Rare Variants in PLXNA4 and Parkinson's Disease.

Approximately 20% of individuals with Parkinson's disease (PD) report a positive family history. Yet, a large portion of causal and disease-modifying variants is still unknown. We used exome sequencing in two affected individuals from a family with late-onset familial PD followed by frequency assessment in 975 PD cases and 1014 ethnically-matched controls and linkage analysis to identify potentially causal variants. Based on the predicted penetrance and the frequencies, a variant in PLXNA4 proved to be the best candidate and PLXNA4 was screened for additional variants in 862 PD cases and 940 controls, revealing an excess of rare non-synonymous coding variants in PLXNA4 in individuals with PD. Although we cannot conclude that the variant in PLXNA4 is indeed the causative variant, these findings are interesting in the light of a surfacing role of axonal guidance mechanisms in neurodegenerative disorders but, at the same time, highlight the difficulties encountered in the study of rare variants identified by next-generation sequencing in diseases with autosomal dominant or complex patterns of inheritance

Personalised profiling to identify clinically relevant changes in tremor due to multiple sclerosis

Background: There is growing interest in sensor-based assessment of upper limb tremor in multiple sclerosis and other movement disorders. However, previously such assessments have not been found to offer any improvement over conventional clinical observation in identifying clinically relevant changes in an individual's tremor symptoms, due to poor test-retest repeatability. Method: We hypothesised that this barrier could be overcome by constructing a tremor change metric that is customised to each individual's tremor characteristics, such that random variability can be distinguished from clinically relevant changes in symptoms. In a cohort of 24 people with tremor due to multiple sclerosis, the newly proposed metrics were compared against conventional clinical and sensor-based metrics. Each metric was evaluated based on Spearman rank correlation with two reference metrics extracted from the Fahn-Tolosa-Marin Tremor Rating Scale: a task-based measure of functional disability (FTMTRS B) and the subject's self-assessment of the impact of tremor on their activities of daily living (FTMTRS C). Results: Unlike the conventional sensor-based and clinical metrics, the newly proposed ’change in scale’ metrics presented statistically significant correlations with changes in self-assessed impact of tremor (max R2>0.5,p< 0.05 after correction for false discovery rate control). They also outperformed all other metrics in terms of correlations with changes in task-based functional performance (R2=0.25 vs. R2=0.15 for conventional clinical observation, both p< 0.05).Conclusions: The proposed metrics achieve an elusive goal of sensor-based tremor assessment: improving on conventional visual observation in terms of sensitivity to change. Further refinement and evaluation of the proposed techniques is required, but our core findings imply that the main barrier to translational impact for this application can be overcome. Sensor-based tremor assessments may improve personalised treatment selection and the efficiency of clinical trials for new treatments by enabling greater standardisation and sensitivity to clinically relevant changes in symptoms

Knowledge gaps and research recommendations for essential tremor

Essential tremor (ET) is a common cause of significant disability, but its etiologies and pathogenesis are poorly understood. Research has been hampered by the variable definition of ET and by non-standardized research approaches. The National Institute of Neurological Disorders and Stroke (USA) invited experts in ET and related fields to discuss current knowledge, controversies, and gaps in our understanding of ET and to develop recommendations for future research. Discussion focused on phenomenology and phenotypes, therapies and clinical trials, pathophysiology, pathology, and genetics. Across all areas, the need for collaborative and coordinated research on a multinational level was expressed. Standardized data collection using common data elements for genetic, clinical, neurophysiological, and pathological studies was recommended. Large cohorts of patients should be studied prospectively to collect bio-samples, characterize the natural history of the clinical syndrome including patient-oriented outcomes, investigate potential etiologies of various phenotypes, and identify pathophysiological mechanisms. In particular, cellular and system-level mechanisms of tremor oscillations should be elucidated because they may yield effective therapeutic targets and biomarkers. A neuropathology consortium was recommended to standardize postmortem analysis and further characterize neuropathological observations in the cerebellum and elsewhere. Furthermore, genome-wide association studies on large patient cohorts (>10,000 patients) may allow the identification of common genes contributing to risk, and whole exome or genome sequencing may enable the identification of genetic risk and causal mutations in cohorts and well-characterized families

Parkinson’s disease mouse models in translational research

Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinson’s disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research