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Relationship: 907


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

Degeneration of dopaminergic neurons of the nigrostriatal pathway leads to Neuroinflammation

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
Inhibition of the mitochondrial complex I of nigro-striatal neurons leads to parkinsonian motor deficits adjacent Moderate Moderate Andrea Terron (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

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

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

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

Several chemokines and chemokines receptors (fraktalkine, CD200) control the neuron-microglia interactions and a loss of this control on the side of neurons can trigger microglial reactivity without any further positive signal required (Blank and Prinz, 2013; Chapman et al., 2000; Streit et al., 2001). Upon neuronal injury, signals termed “Damage-Associated Molecular Patterns (DAMPs)” are released by damaged neurons to promote microglial reactivity (Marin-Teva et al., 2011; Katsumoto et al., 2014). These are for instance detected by Toll-like receptors (TLRs) (for review, see Hayward and Lee, 2014). TLR-2 functions as a master sensing receptor to detect neuronal death and tissue damage in many different neurological conditions including nerve transection injury, traumatic brain injury and hippocampal excitotoxicity (Hayward and Lee, 2014). Astrocytes, the other cellular actor of neuroinflammation besides microglia (Ranshoff and Brown, 2012) are also able to sense tissue injury via e.g. TLR-3 (Farina et al., 2007; Rossi, 2015), and neuronal injury can result in astrocytic activation (Efremova, 2015).

The SNpc can be particularly vulnarable to the inflammatory process; its contains more microglia than astrocytes when compared with other areas of the brain and this can promote stronger neuroinfammation (Mena et al. 2008, Kim et al. 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
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

Kreutzberg and coworkers (1995, 1996) showed that neuronal injury generally leads to activation of microglia and astrocytes. This is a general phenomenon: for instance it is always observed in ischemic damage (stroke; often in the form of glial activation following neuronal injury (Villa 2007)) as well as in stab or freeze injuries (Allahyari and Garcia, 2015). It is also observed regularly when neurons are killed by highly specific neurotoxicants that do not affect glia directly, such as injection of quinolinic acid or of 6-hydroxydopamine into the striatum (Hernandez-Baltazar et al., 2013; Arlicot et al., 2014). The vicious circle of neuronal injury triggering glial activation and glial activation triggering/enhancing neurodegeneration is often assumed to be a key element in the pathogenesis of neurodegenerative diseases, not just PD, but also Alzheimer's disease, prion disease and many others(Hirsch and Hunot, 2009; Tansey and Goldberg, 2009; Griffin et al., 1998; McGeer and Mc Geer, 1998; Blasko et al., 2004; Cacquevel et al., 2004; Rubio-Perez and Morillas-Ruiz, 2012; Thundyil and Lim, 2014; Barbeito et al., 2010).

Innate immune system, mainly microglia and astrocytes is primary involved in Parkinson's disease the(Lucin et al. 2009, Glass et al. 20101, Rocha et al. 2012), and neurons are knowns to actively regulate the microglia response to stress (Mott et al. 2004, Cardona et al. 2006). Presence of reactive microglia has been observed in post-mortem brain tissue from PD patients or in people following intoxication with MPTP as well as in animal models of PD (McGeer et al. 1988, Langston et al. 1999, McGeer et al. 2003, Czlonkowska et al. 1996, Walsh et al. 2011). In co-cultures of neurons and microglia neuronal damage/cell death triggers microglia activation that potentiates MPTP-induced neuronal injury (Gao et al. 2003).

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

• Triggering of glia by injured neurons may not necessarily be due to the damage of neurons, but it may also be due to released synuclein (Sanchez-Guajardo, 2010)

• In a AAV alpha-synucleinoptahy model, it was shown that cytoskeletal perturbation and accumulation of alpha-synuclein were sufficient to induce microglial reactivity, suggesting that neuroinflammation appears early in the disease process and is not a result triggered by cell death (Chung et al., 2009)

• Direct effects of toxicants on glia cannot be completely excluded. They have been reported for most toxicants in one or the other publication (rotenone, paraquat, MPP+) (Zhang et al., 2014; Rappold et al., 2011; Brooks et al., 1989). The overwhelming evidence speaks against such effects for rotenone and MPP+ (Klintworth et al., 2009), but for paraquat there is evidence of direct interaction with microglial membrane NADPH oxidase (Rappold et al., 2011).

