-sensitive proteins	Cell death, release of endogenous DAMPs, inflammation
Laminin	Integrins	Neutrophil recruitment, chemotaxis
S100 proteins or calgranulins	RAGE	Neutrophil recruitment, chemotaxis, cytokine secretion, apoptosis
Hyaluronan	TLR2, TLR4, CD44	DC maturation, cytokine production, adjuvant activity

Activation refers to a phenotypic modification of the resident cells that includes alterations in their secretions, activation of biosynthetic pathways, production of pro-inflammatory proteins and lipids, and morphological changes. While these represent a pleiotropic range of responses that can vary with the tissue, there are a number of common markers or signs of activation that are measurable.

Examples of Common markers are

• NF-kB
• AP-1
• Jnk
• P38/mapk

These described commonalities allow the use of this KE as a hub KE in the AOP network. However, despite the similarities in the inflammatory process, the type of reactive cells and the molecules triggering their reactivity may be tissue-specific. Therefore, for practical reasons, a tissue specific description of the reactive cells and of the triggering factors is necessary in order to specify in a tissue-specific manner, which cell should be considered and what should be measured.

BRAIN

The most easily detectable feature of brain inflammation or neuroinflammation is activation of microglial cells and astrocytes. It is evidenced by changes in shape, increased expression of certain antigens, and accumulation and proliferation of the glial cells in affected regions (Aschner, 1998; Graeber & Streit, 1990; Monnet-Tschudi et al, 2007; Streit et al, 1999; Kraft and Harry, 2011; Claycomb et al., 2013). Upon stimulation by cytokines, chemokines or inflammogens (e.g. from pathogens or from damaged neurons), both glial cell types activate inflammatory signaling pathways, which result in increased expression and/or release of inflammatory mediators such as cytokines, eicosanoids, and metalloproteinases (Dong & Benveniste, 2001) (cf KE: pro-inflammatory mediators, increased), as well as in the production of reactive oxygen species (ROS) and nitrogen species (RNS) (Brown & Bal-Price, 2003). Different types of activation states are possible for microglia and astrocytes, resulting in pro-inflammatory or anti-inflammatory signaling, and other cellular functions (such as phagocytosis) (Streit et al., 1999; Nakajima and Kohsaka, 2004). Therefore, neuroinflammation can have both neuroprotective/neuroreparative and neurodegenerative consequences (Carson et al., 2006; Monnet-Tschudi et al, 2007; Aguzzi et al., 2013 ; Glass et al., 2010). Under normal physiological conditions, microglial cells survey the nervous system for neuronal integrity (Nimmerjahn et al, 2005) and for invading pathogens (Aloisi, 2001; Kreutzberg, 1995; Kreutzberg, 1996; Rivest, 2009). They are the first type of cell activated (first line of defense), and can subsequently induce astrocyte activation (Falsig, 2008). Two distinct states of microglial activation have been described (Gordon, 2003; Kigerl et al, 2009; Maresz et al, 2008; Mosser & Edwards, 2008; Perego et al; Ponomarev et al, 2005): The M1 state is classically triggered by interferon-gamma and/or other pro-inflammatory cytokines, and this state is characterized by increased expression of integrin alpha M (Itgam) and CD86, as well as the release of pro-inflammatory cytokines (TNF-alpha, IL-1beta, IL-6), and it is mostly associated with neurodegeneration. The M2 state is triggered by IL-4 and IL-13 (Maresz et al, 2008; Perego et al, 2011; Ponomarev et al, 2007) and induces the expression of mannose receptor 1 (MRC1), arginase1 (Arg 1) and Ym1/2; it is involved in repair processes. The activation of astrocytes by microglia-derived cytokines or TLR agonists resembles the microglial M1 state (Falsig 2006). Although classification of the M1/M2 polarization of microglial cells may be considered as a simplification of authentic microglial reaction states (Ransohoff, 2016), a similar polarization of reactive astrocytes has been described recently Liddlelow et al., 2017): Interleukin-1 alpha (IL-1a), TNF and subcomponent q (C1q) released by activated microglial cells induce A1-reactive astrocytes, which lose the ability to promote neuronal survival, outgrowth, synaptogenesis and phagocytosis and induce the death of neurons and oligodendrocytes.

Regulatory examples using the KE

Measurement of glial fibrillary acidic protein (GFAP) in brain tissue, whose increase is a marker of astrocyte reactivity, is required by the US EPA in rodent toxicity studies for fuel additives (40 CFR 79.67). It has been used on rare occasions for other toxicant evaluations.

LIVER:

Kupffer cells (KCs) are a specialized population of macrophages that reside in the liver; they were first described by Carl Wilhelm von Kupffer (1829–1902) [Haubrich 2004]. KCs constitute 80%-90% of the tissue macrophages in the reticuloendothelial system and account for approximately 15% of the total liver cell population [Bouwens et al., 1986].   They play an important role in normal physiology and homeostasis as well as participating in the acute and chronic responses of the liver to toxic compounds. Activation of KCs results in the release of an array of inflammatory mediators, growth factors, and reactive oxygen species. This activation appears to modulate acute hepatocyte injury as well as chronic liver responses including hepatic cancer. Understanding the role KCs play in these diverse responses is key to understanding mechanisms of liver injury [Roberts et al.,2007].  Besides the release of inflammatory mediators including cytokines, chemokines, lysosomal and proteolytic enzymes KCs are a main source of TGF-β1 (transforming growth factor-beta 1, the most potent profibrogenic cytokine). In addition latent TGF-β1 can be activated by KC-secreted matrix metalloproteinase 9 (MMP-9)[Winwood and Arthur, 1993; Luckey and Peeterson, 2001] through the release of biologically active substances that promote the pathogenic process. Activated KCs also release ROS like superoxide generated by NOX (NADPH oxidase), thus contributing to oxidative stress. Oxidative stress also activates a variety of transcription factors like NF-κB, PPAR-γ leading to an increased gene expression for the production of growth factors, inflammatory cytokines and chemokines. KCs express TNF-α (Tumor Necrosis Factor-alpha), IL-1 (Interleukin-1) and MCP-1 (monocyte-chemoattractant protein-1), all being mitogens and chemoattractants for hepatic stellate cells (HSCs) and induce the expression of PDGF receptors on HSCs which enhances cell proliferation. Expressed TNF-α, TRAIL (TNF-related apoptosis-inducing ligand), and FasL (Fas Ligand) are not only pro-inflammatory active but also capable of inducing death receptor-mediated apoptosis in hepatocytes [Guo and Friedman, 2007; Friedman 2002; Roberts et al., 2007]. Under conditions of oxidative stress macrophages are further activated which leads to a more enhanced inflammatory response that again further activates KCs though cytokines (Interferon gamma (IFNγ), granulocyte macrophage colony-stimulating factor (GM-CSF), TNF-α), bacterial lipopolysaccharides, extracellular matrix proteins, and other chemical mediators [Kolios et al., 2006; Kershenobich Stalnikowitz and Weissbrod 2003].

Besides KCs, the resident hepatic macrophages, infiltrating bone marrow-derived macrophages, originating from circulating monocytes are recruited to the injured liver via chemokine signals. KCs appear essential for sensing tissue injury and initiating inflammatory responses, while infiltrating Ly-6C+ monocyte-derived macrophages are linked to chronic inflammation and fibrogenesis. The profibrotic functions of KCs (HSC activation via paracrine mechanisms) during chronic hepatic injury remain functionally relevant, even if the infiltration of additional inflammatory monocytes is blocked via pharmacological inhibition of the chemokine CCL2 [Baeck et al., 2012; Tacke and Zimmermann, 2014].

KC activation and macrophage recruitment are two separate events and both are necessary for fibrogenesis, but as they occur in parallel, they can be summarised as one KE.

Probably there is a threshold of KC activation and release above which liver damage is induced. Pre-treatment with gadolinium chloride (GdCl), which inhibits KC function, reduced both hepatocyte and sinusoidal epithelial cell injury, as well as decreased the numbers of macrophages appearing in hepatic lesions and inhibited TGF-β1 mRNA expression in macrophages. Experimental inhibition of KC function or depletion of KCs appeared to protect against chemical-induced liver injury [Ide et al.,2005].  

