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Relationship: 208

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

Neuroinflammation leads to N/A, Neurodegeneration

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 agonists to ionotropic glutamate receptors in adult brain causes excitotoxicity that mediates neuronal cell death, contributing to learning and memory impairment. adjacent Moderate Anna Price (send email) Open for citation & comment WPHA/WNT Endorsed
Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development leads to neurodegeneration with impairment in learning and memory in aging adjacent Low Florianne Tschudi-Monnet (send email) Open for citation & comment WPHA/WNT Endorsed
Binding of Sars-CoV-2 spike protein to ACE 2 receptors expressed on brain cells (neuronal and non-neuronal) leads to neuroinflammation resulting in encephalitis adjacent High Not Specified Anna Price (send email) Under development: Not open for comment. Do not cite

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
human Homo sapiens High NCBI
mouse Mus musculus High NCBI
rat Rattus norvegicus 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
Mixed 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

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

It is well accepted that chronic neuroinflammation is involved in the pathogenesis of neurodegenerative diseases (McNaull et al., 2010; Tansey and Goldberg, 2009; Thundyil and Lim, 2015 ). Chronic neuroinflammation can cause secondary damage (Kraft and Harry, 2011). The mechanisms by which neuroinflammation (i.e. activated microglia and astrocytes) can kill neurons and induce/exacerbate the neurodegenerative process has been suggested to include the release of nitric oxide that causes inhibition of neuronal respiration, ROS and RNS production, and rapid glutamate release resulting in excitotoxic death of neurons (Brown & Bal-Price, 2003; Kraft & Harry, 2011; Taetzsch & Block, 2013). Glial reactivity is also associated with excessive production and release of pro-inflammatory cytokines that not only affect neurons, but also have detrimental feedback effects on microglia (Heneka et al., 2014). For example, sustained exposure to bacterial lipopolysaccharide (LPS) or to other pro-inflammatory mediators was shown to restrict microglial phagocytosis of misfiled and aggregated proteins (Sheng et al., 2003). Systemic immune challenge during pregnancy leading to microglial activation caused increased deposition of amyloid plaques and tau hyperphosphorylation in aged mice (Krstic et al., 2012, 2013), suggesting that neuroinflammation is involved in the amyloid plaques and neurofibrillary tangles formation. There is further evidence that the formation of neurofibrillary tangles is caused by microglial cell-driven neuroinflammation, since LPS-induced systemic inflammation increased tau pathology (Kitazawa et al., 2005).

Sars-CoV-2 specific evidence:

Studies on post-mortem cases indicate that lymphocytes and monocytes infiltrate in brain vessel walls, exacerbating the neuronal degeneration and demyelination process (Wu et al., 2020)

The aberrant immune response characterized by a surge in cytokine levels (e.g., IL-6) derived by SARS-CoV-2 accelerates the process of neurodegeneration that may contribute to the development of neurodegenerative diseases (Debnath et al. 2020).

SARS-CoV-2 can infect human brain organoids resulting in unique metabolic changes and the death of infected and neighbouring neurons. This phenotype is accompanied by impaired synaptogenesis (Song et al., 2020) (Mesci et al, 2020).

Moreover, it is hypothesized that an autoimmune reaction mediated by the cross-reaction between viral particles and myelin basic protein may provide the driving force for neural demyelination, as part of the neurodegenerative process. This hypothesis is supported by the fact that the genome of other coronaviruses like CoV-OC43 and CoV-229E, as well as their antibodies, has been isolated from the CNS of Multiple Sclerosis (MS) patients, and coronavirus-like particles have been found in perivascular cuffs of human MS brain (Montalvan et al. 2020). In fact, the virus might lie dormant in astrocytes and oligodendrocytes and trigger the autoimmunity mediated by molecular mimicry (Mohammadi et al., 2020).

Neurons are the target cells undergoing degeneration during infection, in part due to apoptosis (de Assis et al. 2020). Intracerebral inoculation with CoV-OC43 in susceptible mice led to an acute encephalitis, with neuronal cell death by necrosis and apoptosis (Jacomy H, et al. 2006).

SARS-CoV infection causes neuronal death (even in the absence of encephalitis) in mice transgenic for human ACE2. Death of the animal likely results from dysfunction and/or death of infected neurons, especially those located in cardiorespiratory centres in the medulla. The absence of the host cell receptor prevents severe murine brain disease (Netland J, et al. 2008).