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
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

Beside the rodent models, the concept of vicious circle with neuronal injury leading to neuroinflammation and neuroinflammation triggering or enhancing neurodegeneration is described in several neurodegenerative diseases in human, without any sex restriction (Hirsch and Hunot, 2009; Tansey and Goldberg, 2009; Griffin et al., 1998; McGeer and Mc Geer, 1998; Blasko et al., 2004; Cacquevel et al., 2004; Rubio-Perez and Morillas-Ruiz, 2012; Thundyil and Lim, 2014; Barbeito et al., 2010). Aging is an aggravating factor and increases the risk for developing a neurodegenerative disease (Kawas et al., 2000; Blasko et al., 2004).


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

Abdelsalam RM, Safar MM. 2015. Neuroprotective effects of vildagliptin in rat rotenone Parkinson's disease model: role of RAGE-NFkappaB and Nrf2-antioxidant signaling pathways. J Neurochem 133(5): 700-707.

Allahyari RV, Garcia AD. 2015. Triggering Reactive Gliosis In Vivo by a Forebrain Stab Injury. Journal of visualized experiments : JoVE(100): e52825.

Annese V, Herrero MT, Di Pentima M, Gomez A, Lombardi L, Ros CM, et al. 2015. Metalloproteinase-9 contributes to inflammatory glia activation and nigro-striatal pathway degeneration in both mouse and monkey models of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinsonism. Brain structure & function 220(2): 703-727.

Arlicot N, Tronel C, Bodard S, Garreau L, de la Crompe B, Vandevelde I, et al. 2014. Translocator protein (18 kDa) mapping with [125I]-CLINDE in the quinolinic acid rat model of excitotoxicity: a longitudinal comparison with microglial activation, astrogliosis, and neuronal death. Molecular imaging 13: 4-11.

Barbeito AG, Mesci P, Boillee S. 2010. Motor neuron-immune interactions: the vicious circle of ALS. J Neural Transm 117(8): 981-1000.

Biber K, Neumann H, Inoue K, Boddeke HW. 2007. Neuronal 'On' and 'Off' signals control microglia. Trends Neurosci 30(11): 596-602.

Blank T, Prinz M. 2013. Microglia as modulators of cognition and neuropsychiatric disorders. Glia 61(1): 62-70.

Blasko I, Stampfer-Kountchev M, Robatscher P, Veerhuis R, Eikelenboom P, Grubeck-Loebenstein B. 2004. How chronic inflammation can affect the brain and support the development of Alzheimer's disease in old age: the role of microglia and astrocytes. Aging cell 3(4): 169-176.

Brooks WJ, Jarvis MF, Wagner GC. 1989. Astrocytes as a primary locus for the conversion MPTP into MPP+. J Neural Transm 76(1): 1-12.

Cacquevel M, Lebeurrier N, Cheenne S, Vivien D. 2004. Cytokines in neuroinflammation and Alzheimer's disease. Curr Drug Targets 5(6): 529-534.

Cardona AE, Pioro EP, Sasse ME et al (2006) Control of microglial neurotoxicity by the fractalkine receptor. Nat Neurosci 9:917–924.

Chung CY, Koprich JB, Siddiqi H, Isacson O. 2009. Dynamic changes in presynaptic and axonal transport proteins combined with striatal neuroinflammation precede dopaminergic neuronal loss in a rat model of AAV alpha-synucleinopathy. J Neurosci 29(11): 3365-3373.

Cicchetti F, Lapointe N, Roberge-Tremblay A, Saint-Pierre M, Jimenez L, Ficke BW, et al. 2005. Systemic exposure to paraquat and maneb models early Parkinson's disease in young adult rats. Neurobiol Dis 20(2): 360-371.

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

Czerniczyniec A, Lanza EM, Karadayian AG, Bustamante J, Lores-Arnaiz S. 2015. Impairment of striatal mitochondrial function by acute paraquat poisoning. Journal of bioenergetics and biomembranes 47(5): 395-408.

