Biology subjects

Akkermans, J.

Publications and source records attributed to Akkermans, J..

3 recordsLinked to original sources

bioRxiv2025

Prodromal pathogenesis of CLN7 Batten Disease revealed by multimodal biomarkers in macaques

Neuronal ceroid lipofuscinosis type 7 (CLN7) is a devastating paediatric neurodegenerative disorder with no cure and limited natural history data to guide therapeutic development. Here, we present the first multimodal characterization of prodromal and early-stage CLN7 disease in Japanese macaques carrying a spontaneous CLN7-/- mutation. Using structural T2-weighted MRI for volumetry, [18F]FDG PET for glucose metabolism, and [11C]PBR28 PET for neuroinflammation, we observed region-dependent patterns in volumetric, molecular, and metabolic alterations. MRI confirmed the presence of disease-associated atrophy in many cortical and subcortical brain regions, consistent with human pathology, [18F]FDG PET revealed early cortical and subcortical widespread hypometabolism, and [11C]PBR28 PET imaging detected progressive neuroinflammation in the same brain areas. CSF analyses further showed age-dependent increases in neurofilament light (NfL), providing convergent evidence for neurodegeneration. Together, these results define a prodromal trajectory in CLN7 disease, establish sensitive imaging and fluid biomarkers, and validate the macaque model as a powerful platform for testing interventions.

neuroscience↗

bioRxiv2025

Preclinical evaluation of CHDI-009R for quantification of mutant huntingtin aggregates

Huntingtons disease (HD) is a neurodegenerative disorder caused by an expanded trinucleotide repeat in the huntingtin gene (HTT) that subsequently leads to aggregation of the mutant huntingtin (mHTT) protein. Thus, lowering mHTT is a key therapeutic approach used by several candidate therapeutics currently under investigation. Visualization of the efficiency of these therapeutics through in vivo mHTT quantification rises in importance. For positron emission tomography (PET) imaging of mHTT aggregates, it is critical to characterize the in vivo kinetic profile of newly identified mHTT binders to assess their translational application. Here, we report the evaluation of [11C]CHDI-009R, a PET imaging radioligand with higher affinity and selectivity for mHTT aggregates than previously reported radioligands, in the heterozygous (HET) zQ175DN mouse model of HD and wild-type (WT) littermates at 9 and 3 months of age. [11C]CHDI-009R displayed high stability in plasma and brain, which was reflected in brain kinetics as demonstrated by rapid uptake followed by relatively slow elimination. Kinetic modeling and volume of distribution VT (IDIF) indicated the radioligands ability to quantify mHTT aggregation at 9 months of age with clear genotype differentiation (p<0.0001). [11C]CHDI-009R showed an excellent test-retest reliability in 9-month-old mice (intraclass correlation coefficient: 0.62 - 0.79). A phenotypic difference in mHTT aggregates was also observed in 3-month-old mice in several brain structures (p<0.05) and was confirmed with [3H]CHDI-009R autoradiography. Overall, this study suggests [11C]CHDI-009R is a promising radioligand for the detection of cerebral mHTT aggregates in a mouse model of HD and supports its advance to clinical evaluation.

neuroscience↗

bioRxiv2022

Identification of pre-synaptic density networks using SV2A PET imaging and ICA in healthy and diseased mice

BackgroundSynaptic vesicle glycoprotein 2A (SV2A) is a vesicle glycoprotein involved in neurotransmitter release. SV2A is located on the pre-synaptic terminals of neurons and visualized using the radioligand [11C]UCB-J and positron emission tomography (PET) imaging. Thus, SV2A PET imaging can provide a proxy for pre-synaptic density in health and disease. This study aims to apply independent component analysis (ICA) to SV2A PET data acquired in mice to identify pre-synaptic density networks (pSDNs), explore how ageing affects these pSDNs, and determine the impact of a neurological disorder on these networks. MethodsWe used [11C]UCB-J PET imaging data (n=135) available at different ages (3, 7, 10, and 16 months) in wild-type (WT) C57BL/6J mice and in diseased mice (mouse model of Huntingtons disease, HD). First, ICA was performed on a healthy dataset after it was split into two equal-sized samples (n=36 each) and the analysis was repeated 50 times in different partitions. We tested different model orders (8, 12, and 16) and identified the pSDNs. Next, we investigated the effect of age on the loading weights of the identified pSDNs. Additionally, the identified pSDNs were compared to those of diseased mice to assess the impact of disease on each pSDNs. ResultsModel order 12 resulted in the preferred choice to provide six reliable and reproducible independent components (ICs) as supported by the cluster-quality index (IQ) and regression coefficients ({beta}) values. Temporal analysis showed age-related statistically significant changes on the loading weights in four ICs. ICA in an HD model revealed a statistically significant disease-related effect on the loading weights in several pSDNs in line with the progression of the disease. ConclusionThis study validated the use of ICA on SV2A PET data acquired with [11C]UCB-J for the identification of cerebral pre-synaptic density networks in mice in a rigorous and reproducible manner. Furthermore, we showed that different pSDNs change with age and are affected in a disease condition. These findings highlight the potential value of ICA in understanding pre-synaptic density networks in the mouse brain.

neuroscience↗