bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.08.26.505426

Cholesterol-laden brain-permeable nanoparticles support long-lasting cognitive recovery and motor amelioration in the slow-progressing zQ175DN mouse model of Huntington's Disease

Abstract

Evidence that Huntingtons disease (HD) is characterized by impaired cholesterol biosynthesis in the brain has led to strategies to increase its level in the brain of the rapidly progressing R6/2 mouse model, with a positive therapeutic outcome. Here we tested the long-term efficacy of chronic administration of cholesterol to the brain of the slowly progressing zQ175DN knock-in HD mice in preventing ("early treatment") or reversing ("late treatment") HD symptoms. To do this we used the most advanced formulation of cholesterol loaded brain-permeable nanoparticles (NPs), termed hybrid-g7-NPs-chol, which were injected intraperitoneally. We show that one cycle of treatment with hybrid-g7-NPs-chol, administered in the presymptomatic ("early treatment") or symptomatic ("late treatment") stages is sufficient to normalize cognitive defects up to 5 months, as well as to improve other behavioral and neuropathological parameters. A multiple cycle treatment combining both early and late treatments ("2 cycle treatment") lasting 6 months generates therapeutic effects for more than 11 months, without severe adverse reactions. Sustained cholesterol delivery to the brain of zQ175DN mice also reduces mutant Huntingtin aggregates in both the striatum and cortex and completely normalizes synaptic communication in the striatal medium spiny neurons compared to saline-treated HD mice. Furthermore, through a meta-analysis of published and current data, we demonstrated the power of hybrid-g7-NPs-chol and other strategies able to increase brain cholesterol biosynthesis, to reverse cognitive decline and counteract the formation of mutant Huntingtin aggregates. These results demonstrate that cholesterol delivery via brain-permeable NPs is a therapeutic option to sustainably reverse HD-related behavioral decline and neuropathological signs over time, highlighting the therapeutic potential of cholesterol-based strategies in HD patients.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Birolini, G., Valenza, M., Ottonelli, I., Talpo, F., Minoli, L., Cappelleri, A., Bombaci, M., Caccia, C., Leoni, V., Passoni, A., Favagrossa, M., Nucera, M. R., Colombo, L., Paltrinieri, S., Bagnati, R., Duskey, J. T., Caraffi, R., Vandelli, M. A., Taroni, F., Salmona, M., Scanziani, E., Biella, G., Ruozi, B., Tosi, G., Cattaneo, E.. 2022-08-26. Cholesterol-laden brain-permeable nanoparticles support long-lasting cognitive recovery and motor amelioration in the slow-progressing zQ175DN mouse model of Huntington's Disease. https://doi.org/10.1101/2022.08.26.505426

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↗