Investigators/Authors: Giulietta Riboldi, MD, NYU School of Medicine

Objective: We propose to assess DNA methylation profiles in whole blood (WB) and isolated monocytes from PD patients and controls in order to study the role of the immune system in Parkinson’s disease.

Background:PD is the second most common degenerative disorder worldwide. Despite the identification of many different pathways that may play a central role in this disease, its pathogenic mechanisms are not yet clear. Because of this lack of knowledge, available treatments are mainly symptomatic. Cell-autonomous mechanisms seem to play an important role in the degeneration of the dopaminergic cells, which is the hallmark of the disease. However, microglia activation and its scavenger role in the elimination of alpha-synuclein, the interaction between the T-cell compartment and the role of dopaminergic neurons as antigen presenting cells (APC), all suggest an important role of inflammation in PD.

Methods/Design: Our goal is to profile epigenomic modifications such as DNA methylation in peripheral monocytes. Our rationale is that environmental exposures (smoking, caffeine, alcohol, organophosphate) and biophysical features (such as urate and flavonoids levels, hormonal therapy, physical activities) affect the incidence of PD. Environmental exposure may affect the epigenetic profiles of target tissues and modulate the inflammatory response through their effect on specific cell types.

We will recruit a population of 50 well characterized PD patients and 50 controls (defined as age- matched subjects that do not carry a diagnosis of neurodegenerative disease or chronic autoimmune disorder) and assess their exposure to risk factors previously associated with PD. Collected data will be used to define PD phenotypes and will be correlated with genome wide DNA methylation from isolated CD14+ monocytes. In addition, we aim to perform an integrated genomic analysis to correlate methylation profiles, expression profiles and genotype in whole blood from a large cohort (PPMI study) of PD patients and controls. These analyses will identify impaired pathways and signatures of the disease. Additionally, deconvolution analysis of our data will be performed to detect the contribution of different subpopulations of cells of the immune system to be then confirmed in isolated CD14+ monocytes. Lastly, we will generate genome-wide DNA methylation profiles from CD14+ monocytes in a pilot cohort of PD and control subjects.

Relevance to Diagnosis/Treatment of Parkinson’s disease: The project will help elucidate the role of the immune system in PD. DNA methylation signatures in blood cells could represent an easily accessible biomarker for the diagnosis, phenotypical stratification, and assessment of novel therapies for PD.

November 2020 Project Update:

Because of the recent COVID-19 pandemic and the consequent suspension of research visits at our Institution, our enrollment goal and sample collection has been significantly affected. Despite implementing remote assessments as much as possible, we will need to prologue our enrollment period. Also, pilot analysis of the data collected so far have been delayed by the recent closure of the labs due to the pandemic, further delaying the project.

October 2021 Project Update:

During the past year we successfully enrolled a cohort of subjects with Parkinson’s disease (PD) (n = 50) and non-affected age-matched controls (n = 50). For each enrolled subject, demographic and clinical information were collected, including motor and non-motor features of PD using standardized rating scales and questionnaires, as well as a detailed characterization of exposure to environmental factors (i.e. exposure to rural vs urban environments, smoke, alcohol, caffeine, heavy metal, anti-inflammatory medications, and history of head trauma).

Blood samples from the enrolled subjects are now being processed to isolate peripheral blood mononuclear cell (PBMC) and CD14+ monocytes, the cells of the innate immune system. DNA and RNA will be used to correlate gene expression in these cells type and modifications of the DNA through the assessment of the specific type of change called methylation. A pilot cohort (n = 16 samples) of PD and control subjects was processed to detect methylation changes. Quality control analysis of these data are ongoing, while the rest of the samples are processed and sent for methylation analysis.

In parallel, we also started analyzing the data from the Parkinson's Progression Markers Initiative (PPMI) database, considering clinical features, gene expression data, and methylation data from whole blood (WB). Our preliminary work supported a clear deregulation of gene expression in the inflammatory cells in subjects with PD compared to controls, as we reported in two manuscripts (one under revision and one in preparation). Interestingly, by analyzing previously collected data from the CD14+ monocytes, we were able to identify a set of deregulated genes overlapping with the ones from whole blood (PPMI cohort), suitable for further investigations.

Investigators/Authors:  Katharina Schindelbeck, MD, Feinstein Institute for Medical Research

Objective: We have previously identified and validated imaging network biomarkers to assess the progression of motor and cognitive dysfunction in PD patients using metabolic PET-based as well as functional MRI (fMRI)-based methods. We propose to compare these PET-based and fMRI-based biomarkers in PD patients with and without GBA variants to determine if there are specific network changes associated with GBA mutations that could be used as a biomarker.

Background: Although PD is usually idiopathic, there are individuals with genetic risk factors. Of these, mutations in the GBA gene encoding the enzyme glucocerebrosidase are among the most common. Indeed, individuals carrying GBA mutations as well as the E326K polymorphism can develop PD that is pathologically and clinically indistinguishable from the sporadic disease. These patients, however, tend to experience earlier symptom onset and more rapid disease progression than PD patients without GBA mutations. The relationship between reduced GBA activity and faster disease progression in these patients suggests a potential role for this pathway as a target of disease-modifying therapy.

Methods/Design: We will use PET imaging and resting state fMRI to assess the expression of PD-related motor and cognitive disease network patterns in matched groups of PD patients with and without GBA variants. We will: a) Correlate expression values for these networks with motor and neuropsychological ratings obtained for individual subjects; b) Compare and optimize PD network biomarkers derived using PET and fMRI; and c) Determine whether GBA(+) PD subjects express additional networks independent of the PD motor and cognitive patterns that shed light on their more severe clinical features. We will also assess longitudinal changes in the expression of PD-related motor and cognitive disease network patterns in the GBA(+) and GBA(-) PD patients by scanning them at baseline and 18 months later. We will: a) Compare network progression rates for the two groups; b) Correlate network progression in each group with concurrent changes in clinical ratings measured in the same subjects; and c) Validate the PD-related networks as progression biomarkers.

Relevance to Diagnosis/Treatment of Parkinson’s disease: This study will identify the network biomarker that is most sensitive to disease progression in GBA mutation-positive Parkinson’s disease patients. These determinations will be critical for the design of clinical trials of new disease-modifying drugs.

November 2020 Project Update:

The GBA pathway constitutes a promising target for disease modifying therapies in mutation carriers that are now tested in clinical trials. This study focusses on the development of imaging biomarkers to monitor disease activity at the network level. Such markers could be helpful as outcome measures in clinical trials to objectively assess treatment effects of new anti-GBA therapies.

We studied the effects of mutations in the GBA and LRRK2 genes on PD-related brain networks and in PD patients who underwent PET brain imaging. We found that the patterns of brain connectivity were influenced by the gene mutations.  Brain patterns indicating less efficient information flow were increased in PD patients with GBA mutations, which is likely associated with their more aggressive clinical presentation. By contrast, brain patterns of more benign LRRK2 mutations indicated more efficient and stable information flow through the brain. We also found that disease progression in sporadic PD patients, i.e. those without GBA or LRRK2 mutations, is also associated with less efficient information flow over time.

Thus, connectivity patterns could represent markers to help monitor disease progression and treatment effects. However, these parameters need to be assessed at the individual patient level as opposed to the group averages examined so far.  We will next use functional MRI to quantify disease networks and connectivity patterns at the individual subject level in PD patients with GBA mutations.