How it is Measured or Detected

In General:

Measurement targets are cell surface and intracellular markers; the specific markers may be cell and species-specific. 

Available methods include cytometry, immunohistochemistry, gene expression sequencing; western blotting, ELISA, and functional assays.

BRAIN 

Neuroinflammation, i.e. the activation of glial cells can be measured by quantification of cellular markers (most commonly), or of released mediators (less common). As multiple activation states exist for the two main cell types involved, it is necessary to measure several markers of neuroinflammation:

1. Microglial activation can be detected based on the increased numbers of labeled microglia per volume element of brain tissue (due to increase of binding sites, proliferation, and immigration of cells) or on morphological changes. A specific microglial marker, used across different species, is CD11b. Alternatively various specific carbohydrate structures can be stained by lectins (e.g. IB4). Beyond that, various well-established antibodies are available to detect microglia in mouse tissue (F4/80), phagocytic microglia in rat tissue (ED1) or more generally microglia across species (Iba1). Transgenic mice are available with fluorescent proteins under the control of the CD11b promoter to easily quantify microglia without the need for specific stains.
2. The most frequently used astrocyte marker is glial fibrillary acidic protein, GFAP (99% of all studies) (Eng et al., 2000). This protein is highly specific for astrocytes in the brain, and antibodies are available for immunocytochemical detection. In neuroinflamatory brain regions, the stain becomes more prominent, due to an upregulation of the protein, a shape change/proliferation of the cells, and/or better accessibility of the antibody. Various histological quantification approaches can be used. Occasionally, alternative astrocytic markers, such as vimentin of the S100beta protein, have been used for astrocyte staining (Struzynska et al., 2007). Antibodies for complement component 3 (C3), the most characteristic and highly upregulated marker of A1 neurotoxic reactive astrocytes are commercially available.
3. All immunocytochemical methods can also be applied to cell culture models.
4. In patients, microglial accumulation can be monitored by PET imaging, using [11C]-PK 11195 as a microglial marker (Banati et al., 2002).
5. Activation of glial cells can be assessed in tissue or cell culture models also by quantification of sets of M1/M2 phenotype markers. This can for instance be done by PCR quantification, immunocytochemistry, immunoblotting.

• Itgam, CD86 expression as markers of M1 microglial phenotype
• Arg1, MRC1, as markers of M2 microglial phenotype

(for descriptions of techniques, see Falsig 2004; Lund 2006 ; Kuegler 2010; Monnet-Tschudi et al., 2011; Sandström et al., 2014; von Tobel et al.,  2014)

LIVER:

Kupffer cell activation can be measured by means of expressed cytokines, e.g. tissue levels of TNF-a [Vajdova et al,2004], IL-6 expression, measured by immunoassays or Elisa (offered by various companies), soluble CD163 [Grønbaek etal., 2012; Møller  etal.,2012] or increase in expression of Kupffer cell marker genes such as Lyz, Gzmb, and Il1b, (Genome U34A Array, Affymetrix); [Takahara et al.,2006]

References

Chan JK, Roth J, Oppenheim JJ, Tracey KJ, Vogl T, Feldmann M, Horwood N, Nanchahal J., Alarmins: awaiting a clinical response. J Clin Invest. 2012 Aug;122(8):2711-9.

Davies LC, Jenkins SJ, Allen JE, Taylor PR, Tissue-resident macrophages, Nat Immunol. 2013 Oct;14(10):986-95. 

Escamilla-Tilch M, Filio-Rodríguez G, García-Rocha R, Mancilla-Herrera I, Mitchison NA, Ruiz-Pacheco JA, Sánchez-García FJ, Sandoval-Borrego D, Vázquez-Sánchez EA, The interplay between pathogen-associated and danger-associated molecular patterns: an inflammatory code in cancer? Immunol Cell Biol. 2013 Nov-Dec;91(10):601-10.

Hussell T, Bell TJ, Alveolar macrophages: plasticity in a tissue-specific context, Nat Rev Immunol. 2014 Feb;14(2):81-93.

Kim ND, Luster AD. The role of tissue resident cells in neutrophil recruitment ,Trends Immunol. 2015 Sep;36(9):547-55.

Saïd-Sadier N, Ojcius DM., Alarmins, inflammasomes and immunity. Biomed J. 2012 Nov-Dec;35(6):437-49.

Schaefer L, Complexity of danger: the diverse nature of damage-associated molecular patterns, J Biol Chem. 2014 Dec 19;289(51):35237-45.

BRAIN:

Aschner M (1998) Immune and inflammatory responses in the CNS: modulation by astrocytes. ToxicolLett 103: 283-287

Banati, R. B. (2002). "Visualising microglial activation in vivo." Glia 40: 206-217.

Brown GC, Bal-Price A (2003) Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria. Mol Neurobiol 27: 325-355

Charleston JS, Body RL, Bolender RP, Mottet NK, Vahter ME, Burbacher TM. 1996. Changes in the number of astrocytes and microglia in the thalamus of the monkey Macaca fascicularis following long-term subclinical methylmercury exposure. NeuroToxicology 17: 127-138.

Charleston JS, Bolender RP, Mottet NK, Body RL, Vahter ME, Burbacher TM. 1994. Increases in the number of reactive glia in the visual cortex of Macaca fascicularis following subclinical long-term methyl mercury exposure. ToxicolApplPharmacol 129: 196-206.

Dong Y, Benveniste EN (2001) Immune Function of Astrocytes. Glia 36: 180-190

Eng LF, Ghirnikar RS, Lee YL (2000) Glial Fibrillary Acidic Protein: GFAP-Thirty-One Years (1969-2000). NeurochemRes 25: 1439-1451

Eskes C, Honegger P, Juillerat-Jeanneret L, Monnet-Tschudi F. 2002. Microglial reaction induced by noncytotoxic methylmercury treatment leads to neuroprotection via interactions with astrocytes and IL-6 release. Glia 37(1): 43-52.

Falsig J, Latta M, Leist M. Defined inflammatory states in astrocyte cultures correlation with susceptibility towards CD95-driven apoptosis. J Neurochem. 2004  Jan;88(1):181-93.

Falsig J, Pörzgen P, Lund S, Schrattenholz A, Leist M. The inflammatory transcriptome of reactive murine astrocytes and implications for their innate immune function. J Neurochem. 2006 Feb;96(3):893-907.

Falsig J, van Beek J, Hermann C, Leist M. Molecular basis for detection of invading pathogens in the brain. J Neurosci Res. 2008 May 15;86(7):1434-47.

Glass CK, Saijo K, Winner B, Marchetto MC, Gage FH (2010). Mechanisms underlying inflammation in neurodegeneration. Cell. 2010 Mar 19;140(6):918-34.

Gordon S (2003) Alternative activation of macrophages. Nat Rev Immunol 3: 23-35

Graeber MB, Streit WJ (1990) Microglia: immune network in the CNS. Brain Pathol 1: 2-5

Kigerl KA, Gensel JC, Ankeny DP, Alexander JK, Donnelly DJ, Popovich PG (2009) Identification of two distinct macrophage subsets with divergent effects causing either neurotoxicity or regeneration in the injured mouse spinal cord. J Neurosci 29: 13435-13444

Kuegler PB, Zimmer B, Waldmann T, Baudis B, Ilmjärv S, Hescheler J, Gaughwin P, Brundin P, Mundy W, Bal-Price AK, Schrattenholz A, Krause KH, van Thriel C, Rao MS, Kadereit S, Leist M. Markers of murine embryonic and neural stem cells, neurons and astrocytes: reference points for developmental neurotoxicity testing. ALTEX. 2010;27(1):17-42

Kreutzberg GW (1995) Microglia, the first line of defence in brain pathologies. Arzneimttelforsch 45: 357-360

Kreutzberg GW (1996) Microglia : a sensor for pathological events in the CNS. Trends Neurosci 19: 312-318

Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, et al. 2017. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541(7638): 481-487.

Little AR, Miller DB, Li S, Kashon ML, O'Callaghan JP. 2012. Trimethyltin-induced neurotoxicity: gene expression pathway analysis, q-RT-PCR and immunoblotting reveal early effects associated with hippocampal damage and gliosis. Neurotoxicol Teratol 34(1): 72-82.

