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

Stress-induced precocious aging in PD-patient iPSC-derived NSCs may underlie the pathophysiology of Parkinson's disease.

Parkinson's disease (PD) is an aging-related degenerative disorder arisen from the loss of dopaminergic neurons in substantia nigra. Although many genetic mutations have been implicated to be genetically linked to PD, the low incidence of familial PD carried with mutations suggests that there must be other factors such as oxidative stress, mitochondrial dysfunction, accumulation of misfolded proteins, and enhanced inflammation, which are contributable to the pathophysiology of PD. The major efforts of current research have been devoted to unravel the toxic effect of multiple factors, which directly cause the degeneration of dopaminergic neurons in adulthood. Until recently, several studies have demonstrated that NSCs had compromised proliferation and differentiation capacity in PD animal models or PD patient-derived iPS models, suggesting that the pathology of PD may be rooted in some cellular aberrations at early developmental stage but the mechanism remains to be elusive. Based on the early-onset PD patient-specific iPSCs, we found that PD-patient iPSC-derived NSCs were more susceptible to stress and became functionally compromised by radiation or oxidative insults. We further unraveled that stress-induced SIRT1 downregulation leading to autophagic dysfunction, which were responsible for these deficits in PD-NSCs. Mechanistically, we demonstrated that stress-induced activation of p38 MAPK suppressed SIRT1 expression, which in turn augmented the acetylation of multiple ATG proteins of autophagic complex and eventually led to autophagic deficits. Our studies suggest that early developmental deficits may, at least partially, contribute to the pathology of PD and provide a new avenue for developing better therapeutic interventions to PD

The influence of obstructive sleep apnea and continuous positive airway pressure on the nasal microbiome

OBJECTIVE: The aim of this study was to investigate the influence of obstructive sleep apnea and continuous positive airway pressure on the nasal microbiome. PATIENTS AND METHODS: Endonasal swabs from the olfactory groove of 22 patients with moderate and severe obstructive sleep apnea (OSA) and a control group of 17 healthy controls were obtained at the Department of Otorhinolaryngology of the Friedrich-Alexander-Universität Erlangen-Nürnberg. 16S rRNA gene sequencing was performed to further evaluate the endonasal microbiome. In a second step, the longitudinal influence of continuous positive airway pressure (CPAP) therapy on the nasal microbiome was investigated (3-6 and 6-9 months). RESULTS: Analysis of the bacterial load and β-diversity showed no significant differences between the groups, although patients with severe OSA showed increased α-diversity compared to the control group, while those with moderate OSA showed decreased α-diversity. The evaluation of longitudinal changes in the nasal microbiota during CPAP treatment showed no significant difference in α- or β-diversity. However, the number of bacteria for which a significant difference between moderate and severe OSA was found in the linear discriminant analysis decreased during CPAP treatment. CONCLUSIONS: Long-term CPAP treatment showed an alignment of the composition of the nasal microbiome in patients with moderate and severe OSA as well as an alignment of biodiversity with that of the healthy control group. This change in the composition of the microbiome could be both part of the therapeutic effect in CPAP therapy and a promoting factor of the adverse side effects of the therapy. Further studies are needed to investigate whether the endonasal microbiome is related to CPAP compliance and whether CPAP compliance can be positively influenced in the future by therapeutic modification of the microbiome

Gait variability as digital biomarker of disease severity in Huntington’s disease

Background Impaired gait plays an important role for quality of life in patients with Huntington's disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments. Objective To objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington's Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC). Methods Patients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age- and gender-matched controls performed a defined gait test (4 x 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV). Results Stride length (- 15%) and gait velocity (- 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416 <= r(Sp) <= 0.690). Objective gait variability parameters correlated with disease stage based upon TFC. Conclusions Sensor-based gait variability parameters were identified as clinically most relevant digital biomarker for gait impairment in HD. Altered gait variability represents characteristic irregularity of gait in HD and reflects disease severity