Study Traces Unique and Shared Cellular Features Found in 6 Neurodegenerative Diseases

Resume: Multiple neurodegenerative disorders harbor similar fundamental dysfunctional cellular processes.

Fountain: university of arizona

A bewildering variety of neurodegenerative diseases are known to attack different regions of the brain, causing severe cognitive and motor deficits. The combined impact of these (often fatal) diseases has inflicted a devastating cost on society.

New insights suggest that many of these conditions originate from a constellation of common processes, manifesting in different ways as each disease develops.

In a study appearing in the current issue of Alzheimer’s and Dementia: The Journal of the Alzheimer’s AssociationCorresponding author Carol Huseby of Arizona State University and colleagues discuss cellular alterations in six different neurodegenerative diseases: amyotrophic lateral sclerosis or Lou Gehrig’s disease, Alzheimer’s disease, Friedreich’s ataxia, frontotemporal dementia, Huntington’s disease, and Parkinson. Carol Huseby is an investigator with the ASU-Banner Neurodegenerative Diseases Research Center.

The study uses an innovative approach, including machine learning analysis of RNA found in whole blood. By comparing multiple diseases, researchers can identify which RNA markers are found in various neurodegenerative diseases and which are unique to each disease.

“It appears that multiple neurodegenerative diseases harbor similar fundamental dysfunctional cellular processes,” says Huseby, an investigator with the ASU-Banner Neurodegenerative Diseases Research Center.

“Differences between diseases may be key to uncovering the vulnerabilities of regional cell types and therapeutic targets for each disease.”

The blood samples used for the study were derived from a publicly available dataset known as the Gene Expression Omnibus. Each of the six neurodegenerative diseases were tested. As the machine learning algorithm analyzed thousands of genes, it assembled sets of RNA transcripts that optimally classified each disease, comparing the data with RNA samples from healthy patients’ blood.

Selected RNA transcripts reveal eight common themes across the six neurodegenerative diseases: regulation of transcription, degranulation (a process involved in inflammation), immune response, protein synthesis, cell death or apoptosis, cytoskeletal components, ubiquitylation/proteasome ( involved in protein breakdown) and mitochondrial complexes (which oversee energy use in cells). The eight cellular dysfunctions discovered are associated with identifiable pathologies in the brain characteristic of each disease.

The study also identified rare transcripts for each disease, which may represent unexplored disease pathways. Such disease-specific outliers can be explored as a potential source of diagnostic biomarkers.

For example, while synaptic loss was a common feature in all six diseases analyzed, transcripts related to a phenomenon known as splicing some regulation were only detected in the case of Alzheimer’s disease. (The spliceosome is a protein complex found in the cell nucleus, essential for proper cell function. RNA mis-splicing is associated with disease.)

Research into blood biomarkers for neurodegenerative diseases, coupled with powerful statistical methods using artificial intelligence, has opened a new window on these serious conditions. Blood samples can be easily taken from living patients at all stages of health and disease, providing a powerful new tool for early diagnosis.

According to the United Nations, when all neurodegenerative diseases are considered, the global number of deaths may exceed a staggering 1 billion people. The course of many of these diseases is protracted and ruthless, causing not only severe suffering for patients, but also a huge financial burden on health care systems.

New methods of early diagnosis, improved treatments and possible methods of prevention are urgently needed.

However, most neurodegenerative diseases have been difficult to accurately diagnose and stubbornly resistant to treatment, including Alzheimer’s disease (AD), the leading cause of dementia.

Although genetic factors play a role in the development of AD, most cases are considered sporadic, meaning the underlying causes are unclear.

This shows a diagram of the brain.
Illustration shows cell types and brain regions affected by six different neurodegenerative diseases: Friedreich’s ataxia (purple); Huntington’s disease (blue); frontotemporal dementia (yellow); amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND) or Lou Gehrig’s disease (green); Parkinson’s disease (orange); and Alzheimer’s disease (pink). Credit: Shireen Dooling

This is also the case for three other diseases highlighted in the study: frontotemporal dementia, ALS and Parkinson’s disease. Huntington’s disease and Friedreich’s ataxia appear to be genetically determined and are said to be familial.

Signs of neurodegeneration are detectable in both the central nervous system and the peripheral vascular system. Diseases can also migrate from their point of origin to distant regions of the brain, where they inflict most of their damage.

The study describes clusters or trees of RNA selected by the machine learning process, which uncovers gene expression patterns common to the six neurodegenerative diseases explored in the study, as well as expression profiles that are distinct and disease-dependent.

The machine learning algorithm creates and statistically compares thousands of these trees to select clusters of 20 transcripts that most closely align with known disease pathways in the diseases under study.

The findings offer clues about common cellular features that may play a role in the initiation processes of neurodegeneration. The study also raises perplexing questions about how different forms of disease ultimately develop from these common elements.

From the RNA transcripts extracted from the blood, about 10,000 genes are expressed. The machine learning algorithm, known as Random Forest, sorts the data and compares the results with gene expression profiles known to be associated with disease-linked biological pathways.

Whole blood screening and full RNA profiling can overcome the limitations of many other forms of testing, which are often less comprehensive, expensive, highly invasive, and time-consuming.

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This shows a girl with a book and a teddy bear.

Diagnosis through whole blood, on the other hand, can be performed at low cost practically anywhere in the world. Blood results can be tracked over time, providing a valuable window into disease progression. Research of this type can also foster new modes of treatment.

The results suggest a tantalizing possibility: transcriptional changes shared by multiple types of diseases may provide the initial seeds that then develop into each of the various brain conditions. The mechanisms responsible for these common factors germinating to produce various diseases and symptoms, attacking different regions of the brain, remain a central puzzle to be solved.

Future research will explore transcriptional impacts on neurons in addition to blood cells, as well as the underlying mechanisms that set the stage for neurodegenerative diseases to develop and evolve into their various pathologies.

About this research news in neurology and genetics

Author: press office
Fountain: university of arizona
Contact: Press Office – University of Arizona
Picture: Image is credited to Shireen Dooling.

original research: closed access.
Blood RNA transcripts reveal similar and differential alterations in fundamental cellular processes in Alzheimer’s disease and other neurodegenerative diseases” by Carol J. Huseby et al. Alzheimer’s and dementia


Blood RNA transcripts reveal similar and differential alterations in fundamental cellular processes in Alzheimer’s disease and other neurodegenerative diseases


Dysfunctional processes in Alzheimer’s disease and other neurodegenerative diseases lead to neuronal degeneration in the central and peripheral nervous systems. Research shows that neurodegeneration of any kind is a systemic disease that can even start outside of the region vulnerable to the disease. Neurodegenerative diseases are defined by the vulnerabilities and pathology that occurs in the affected regions.


A randomized forest machine learning analysis on whole blood transcriptomes of six neurodegenerative diseases generated disease classification unbiased RNA transcripts that were subsequently subjected to pathway analyses.


We report that the selected blood transcriptome transcripts for each of the neurodegenerative diseases represent fundamental cellular biological processes including regulation of transcription, degranulation, immune response, protein synthesis, apoptosis, cytoskeletal components, ubiquity. /proteasome and mitochondrial complexes that are also affected in the brain and reveal common themes in six neurodegenerative diseases.


Neurodegenerative diseases share common dysfunctions in fundamental cellular processes. Identifying regional vulnerabilities will reveal unique disease mechanisms.

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