Tackling the Ethical Challenges Associated with Premature Delivery of Stem Cell-Based Therapies: Report of a Focus Session at the ISSCR 2018 Annual Meeting

Despite a lack of clear evidence of safety and efficacy developed under traditional clinical translation pathways for medical therapies, there has been a surge of unproven clinical uses of stem cell-based interventions, often enabled by businesses marketing these purported therapies directly to patients. This is made possible in part by the diverse regulatory and policy environments in different countries. While the desire of patients to access therapeutic interventions is completely understandable, many stand to be harmed by receiving interventions that have not been adequately tested. This possibility raises substantial ethical concerns and has the potential to diminish public trust in cellular therapies more generally, incur health care costs due to adverse events and derail research into regenerative medicines. Regulators, scientists, clinicians, professional societies and patient advocacy groups need to work collaboratively to articulate the legal and ethical expectations of stem cell-based therapies and create novel approaches to their development, translation, regulation and governance.Non

Huntingtin aggregation impairs autophagy leading to Argonaute-2 accumulation and global microRNA dysregulation

Many neurodegenerative diseases are characterized by the presence of intracellular protein aggregates resulting in alterations in autophagy. However, the consequences of impaired autophagy on neuronal function remain poorly understood. In this study, we used cell culture and mouse models of huntingtin protein aggregation, as well as post-mortem material from patients with Huntington’s disease to demonstrate that Argonaute-2 (AGO2) accumulates in the presence of neuronal protein aggregates and that this is due to impaired autophagy. Accumulation of AGO2, a key factor of the RNA-induced silencing complex that executes microRNA functions, results in global alterations of microRNA levels and activity. Together these results demonstrate that impaired autophagy found in neurodegenerative diseases not only influences protein aggregation, but also directly contributes to global alterations of intracellular post-transcriptional networks.The work was supported by grants from the Swedish Research Council (# K2014-62X-22527-01-3 and K2014-62X-20404-08-5), the Swedish Foundation for Strategic Research (# FFL12-0074), the Swedish Brain Foundation (# FO2014-0106); the Swedish excellence project Basal Ganglia Disorders Linnaeus Consortium (Bagadilico), and the Swedish Government Initiative for Strategic Research Areas (MultiPark & StemTherapy)

Dermal fibroblasts from patients with Parkinson’s disease have normal GCase activity and autophagy compared to patients with PD and GBA mutations

Background: Recently, the development of Parkinson’s disease (PD) has been linked to a number of genetic risk factors, of which the most common is glucocerebrosidase (GBA) mutations. Methods: We investigated PD and Gaucher Disease (GD) patient derived skin fibroblasts using biochemistry assays. Results: PD patient derived skin fibroblasts have normal glucocerebrosidase (GCase) activity, whilst patients with PD and GBA mutations have a selective deficit in GCase enzyme activity and impaired autophagic flux. Conclusions: This data suggests that only PD patients with a GBA mutation have altered GCase activity and autophagy, which may explain their more rapid clinical progression.We are grateful to an NIHR award of a Biomedical Research Centre to Addenbrookes Hospital and the University of Cambridge. We are also grateful to the Rosetrees Trust, the WT-MRC Stem Cell Institute and the Canadian Institutes of Health Research (CIHR) fellowship (358492) for the funding for this work

Review: The spectrum of clinical features seen with alpha synuclein pathology.

It has been recognized for many years that a number of chronic neurodegenerative diseases of the CNS are characterized by the development of intracellular inclusion bodies, but it is only relatively recently that the core proteins defining these pathologies have been defined. One such protein is alpha synuclein, that was found to be the main component of Lewy bodies in the late 1990s, and this discovery reinforced the emerging view that alpha synuclein was intimately linked to diseases characterized by this type of pathology--namely Parkinson's disease (PD) and Dementia with Lewy Bodies (DLB). Furthermore at around this time, this same protein was also found within the glial inclusion bodies of patients dying with multiple system atrophy (MSA). These three disorders constitute the majority of patients with an 'alpha synucleinopathy', although there are a number of rarer conditions that can also cause this pathology including inherited metabolic disorders such as Gaucher's disease (GD). In this review, we will concentrate on PD, the commonest alpha synucleinopathy, and its associated dementia (PDD), as well as discussing DLB and MSA and will highlight how the clinical features of these conditions vary as a function of pathology.Our own work on the natural history of Parkinson’s disease has been supported by the Wellcome Trust; MRC; Patrick Berthoud Trust; Cure Parkinson’s Trust; Parkinson’s UK and the Cambridge NIHR Biomedical Research Centre.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/nan.1230

Parkinson's Disease in a Dish: What Patient Specific-Reprogrammed Somatic Cells Can Tell Us about Parkinson's Disease, If Anything?