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

Neuroinflammation is a component of neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease (Neumann, 2001) which may play a secondary or an active primary role in the disease process (Hirsch and Hunot, 2009). McNaull and coworkers (McNaull et al., 2010) suggested that early developmental onset of brain inflammation could be linked with late onset of Alzheimer’s disease. A recent paper by Krstic and coworkers (2012) showed that a systemic immune challenge during late gestation predispose mice to develop Alzheimer’s like pathology when aging, suggesting a causal link between systemic inflammation, neuroinflammation, and the onset of Alzheimer’s disease. Regarding toxicant-induced neuroinflammation, microglial/astrocyte activation and chronic neuron damage may continue for years after initial exposure (Taetsch and Block, 2013), suggesting that chronical neuroinflammation and neurodegeneration have a slow long-term temporal evolution. Ongoing neuroinflammation can be visualized in patients using the positron emission tomography (PET) ligand [11C] (R)-PK11195 (Cagnin et al., 2001). Recent genome-wide association study (GWAS) analyses of sporadic Alzheimer's disease revealed a set of genes that point to a pathogenic role of neuroinflammation in Alzheimer's disease (for review, see Heneka et al., 2014). High levels of pro-inflammatory cytokines produced by activated microglia and astrocytes are detected in the brain of Alzheimer's subjects and animal models (McGeer and McGeer, 1998; Janelsins et al., 2005). 

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

Long-term treatments with NSAIDs (non-steroidal anti-inflammatory drugs) have a preventive effect on Alzheimer's disease development (Piertrzick and Behl, 2005; Wang et al., 2015), but such treatment has no effect or is even detrimental if administered once the disease is at an advanced stage (Lichtenstein et al., 2010), This may be due to the dual protective/destructive effects of neuroinflammation and to its complexity.

Serum Pb level negatively correlates with verbal memory score, but not with abnormal cognition in Alzheimer's disease (Park et al., 2014). Epidemiologic studies are not well-suited to accomodate the long latency period between exposures during early life and late onset of Alzheimer's disease, even if bone Pb content is an accurate measurement of historical Pb exposure in adult (Bakulski et al., 2012).

Besides neuroinflammation or effects associated with neuroinflammation, other mechanisms may be involved in neurodegeneration with Abeta and tau accumulation: Pb-induced epigenetic modifications of genes involved in the amyloid cascade or tau expression may contribute to the accumulation of Abeta and tau accumulation following developmental exposure to Pb (Zawia and Basha, 2005; Basha and Reddy, 2010). Also oxidative damage to DNA was shown to be involved in delayed effects observed in old rats (PD 600), if exposed early postanatally (PD 1 to 20) (Bolin et al., 2006)

Gap of knowledge: there are no studies showing that glufosinate-induced neuroinflammation leads to neurodegeneration.

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

The hypotheisis of developmental origin of Pb-induced neurodegeneration was tested and observed in Zebra fish by Lee and Freeman (2014).

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

Ashok A, Rai NK, Tripathi S, Bandyopadhyay S., Exposure to As-, Cd-, and Pb-mixture induces Abeta, amyloidogenic APP processing and cognitive impairments via oxidative stress-dependent neuroinflammation in young rats. Toxicol Sci., 2015, 143(1): 64-80.

Bakulski KM, Park SK, Weisskopf MG, Tucker KL, Sparrow D, Spiro A, 3rd, et al. 2014. Lead exposure, B vitamins, and plasma homocysteine in men 55 years of age and older: the VA normative aging study. Environ Health Perspect 122(10): 1066-1074.

Basha MR, Murali M, Siddiqi HK, Ghosal K, Siddiqi OK, Lashuel HA, et al. 2005. Lead (Pb) exposure and its effect on APP proteolysis and Abeta aggregation. FASEB J 19(14): 2083-2084.

Basha R, Reddy GR. 2010. Developmental exposure to lead and late life abnormalities of nervous system. Indian journal of experimental biology 48(7): 636-641.

Bihaqi SW, Huang H, Wu J, Zawia NH. 2011. Infant exposure to lead (Pb) and epigenetic modifications in the aging primate brain: implications for Alzheimer's disease. J Alzheimers Dis 27(4): 819-833.

