New blood test identifies genetic risk factor for Parkinson’s disease in human studies.


A new blood test may more accurately identify blood signatures, or biomarkers, for Parkinson’s disease (PD), according to a new study published in the journal Movement Disorders. The study, conducted by researchers at Mount Sinai applies a new approach to looking for blood biomarkers for both patients with and without a known genetic risk factor for PD. This paper is the fourth from the team reporting new computational techniques to improve the identification of reliable blood biomarkers.

While biomarkers, such as bad cholesterol level in the case of heart disease, hasten diagnoses by offering accurate measures of disease progression, there are currently no fully validated biomarkers for PD state the team.

The current study analyzed the blood of four groups of mice with genetic material (e.g. ribonucleic acids or RNA) predicted by researchers to form part of a PD signature. Researchers also examined the blood of a group of Ashkenazi Jewish patients living with PD, as well as a separate group of healthy controls.

About half of the human subjects, both symptomatic PD patients and healthy controls, have small changes in their DNA code called mutations in a gene known to increase the likelihood of developing Parkinson’s leucine-rich repeat kinase 2, or LRRK2. Just one to two percent of Parkinson’s patients carry this gene mutation, and many LRRK2 mutation carriers are from the Ashkenazi Jewish population. The other samples studied came from individuals without the mutation, 50% of which had clinical PD.

After comparing the mouse and human blood samples, the researchers identified RNA signatures that can be measured in blood samples that correlate with the disease-causing mutations in the LRRK2 gene in PD patients.  While LRRK2 mutations contribute to PD risk in a small percentage of patients, researchers believe related pathways also play a role in much more common, non-inherited cases of PD. Studying it may speed progress toward treatments that would benefit everyone with the disease, not just those with genetic mutations.

To the researchers knowledge this is the first time a study has compared animal models and clinical samples to look at RNA expression patterns of biomarkers in PD.  The other goal of the current study is to use this approach to identify subtypes of the disease so that treatment can be targeted more accurately and in addition, incorporated with clinical trials that facilitate the ability to identify new therapeutic and disease modifying agents.

The team state that Parkinson’s disease (PD) is a chronic and progressive movement disorder affecting nearly one million people in the U.S. PD involves the malfunction and death of vital nerve cells in the brain, called neurons. Some of these dying neurons produce dopamine, a chemical that sends messages to the part of the brain that controls movement and coordination. As PD progresses, the amount of dopamine produced in the brain decreases, leaving a person unable to control movement normally. The cause of PD is unknown and there is presently no cure.

The goal of this research is to improve early disease detection, especially in people who are carrying a predisposing genetic mutation state the researchers, adding that if they can improve the medical community’s ability to diagnose the disease more specifically and identify new subtypes, this can help overcome the hurdle in developing new treatments for Parkinson’s and other brain diseases. The next step is to replicate this approach in a larger sample, where the team will track patients longitudinally and see how profiles are changing over time.

Source:  The Mount Sinai Health System 

 

Human phenotype heatmap and drug effect. (A) Blood expression profiles discriminate between individuals with PD symptoms (PD patients, in blue) and healthy controls (Controls, in black). Marker values were used to fit a linear model using gender, genotype, and phenotype (i.e., presence or absence of PD). Markers that were associated with phenotype at a q value of 0.1 were used to construct a heatmap. Each marker value is scaled to have 0 mean and 1 standard deviation. Markers were clustered with hierarchical clustering using Euclidean distance. Samples are ordered according to a score that is the sum of the scaled marker values multiplied by the sign of the direction of change in the PD-affected group. This score produces significant discrimination between PD patients and healthy controls with an area under the curve (AUC) of 0.79 (P = 1.89 × 10−5) using 5-fold cross-validated support vector machine (SVM) classification using all genes. (B) Example of a marker associated with disease status and l-dopa dose. Box plot and linear fit showing MyD88 gene expression by l-dopa dosage. MyD88 gene expression is significantly higher in PD patients, yet it is lowered by l-dopa administration. Expression profiles of PD patients who received higher doses of l-dopa look more like those of normal controls. (C) In order to evaluate the relative and potentially confounding effects of disease presence and l-dopa dose on the expression of each gene, we plotted the t-statistic obtained for each gene comparing PD patients and asymptomatic individuals (x-axis) and the t-statistic obtained from a regression against l-dopa dose (y-axis, as shown for MyD88 in B). These paired t-values were then fit by a linear regression, showing an inverse correlation. The expression profiles of PD patients who received higher doses of l-dopa look more like those of normal controls. Because gene effect sizes are not independent, significance was evaluated by a permutation test: 500 random correlation values were generated from permuting the l-dopa doses and deriving the corresponding effect sizes. The inverse relationship is significant (P = 0.02) and further corroborates the argument that the observed expression profiles are the result of the underlying disease biology.  Low-Variance RNAs Identify Parkinson's Disease Molecular Signature in Blood.  Sealfon et al 2015.

Human phenotype heatmap and drug effect. (A) Blood expression profiles discriminate between individuals with PD symptoms (PD patients, in blue) and healthy controls (Controls, in black). Marker values were used to fit a linear model using gender, genotype, and phenotype (i.e., presence or absence of PD). Markers that were associated with phenotype at a q value of 0.1 were used to construct a heatmap. Each marker value is scaled to have 0 mean and 1 standard deviation. Markers were clustered with hierarchical clustering using Euclidean distance. Samples are ordered according to a score that is the sum of the scaled marker values multiplied by the sign of the direction of change in the PD-affected group. This score produces significant discrimination between PD patients and healthy controls with an area under the curve (AUC) of 0.79 (P = 1.89 × 10−5) using 5-fold cross-validated support vector machine (SVM) classification using all genes. (B) Example of a marker associated with disease status and l-dopa dose. Box plot and linear fit showing MyD88 gene expression by l-dopa dosage. MyD88 gene expression is significantly higher in PD patients, yet it is lowered by l-dopa administration. Expression profiles of PD patients who received higher doses of l-dopa look more like those of normal controls. (C) In order to evaluate the relative and potentially confounding effects of disease presence and l-dopa dose on the expression of each gene, we plotted the t-statistic obtained for each gene comparing PD patients and asymptomatic individuals (x-axis) and the t-statistic obtained from a regression against l-dopa dose (y-axis, as shown for MyD88 in B). These paired t-values were then fit by a linear regression, showing an inverse correlation. The expression profiles of PD patients who received higher doses of l-dopa look more like those of normal controls. Because gene effect sizes are not independent, significance was evaluated by a permutation test: 500 random correlation values were generated from permuting the l-dopa doses and deriving the corresponding effect sizes. The inverse relationship is significant (P = 0.02) and further corroborates the argument that the observed expression profiles are the result of the underlying disease biology. Low-Variance RNAs Identify Parkinson’s Disease Molecular Signature in Blood. Sealfon et al 2015.

 

 

 

 

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s