Technologies allowing for the derivation of patient-specific neurons from somatic cells are emerging as powerful in vitro tools to investigate the intrinsic cellular pathological behaviours of the diseases that affect these patients. While the use of patient-derived neurons to model Parkinson's disease (PD) has only just begun, these approaches have allowed us to begin investigating disease pathogenesis in a unique way. In this paper, we discuss the advances made in the field of cellular reprogramming to model PD and discuss the pros and cons associated with the use of such cells.Peer Reviewe

Progression of Neuropsychiatric Symptoms over Time in an Incident Parkinson's Disease Cohort (ICICLE-PD).

BACKGROUND: Cross-sectional studies have identified that the prevalence of neuropsychiatric symptoms (NPS) in Parkinson's disease (PD) ranges from 70-89%. However, there are few longitudinal studies determining the impact of NPS on quality of life (QoL) in PD patients and their caregivers. We seek to determine the progression of NPS in early PD. METHODS: Newly diagnosed idiopathic PD cases (n = 212) and age-matched controls (n = 99) were recruited into a longitudinal study. NPS were assessed using the Neuropsychiatric Inventory with Caregiver Distress scale (NPI-D). Further neuropsychological and clinical assessments were completed by participants, with reassessment at 18 and 36 months. Linear mixed-effects modelling determined factors associated with NPI-D and QoL over 36 months. RESULTS: Depression, anxiety, apathy and hallucinations were more frequent in PD than controls at all time points (p < 0.05). Higher motor severity at baseline was associated with worsening NPI-D scores over time (β = 0.1, p < 0.05), but not cognition. A higher NPI total score was associated with poorer QoL at any time point (β = 0.3, p < 0.001), but not changed in QoL scores. CONCLUSION: NPS are significantly associated with poorer QoL, even in early PD. Screening for NPS from diagnosis may allow efficient delivery of better support and treatment to patients and their families

Modelling the natural history of Huntington's disease progression.

BACKGROUND: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington's disease (HD) as an example, we describe a novel approach to model HD and show that the progression of a neurological disorder can be predicted for individual patients. METHODS: Starting with an initial cohort of 343 patients with HD that we have followed since 1995, we used data from 68 patients that satisfied our filtering criteria to model disease progression, based on the Unified Huntington's Disease Rating Scale (UHDRS), a measure that is routinely used in HD clinics worldwide. RESULTS: Our model was validated by: (A) extrapolating our equation to model the age of disease onset, (B) testing it on a second patient data set by loosening our filtering criteria, (C) cross-validating with a repeated random subsampling approach and (D) holdout validating with the latest clinical assessment data from the same cohort of patients. With UHDRS scores from the past four clinical visits (over a minimum span of 2 years), our model predicts disease progression of individual patients over the next 2 years with an accuracy of 89-91%. We have also provided evidence that patients with similar baseline clinical profiles can exhibit very different trajectories of disease progression. CONCLUSIONS: This new model therefore has important implications for HD research, most obviously in the development of potential disease-modifying therapies. We believe that a similar approach can also be adapted to model disease progression in other chronic neurological disorders.This study was supported by the Cotswold Trust, the Rosetrees Trust, donations to the Huntington’s disease clinic in the John van Geest Centre for Brain Repair, and NIHR award of the Biomedical Research Centre - Cambridge University NHS Foundation Trust. This project was also supported by EPSRC through projects EP/I03210X/1 and EP/G066477/1.This article has been accepted for publication in Journal of Neurology, Neurosurgery, and Psychiatry, following peer review. The definitive copyedited, typeset version J Neurol Neurosurg Psychiatry doi:10.1136/jnnp-2014-308153 is available online at: http://jnnp.bmj.com/content/early/2014/12/16/jnnp-2014-308153.long

Neuroendocrine abnormalities in Parkinson's disease

Neuroendocrine abnormalities are common in Parkinson's disease (PD) and include disruption of melatonin secretion, disturbances of glucose, insulin resistance and bone metabolism, and body weight changes. They have been associated with multiple non-motor symptoms in PD and have important clinical consequences, including therapeutics. Some of the underlying mechanisms have been implicated in the pathogenesis of PD and represent promising targets for the development of disease biomarkers and neuroprotective therapies. In this systems-based review, we describe clinically relevant neuroendocrine abnormalities in Parkinson's disease to highlight their role in overall phenotype. We discuss pathophysiological mechanisms, clinical implications, and pharmacological and non-pharmacological interventions based on the current evidence. We also review recent advances in the field, focusing on the potential targets for development of neuroprotective drugs in Parkinson's disease and suggest future areas for research

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials

Statins are underused in recent-onset Parkinson's disease with increased vascular risk : findings from the UK Tracking Parkinson's and Oxford Parkinson's Disease Centre (OPDC) discovery cohorts

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.Peer reviewedPublisher PD