The spatiotemporal changes in dopamine, neuromelanin and iron characterizing Parkinson’s disease

Dopamine transporter; Iron; NeuromelaninTransportador de dopamina; Hierro; NeuromelaninaTransportador de dopamina; Ferro; NeuromelaninaIn Parkinson’s disease, there is a progressive reduction in striatal dopaminergic function, and loss of neuromelanin-containing dopaminergic neurons and increased iron deposition in the substantia nigra. We tested the hypothesis of a relationship between impairment of the dopaminergic system and changes in the iron metabolism. Based on imaging data of patients with prodromal and early clinical Parkinson’s disease, we assessed the spatiotemporal ordering of such changes and relationships in the sensorimotor, associative and limbic territories of the nigrostriatal system. Patients with Parkinson’s disease (disease duration < 4 years) or idiopathic REM sleep behaviour disorder (a prodromal form of Parkinson’s disease) and healthy controls underwent longitudinal examination (baseline and 2-year follow-up). Neuromelanin and iron sensitive MRI and dopamine transporter single-photon emission tomography were performed to assess nigrostriatal levels of neuromelanin, iron, and dopamine. For all three functional territories of the nigrostriatal system, in the clinically most and least affected hemispheres separately, the following was performed: cross-sectional and longitudinal intergroup difference analysis of striatal dopamine and iron, and nigral neuromelanin and iron; in Parkinson’s disease patients, exponential fitting analysis to assess the duration of the prodromal phase and the temporal ordering of changes in dopamine, neuromelanin or iron relative to controls; and voxel-wise correlation analysis to investigate concomitant spatial changes in dopamine-iron, dopamine-neuromelanin and neuromelanin-iron in the substantia nigra pars compacta. The temporal ordering of dopaminergic changes followed the known spatial pattern of progression involving first the sensorimotor, then the associative and limbic striatal and nigral regions. Striatal dopaminergic denervation occurred first followed by abnormal iron metabolism and finally neuromelanin changes in the substantia nigra pars compacta, which followed the same spatial and temporal gradient observed in the striatum but shifted in time. In conclusion, dopaminergic striatal dysfunction and cell loss in the substantia nigra pars compacta are interrelated with increased nigral iron content.The ICEBERG study was funded by grants from the Investissements d'Avenir, IAIHU-06 (Paris Institute of Neurosciences – IHU), ANR-11-INBS-0006, Fondation d’Entreprise EDF, Biogen Inc., Fondation Thérèse and René Planiol, Fondation Saint Michel, Unrestricted support for Research on Parkinson’s disease from Energipole (M. Mallart), M.Villain and Société Française de Médecine Esthétique (M. Legrand)

Lack of evidence for a role of genetic variation in TMEM230 in the risk for Parkinson's disease in the Caucasian population

Mutations in . TMEM230 have recently been associated to Parkinson's disease (PD). To further understand the role of this gene in the Caucasian population, we interrogated our large repository of next generation sequencing data from unrelated PD cases and controls, as well as multiplex families with autosomal dominant PD. We identified 2 heterozygous missense variants in 2 unrelated PD cases and not in our control database (p.Y106H and p.I162V), and a heterozygous missense variant in 2 PD cases from the same family (p.A163T). However, data presented herein is not sufficient to support the role of any of these variants in PD pathology. A series of unified sequence kernel association tests also failed to show a cumulative effect of rare variation in this gene on the risk of PD in the general Caucasian population. Further evaluation of genetic data from different populations is needed to understand the genetic role of . TMEM230 in PD etiology

PTPA variants and impaired PP2A activity in early-onset parkinsonism with intellectual disability

