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Relationship: 1718

Title

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

Cell injury/death leads to Tissue resident cell activation

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

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Binding of electrophilic chemicals to SH(thiol)-group of proteins and /or to seleno-proteins involved in protection against oxidative stress during brain development leads to impairment of learning and memory adjacent Moderate Brendan Ferreri-Hanberry (send email) Under development: Not open for comment. Do not cite EAGMST Under Review
Protein Alkylation leading to Liver Fibrosis adjacent High Brendan Ferreri-Hanberry (send email) Open for citation & comment TFHA/WNT Endorsed

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
Monkey Monkey High NCBI
human and other cells in culture human and other cells in culture High NCBI
human Homo sapiens High NCBI

Sex Applicability

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

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
During brain development, adulthood and aging High
All life stages High

Key Event Relationship Description

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

The pioneering work of Kreutzberg and coworkers (1995, 1996) has shown that neuronal injury leads to neuroinflammation, with microglia and astrocyte reactivity. Several chemokines and chemokines receptors (fraktalkine, CD200) control the neuron-microglia interactions, and a loss of this control can trigger microglial reactivity (Blank and Prinz, 2013; Chapman et al., 2000; Streit et al., 2001). Upon injury causing neuronal death (mainly necrotic), signals termed Damage-Associated Molecular Patterns (DAMPs) are released by damaged neurons and promote microglial reactivity (Marin-Teva et al., 2011; Katsumoto et al., 2014). Toll-like receptors (TLRs) are pattern-recognition receptors that recognize specific pathogen- and danger-associated molecular signatures (PAMPs and DAMPs) and subsequently initiate inflammatory and immune responses. Microglial cells express TLRs, mainly TLR-2, which can detect neuronal cell death (for review, see Hayward and Lee, 2014). TLR-2 functions as a master sentry receptor to detect neuronal death and tissue damage in many different neurological conditions including nerve trans-section injury, traumatic brain injury and hippocampal excitotoxicity (Hayward and Lee, 2014). Astrocytes, the other cellular mediator of neuroinflammation (Ranshoff and Brown, 2012) are also able to sense tissue injury via TLR-3 (Farina et al., 2007; Rossi, 2015).

LIVER:

Damaged hepatocytes release reactive oxygen species (ROS), cytokines such as TGF-β1 and TNF-α, and chemokines which lead to oxidative stress, inflammatory signalling and finally activation of the resident macrophages in the liver, Kupffer cells (KCs). ROS generation in hepatocytes results from oxidative metabolism by NADH oxidase (NOX) and cytochrome 2E1 activation as well as through lipid peroxidation. Damaged liver cells trigger a sterile inflammatory response with activation of innate immune cells through release of damage-associated molecular patterns (DAMPs), which activate KCs through toll-like receptors and recruit activated neutrophils and monocytes into the liver. Central to this inflammatory response is the promotion of ROS formation by these phagocytes. Upon initiation of apoptosis hepatocytes undergo genomic DNA fragmentation and formation of apoptotic bodies; these apoptotic bodies are consecutively engulfed by KCs and cause their activation. This increased phagocytic activity strongly up-regulates NOX expression in KCs, a superoxide producing enzyme of phagocytes with profibrogenic activity, as well as nitric oxide synthase (iNOS) mRNA transcriptional levels with consequent harmful reaction between ROS and nitricoxide (NO), like the generation of cytotoxic peroxinitrite (N2O3). ROS and/or diffusible aldehydes also derive from liver sinusoidal endothelial cells (LSECs) which are additional initial triggers of KC activation. [Winwood and Arthur,1993; Luckey and Petersen, 2001; Roberts et al., 2007; Malhi, H. et al., 2010; Canbay et al., 2004; Orrenius et al., 2012; Kisseleva and Brenner, 2008; Jaeschke, 2011; Li et al., 2008; Poli, 2000]

Evidence Supporting this KER

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

LIVER:

There is convincing theoretical evidence that hepatocyte injury and apoptosis causes KC activation, as well as inflammation and oxidative stress. But there are only limited experimental studies which could show that there is a direct relationship between these two events with temporal concordance. Specific markers for activated KCs have not been identified yet. KC activation cannot be detected morphologically by staining techniques since cell morphology does not change, but cytokines release can be measured (with the caveat that KCs activate spontaneously in vitro) and used as marker for KC activation. [Canbay et al., 2003; Soldatow et al., 2013] Tukov et al. examined the effects of KCs cultured in contact with rat hepatocytes. They found that by adding KCs to the cultures they could mimic in vivo drug-induced inflammatory responses. Experiments on cells of the macrophage lineage showed significant aldehyde-induced stimulation of the activity of protein kinase C, an enzyme involved in several signal transduction pathways. Further, 4-Hydroxynonenal (HNE) was demonstrated to up-regulate TGF-β1 expression and synthesis in isolated rat KCs. [Tukov et al., 2006] Canbay et al could prove that engulfment of hepatocyte apoptotic bodies stimulated KC generation of cytokines. [LeCluyse et al., 2012]    

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

It is widely accepted that cell/neuronal injury and death lead to neuroinflammation (microglial and astrocyte reactivities) in adult brain. In the developing brain, neuroinflammation was observed after neurodegeneration induced by excitotoxic lesions (Acarin et al., 1997; Dommergues et al., 2003) or after ethanol exposure (Tiwari et al., 2012; Ahmad et al., 2016). It is important to note that physiological activation of microglial cells is observed during normal brain development for removal of apoptotic debris (Ashwell 1990, 1991). But exposure to toxicant (ethanol), excitotoxic insults (kainic acid) or traumatic brain injury during development can also induce apoptosis in hippocampus and cerebral cortex, as measured either by TUNEL, BID or caspase 3 upregulation associated to an inflammatory response, as evidenced by increased level of pro- inflammatory cytokines IL-1b, TNF-a, of NO, of p65 NF-kB or of the marker of astrogliosis, glial fibrillary acidic protein (GFAP), suggesting that, during brain development, neuroinflammation can also be triggerred by apoptosis induced by several types of insult (Tiwari and Chopra, 2012; Baratz et al., 2015; Mesuret et al., 2014).

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

Mercury

Mouse developmental exposure to 50 mM of HgCl2 in maternal drinking water from GD8 to PD21 did not induce any change in GM-CSF, IFN-g, IL-1b, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70. IL-13, IL-17, MCP1, MIP2 and TNF-a measured by Luminex in brain slices of PD21 and PD70. No sex differences, but brain increase of IgG and increased sociability in females (Zhang et al., 2012).

3D rat brain cell cultures treated for 10 days with HgCl2 or MeHgCl (10-10 - 10-6 M) exhibited increased apotosis measured by TUNEL, but exclusively in immature cultures. The proportion of cells undergoing apoptotis  was highest for astrocytes than for neurons. But the apoptotic nuclei were not associated with reactive microglial cells as evidenced by double staining (Monnet-Tschudi, 1998).

Acrylamide

A 2 weeks exposure to acrylamide in drinking water (44mg/kg/day) induced behavioral effects, such a decreased in locomotor activity, but with no effect at gene level on neuronal and inflammatory markers analyzed in somatosensory and motor cortex (Bowyer et al., 2009).

LIVER:

The detailed mechanisms of the KC - hepatocyte interaction and its consequences for both normal and toxicant-driven liver responses remain to be determined. KC activation followed by cytokine release is associated in some cases with evident liver damage, whereas in others this event is unrelated to liver damage or may be even protective; apparently this impact is dependent on the quantity of KC activation; excessive or prolonged release of KC mediators can switch an initially protective mechanism to a damaging inflammatory response. Evidence suggests that low levels of cytokine release from KCs constitute a survival signal that protects hepatocytes from cell death and in some cases, stimulates proliferation. [Roberts et al., 2007]   

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help
Time-scale
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help
Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help
Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Domain of Applicability

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

Liver:

Human [Winwood and Arthur,1993; Roberts et al., 2007; Kolios et al., 2006]    

Rat [Tukov et al., 2006; Roberts et al., 2007]

References

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

Acarin L, González B, Castellano B, Castro AJ. 1997. Quantitative analysis of microglial reaction to a cortical excitotoxic lesion in the early postnatal brain. ExpNeurol 147: 410-417.

Ahmad A, Shah SA, Badshah H, Kim MJ, Ali T, Yoon GH, et al. 2016. Neuroprotection by Vitamin C Against Ethanol-Induced Neuroinflammation Associated Neurodegeneration in the Developing Rat Brain. CNS Neurol Disord Drug Targets 15(3): 360-370.

