Peripheral nerve injury elicits microstructural and neurochemical changes in the striatum and substantia nigra of a DYT-TOR1A mouse model with dystonia-like movements

Peripheral nerve injury induces dystonia-like movements and dysregulation in the energy metabolism: A multi-omics descriptive study in Thap1+/- mice

DYT-THAP1 dystonia is a monogenetic form of dystonia, a movement disorder characterized by the involuntary co-contraction of agonistic and antagonistic muscles. The disease is caused by mutations in the THAP1 gene, although the precise mechanisms by which these mutations contribute to the pathophysiology of dystonia remain unclear. The incomplete penetrance of DYT-THAP1 dystonia, estimated at 40 to 60 %, suggests that an environmental trigger may be required for the manifestation of the disease in genetically predisposed individuals. To investigate the gene-environment interaction in the development of dystonic features, we performed a sciatic nerve crush injury in a genetically predisposed DYT-THAP1 heterozygous knockout mouse model (Thap1+/-). We employed a multi-omic assessment to study the pathophysiological pathways underlying the disease. Phenotypic analysis using an unbiased deep learning algorithm revealed that nerve-injured Thap1+/- mice exhibited significantly more dystonia like movements (DLM) over the course of the 12-week experiment compared to naive Thap1+/- mice. In contrast, nerve-injured wildtype (wt) mice only showed a significant increase in DLM compared to their naive counterpart during the first weeks after injury. Furthermore, at week 11 after nerve crush, nerve-injured Thap1+/- mice displayed significantly more DLM than nerve-injured wt counterparts. Multi-omic analysis of the cerebellum, striatum and cortex in nerve-injured Thap1+/- mice revealed differences that are indicative of an altered energy metabolism compared to naive Thap1+/- and nerve-injured wt animals. These findings suggest that aberrant energy metabolism in brain regions relevant to dystonia may underlie the dystonic phenotype observed in nerve injured Thap1+/- mice.

Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning

Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.

Disturbed brain energy metabolism in a rodent model of DYT-TOR1A dystonia

DYT-TOR1A (DYT1) dystonia, characterized by reduced penetrance and suspected environmental triggers, is explored using a "second hit" DYT-TOR1A rat model. We aim to investigate the biological mechanisms driving the conversion into a dystonic phenotype, focusing on the striatum's role in dystonia pathophysiology. Sciatic nerve crush injury was induced in ∆ETorA rats, lacking spontaneous motor abnormalities, and wild-type (wt) rats. Twelve weeks post-injury, unbiased RNA-sequencing was performed on the striatum to identify differentially expressed genes (DEGs) and pathways. Fenofibrate, a PPARα agonist, was introduced to assess its effects on gene expression. 18F-FDG autoradiography explored metabolic alterations in brain networks. Low transcriptomic variability existed between naïve wt and ∆ETorA rats (17 DEGs). Sciatic nerve injury significantly impacted ∆ETorA rats (1009 DEGs) compared to wt rats (216 DEGs). Pathway analyses revealed disruptions in energy metabolism, specifically in fatty acid β-oxidation and glucose metabolism. Fenofibrate induced gene expression changes in wt rats but failed in ∆ETorA rats. Fenofibrate increased dystonia-like movements in wt rats but reduced them in ∆ETorA rats. 18F-FDG autoradiography indicated modified glucose metabolism in motor and somatosensory cortices and striatum in both ∆ETorA and wt rats post-injury. Our findings highlight perturbed energy metabolism pathways in DYT-TOR1A dystonia, emphasizing compromised PPARα agonist efficacy in the striatum. Furthermore, we identify impaired glucose metabolism in the brain network, suggesting a potential shift in energy substrate utilization in dystonic DYT-TOR1A rats. These results contribute to understanding the pathophysiology and potential therapeutic targets for DYT-TOR1A dystonia.

Gene-environment interaction elicits dystonia-like features and impaired translational regulation in a DYT-TOR1A mouse model

DYT-TOR1A dystonia is the most common monogenic dystonia characterized by involuntary muscle contractions and lack of therapeutic options. Despite some insights into its etiology, the disease's pathophysiology remains unclear. The reduced penetrance of about 30% suggests that extragenetic factors are needed to develop a dystonic phenotype. In order to systematically investigate this hypothesis, we induced a sciatic nerve crush injury in a genetically predisposed DYT-TOR1A mouse model (DYT1KI) to evoke a dystonic phenotype. Subsequently, we employed a multi-omic approach to uncover novel pathophysiological pathways that might be responsible for this condition. Using an unbiased deep-learning-based characterization of the dystonic phenotype showed that nerve-injured DYT1KI animals exhibited significantly more dystonia-like movements (DLM) compared to naive DYT1KI animals. This finding was noticeable as early as two weeks following the surgical procedure. Furthermore, nerve-injured DYT1KI mice displayed significantly more DLM than nerve-injured wildtype (wt) animals starting at 6 weeks post injury. In the cerebellum of nerve-injured wt mice, multi-omic analysis pointed towards regulation in translation related processes. These observations were not made in the cerebellum of nerve-injured DYT1KI mice; instead, they were localized to the cortex and striatum. Our findings indicate a failed translational compensatory mechanisms in the cerebellum of phenotypic DYT1KI mice that exhibit DLM, while translation dysregulations in the cortex and striatum likely promotes the dystonic phenotype.