The protein phosphatase 2A complex (PP2A), the major Ser/Thr phosphatase in the brain, is involved in a number of signalling pathways and functions, including the regulation of crucial proteins for neurodegeneration, such as alpha-synuclein, tau and LRRK2. Here, we report the identification of variants in the PTPA/PPP2R4 gene, encoding a major PP2A activator, in two families with early-onset parkinsonism and intellectual disability. We carried out clinical studies and genetic analyses, including genome-wide linkage analysis, whole-exome sequencing, and Sanger sequencing of candidate variants. We next performed functional studies on the disease-associated variants in cultured cells and knock-down of ptpa in Drosophila melanogaster. We first identified a homozygous PTPA variant, c.893T&gt;G (p.Met298Arg), in patients from a South African family with early-onset parkinsonism and intellectual disability. Screening of a large series of additional families yielded a second homozygous variant, c.512C&gt;A (p.Ala171Asp), in a Libyan family with a similar phenotype. Both variants co-segregate with disease in the respective families. The affected subjects display juvenile-onset parkinsonism and intellectual disability. The motor symptoms were responsive to treatment with levodopa and deep brain stimulation of the subthalamic nucleus. In overexpression studies, both the PTPA p.Ala171Asp and p.Met298Arg variants were associated with decreased PTPA RNA stability and decreased PTPA protein levels; the p.Ala171Asp variant additionally displayed decreased PTPA protein stability. Crucially, expression of both variants was associated with decreased PP2A complex levels and impaired PP2A phosphatase activation. PTPA orthologue knock-down in Drosophila neurons induced a significant impairment of locomotion in the climbing test. This defect was age-dependent and fully reversed by L-DOPA treatment. We conclude that bi-allelic missense PTPA variants associated with impaired activation of the PP2A phosphatase cause autosomal recessive early-onset parkinsonism with intellectual disability. Our findings might also provide new insights for understanding the role of the PP2A complex in the pathogenesis of more common forms of neurodegeneration.</p

Genotype-phenotype correlation in PRKN-associated Parkinson's disease

Bi-allelic pathogenic variants in PRKN are the most common cause of autosomal recessive Parkinson's disease (PD). 647 patients with PRKN-PD were included in this international study. The pathogenic variants present were characterised and investigated for their effect on phenotype. Clinical features and progression of PRKN-PD was also assessed. Among 133 variants in index cases (n = 582), there were 58 (43.6%) structural variants, 34 (25.6%) missense, 20 (15%) frameshift, 10 splice site (7.5%%), 9 (6.8%) nonsense and 2 (1.5%) indels. The most frequent variant overall was an exon 3 deletion (n = 145, 12.3%), followed by the p.R275W substitution (n = 117, 10%). Exon3, RING0 protein domain and the ubiquitin-like protein domain were mutational hotspots with 31%, 35.4% and 31.7% of index cases presenting mutations in these regions respectively. The presence of a frameshift or structural variant was associated with a 3.4 ± 1.6 years or a 4.7 ± 1.6 years earlier age at onset of PRKN-PD respectively (p &lt; 0.05). Furthermore, variants located in the N-terminus of the protein, a region enriched with frameshift variants, were associated with an earlier age at onset. The phenotype of PRKN-PD was characterised by slow motor progression, preserved cognition, an excellent motor response to levodopa therapy and later development of motor complications compared to early-onset PD. Non-motor symptoms were however common in PRKN-PD. Our findings on the relationship between the type of variant in PRKN and the phenotype of the disease may have implications for both genetic counselling and the design of precision clinical trials

Genotype–phenotype correlation in PRKN- associated Parkinson’s disease

Bi-allelic pathogenic variants in PRKN are the most common cause of autosomal recessive Parkinson’s disease (PD). 647 patients with PRKN-PD were included in this international study. The pathogenic variants present were characterised and investigated for their effect on phenotype. Clinical features and progression of PRKN-PD was also assessed. Among 133 variants in index cases (n = 582), there were 58 (43.6%) structural variants, 34 (25.6%) missense, 20 (15%) frameshift, 10 splice site (7.5%%), 9 (6.8%) nonsense and 2 (1.5%) indels. The most frequent variant overall was an exon 3 deletion (n = 145, 12.3%), followed by the p.R275W substitution (n = 117, 10%). Exon3, RING0 protein domain and the ubiquitin-like protein domain were mutational hotspots with 31%, 35.4% and 31.7% of index cases presenting mutations in these regions respectively. The presence of a frameshift or structural variant was associated with a 3.4 ± 1.6 years or a 4.7 ± 1.6 years earlier age at onset of PRKN-PD respectively (p < 0.05). Furthermore, variants located in the N-terminus of the protein, a region enriched with frameshift variants, were associated with an earlier age at onset. The phenotype of PRKN-PD was characterised by slow motor progression, preserved cognition, an excellent motor response to levodopa therapy and later development of motor complications compared to early-onset PD. Non-motor symptoms were however common in PRKN-PD. Our findings on the relationship between the type of variant in PRKN and the phenotype of the disease may have implications for both genetic counselling and the design of precision clinical trials

Motor abnormalities progression in prodromal PD

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Longitudinal association between dopamine agonists and weight in Parkinson’s disease