Liu Y, Hu J, Wu J, Zhu C, Hui Y, Han Y, et al. 2012. alpha7 nicotinic acetylcholine receptor-mediated neuroprotection against dopaminergic neuron loss in an MPTP mouse model via inhibition of astrocyte activation. J Neuroinflammation 9: 98.

Lund S, Christensen KV, Hedtjärn M, Mortensen AL, Hagberg H, Falsig J, Hasseldam H, Schrattenholz A, Pörzgen P, Leist M. The dynamics of the LPS triggered inflammatory response of murine microglia under different culture and in vivo conditions. J Neuroimmunol. 2006 Nov;180(1-2):71-87.

Maresz K, Ponomarev ED, Barteneva N, Tan Y, Mann MK, Dittel BN (2008) IL-13 induces the expression of the alternative activation marker Ym1 in a subset of testicular macrophages. J Reprod Immunol 78: 140-148

Monnet-Tschudi F, Zurich MG, Honegger P (2007) Neurotoxicant-induced inflammatory response in three-dimensional brain cell cultures. Hum Exp Toxicol 26: 339-346

Monnet-Tschudi, F., A. Defaux, et al. (2011). "Methods to assess neuroinflammation." Curr Protoc Toxicol Chapter 12: Unit12 19.            

Mosser DM, Edwards JP (2008) Exploring the full spectrum of macrophage activation. Nat Rev Immunol 8: 958-969

Nakajima K, Kohsaka S. 2004. Microglia: Neuroprotective and neurotrophic cells in the central nervous system. Current Drug Targets-Cardiovasc & Haematol Disorders 4: 65-84.

Perego C, Fumagalli S, De Simoni MG (2011) Temporal pattern of expression and colocalization of microglia/macrophage phenotype markers following brain ischemic injury in mice. J Neuroinflammation 8: 174

Ponomarev ED, Maresz K, Tan Y, Dittel BN (2007) CNS-derived interleukin-4 is essential for the regulation of autoimmune inflammation and induces a state of alternative activation in microglial cells. J Neurosci 27: 10714-10721

Ponomarev ED, Shriver LP, Maresz K, Dittel BN (2005) Microglial cell activation and proliferation precedes the onset of CNS autoimmunity. J Neurosci Res 81: 374-389

Ransohoff RM. 2016. A polarizing question: do M1 and M2 microglia exist? Nat Neurosci 19(8): 987-991.

Sandstrom von Tobel, J., D. Zoia, et al. (2014). "Immediate and delayed effects of subchronic Paraquat exposure during an early differentiation stage in 3D-rat brain cell cultures." Toxicol Lett. DOI : 10.1016/j.toxlet.2014.02.001

Struzynska L, Dabrowska-Bouta B, Koza K, Sulkowski G (2007) Inflammation-Like Glial Response in Lead-Exposed Immature Rat Brain. Toxicol Sc 95:156-162

von Tobel, J. S., P. Antinori, et al. (2014). "Repeated exposure to Ochratoxin A generates a neuroinflammatory response, characterized by neurodegenerative M1 microglial phenotype." Neurotoxicology 44C: 61-70.

Venneti S, Lopresti BJ, Wiley CA. 2006. The peripheral benzodiazepine receptor (Translocator protein 18kDa) in microglia: from pathology to imaging. Prog Neurobiol 80(6): 308-322.

Xu DP, Zhang K, Zhang ZJ, Sun YW, Guo BJ, Wang YQ, et al. 2014. A novel tetramethylpyrazine bis-nitrone (TN-2) protects against 6-hydroxyldopamine-induced neurotoxicity via modulation of the NF-kappaB and the PKCalpha/PI3-K/Akt pathways. Neurochem Int 78: 76-85.

Zurich M-G, Eskes C, Honegger P, Bérode M, Monnet-Tschudi F. 2002. Maturation-dependent neurotoxicity of lead aceate in vitro: Implication of glial reactions. J Neurosc Res 70: 108-116.

LIVER:

Baeck, C. et al. (2012), Pharmacological inhibition of the chemokine CCL2 (MCP-1) diminishes liver macrophage infiltration and steatohepatitis in chronic hepatic injury, Gut, vol. 61, no. 3, pp.416–426.

Boltjes, A. et al. (2014), The role of Kupffer cells in hepatitis B and hepatitis C virus infections, J Hepatol, vol. 61, no. 3, pp. 660-671.

Bouwens, L. et al. (1986), Quantitation, tissue distribution and proliferation kinetics of Kupffer cells in normal rat liver, Hepatology, vol. 6, no. 6, pp. 718-722.

Dalton, S.R. et al. (2009), Carbon tetrachloride-induced liver damage in asialoglycoprotein receptor-deficient mice, Biochem Pharmacol, vol. 77, no. 7, pp. 1283-1290.

Friedman, S.L. (2002), Hepatic Fibrosis-Role of Hepatic Stellate Cell Activation, MedGenMed, vol. 4, no. 3, pp. 27.

Grønbaek, H. et al. (2012), Soluble CD163, a marker of Kupffer cell activation, is related to portal hypertension in patients with liver cirrhosis, Aliment Pharmacol Ther, vol 36, no. 2, pp. 173-180.

Guo, J. and S.L. Friedman (2007), Hepatic Fibrogenesis, Semin Liver Dis, vol. 27, no. 4, pp. 413-426.

Haubrich, W.S. (2004), Kupffer of Kupffer cells, Gastroenterology, vol. 127, no. 1, p. 16

Ide, M. et al. (2005), Effects of gadolinium chloride (GdCl(3)) on the appearance of macrophage populations and fibrogenesis in thioacetamide-induced rat hepatic lesions, J. Comp. Path, vol. 133, no. 2-3, pp. 92–102.

Kegel, V. et al. (2015), Subtoxic concentrations of hepatotoxic drugs lead to Kupffer cell activation in a human in vitro liver model: an approach to study DILI, Mediators Inflamm, 2015:640631, http://doi.org/10.1155/2015/640631.

Kershenobich Stalnikowitz, D. and A.B. Weissbrod (2003), Liver Fibrosis and Inflammation. A Review, Annals of Hepatology, vol. 2, no. 4, pp.159-163.

Kolios, G., V. Valatas and E. Kouroumalis (2006), Role of Kupffer Cells in the Pathogenesis of Liver Disease, World J.Gastroenterol, vol. 12, no. 46, pp. 7413-7420.

Luckey, S.W., and D.R. Petersen (2001), Activation of Kupffer cells during the course of carbon tetrachloride-induced liver injury and fibrosis in rats, Exp Mol Pathol, vol. 71, no. 3, pp. 226-240.

Møller, H.J. (2012), Soluble CD163.Scand J Clin Lab Invest, vol. 72, no. 1, pp. 1-13.

Roberts, R.A. et al. (2007), Role of the Kupffer cell in mediating hepatic toxicity and carcinogenesis, Toxicol Sci, vol. 96, no. 1, pp. 2-15.

Su, G.L. et al. (2002), Activation of human and mouse Kupffer cells by lipopolysaccharide is mediated by CD14, Am J Physiol Gastrointest Liver Physiol, vol. 283, no. 3, pp. G640-645.

Tacke, F. and H.W. Zimmermann (2014), Macrophage heterogeneity in liver injury and fibrosis, J Hepatol, vol. 60, no. 5, pp. 1090-1096.

Takahara, T et al. (2006), Gene expression profiles of hepatic cell-type specific marker genes in progression of liver fibrosis, World J Gastroenterol, vol. 12, no. 40, pp. 6473-6499.

Vajdova, K. et al. (2004), Ischemic preconditioning and intermittent clamping improve murine hepatic microcirculation and Kupffer cell function after ischemic injury, Liver Transpl, vol. 10, no. 4, pp. 520–528

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Event: 1493: Increased Pro-inflammatory mediators

Short Name: Increased pro-inflammatory mediators

Key Event Component

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

LIVER:

Human [Santibañez et al., 2011]

Rat [Luckey and Petersen, 2001]

Mouse [Nan et al., 2013]

BRAIN:

Falsig 2004; Lund 2006 ; Kuegler 2010; Monnet-Tschudi et al., 2011; Sandström et al., 2014; von Tobel et al.,  2014

Key Event Description

Inflammatory mediators are soluble, diffusible molecules that act locally at the site of tissue damage and infection, and at more distant sites. They can be divided into exogenous and endogenous mediators.