Aschner, M., Wu, Q., Friedman, M.A., 2005. Effects of acrylamide on primary neonatal rat astrocyte functions. Ann N Y Acad Sci. 1053, 444-54.

Ashwell K. 1990. Microglia and cell death in the developing mouse cerebellum. DevBrain Res 55: 219-230.

Ashwell K. 1991. The distribution of microglia and cell death in the fetal rat forebrain. DevBrain Res 58: 1-12.

Baratz R, Tweedie D, Wang JY, Rubovitch V, Luo W, Hoffer BJ, et al. 2015. Transiently lowering tumor necrosis factor-alpha synthesis ameliorates neuronal cell loss and cognitive impairments induced by minimal traumatic brain injury in mice. J Neuroinflammation 12: 45.

Blank T, Prinz M. Microglia as modulators of cognition and neuropsychiatric disorders. Glia, 2013, 61: 62-70.

Bowyer, J.F., et al., 2009. The mRNA expression and histological integrity in rat forebrain motor and sensory regions are minimally affected by acrylamide exposure through drinking water. Toxicol Appl Pharmacol. 240, 401-11.

Chapman GA, Moores K, Harrison D, Campbell CA, Stewart BR, Strijbos PJLM. Fractalkine Cleavage from Neuronal Membrans Represents an Acute Event in Inflammatory Response to Excitotoxic Brain Damage. J Neurosc., 2000, 20 RC87: 1-5.

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

Thomas Curtis, J., et al., 2011. Chronic inorganic mercury exposure induces sex-specific changes in central TNFalpha expression: importance in autism? Neurosci Lett. 504, 40-4.

Dommergues MA, Plaisant F, Verney C, Gressens P. 2003. Early microglial activation following neonatal excitotoxic brain damage in mice: a potential target for neuroprotection. Neuroscience 121(3): 619-628.

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.

Farina C, Aloisi F, Meinl E. Astrocytes are active players in cerebral innate immunity. Trends Immunol, 2007, 28(3): 138-145.

Godefroy, D., et al., 2012. The chemokine CCL2 protects against methylmercury neurotoxicity. Toxicol Sci. 125, 209-18.

Hayward JH, Lee SJ. A Decade of Research on TLR2 Discovering Its Pivotal Role in Glial Activation and Neuroinflammation in Neurodegenerative Diseases. Experimental Neurobiology, 2014, 23(2): 138-147.

Katsumoto A, Lu H, Miranda AS, Ransohoff RM. Ontogeny and functions of central nervous system macrophages. J Immunol., 2014, 193(6): 2615-2621.

Kempuraj, D., et al., 2010. Mercury induces inflammatory mediator release from human mast cells. J Neuroinflammation. 7, 20.

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

Kreutzberg GW. Microglia : a sensor for pathological events in the CNS. Trends Neurosci., 2006, 19: 312-318.

Lohren, H., et al., 2015. Toxicity of organic and inorganic mercury species in differentiated human neurons and human astrocytes. J Trace Elem Med Biol. 32, 200-8.

Malfa, G.A., et al., 2014. "Reactive" response evaluation of primary human astrocytes after methylmercury exposure. J Neurosci Res. 92, 95-103.

Marin-Teva JL, Cuadros MA, Martin-Oliva D, Navascues J., Microglia and neuronal cell death. Neuron glia biology, 2011, 7(1): 25-40.

Mesuret G, Engel T, Hessel EV, Sanz-Rodriguez A, Jimenez-Pacheco A, Miras-Portugal MT, et al. 2014. P2X7 receptor inhibition interrupts the progression of seizures in immature rats and reduces hippocampal damage. CNS neuroscience & therapeutics 20(6): 556-564.

Ni, M., et al., 2011. Comparative study on the response of rat primary astrocytes and microglia to methylmercury toxicity. Glia. 59, 810-20.

Ni, M., et al., 2012. Glia and methylmercury neurotoxicity. J Toxicol Environ Health A. 75, 1091-101.

Ransohoff RM, Brown MA. Innate immunity in the central nervous system. J Clin Invest., 2012, 122(4): 1164-1171.

Roda, E., et al., 2008. Cerebellum cholinergic muscarinic receptor (subtype-2 and -3) and cytoarchitecture after developmental exposure to methylmercury: an immunohistochemical study in rat. J Chem Neuroanat. 35, 285-94.

