A study found that multiple neurodegenerative diseases have common and unique dysfunctional cellular processes.
The six neurodegenerative diseases that showed this commonality, include amyotrophic lateral sclerosis or Lou Gehrig’s disease, Alzheimer’s disease, Friedreich’s ataxia, frontotemporal dementia, Huntington’s disease, and Parkinson’s disease.
For the study, published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, the researchers deployed machine learning analysis of RNA from blood samples. The blood samples were taken from a publicly available database called the Gene Expression Omnibus. The research team then compared multiple diseases to identify RNA markers that occur across several neurodegenerative diseases and those that are unique to each disorder.
The study found eight common points across six neurodegenerative diseases. These are transcription regulation, degranulation (associated with inflammation process), immune response, protein synthesis, cell death or apoptosis, cytoskeletal components, ubiquitylation/proteasome (part of protein degradation), and mitochondrial complexes (oversee energy expenditure in cells), according to NewsMedical.
These eight cellular dysfunctions are linked with identifiable pathologies in the brain for each disease. Besides these eight, the study also found unique markers for each disease.
For instance, transcripts related to a process known as spliceosome regulation were detected just in the case of Alzheimer’s disease.
“It appears that multiple neurodegenerative diseases harbor similar fundamental dysfunctional cellular processes. Differences between diseases may be key to discovering regional cell-type vulnerabilities and therapeutic targets for each disease,” first author of the study, Carol Huseby, a researcher at ASU-Banner Neurodegenerative Disease Research Center, said, the outlet reported.
According to the United Nations, the global death toll from all neurodegenerative diseases combined might be 1 billion people, as per the outlet.
Against this backdrop, the need for novel methods of early diagnosis, better treatments, and even methods of prevention is supreme.
The RNA transcripts extracted from blood are fed to the machine learning algorithm, known as Random Forest, which then analyzes the data and compares results with known patterns associated with disease-linked biological pathways.
Usual other forms of testing are often less detailed, expensive, invasive, and labor-intensive. In contrast, diagnosis through whole blood is a low-cost alternative. It can be easily collected in patients at all stages of life.
Additionally, changes in the blood result can be tracked, which can help trace disease progression.
The study opens up the possibility that RNA changes common in multiple diseases may later develop into individual neurodegenerative diseases. However, the mechanism behind this phenomenon remains a mystery.