Exogenous mediators of inflammation are bacterial products or toxins like endotoxin or LPS. Endogenous mediators of inflammation are produced from within the (innate and adaptive) immune system itself, as well as other systems. They can be derived from molecules that are normally present in the plasma in an inactive form, such as peptide fragments of some components of complement, coagulation, and kinin systems. Or they can be released at the site of injury by a number of cell types that either contain them as preformed molecules within storage granules, e.g. histamine, or which can rapidly switch on the machinery required to synthesize the mediators.

Table1: a non-exhaustive list of examples for pro-inflammatory mediators

Classes of inflammatory mediators	Examples
Pro-inflammatory cytokines	TNF-a, Interleukins (IL-1, IL-6, IL-8), Interferons  (IFN-g), chemokines (CXCL, CCL, GRO-α, MCP-1), GM-CSF
Prostaglandins	PGE2
Bradykinin
Vasoactive amines	histamine, serotonin
Reactive oxygen species (ROS)	O2-, H2O2
Reactive nitrogen species (RNS)	NO, iNOS

The increased production of pro-inflammatory mediators can have negative consequences on the parenchymal cells leading even to cell death, as described for TNF-a or peroxynitrite on neurons (Chao et al., 1995; Brown and Bal-Price, 2003). In addition, via a feedback loop, they can act on the reactive resident cells thus maintaining or exacerbating their reactive state; and by modifying elements of their signalling pathways, they can favour the M1 phenotypic polarization and the chronicity of the inflammatory process (Taetzsch et al., 2015).

Basically, this event occurs equally in various tissues and does not require tissue-specific descriptions. Nevertheless, there are some specificities such as the release of glutamate by brain reactive glial cells (Brown and Bal-Price, 2003; Vesce et al., 2007).The differences may rather reside in the type of insult favouring the increased expression and/or release of a specific class of inflammatory mediators, as well the time after the insult reflecting different stages of the inflammatory process. For these reasons, the analyses of the changes of a battery of inflammatory mediators rather than of a single one is a more adequate measurement of this KE.

Regulatory examples using the KE

CD54 and CD 86 as well as IL-8 expression is used to assess skin sensitization potential (OECD TG 442E). IL-2 expression is used to assess immunotoxicity (and will become an OECD test guideline); for the latter see also doi: 10.1007/s00204-018-2199-7.

 

LIVER:

When activated, resident macrophages (Kupffer cells) release inflammatory mediators including cytokines, chemokines, lysosomal, and proteolytic enzymes and are a main source of TGF-β1 - the most potent pro-fibrogenic cytokine. Following the role of TGF-β is described in more detail.

Transforming growth factor β (TGF-β) is a pleiotropic cytokine with potent regulatory and

inflammatory activity [Sanjabi et al., 2009; Li and Flavell, 2008a;2008b]. The multi-faceted effects of TGF-β on numerous immune functions are cellular and environmental context dependent [Li et al., 2006]. TGF-β binds to TGF-β receptor II (TGF-βRII) triggering the kinase activity of the cytoplasmic domain that in turn activates TGF-βRI. The activated receptor complex leads to nuclear translocation of Smad molecules,

and transcription of target genes [Li et al., 2006a]. The role of TGF-β as an immune modulator of T cell activity is best exemplified by the similarities between TGF-β1 knockout and T cell specific

TGF-β receptor II knockout mice [Li et al., 2006b; Marie et al., 2006;Shull et al., 1992]. The animals in both of these models develop severe multi-organ autoimmunity and succumb to death within a few weeks after birth [Li et al., 2006b; Marie et al., 2006; Shull et al., 1992]. In addition, in mice where TGF-β signaling is blocked specifically in T cells, the development of natural killer T (NKT) cells, natural regulatory T (nTreg) cells, and CD8+ T cells was shown to be dependent on TGF-β signaling in the thymus [Li et al., 2006b; Marie et al., 2006].

TGF-β plays a major role under inflammatory conditions. TGF-β in the presence of IL-6 drives the differentiation of T helper 17 (Th17) cells, which can promote further inflammation and augment autoimmune conditions [Korn et al., 2009]. TGF-β orchestrates the differentiation of both Treg and Th17 cells in a concentration-dependent manner [Korn et al., 2008]. In addition, TGF-β in combination with IL-4, promotes the differentiation of IL-9- and IL-10-producing T cells, which lack

suppressive function and also promote tissue inflammation [Dardalhon  et al., 2008; Veldhoen et al., 2008]. The biological effects of TGF-β under inflammatory conditions on effector and memory CD8+ T cells are much less understood. In a recent study, it was shown that TGF-β has a drastically opposing role on naïve compared to antigen-experienced/memory CD8+ T cells [Filippi et al., 2008]. When cultured in vitro, TGF-β suppressed naïve CD8+ T cell activation and IFN-γ production, whereas TGF-β enhanced survival of memory CD8+ T cells and increased the production of IL-17 and IFN-γ [Filippi et al., 2008]. TGF-β also plays an important role in suppressing the cells of the innate immune system.

The transforming growth factor beta (TGF-β) family of cytokines are ubiquitous, multifunctional, and essential to survival. They play important roles in growth and development, inflammation and repair, and host immunity. The mammalian TGF-β isoforms (TGF-β1, β2 and β3) are secreted as latent precursors and have multiple cell surface receptors of which at least two mediate signal transduction. Autocrine and paracrine effects of TGF-βs can be modified by extracellular matrix, neighbouring cells and other cytokines. The vital role of the TGF-β family is illustrated by the fact that approximately 50% of TGF-1 gene knockout mice die in utero and the remainder succumb to uncontrolled inflammation after birth. The role of TGF-β in homeostatic and pathogenic processes suggests numerous applications in the diagnosis and treatment of various diseases characterised by inflammation and fibrosis. [Clark and Coker, 1998; Santibañez et al., 2011; Pohlers et al., 2009] Abnormal TGF-β regulation and function are implicated in a growing number of fibrotic and inflammatory pathologies, including pulmonary fibrosis, liver cirrhosis, glomerulonephritis and diabetic nephropathy, congestive heart failure, rheumatoid arthritis, Marfan syndrome, hypertrophic scars, systemic sclerosis, myocarditis, and Crohn’s disease. [Gordon and Globe,2008] TGF-β1 is a polypeptide member of the TGF-β superfamily of cytokines. TGF-β is synthesized as a non-active pro-form, forms a complex with two latent associated proteins latency-associated protein (LAP) and latent TGF- β binding protein (LTBP) and undergoes protolithic cleavage by the endopeptidase furin to generate the mature TGF-β dimer. Among the TGF-βs, six distinct isoforms have been discovered although only the TGF-β1, TGF-β2 and TGF-β3 isoforms are expressed in mammals, and their human genes are located on chromosomes 19q13, 1q41 and 14q24, respectively. Out of the three TGF-β isoforms (β1, β2 and β3) only TGF-β1 was linked to fibrogenesis and is the most potent fibrogenic factor for hepatic stellate cells. [Roberts, 1998; Govinden and Bhoola, 2003]. During fibrogenesis, tissue and blood levels of active TGF-β are elevated and overexpression of TGF-β1 in transgenic mice can induce fibrosis. Additionally, experimental fibrosis can be inhibited by anti-TGF-β treatments with neutralizing antibodies or soluble TGF-β receptors [Qi et al.; 1999; Shek and Benyon , 2004; De Gouville et al., 2005; Chen et al., 2009]. TGF-β1 induces its own mRNA to sustain high levels in local sites of injury. The effects of TGF-β1 are classically mediated by intracellular signalling via Smad proteins. Smads 2 and 3 are stimulatory whereas Smad 7 is inhibitory. [Parsons et al., 2013; Friedman, 2008; Kubiczkova et al., 2012] Smad1/5/8, MAP kinase (mitogen-activated protein) and PI3 kinase are further signalling pathways in different cell types for TGF-β1 effects.