Rossi D. Astrocyte physiopathology: At the crossroads of intercellular networking, inflammation and cell death. Prog Neurobiol., 2015, 130: 86-120.

Santhanasabapathy, R., et al., 2015. Farnesol quells oxidative stress, reactive gliosis and inflammation during acrylamide-induced neurotoxicity: Behavioral and biochemical evidence. Neuroscience. 308, 212-27.

Streit WJ, Conde J, Harrison JK. Chemokines and Alzheimer's disease. Neurobiol Aging., 2001, 22: 909-913.

Tiwari V, Chopra K. 2012. Attenuation of oxidative stress, neuroinflammation, and apoptosis by curcumin prevents cognitive deficits in rats postnatally exposed to ethanol. Psychopharmacology (Berl) 224(4): 519-535

Wang, Y.T., et al., 2017. Acrolein acts as a neurotoxin in the nigrostriatal dopaminergic system of rat: involvement of alpha-synuclein aggregation and programmed cell death. Sci Rep. 7, 45741.

Yamamoto, M., et al., 2012. Increased expression of aquaporin-4 with methylmercury exposure in the brain of the common marmoset. J Toxicol Sci. 37, 749-63.

Zhang, Y., Bolivar, V.J., Lawrence, D.A., 2012. Developmental exposure to mercury chloride does not impair social behavior of C57BL/6 x BTBR F(1) mice. J Immunotoxicol. 9, 401-10.

Zhao, M., et al., 2017. Effect of acrylamide-induced neurotoxicity in a primary astrocytes/microglial co-culture model. Toxicol In Vitro. 39, 119-125.

Zhao, M., et al., 2017. Acrylamide-induced neurotoxicity in primary astrocytes and microglia: Roles of the Nrf2-ARE and NF-kappaB pathways. Food Chem Toxicol. 106, 25-35.

Zhao, W.Z., et al., 2017. Neuroprotective Effects of Baicalein on Acrolein-induced Neurotoxicity in the Nigrostriatal Dopaminergic System of Rat Brain. Mol Neurobiol.

LIVER:

  • Canbay, A. et al. (2003), Kupffer cell engulfment of apoptotic bodies stimulates death ligand and cytokine expression, Hepatology, vol. 38, no. 5, pp. 1188-1198.
  • Canbay, A., S.L. Friedman and G.J. Gores (2004), Apoptosis: the nexus of liver injury and fibrosis, Hepatology, vol. 39, no. 2, pp. 273-278.
  • Jaeschke, H. (2011), Reactive oxygen and mechanisms of inflammatory liver injury: Present concepts, J Gastroenterol Hepatol. vol. 26, suppl. 1, pp. 173-179.
  • Kisseleva T and Brenner DA, (2008), Mechanisms of Fibrogenesis, Exp Biol Med, vol. 233, no. 2, pp. 109-122.
  • 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.
  • LeCluyse, E.L. et al. (2012), Organotypic liver culture models: meeting current challenges in toxicity testing, Crit Rev Toxicol, vol. 42, no. 6, 501-548.
  • 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.
  • 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.
  • Malhi, H. et al. (2010), Hepatocyte death: a clear and present danger, Physiol Rev, vol. 90, no. 3, pp. 1165-1194.
  • Orrenius, S., P. Nicotera and B. Zhivotovsky (2011), Cell death mechanisms and their implications in toxicology, Toxicol. Sci, vol. 119, no. 1, pp. 3-19.
  • Poli, G. (2000), Pathogenesis of liver fibrosis: role of oxidative stress, Mol Aspects Med, vol. 21, no. 3, pp. 49 – 98.
  • 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.
  • Soldatow, V.Y. et al. (2013), In vitro models for liver toxicity testing, Toxicol Res, vol. 2, no.1, pp. 23–39.
  • Tukov, F.F. et al. (2006), Modeling inflammation-drug interactions in vitro: a rat Kupffer cell-hepatocyte co-culture system, Toxicol In Vitro, vol. 20, no. 8, pp. 1488-1499.
  • Winwood, P.J., and M.J. Arthur (1993), Kupffer cells: their activation and role in animal models of liver injury and human liver disease, Semin Liver Dis, vol. 13, no. 1, pp. 50-59.