Bihaqi SW, Zawia NH. 2013. Enhanced taupathy and AD-like pathology in aged primate brains decades after infantile exposure to lead (Pb). Neurotoxicology 39: 95-101.

Bihaqi SW, Bahmani A, Subaiea GM, Zawia NH. 2014. Infantile exposure to lead and late-age cognitive decline: relevance to AD. Alzheimer's & dementia : the journal of the Alzheimer's Association 10(2): 187-195.

Bolin CM, Basha R, Cox D, Zawia NH, Maloney B, Lahiri DK, et al. 2006. Exposure to lead and the developmental origin of oxidative DNA damage in the aging brain. Faseb J 20(6): 788-790.

Brown GC, Bal-Price A., Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria. Mol Neurobiol., 2003, 27(3): 325-355.

Cagnin A, Brooks DJ, Kennedy AM, Gunn RN, Myers R, Turkheimer FE, et al., In-vivo measurement of activated microglia in dementia. Lancet, 2001, 358(9280): 461-467.

Gassowska M, Baranowska-Bosiacka I, Moczydlowska J, Tarnowski M, Pilutin A, Gutowska I, et al. 2016. Perinatal exposure to lead (Pb) promotes Tau phosphorylation in the rat brain in a GSK-3beta and CDK5 dependent manner: Relevance to neurological disorders. Toxicology 347-349: 17-28.

Gu H, Robison G, Hong L, Barrea R, Wei X, Farlow MR, et al. 2012. Increased beta-amyloid deposition in Tg-SWDI transgenic mouse brain following in vivo lead exposure. Toxicol Lett 213(2): 211-219.

Heneka MT, Kummer MP, Latz E., Innate immune activation in neurodegenerative disease. Nat Rev Immunol., 2014, 14(7): 463-477.

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

Janelsins MC, Mastrangelo MA, Oddo S, LaFerla FM, Federoff HJ, Bowers WJ., Early correlation of microglial activation with enhanced tumor necrosis factor-alpha and monocyte chemoattractant protein-1 expression specifically within the entorhinal cortex of triple transgenic Alzheimer's disease mice. J Neuroinflammation, 2005, 2: 23.

Kasten-Jolly J, Heo Y, Lawrence DA. 2011. Central nervous system cytokine gene expression: modulation by lead. J Biochem Mol Toxicol 25(1): 41-54.

Kasten-Jolly J, Pabello N, Bolivar VJ, Lawrence DA. 2012. Developmental lead effects on behavior and brain gene expression in male and female BALB/cAnNTac mice. Neurotoxicology 33(5): 1005-1020.

Kitazawa M, Oddo S, Yamasaki TR, Green KN, LaFerla FM., Lipopolysaccharide-induced inflammation exacerbates tau pathology by a cyclin-dependent kinase 5-mediated pathway in a transgenic model of Alzheimer's disease. J Neurosci., 2005, 25(39): 8843-8853.

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.

Krstic, D., A. Madhusudan, et al., 2012. Systemic immune challenges trigger and drive Alzheimer-like neuropathology in mice. J Neuroinflammation, 2012, 9: 151.

Krstic D, Knuesel I. 2013. Deciphering the mechanism underlying late-onset Alzheimer disease. Nature reviews Neurology 9(1): 25-34.

Kumawat KL, Kaushik DK, Goswami P, Basu A. 2014. Acute exposure to lead acetate activates microglia and induces subsequent bystander neuronal death via caspase-3 activation. Neurotoxicology 41: 143-153.

Lee J, Freeman JL. 2014. Zebrafish as a model for investigating developmental lead (Pb) neurotoxicity as a risk factor in adult neurodegenerative disease: a mini-review. Neurotoxicology 43: 57-64.

Lichtenstein MP, Carriba P, Masgrau R, Pujol A, Galea E., Staging anti-inflammatory therapy in Alzheimer's disease. Frontiers in Aging Neuroscience, 2010, 2: 142.

Liu MC, Liu XQ, Wang W, Shen XF, Che HL, Guo YY, et al. 2012. Involvement of microglia activation in the lead induced long-term potentiation impairment. PLoS One 7(8): e43924.