Efremova L, Schildknecht S, Adam M, Pape R, Gutbier S, Hanf B, et al. 2015. Prevention of the degeneration of human dopaminergic neurons in an astrocyte co-culture system allowing endogenous drug metabolism. Br J Pharmacol 172(16): 4119-4132.

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

Gao HM, Hong JS, Zhang W, Liu B. 2002. Distinct role for microglia in rotenone-induced degeneration of dopaminergic neurons. J Neurosci 22(3): 782-790.

Gao H, Liu B, Zhang W, Hong J (2003) Critical role of microglial NADPH oxidase-derived free radicals in the in vitro MPTP model of Parkinson’s disease. FASEB J 17:1954–1956. doi:10.1096/fj.03-0109fje.

Glass CK, Saijo K, Winner B et al (2010) Mechanisms underlying inflammation in neurodegeneration. Cell 140:918–934. doi:10.1016/j.cell.2010.02.016.

Czlonkowska A, Kohutnicka M, Kurkowska-Jastrzebska I, Czlonkowski A (1996) Microglial reaction in MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) induced Parkinson’s disease mice model. Neurodegeneration 5:137–143.

Griffin WS, Sheng JG, Royston MC, Gentleman SM, McKenzie JE, Graham DI, et al. 1998. Glial-neuronal interactions in Alzheimer's disease: the potential role of a 'cytokine cycle' in disease progression. Brain Pathol 8(1): 65-72.

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

Hernandez-Baltazar D, Mendoza-Garrido ME, Martinez-Fong D. 2013. Activation of GSK-3beta and caspase-3 occurs in Nigral dopamine neurons during the development of apoptosis activated by a striatal injection of 6-hydroxydopamine. PLoS One 8(8): e70951.

Hirsch EC, Hunot S. 2009. Neuroinflammation in Parkinson's disease: a target for neuroprotection? Lancet Neurol 8(4): 382-397.

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

Kawas C, Gray S, Brookmeyer R, Fozard J, Zonderman A. 2000. Age-specific incidence rates of Alzheimer's disease: the Baltimore Longitudinal Study of Aging. Neurology 54(11): 2072-2077.

Kim WG, Mohney RP, Wilson B et al (2000) Regional difference in susceptibility to lipopolysaccharide-induced neurotoxicity in the rat brain: role of microglia. J Neurosci: Off J Soc Neurosci 20:6309–6316.

Klintworth H, Garden G, Xia Z. 2009. Rotenone and paraquat do not directly activate microglia or induce inflammatory cytokine release. Neurosci Lett 462(1): 1-5.

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.

Langston JW, Forno LS, Tetrud J et al (1999) Evidence of active nerve cell degeneration in the substantia nigra of humans years after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine exposure. Ann Neurol 46:598–605.

Lucin KM, Wyss-Coray T (2009) Immune activation in brain aging and neurodegeneration: too much or too little? Neuron 64:110–122

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

McGeer PL, McGeer EG. 1998. Glial cell reactions in neurodegenerative diseases: Pathophysiology and therapeutic interventions. Alzheimer DisAssocDisord 12 Suppl. 2: S1-S6.

McGeer PL, Itagaki S, Boyes BE, McGeer EG (1988) Reactive microglia are positive for HLA-DR in the substantia nigra of Parkinson’s and Alzheimer's disease brains. Neurology 38:1285–1285.

McGeer PL, Schwab C, Parent A, Doudet D (2003) Presence of reactive microglia in monkey substantia nigra years after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine administration. Ann Neurol 54:599–604. doi:10.1002/ana.10728.

Mena M a, Yébenes G d J (2008) Immune activation in brain aging and neurodegeneration: too much or too little? Neuroscientist 14:544–560. doi:10.1177/1073858408322839.

Mitra S, Chakrabarti N, Bhattacharyya A. 2011. Differential regional expression patterns of alpha-synuclein, TNF-alpha, and IL-1beta; and variable status of dopaminergic neurotoxicity in mouse brain after Paraquat treatment. J Neuroinflammation 8: 163.

Mott RT, Ait-Ghezala G, Town T et al (2004) Neuronal expression of CD22: novel mechanism for inhibiting microglial proinflammatory cytokine production. Glia 46:369–379.