TGF-β is found in all tissues, but is particularly abundant in bone, lung, kidney and placental tissue. TGF-β is produced by many, but not all parenchymal cell types, and is also produced or released by infiltrating cells such as lymphocytes, monocytes/macrophages, and platelets. Following wounding or inflammation, all these cells are potential sources of TGF-β. In general, the release and activation of TGF-β stimulates the production of various extracellular matrix proteins and inhibits the degradation of these matrix proteins. [Branton and Kopp, 1999]

TGF-β 1 is produced by every leukocyte lineage, including lymphocytes, macrophages, and dendritic cells, and its expression serves in both autocrine and paracrine modes to control the differentiation, proliferation, and state of activation of these immune cells. [Letterio and Roberts; 1998]

In the liver TGF-β1 is released by activated Kupffer cells, liver sinusoidal endothelial cells, and platelets; in the further course of events also activated hepatic stellate cells express TGF-β1. Hepatocytes do not produce TGF-β1 but are implicated in intracellular activation of latent TGF-β1. [Roth et al., 1998; Kisseleva and Brenner, 2007; Kisseleva and Brenner, 2008; Poli, 2000; Liu et al., 2006]

TGF-β1 is the most established mediator and regulator of epithelial-mesenchymal-transition (EMT) which further contributes to the production of extracellular matrix. It has been shown that TGF-β1 mediates EMT by inducing snail-1 transcription factor and tyrosine phosphorylation of Smad2/3 with subsequent recruitment of Smad4. [Kolios et al., 2006; Bataller and Brenner, 2005; Guo and Friedman,2007; Brenner,2009; Kaimori et al., 2007; Gressner et al., 2002; Kershenobich Stalnikowitz and Weisssbrod, 2003; Li et al., 2008; Matsuoka and Tsukamoto, 1990; Kisseleva and Brenner, 2008; Poli, 200; Parsons et al., 2007; Friedman 2008; Liu et al., 2006]

TGF-β1 induces apoptosis and angiogenesis in vitro and in vivo through the activation of vascular endothelial growth factor (VEGF) High levels of VEGF and TGF-β1 are present in many tumors. Crosstalk between the signalling pathways activated by these growth factors controls endothelial cell apoptosis and angiogenesis. [Clark and Coker; 1998]

 

How it is Measured or Detected

The specific type of measurement(s) might vary with tissue, environment and context and will need to be described for different tissue contexts  as used within different AOP descriptions.

In general, quantification of inflammatory markers can be done by:

• qRT-PCR (mRNA expression)
• ELISA
• Immunocytochemistry
• Immunoblotting

For descriptions of techniques, see Falsig 2004; Lund 2006 ; Kuegler 2010; Monnet-Tschudi et al., 2011; Sandström et al., 2014; von Tobel et al.,  2014
 

LIVER:

There are several assays for TGB-β1 measurement available.

e.g. Human TGF-β1 ELISA Kit. The Human TGF-β 1 ELISA (Enzyme –Linked Immunosorbent Assay) kit is an in vitro enzyme-linked immunosorbent assay for the quantitative measurement of human TGF-β1 in serum, plasma, cell culture supernatants, and urine. This assay employs an antibody specific for human TGF-β1 coated on a 96-well plate. Standards and samples are pipetted into the wells and TGF-β1 present in a sample is bound to the wells by the immobilized antibody. The wells are washed and biotinylated anti-human TGF-β1 antibody is added. After washing away unbound biotinylated antibody, HRP- conjugated streptavidin is pipetted to the wells. The wells are again washed, a TMB substrate solution is added to the wells and colour develops in proportion to the amount of TGF-β1 bound. The StopSolution changes the colour from blue to yellow, and the intensity of the colour is measured at 450 nm [Mazzieri et al., 2000]

References

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Dong Y, Benveniste EN (2001) Immune Function of Astrocytes. Glia 36: 180-190

Falsig J, Latta M, Leist M. Defined inflammatory states in astrocyte cultures correlation with susceptibility towards CD95-driven apoptosis. J Neurochem. 2004  Jan;88(1):181-93.

Falsig J, Pörzgen P, Lund S, Schrattenholz A, Leist M. The inflammatory transcriptome of reactive murine astrocytes and implications for their innate immune function. J Neurochem. 2006 Feb;96(3):893-907.

Falsig J, van Beek J, Hermann C, Leist M. Molecular basis for detection of invading pathogens in the brain. J Neurosci Res. 2008 May 15;86(7):1434-47.

Hamadi N, Sheikh A, Madjid N, Lubbad L, Amir N, Shehab SA, Khelifi-Touhami F, Adem A: Increased pro-inflammatory cytokines, glial activation and oxidative stress in the hippocampus after short-term bilateral adrenalectomy. BMC Neurosci 2016, 17:61.

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Lund S, Christensen KV, Hedtjärn M, Mortensen AL, Hagberg H, Falsig J, Hasseldam H, Schrattenholz A, Pörzgen P, Leist M. The dynamics of the LPS triggered inflammatory response of murine microglia under different culture and in vivo conditions. J Neuroimmunol. 2006 Nov;180(1-2):71-87.

Monnet-Tschudi, F., A. Defaux, et al. (2011). "Methods to assess neuroinflammation." Curr Protoc Toxicol Chapter 12: Unit12 19.

Sandstrom von Tobel, J., D. Zoia, et al. (2014). "Immediate and delayed effects of subchronic Paraquat exposure during an early differentiation stage in 3D-rat brain cell cultures." Toxicol Lett. DOI : 10.1016/j.toxlet.2014.02.001

Taetzsch T, Levesque S, McGraw C, Brookins S, Luqa R, Bonini MG, Mason RP, Oh U, Block ML (2015) Redox regulation of NF-kappaB p50 and M1 polarization in microglia. Glia 5, 63:423-440.

Vesce S, Rossi D, Brambilla L, Volterra A (2007) Glutamate release from astrocytes in physiological conditions and in neurodegenerative disorders characterized by neuroinflammation. Int Rev Neurobiol. 82 :57-71.

 LIVER:

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• Branton, M.H. and J.B. Kopp (1999), TGF-beta and fibrosis, Microbes Infect, vol. 1, no. 15, pp. 1349-1365.
• Brenner, D.A. (2009), Molecular Pathogenesis of Liver Fibrosis, Trans Am Clin Climatol Assoc, vol. 120, pp. 361–368.
• Cheng, K., N.Yang and R.I. Mahato (2009), TGF-beta1 gene silencing for treating liver fibrosis, Mol Pharm, vol. 6, no. 3, pp. 772–779.
• Clark, D.A. and R.Coker (1998), Transforming growth factor-beta (TGF-beta), Int J Biochem Cell Biol, vol. 30, no. 3, pp. 293-298.
• Dardalhon V, Awasthi A, Kwon H, Galileos G, Gao W, Sobel RA, Mitsdoerffer M, Strom TB,
• De Gouville, A.C. et al. (2005), Inhibition of TGF-beta signaling by an ALK5 inhibitor protects rats from dimethylnitrosamine-induced liver fibrosis, Br J Pharmacol, vol. 145, no. 2, pp. 166–177.
• Filippi CM, Juedes AE, Oldham JE, Ling E, Togher L, Peng Y, Flavell RA, von Herrath MG, Transforming growth factor-beta suppresses the activation of CD8+ T-cells when naive but promotes their survival and function once antigen experienced: a two-faced impact on autoimmunity. Diabetes 2008;57:2684–2692.
• Friedman, S.L. (2008), Mechanisms of Hepatic Fibrogenesis, Gastroenterology, vol. 134, no. 6, pp. 1655–1669.
• Gordon, K.J. and G.C. Blobe (2008), Role of transforming growth factor-β superfamily signalling pathways in human disease, Biochim Biophys Acta, vol. 1782, no. 4, pp. 197–228.
• Govinden, R. and K.D. Bhoola (2003), Genealogy, expression, and cellular function of transforming growth factor-β, Pharmacol. Ther, vol. 98, no. 2, pp. 257–265.
• Gressner, A.M. et al. (2002), Roles of TGF-β in hepatic fibrosis. Front Biosci, vol. 7, pp. 793-807.
• Guo, J. and S.L. Friedman (2007), Hepatic fibrogenesis, Semin Liver Dis, vol. 27, no. 4, pp. 413-426.
• Kaimori, A. et al. (2007), Transforming growth factor-beta1 induces an epithelial-to-mesenchymal transition state in mouse hepatocytes in vitro, J Biol Chem, vol. 282, no. 30, pp. 22089-22101.
• Kershenobich Stalnikowitz, D. and A.B. Weisssbrod (2003), Liver Fibrosis and Inflammation. A Review, Annals of Hepatology, vol. 2, no. 4, pp.159-163.
• Kisseleva T and Brenner DA, (2008), Mechanisms of Fibrogenesis, Exp Biol Med, vol. 233, no. 2, pp. 109-122.
• Kisseleva, T. and Brenner, D.A. (2007), Role of hepatic stellate cells in fibrogenesis and the reversal of fibrosis, Journal of Gastroenterology and Hepatology, vol. 22, Suppl. 1; pp. S73–S78.
• Kolios, G., V. Valatas and E. Kouroumalis (2006), Role of Kupffer cells in the pathogenesis of liver disease, World J.Gastroenterol, vol. 12, no. 46, pp. 7413-7420.
• Korn T, Mitsdoerffer M, Croxford AL, Awasthi A, Dardalhon VA, Galileos G, Vollmar P, Stritesky GL, Kaplan MH, Waisman A, Kuchroo VK, Oukka M., IL-6 controls Th17 immunity in vivo by inhibiting the conversion of conventional T cells into Foxp3+ regulatory T cells, Proceedings of the National Academy of Sciences Nov 2008, 105 (47) 18460-18465; DOI: 10.1073/pnas.0809850105
• Korn T, Bettelli E, Oukka M, Kuchroo VK. IL-17 and Th17 Cells. Annu Rev Immunol. 2009
• Kubiczkova, L. et al, (2012), TGF-β - an excellent servant but a bad master, J Transl Med, vol. 10, p. 183.
• Letterio, J.J. and A.B. Roberts (1998), Regulation of immune responses by TGF-beta, Annu Rev Immunol, vol.16, pp. 137-161.
• Li MO, Flavell RA. Contextual regulation of inflammation: a duet by transforming growth factor-beta and interleukin-10. Immunity 2008a;28:468–476.
• Li MO, Flavell RA. TGF-beta: a master of all T cell trades. Cell 2008b;134:392–404.
• Li MO, Sanjabi S, Flavell RA. Transforming growth factor-beta controls development, homeostasis, and tolerance of T cells by regulatory T cell-dependent and -independent mechanisms. Immunity 2006b;25:455–471.
• Li MO, Wan YY, Sanjabi S, Robertson AK, Flavell RA. Transforming growth factor-beta regulation of immune responses. Annu Rev Immunol 2006a;24:99–146.
• Li, Jing-Ting et al. (2008), Molecular mechanism of hepatic stellate cell activation and antifibrotic therapeutic strategies, J Gastroenterol, vol. 43, no. 6, pp. 419–428.
• Liu, Xingjun et al. (2006), Therapeutic strategies against TGF-beta signaling pathway in hepatic fibrosis. Liver Int, vol.26, no.1, pp. 8-22.
• Luckey, S.W., and D.R. Petersen (2001), Activation of Kupffer cells during the course of carbon tetrachloride-induced liver injury and fibrosis in rats, Exp Mol Pathol, vol. 71, no. 3, pp. 226-240.
• Marie JC, Liggitt D, Rudensky AY. Cellular mechanisms of fatal early-onset autoimmunity in mice with the T cell-specific targeting of transforming growth factor-beta receptor. Immunity 2006;25:441–454.
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• Mazzieri, R .et al. (2000), Measurements of Active TGF-β Generated by Culture Cells, Methods in Molecular Biology, vol. 142, pp. 13-27.
• Nan, Y.M. et al. (2013), Activation of peroxisome proliferator activated receptor alpha ameliorates ethanol mediated liver fibrosis in mice, Lipids Health Dis, vol. 12, p.11.
• Parsons, C.J., M.Takashima and R.A. Rippe (2007), Molecular mechanisms of hepatic fibrogenesis. J Gastroenterol Hepatol, vol. 22, Suppl.1, pp. S79-S84.
• Pohlers , D. et al. (2009), TGF-β and fibrosis in different organs – molecular pathway imprints, Biochim. Biophys. Acta, vol. 1792, no. 8, pp.746–756.
• Poli, G. (2000), Pathogenesis of liver fibrosis: role of oxidative stress, Mol Aspects Med, vol. 21, no. 3, pp. 49 – 98.
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• Roth, S., K. Michel and A.M. Gressner (1998), (Latent) transforming growth factor beta in liver parenchymal cells, its injury-dependent release, and paracrine effects on rat HSCs, Hepatology, vol. 27, no. 4, pp. 1003-1012.
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• Santibañez J.F., M. Quintanilla and C. Bernabeu (2011), TGF-β/TGF-β receptor system and its role in physiological and pathological conditions, Clin Sci (Lond), vol. 121, no. 6, pp. 233-251.
• Shek, F.W. and R.C. Benyon (2004), How can transforming growth factor beta be targeted usefully to combat liver fibrosis? Eur J Gastroenterol Hepatol, vol. 16, no. 2, pp.123-126.
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Event: 386: Decrease of neuronal network function

Short Name: Neuronal network function, Decreased

Key Event Component

AOPs Including This Key Event

Biological Context

Organ term

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

In vitro studies in brain slices applying electrophysiological techniques showed significant variability among species (immature rats, rabbits and kittens) related to synaptic latency, duration, amplitude and efficacy in spike initiation (reviewed in Erecinska et al., 2004).

Key Event Description

Biological state: There are striking differences in neuronal network formation and function among the developing and mature brain. The developing brain shows a slow maturation and a transient passage from spontaneous, long-duration action potentials to synaptically-triggered, short-duration action potentials.

Furthermore, at this precise developmental stage the neuronal network is characterised by "hyperexcitability”, which is related to the increased number of local circuit recurrent excitatory synapses and the lack of γ-amino-butyric acid A (GABAA)-mediated inhibitory function that appears much later. This “hyperexcitability” disappears with maturation when pairing of the pre- and postsynaptic partners occurs and synapses are formed generating population of postsynaptic potentials and population of spikes followed by developmental GABA switch. Glutamatergic neurotransmission is dominant at early stages of development and NMDA receptor-mediated synaptic currents are far more times longer than those in maturation, allowing more calcium to enter the neurons. The processes that are involved in increased calcium influx and the subsequent intracellular events seem to play a critical role in establishment of wiring of neural circuits and strengthening of synaptic connections during development (reviewed in Erecinska et al., 2004). Neurons that do not receive glutaminergic stimulation are undergoing developmental apoptosis.

During the neonatal period, the brain is subject to profound alterations in neuronal circuitry due to high levels of synaptogenesis and gliogenesis. For example, in neuroendocrine regions such as the preoptic area-anterior hypothalamus (POA-AH), the site of gonadotropin-releasing hormone (GnRH) system is developmentally regulated by glutamatergic neurons. The changes in the expression of the N-methyl-D-aspartate (NMDA) receptor subunits NR1 and NR2B system begin early in postnatal development, before the onset of puberty, thereby playing a role in establishing the appropriate environment for the subsequent maturation of GnRH neurons (Adams et al., 1999).

Biological compartments: Neural network formation and function happen in all brain regions but it appears to onset at different time points of development (reviewed in Erecinska et al., 2004). Glutamatergic neurotransmission in hippocampus is poorly developed at birth. Initially, NMDA receptors play important role but the vast majority of these premature glutamatergic synapses are “silent” possibly due to delayed development of hippocampal AMPA receptors. In contrast, in the cerebral cortex the maturation of excitatory glutamatergic neurotransmission happens much earlier. The “silent” synapses disappear by PND 7-8 in both brain regions mentioned above.

