Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.02.19.580982

Pathologic α-Synuclein-NOD2 Interaction and RIPK2 Activation Drives Microglia-Induced Neuroinflammation in Parkinson's Disease

Abstract

Pathological aggregation of -Synuclein (-Syn) and neuroinflammation are closely linked to Parkinsons disease (PD). However, the specific regulators of the neuroinflammation caused by pathological -syn remain obscure. In this study, we show that NOD2/RIPK2 signaling is a crucial regulator of neuroinflammation in PD. Pathological -syn binds to NOD2, causing self-oligomerization and complex formation with RIPK2, leading to RIPK2 ubiquitination and activation of MAPK and NF-kB. Notably, this NOD2/RIPK2 signaling is particularly active in microglia of human PD brains and the -Syn preformed fibril (-Syn PFF) mouse model. Depleting NOD2 or RIPK2 reduces neuroinflammation and protects against dopamine neuron degeneration in a pathologic -Syn mouse model by blocking the formation of neurotoxic reactive astrocytes caused by microglia activation. The discovery of NOD2/RIPK2 signaling as a key regulator of neuroinflammation in PD provides a new understanding of -Syn-driven neuroinflammation and neurodegeneration in PD and a potential new therapeutic strategy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/580982v2_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@10c010aorg.highwire.dtl.DTLVardef@1183b23org.highwire.dtl.DTLVardef@1d289dborg.highwire.dtl.DTLVardef@158b6ef_HPS_FORMAT_FIGEXP M_FIG C_FIG In briefPathological -Synuclein (-Syn) binds to the microglial NOD2 protein, which in turn triggers NOD2/RIPK2 complex and RIPK2 phosphorylation/ubiquitination. This process activates the NF-kB/MAPK pathways, ultimately leading to neurotoxic reactive astrocyte-induced dopaminergic neurodegeneration. Depletion of RIPK2 (RIPK2 KO) or NOD2 (NOD2) protects dopamine neurons in a mouse model of Parkinsons disease (PD). These findings provide insights into -Syn-driven neuroinflammation and offer potential therapeutic strategies for PD. HighlightsNOD2/RIPK2 signaling is identified as a crucial regulator of neuroinflammation in PD. NOD2/RIPK2 signaling is highly active in microglia in human PD brains and -Syn PFF mouse models. Pathological -Syn binds to NOD2, triggering self-oligomerization and RIPK2 complex formation, leading to MAPK and NF-kB activation Genetic depletion of NOD2 or RIPK2 reduces neuroinflammation and protects dopamine neurons by blocking the formation of neurotoxic reactive astrocytes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ko, H., Dawson, T. M., Seo, B. A., Kwon, S.-H., Kim, D., Kim, H.-B., Ma, S.-X., Gadhave, K., Burgess, N., Mao, X., Rosenthal, L. S., Redding-Ochoa, J., Troncoso, J. C., Lee, S., Dawson, V. L.. 2024-02-25. Pathologic α-Synuclein-NOD2 Interaction and RIPK2 Activation Drives Microglia-Induced Neuroinflammation in Parkinson's Disease. https://doi.org/10.1101/2024.02.19.580982

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

CoReg, tracking-based motion compensation for in vivo calcium imaging

In vivo calcium imaging enables the observation of neural activity with high spatial and temporal resolution. However, recordings are frequently affected by motion artifacts caused by animal movement, physiological processes, tissue drift, or instability of the imaging setup. These artifacts make signal extraction harder and reduce the consistency of downstream analyses. Current registration methods can correct rigid and non-rigid motion, but often estimate motion from individual frames or image patches and make limited use of temporal information. In addition, activity-driven fluorescence changes can make it difficult to distinguish changes in image appearance from actual motion. Here, we treat motion correction in calcium imaging as a point tracking problem and introduce CoReg, an open-source Python- based software tool with a graphical user interface. We benchmark CoReg against established registration methods using fluorescence imaging datasets from multiple species. CoReg performs competitively with or better than existing approaches across several settings. We further show that its performance is robust to the choice of tracked points, making the method straightforward to apply. CoReg includes implementations of both our method and the evaluated established methods. Our results show that tracking-based registration provides a simple and reliable approach for motion correction in vivo calcium imaging.

