Study reveals shared and unique cellular features found in 6 neurodegenerative diseases

Study reveals shared and unique cellular features found in 6 neurodegenerative diseases

Abstract: Multiple neurodegenerative disorders contain similar fundamental dysfunctional cellular processes.

Source: University of Arizona

A bewildering array of neurodegenerative diseases are known to attack different regions of the brain, causing severe cognitive and motor deficits. The combined impact of these (generally fatal) diseases took a devastating toll on society.

New insights suggest that many of these diseases have their origins in a constellation of shared processes, which unfold in different ways as each disease develops.

In a study that appears in the current issue of the journal Alzheimer’s disease and dementia: Journal of the Alzheimer’s Associationcorresponding author Carol Huseby of Arizona State University and her colleagues look at cellular changes 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’s disease. Carol Huseby is a researcher with the ASU-Banner Research Center for Neurodegenerative Diseases.

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

“Multiple neurodegenerative diseases appear to contain similar underlying dysfunctional cellular processes,” says Huseby, a researcher at the ASU-Banner Center for Neurodegenerative Disease Research.

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

The blood samples used for the study were derived from a publicly available data set known as the Gene Expression Omnibus. Each of the six neurodegenerative diseases was examined. As the machine learning algorithm combed through thousands of genes, it assembled sets of RNA transcripts that optimally classified each disease by comparing the data to RNA samples from the blood of healthy patients.

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

The study also identified unusual transcripts for each disease, which may represent unexplored disease pathways. Such disease-specific extremes 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 associated with a phenomenon known as spliceosome regulation were only detected in Alzheimer’s disease. (The spliceosome is a protein complex found in the cell nucleus, essential for proper cell function. Improper splicing of RNA is associated with disease.)

Research on blood biomarkers for neurodegenerative diseases, together with powerful statistical methods using artificial intelligence, has opened a new window into these serious diseases. Blood can be easily sampled 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 taken into account, the number of deaths worldwide could exceed a staggering one billion people. The course of many such diseases is protracted and relentless, causing not only severe suffering for patients, but also a huge economic burden on health care systems.

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

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

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

The illustration shows the 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 genetic and are said to run in families.

Signs of neurodegeneration are visible in the central nervous and peripheral vascular systems. Diseases can also migrate from their place of origin to distant areas of the brain, where they cause the most damage.

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

Thousands of such trees are generated and statistically compared using a machine learning algorithm to select groups of 20 transcripts that are closest to known disease pathways in the diseases under study.

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

About 10,000 genes are expressed from RNA transcripts extracted from blood. A machine learning algorithm, known as Random Forest, categorizes the data and compares the results to gene expression profiles known to be associated with disease-related biological pathways.

Whole blood screening and complete RNA profiling can overcome the limitations of many other forms of testing, which are often less comprehensive and expensive, highly invasive and labor intensive.

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In contrast, diagnosis via whole blood can be performed at low cost almost anywhere in the world. Blood results can be tracked over time, providing valuable insight into disease progression. Research of this kind can also lead to new treatments.

The results suggest a tantalizing possibility: Transcriptional changes shared by multiple disease types may provide the germs that later develop into each of the different brain disorders. The mechanisms responsible for the germination of these common factors that produce different diseases and symptoms, attacking different regions of the brain, remain a central puzzle to be solved.

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

About this neurology and genetics news

Author: Press office
Source: University of Arizona
Contact: Press Office – University of Arizona
Picture: Image credit to Shireen Dooling

Original research: Closed access.
Blood RNA transcripts reveal similar and distinct changes in fundamental cellular processes in Alzheimer’s disease and other neurodegenerative diseases” Carol J. Huseby et al. Alzheimer’s disease and dementia


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


Dysfunctional processes in Alzheimer’s disease and other neurodegenerative diseases lead to neural degeneration in the central and peripheral nervous system. Research shows that neurodegeneration of any kind is a systemic disease that can even begin outside of the disease-susceptible area. Neurodegenerative diseases are defined by the vulnerability and pathology that occurs in the affected regions.


Random forest machine learning analysis of whole blood transcriptomes from six neurodegenerative diseases generated unbiased RNA transcripts for disease classification that were subsequently subjected to pathway analysis.

the results

We report that blood transcriptome transcripts selected for each of the neurodegenerative diseases represent fundamental cellular biological processes including transcriptional regulation, degranulation, immune response, protein synthesis, apoptosis, cytoskeletal components, ubiquitylation/proteasome, and mitochondrial complexes that are also implicated 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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