There is strong evidence suggesting that NMDA receptor subunit composition controls synaptogenesis and synapse stabilization (Gambrill and Barria, 2011). It is established fact that during early postnatal development in the rat hippocampus, synaptogenesis occurs in parallel with a developmental switch in the subunit composition of NMDA receptors from NR2B to NR2A. It is suggested that early expression of NR2A in organotypic hippocampal slices reduces the number of synapses and the volume and dynamics of spines. In contrast, overexpression of NR2B does not affect the normal number and growth of synapses. However, it does increase spine motility, adding and retracting spines at a higher rate. The C terminus of NR2B, and specifically its ability to bind CaMKII, is sufficient to allow proper synapse formation and maturation. Conversely, the C terminus of NR2A was sufficient to stop the development of synapse number and spine growth. These results indicate that the ratio of synaptic NR2B over NR2A controls spine motility and synaptogenesis, and suggest a structural role for the intracellular C terminus of NR2 in recruiting the signalling and scaffolding molecules necessary for proper synaptogenesis. Interestingly, it was found that genetic deletion of NR3A accelerates glutamatergic synaptic transmission, as measured by AMPAR-mediated postsynaptic currents recorded in hippocampal CA1. Consistent, the deletion of NR3A accelerates the expression of the glutamate receptor subunits NR1, NR2A, and GluR1 sugesting that glutamatergic synapse maturation is critically dependent upon activation of NMDA-type glutamate receptors (Henson et al., 2012).

General role in biology: The development of neuronal networks can be distinguished into two phases: an early ‘establishment’ phase of neuronal connections, where activity-dependent and independent mechanisms could operate, and a later ‘maintenance’ phase, which appears to be controlled by neuronal activity (Yuste and Sur, 1999). These neuronal networks facilitate information flow that is necessary to produce complex behaviors, including learning and memory (Mayford et al., 2012).

How it is Measured or Detected

Methods that have been previously reviewed and approved by a recognized authority should be included in the Overview section above. All other methods, including those well established in the published literature, should be described here. Consider the following criteria when describing each method: 1. Is the assay fit for purpose? 2. Is the assay directly or indirectly (i.e. a surrogate) related to a key event relevant to the final adverse effect in question? 3. Is the assay repeatable? 4. Is the assay reproducible?

In vivo: The recording of brain activity by using electroencephalography (EEG), electrocorticography (ECoG) and local field potentials (LFP) assists towards the collection of signals generated by multiple neuronal cell networks. Advances in computer technology have allowed quantification of the EEG and expansion of quantitative EEG (qEEG) analysis providing a sensitive tool for time-course studies of different compounds acting on neuronal networks' function (Binienda et al., 2011). The number of excitatory or inhibitory synapses can be functionally studied at an electrophysiological level by examining the contribution of glutamatergic and GABAergic synaptic inputs. The number of them can be determined by variably clamping the membrane potential and recording excitatory and inhibitory postsynaptic currents (EPSCs or IPSCs) (Liu, 2004).

In vitro: Microelectrode array (MEA) recordings are also used to measure electrical activity in cultured neurons (Keefer et al., 2001, Gramowski et al., 2000; Gopal, 2003; Johnstone et al., 2010). MEAs can be applied in high throughput platforms to facilitate screening of numerous chemical compounds (McConnell et al., 2012). Using selective agonists and antagonists of different classes of receptors their response can be evaluated in a quantitative manner (Novellino et al., 2011; Hogberg et al., 2011).

Patch clamping technique can also be used to measure neuronal network activity.In some cases, if required, planar patch clamping technique can also be used to measure neuronal networks activity (e.g., Bosca et al., 2014).

References

Adams MM, Flagg RA, Gore AC., Perinatal changes in hypothalamic N-methyl-D-aspartate receptors and their relationship to gonadotropin-releasing hormone neurons. Endocrinology. 1999 May;140(5):2288-96.

Binienda ZK, Beaudoin MA, Thorn BT, Ali SF. (2011) Analysis of electrical brain waves in neurotoxicology: γ-hydroxybutyrate. Curr Neuropharmacol. 9: 236-239.

Bosca, A., M. Martina, and C. Py (2014) Planar patch clamp for neuronal networks--considerations and future perspectives. Methods Mol Biol, 2014. 1183: p. 93-113.

Erecinska M, Cherian S, Silver IA. (2004) Energy metabolism in mammalian brain during development. Prog Neurobiol. 73: 397-445.

Gambrill AC, Barria A. NMDA receptor subunit composition controls synaptogenesis and synapse stabilization. Proc Natl Acad Sci U S A. 2011:108(14):5855-60.

Gopal K. (2003) Neurotoxic effects of mercury on auditory cortex networks growing on microelectrode arrays: a preliminary analysis. Neurotoxicol Teratol. 25: 69-76.

Gramowski A, Schiffmann D, Gross GW. (2000) Quantification of acute neurotoxic effects of trimethyltin using neuronal networks cultures on microelectrode arrays. Neurotoxicology 21: 331-342.

Henson MA, Larsen RS, Lawson SN, Pérez-Otaño I, Nakanishi N, Lipton SA, Philpot BD. (2012) Genetic deletion of NR3A accelerates glutamatergic synapse maturation. PLoS One. 7(8).

Hogberg HT, Sobanski T, Novellino A, Whelan M, Weiss DG, Bal-Price AK. (2011) Application of micro-electrode arrays (MEAs) as an emerging technology for developmental neurotoxicity: evaluation of domoic acid-induced effects in primary cultures of rat cortical neurons. Neurotoxicology 32: 158-168.

Johnstone AFM, Gross GW, Weiss D, Schroeder O, Shafer TJ. (2010) Use of microelectrode arrays for neurotoxicity testing in the 21st century Neurotoxicology 31: 331-350.

Keefer E, Norton S, Boyle N, Talesa V, Gross G. (2001) Acute toxicity screening of novel AChE inhibitors using neuronal networks on microelectrode arrays. Neurotoxicology 22: 3-12.

Liu G. (2004) Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat Neurosci. 7: 373-379.

Mayford M, Siegelbaum SA, Kandel ER. (2012) Synapses and memory storage. Cold Spring Harb Perspect Biol. 4. pii: a005751.

McConnell ER, McClain MA, Ross J, LeFew WR, Shafer TJ. (2012) Evaluation of multi-well microelectrode arrays for neurotoxicity screening using a chemical training set. Neurotoxicology 33: 1048-1057.

Novellino A, Scelfo B, Palosaari T, Price A, Sobanski T, Shafer TJ, Johnstone AF, Gross GW, Gramowski A, Schroeder O, Jügelt K, Chiappalone M, Benfenati F, Martinoia S, Tedesco MT, Defranchi E, D'Angelo P, Whelan M. (2011) Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals. Front Neuroeng. 4: 4.

Yuste R, Peinado A, Katz LC. (1992) Neuronal domains in developing neocortex. Science 257: 665-669.

List of Adverse Outcomes in this AOP

Event: 341: Impairment, Learning and memory

Short Name: Impairment, Learning and memory

Key Event Component

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Basic forms of learning behavior such as habituation have been found in many taxa from worms to humans (Alexander, 1990). More complex cognitive processes such as executive function likely reside only in higher mammalian species such as non-human primates and humans. Recently, larval zebrafish has also been suggested as a model for the study of learning and memory (Roberts et al., 2013).

Key Event Description

 

Learning can be defined as the process by which new information is acquired to establish knowledge by systematic study or by trial and error (Ono, 2009). Two types of learning are considered in neurobehavioral studies: a) associative learning and b) non-associative learning. Associative learning is based on making associations between different events. In associative learning, a subject learns the relationship among two different stimuli or between the stimulus and the subject’s behaviour. On the other hand, non-associative learning can be defined as an alteration in the behavioural response that occurs over time in response to a single type of stimulus. Habituation and sensitization are some examples of non-associative learning.

The memory formation requires acquisition, retention and retrieval of information in the brain, which is characterised by the non-conscious recall of information (Ono, 2009). There are three main categories of memory, including sensory memory, short-term or working memory (up to a few hours) and long-term memory (up to several days or even much longer).