neuroscience↗

A shared slowing axis and phenotype-specific rhythms link clinical EEG to FDG-PET dementia phenotypes

Early and accurate identification of neurodegenerative disorders has notable implications for treatment and quality of life. Whereas neuroimaging-based biomarkers can accurately identify neurodegenerative subtypes, EEG represents a low-cost alternative that could expand access to dementia care. Here, we investigated whether six data-driven EEG latent factors, derived by nonnegative tensor decomposition of clinical EEGs, differ systematically across FDG-PET-defined neurodegenerative phenotypes. In 455 patients with EEG and FDG-PET acquired within one year of each other, factor expression was compared across 14 diagnostic classes assigned by StateViewer, a validated artificial intelligence framework for FDG-PET interpretation. To separate disease-general severity from phenotype-specific topography, each factor was also regressed on a subject-level slowing index. Finally, we related the factors to regional FDG uptake and to neuropsychological performance (n = 98) and assessed their potential utility for single-subject classification. Four factors reflecting neurophysiological oscillations (posterior alpha, Rolandic beta, frontotemporal delta-theta and posterior theta-slow alpha) varied significantly across phenotypes, with attenuation of faster and augmentation of slower rhythms most pronounced in dementia with Lewy bodies (DLB), dysexecutive Alzheimer disease (AD) and posterior cortical atrophy, whereas factors dominated by eye-movement and muscle artifact showed no robust phenotypic association. Alpha and beta effects were largely attributable to overall slowing, whereas posterior theta-slow alpha became more phenotype-specific after adjustment (eta-squared 0.082 to 0.147), dissociating the Alzheimer disease spectrum from frontotemporal syndromes. The slowing index tracked with an FDG-PET pattern highly associated with DLB across the cohort, irrespective of assigned phenotype (r = 0.51, P < 0.001). Consistent with a model in which dementia phenotypes reflect altered hippocampal-neocortical oscillatory coupling (stochastic latent oscillatory diffusion), frontotemporal delta-theta tracked a limbocortical axis of metabolic pattern expression, rising with neocortical and falling with limbic-predominant involvement, independent of overall slowing and phenotype. Each factor mapped onto a distinct metabolic topography, with frontotemporal delta-theta resembling Alzheimer-type hypometabolism and posterior theta-slow alpha the medial occipital hypometabolism of DLB. In terms of neuropsychological performance, Rolandic beta was associated with executive functions, and frontotemporal delta-theta with memory, visuospatial and global performance. Using these four factors alone, an exploratory logistic regression classifier separated cognitively unimpaired participants from DLB, posterior cortical atrophy, typical AD, and dysexecutive AD (area under the curve > 0.8) but poorly discriminated frontotemporal dementia phenotypes canonically associated with 4-repeat tau. Thus, data-driven decomposition of clinical EEG separates oscillatory activity from artifact into factors that track metabolic phenotype, regional metabolism and cognition, capturing both overall slowing and phenotype-specific patterns. This work informs the further development of scalable, biology-informed EEG-based phenotyping of neurodegenerative disease.

neuroscience↗

An application of fNIRS for the prediction of hearing thresholds in sleeping infants: a proof of principle

This study applied a two-component-response modelling approach to fNIRS recordings obtained from naturally sleeping infants presented with auditory stimuli at different sound intensities. We investigated whether the relationship between fNIRS response measures and stimulus intensity could be modelled to estimate individual auditory hearing thresholds. In the normal-hearing group, 75% of infants had threshold estimates for the sound /ba/ within the expected normal-hearing range, with an estimated mean equivalent normal loudness (ENL) of 10.72 dB HL (SD 8.64 dB HL). A case study further explored the potential application of this approach in an infant with hearing impairment, highlighting the importance of reliable responses at lower stimulus intensities and appropriate regression model selection. These findings provide preliminary evidence that cortical haemodynamic responses can be quantitatively related to sound intensity for estimating auditory sensitivity.

neuroscience↗