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 evaluation 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 discover RNA markers that occur across several neurodegenerative diseases and people which are unique to every disorder.
The study found eight common points across six neurodegenerative diseases. These are transcription regulation, degranulation (related to inflammation process), immune response, protein synthesis, cell death or apoptosis, cytoskeletal components, ubiquitylation/proteasome (a part of protein degradation), and mitochondrial complexes (oversee energy expenditure in cells), in accordance with NewsMedical.
These eight cellular dysfunctions are linked with identifiable pathologies within the brain for every disease. Besides these eight, the study also found unique markers for every disease.
As an example, transcripts related to a process often known as spliceosome regulation were detected just within the case of Alzheimer’s disease.
“It seems that multiple neurodegenerative diseases harbor similar fundamental dysfunctional cellular processes. Differences between diseases could also be key to discovering regional cell-type vulnerabilities and therapeutic targets for every disease,” first creator of the study, Carol Huseby, a researcher at ASU-Banner Neurodegenerative Disease Research Center, said, the outlet reported.
In line with the United Nations, the worldwide death toll from all neurodegenerative diseases combined is likely to be 1 billion people, as per the outlet.
Against this backdrop, the necessity for novel methods of early diagnosis, higher treatments, and even methods of prevention is supreme.
The RNA transcripts extracted from blood are fed to the machine learning algorithm, often known as Random Forest, which then analyzes the info and compares results with known patterns related to disease-linked biological pathways.
Usual other types of testing are sometimes less detailed, expensive, invasive, and labor-intensive. In contrast, diagnosis through whole blood is a low-cost alternative. It might probably be easily collected in patients in any respect stages of life.
Moreover, changes within the blood result could be tracked, which might help trace disease progression.
The study opens up the chance that RNA changes common in multiple diseases may later turn into individual neurodegenerative diseases. Nonetheless, the mechanism behind this phenomenon stays a mystery.