Learning and memory depend upon the coordinated action of different brain regions and neurotransmitter systems constituting functionally integrated neural networks (D’Hooge and DeDeyn, 2001). Among the many brain areas engaged in the acquisition of, or retrieval of, a learned event, the hippocampal-based memory systems have received the most study. For example, the hippocampus has been shown to be critical for spatial-temporal memory, visio-spatial memory, verbal and narrative memory, and episodic and autobiographical memory (Burgess et al., 2000; Vorhees and Williams, 2014). However, there is substantial evidence that fundamental learning and memory functions are not mediated by the hippocampus alone but require a network that includes, in addition to the hippocampus, anterior thalamic nuclei, mammillary bodies cortex, cerebellum and basal ganglia (Aggleton and Brown, 1999; Doya, 2000; Mitchell et al., 2002, Toscano and Guilarte, 2005; Gilbert et al., 2006, 2016). Thus, damage to variety of brain structures can potentially lead to impairment of learning and memory. The main learning areas and pathways are similar in rodents and primates, including man (Eichenbaum, 2000; Stanton and Spear, 1990).While the prefrontal cortex and frontostriatal neuronal circuits have been identified as the primary sites of higher-order cognition in vertebrates, invertebrates utilize paired mushroom bodies, shown to contain ~300,000 neurons in honey bees (Menzel, 2012; Puig et al., 2014).

For the purposes of this KE (AO), impaired learning and memory is defined as an organism’s inability to establish new associative or non-associative relationships, or sensory, short-term or long-term memories which can be measured using different behavioural tests described below.

How it is Measured or Detected

In laboratory animals: in rodents, a variety of tests of learning and memory have been used to probe the integrity of hippocampal function. These include tests of spatial learning like the radial arm maze (RAM), the Barnes maze, passive avoidance and Spontaneous alternation and most commonly, the Morris water maze (MWM). Test of novelty such as novel object recognition, and fear based context learning are also sensitive to hippocampal disruption. Finally, trace fear conditioning which incorporates a temporal component upon traditional amygdala-based fear learning engages the hippocampus. A brief description of these tasks follows.

1) RAM, Barnes, MWM are examples of spatial tasks, animals are required to learn the location of a food reward (RAM); an escape hole to enter a preferred dark tunnel from a brightly lit open field area (Barnes maze), or a hidden platform submerged below the surface of the water in a large tank of water (MWM) (Vorhees and Williams, 2014).

2) Novel Object recognition. This is a simpler task that can be used to probe recognition memory. Two objects are presented to animal in an open field on trial 1, and these are explored. On trial 2, one object is replaced with a novel object and time spent interacting with the novel object is taken evidence of memory retention – I have seen one of these objects before, but not this one (Cohen and Stackman, 2015).

3) Contextual Fear conditioning is a hippocampal based learning task in which animals are placed in a novel environment and allowed to explore for several minutes before delivery of an aversive stimulus, typically a mild foot shock. Upon reintroduction to this same environment in the future (typically 24-48 hours after original training), animals will limit their exploration, the context of this chamber being associated with an aversive event. The degree of suppression of activity after training is taken as evidence of retention, i.e., memory (Curzon et al., 2009).

4) Trace fear conditioning. Standard fear conditioning paradigms require animals to make an association between a neutral conditioning stimulus (CS, a light or a tone) and an aversive stimulus (US, a footshock). The unconditioned response (CR) that is elicited upon delivery of the footshock US is freezing behavior. With repetition of CS/US delivery, the previously neutral stimulus comes to elicit the freezing response. This type of learning is dependent on the amygdala, a brain region associated with, but distinct from the hippocampus. Introducing a brief delay between presentation of the neutral CS and the aversive US, a trace period, requires the engagement of the amygdala and the hippocampus (Shors et al., 2001).

In humans:  A variety of standardized learning and memory tests have been developed for human neuropsychological testing, including children (Rohlman et al., 2008). These include episodic autobiographical memory, perceptual motor tests, short and  long term memory tests, working memory tasks, word pair recognition memory; object location recognition memory. Some have been incorporated in general tests of intelligence (IQ) such as the Wechsler Adult Intelligence Scale (WAIS) and the Wechsler. Modifications have been made and norms developed for incorporating of tests of learning and memory in children. Examples of some of these tests include:

1) Rey Osterieth Complex Figure test (RCFT) which probes a variety of functions including as visuospatial abilities, memory, attention, planning, and working memory (Shin et al., 2006).

2) Children’s Auditory Verbal Learning Test (CAVLT) is a free recall of presented word lists that yields measures of Immediate Memory Span, Level of Learning, Immediate Recall, Delayed Recall, Recognition Accuracy, and Total Intrusions. (Lezak 1994; Talley, 1986).

3) Continuous Visual Memory Test (CVMT) measures visual learning and memory. It is a free recall of presented pictures/objects rather than words but that yields similar measures of Immediate Memory Span, Level of Learning, Immediate Recall, Delayed Recall, Recognition Accuracy, and Total Intrusions. (Lezak, 1984; 1994).

4) Story Recall from Wechsler Memory Scale (WMS) Logical Memory Test Battery, a standardized neurospychological test designed to measure memory functions (Lezak, 1994; Talley, 1986).

5) Autobiographical memory (AM) is the recollection of specific personal events in a multifaceted higher order cognitive process. It includes episodic memory- remembering of past events specific in time and place, in contrast to semantic autobiographical memory is the recollection of personal facts, traits, and general knowledge. Episodic AM is associated with greater activation of the hippocampus and a later and more gradual developmental trajectory. Absence of episodic memory in early life (infantile amnesia) is thought to reflect immature hippocampal function (Herold et al., 2015; Fivush, 2011).

6) Staged Autobiographical Memory Task. In this version of the AM test, children participate in a staged event involving a tour of the hospital, perform a series of tasks (counting footprints in the hall, identifying objects in wall display, buy lunch, watched a video). It is designed to contain unique event happenings, place, time, visual/sensory/perceptual details. Four to five months later, interviews are conducted using Children’s Autobiographical Interview and scored according to standardized scheme (Willoughby et al., 2014).

In Honey Bees: For over 50 years an assay for evaluating olfactory conditioning of the proboscis extension reflex (PER) has been used as a reliable method for evaluating appetitive learning and memory in honey bees (Guirfa and Sandoz, 2012; LaLone et al., 2017). These experiments pair a conditioned stimulus (e.g., an odor) with an unconditioned stimulus (e.g., sucrose) provided immediately afterward, which elicits the proboscis extension (Menzel, 2012). After conditioning, the odor alone will lead to the conditioned PER. This methodology has aided in the elucidation of five types of olfactory memory phases in honey bee, which include early short-term memory, late short-term memory, mid-term memory, early long-term memory, and late long-term memory (Guirfa and Sandoz, 2012). These phases are dependent on the type of conditioned stimulus, the intensity of the unconditioned stimulus, the number of conditioning trials, and the time between trials. Where formation of short-term memory occurs minutes after conditioning and decays within minutes, memory consolidation or stabilization of a memory trace after initial acquisition leads to mid-term memory, which lasts 1 d and is characterized by activity of the cAMP-dependent PKA (Guirfa and Sandoz, 2012). Multiple conditioning trials increase the duration of the memory after learning and coincide with increased Ca2+-calmodulin-dependent PKC activity (Guirfa and Sandoz, 2012). Early long-term memory, where a conditioned response can be evoked days to weeks after conditioning requires translation of existing mRNA, whereas late long-term memory requires de novo gene transcription and can last for weeks (Guirfa andSandoz, 2012)."

Regulatory Significance of the AO

A prime example of impairments in learning and memory as the adverse outcome for regulatory action is developmental lead exposure and IQ function in children (Bellinger, 2012). Most methods are well established in the published literature and many have been engaged to evaluate the effects of developmental thyroid disruption. The US EPA and OECD Developmental Neurotoxicity (DNT) Guidelines (OCSPP 870.6300 or OECD TG 426) as well as OECD TG 443 (OECD, 2018) both require testing of learning and memory (USEPA, 1998; OECD, 2007) advising to use the following tests passive avoidance, delayed-matching-to-position for the adult rat and for the infant rat, olfactory conditioning, Morris water maze, Biel or Cincinnati maze, radial arm maze, T-maze, and acquisition and retention of schedule-controlled behaviour.  These DNT Guidelines have been deemed valid to identify developmental neurotoxicity and adverse neurodevelopmental outcomes (Makris et al., 2009).

Also, in the frame of the OECD GD 43 (2008) on reproductive toxicity, learning and memory testing may have potential to be applied in the context of developmental neurotoxicity studies. However, many of the learning and memory tasks used in guideline studies may not readily detect subtle impairments in cognitive function associated with modest degrees of developmental thyroid disruption (Gilbert et al., 2012).

References

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Appendix 2

List of Key Event Relationships in the AOP