Purisai MG, McCormack AL, Cumine S, Li J, Isla MZ, Di Monte DA. 2007. Microglial activation as a priming event leading to paraquat-induced dopaminergic cell degeneration. Neurobiol Dis 25(2): 392-400.

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

Rappold PM, Cui M, Chesser AS, Tibbett J, Grima JC, Duan L, et al. 2011. Paraquat neurotoxicity is mediated by the dopamine transporter and organic cation transporter-3. Proc Natl Acad Sci U S A 108(51): 20766-20771.

Rocha SM, Cristovão AC, Campos FL et al (2012) Astrocyte-derived GDNF is a potent inhibitor of microglial activation. Neurobiol Dis 47:407–415. doi:10.1016/j.nbd.2012.04.014

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

Rubio-Perez JM, Morillas-Ruiz JM. 2012. A review: inflammatory process in Alzheimer's disease, role of cytokines. ScientificWorldJournal 2012: 756357.

Sanchez-Guajardo V, Febbraro F, Kirik D, Romero-Ramos M. Microglia acquire distinct activation profiles depending on the degree of alpha-synuclein neuropathology in a rAAV based model of Parkinson's disease. PLoS One. 2010 Jan 20;5(1):e8784.

Sandstrom von Tobel J, Zoia D, Althaus J, Antinori P, Mermoud J, Pak HS, et al. 2014. Immediate and delayed effects of subchronic Paraquat exposure during an early differentiation stage in 3D-rat brain cell cultures. Toxicol Lett. 10.1016/j.toxlet.2014.02.001

Shan S, Hong-Min T, Yi F, Jun-Peng G, Yue F, Yan-Hong T, et al. 2011. New evidences for fractalkine/CX3CL1 involved in substantia nigral microglial activation and behavioral changes in a rat model of Parkinson's disease. Neurobiol Aging 32(3): 443-458.

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

Sung YH, Kim SC, Hong HP, Park CY, Shin MS, Kim CJ, et al. 2012. Treadmill exercise ameliorates dopaminergic neuronal loss through suppressing microglial activation in Parkinson's disease mice. Life sciences 91(25-26): 1309-1316.

Tansey MG, Goldberg MS. 2009. Neuroinflammation in Parkinson's disease: Its role in neuronal death and implications for therapeutic intervention. Neurobiol Dis.

Teismann P, Sathe K, Bierhaus A, Leng L, Martin HL, Bucala R, et al. 2012. Receptor for advanced glycation endproducts (RAGE) deficiency protects against MPTP toxicity. Neurobiol Aging 33(10): 2478-2490.

Thundyil J, Lim KL. 2014. DAMPs and Neurodegeneration. Ageing research reviews.

Villa P, van Beek J, Larsen AK, Gerwien J, Christensen S, Cerami A, Brines M, Leist M, Ghezzi P, Torup L. Reduced functional deficits, neuroinflammation, and secondary tissue damage after treatment of stroke by nonerythropoietic erythropoietin derivatives. J Cereb Blood Flow Metab. 2007 Mar;27(3):552-63

Walsh S, Finn DP, Dowd E (2011) Time-course of nigrostriatal neurodegeneration and neuroinflammation in the 6-hydroxydopamine-induced axonal and terminal lesion models of Parkinson’s disease in the rat. Neuroscience 175:251–261.

Wang XJ, Zhang S, Yan ZQ, Zhao YX, Zhou HY, Wang Y, et al. 2011. Impaired CD200-CD200R-mediated microglia silencing enhances midbrain dopaminergic neurodegeneration: roles of aging, superoxide, NADPH oxidase, and p38 MAPK. Free Radic Biol Med 50(9): 1094-1106.

Zhang S, Wang XJ, Tian LP, Pan J, Lu GQ, Zhang YJ, et al. 2011. CD200-CD200R dysfunction exacerbates microglial activation and dopaminergic neurodegeneration in a rat model of Parkinson's disease. J Neuroinflammation 8: 154.

Zhang XY, Chen L, Yang Y, Xu DM, Zhang SR, Li CT, et al. 2014. Regulation of rotenone-induced microglial activation by 5-lipoxygenase and cysteinyl leukotriene receptor 1. Brain Res 1572: 59-71.