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

McNaull BB, Todd S, McGuinness B, Passmore AP., Inflammation and Anti-Inflammatory Strategies for Alzheimer's Disease - A Mini-Review. Gerontology, 2010, 56: 3-14.

Neumann H., Control of Glial Immune Function by Neurons. Glia, 2001, 36: 191-199

Park JH, Lee DW, Park KS, Joung H. 2014. Serum trace metal levels in Alzheimer's disease and normal control groups. American journal of Alzheimer's disease and other dementias 29(1): 76-83.

Pietrzik, C. and C. Behl., Concepts for the treatment of Alzheimer's disease: molecular mechanisms and clinical application. Int J Exp Pathol., 2005, 86(3): 173-185.

Ryan JC, Morey JS, Ramsdell JS, Van Dolah FM. Acute phase gene expression in mice exposed to the marine neurotoxin domoic acid. Neuroscience 2005. 136: 1121-1132.

Ryan JC, Cross CA, Van Dolah FM. Effects of COX inhibitors on neurodegeneration and survival in mice exposed to the marine neurotoxin domoic acid. Neurosci Lett. 2011. 487: 83-87.

Sheng JG, Bora SH, Xu G, Borchelt DR, Price DL, Koliatsos VE., Lipopolysaccharide-induced-neuroinflammation increases intracellular accumulation of amyloid precursor protein and amyloid beta peptide in APPswe transgenic mice. Neurobiol Dis., 2003, 14(1): 133-145.

Stewart WF, Schwartz BS, Davatzikos C, Shen D, Liu D, Wu X, et al. 2006. Past adult lead exposure is linked to neurodegeneration measured by brain MRI. Neurology 66(10): 1476-1484.

Taetzsch T, Block ML., Pesticides, microglial NOX2, and Parkinson's disease. J Biochem Mol Toxicol., 2013, 27(2): 137-149.

Tansey MG, Goldberg MS., Neuroinflammation in Parkinson's disease: Its role in neuronal death and implications for therapeutic intervention. Neurobiol Dis., 2010, 37(3):510-8.

Thundyil J, Lim KL. 2015. DAMPs and neurodegeneration. Ageing research reviews 24(Pt A): 17-28.

Wang J, Tan L, Wang HF, Tan CC, Meng XF, Wang C, Tang SW, Yu JT (2015) Anti-inflammatory drugs and risk of Alzheimer's disease: an updated systematic review and meta-analysis. J Alzheimers Dis 44: 385-96

Zawia NH, Basha MR. 2005. Environmental risk factors and the developmental basis for Alzheimer's disease. Rev Neurosci 16(4): 325-337.

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.

Sars-CoV-2-related references:

Debnath M et al. Changing dynamics of psychoneuroimmunology during COVID-19 pandemic. Brain Behav Immun Health. 2020 May;5:100096.

Jacomy H, et al. Human coronavirus OC43 infection induces chronic encephalitis leading to disabilities in BALB/C mice. Virology. (2006) 349:335–46

Mesci P et al. Sofosbuvir protects human brain organoids against SARS-CoV-2. bioRxiv. 2020. Available at: doi: https://doi.org/10.1101/2020.05.30.125856

Mohammadi S. et al. Understanding the Immunologic Characteristics of Neurologic Manifestations of SARS-CoV-2 and Potential Immunological Mechanisms. Mol Neurobiol. 2020 Dec;57(12):5263-5275.

Montalvan V, Lee J, Bueso T, De Toledo J, Rivas K (2020) Neurological manifestations of COVID-19 and other coronavirus infections: a systematic review. Clin Neurol Neurosurg. 2020 Jul;194:105921.

Netland J, et al. Severe acute respiratory syndrome coronavirus infection causes neuronal death in the absence of encephalitis in mice transgenic for human ACE2. J Virol. 2008;82:7264–7275.

Song et al. Neuroinvasive potential of SARS-CoV-2 revealed in a human brain organoid model. bioRxiv. 2020. Available at: https://www.biorxiv.org/content/10.1101/2020.06.25.169946v1

Wu Y, et al. Nervous system involvement after infection with COVID19 and other coronaviruses. Brain Behav Immun. 2020 Jul;87:18-22.

Zanin L, et al. SARS-CoV-2 can induce brain and spine demyelinating lesions. Acta Neurochir (Wien). 2020 Jul;162(7):1491-1494.