[[abstract]]Objective To examine the longitudinal relation of dopamine agonists (DA) use with body mass index (BMI) change and weight gain in Parkinson's disease (PD). Methods In a cohort of 356 patients with PD annually followed up to 6 years, BMI, antiparkinsonian drugs use, and impulse control disorders (ICDs) were assessed at each visit. DA dose trajectories were estimated using latent class mixed models. The association of DA use with BMI change and weight gain was examined using latent-process mixed models and time-dependent Cox models respectively, while adjusting for disease severity and levodopa (LD) use. Results In the mixed model, BMI (kg/m2) increased over the follow-up in DA users (betaDA×time = 0.13, 95% CI = 0.02, 0.24) compared to non-users, while it decreased in LD users (betaLD×time = −0.26, 95% CI = −0.38, −0.13). We identified three trajectories of average daily DA dose over the follow-up. Patients in the high trajectory gained more weight than patients who never used DA (P = .001) and in the low (P = .02) or moderate (P = .04) trajectories. The incidence of weight gain of ≥6 kg was 2.10-fold (95% CI = 1.03, 4.28) higher in DA users compared to non-users, while LD users were less likely to gain weight (HR = 0.60, 95% CI = 0.33, 1.11). Associations decreased in analyses adjusted for compulsive eating or ICDs. Conclusion Weight increased in DA users over 6 years, and DA use was associated with increased incidence of weight gain. These associations were partially explained by compulsive eating. Alternatively, weight decreased in LD users. These findings warrant careful monitoring of compulsive eating and weight in PD patients

Machine learning-based prediction of impulse control disorders in Parkinson's disease from clinical and genetic data

International audienceGoal: Impulse control disorders (ICDs) are frequent non-motor symptoms occurring during the course of Parkinson's disease (PD). The objective of this study was to estimate the predictability of the future occurrence of these disorders using longitudinal data, the first study using cross-validation and replication in an independent cohort. Methods: We used data from two longitudinal PD cohorts (training set: PPMI, Parkinson's Progression Markers Initiative; test set: DIGPD, Drug Interaction With Genes in Parkinson's Disease). We included 380 PD subjects from PPMI and 388 PD subjects from DIGPD, with at least two visits and with clinical and genetic data available, in our analyses. We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs. We quantified performance using the area under the receiver operating characteristic curve (ROC AUC) and average precision. We compared these models to a trivial model predicting ICDs at the next visit with the status at the most recent visit. Results: The recurrent neural network (PPMI: 0.85 [0.80 -- 0.90], DIGPD: 0.802 [0.78 -- 0.83]) was the only model to be significantly better than the trivial model (PPMI: ROC AUC = 0.75 [0.69 -- 0.81]; DIGPD: 0.78 [0.75 -- 0.80]) on both cohorts. We showed that ICDs in PD can be predicted with better accuracy with a recurrent neural network model than a trivial model. The improvement in terms of ROC AUC was higher on PPMI than on DIGPD data, but not clinically relevant in both cohorts. Conclusions: Our results indicate that machine learning methods are potentially useful for predicting ICDs, but further works are required to reach clinical relevance

Social Cognitive Impairment in Early Parkinson's Disease: a novel "Mild Impairment"?

International audienceIntroductionSocial cognition (SC) deficit has recently been described in the early stages of Parkinson's disease (PD), but findings remain unclear. Our objective was to determine the frequency of SC impairment in newly-diagnosed PD patients and whether it is independent of Mild Cognitive Impairment (MCI).MethodsWe enrolled 109 patients with idiopathic PD diagnosed within the previous four years (ICEBERG cohort) and 39 healthy participants. SC was evaluated using the Mini-Social Cognition and Emotional Assessment (Mini-SEA) that allows a multi-domain assessment of SC. Relationships between SC and clinical characteristics, global cognitive efficiency, mood, anxiety, apathy and impulse control disorders, were also evaluated.Results30% of patients had significant socio-emotional impairment. Moreover, SC deficit in isolation was 3.5 times more frequent than MCI in isolation (20.2% vs 5.5% respectively). Both emotion identification and Theory of Mind were impaired compared to healthy participants. No effect of age, level of education, disease severity, dopamine replacement therapy, or global cognitive efficiency were found. Only scores on the Frontal Assessment Battery were correlated with SC abilities.ConclusionSC impairment is frequent in early PD and should be given more consideration. It often occurs in the absence of any other cognitive disorder and may represent the most common neuropsychological deficit in early-stage PD. In line with the definition of PD-MCI criteria, we consider the addition of a sixth MCI sub-type termed “Mild Social Cognition Impairment (MSCI)”. Further studies are required to validate the addition of this new MCI domain

X-Vectors: new quantitative biomarkers for early Parkinson's disease detection from speech

International audienceMany articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients-Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7-15% improvement). This result was observed for both recording types (high-quality microphone and telephone)