Study tracks shared and unique cellular markers found in 6 neurodegenerative diseases

TECHNOLOGY

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

Source: University of Arizona

A bewildering range of neurodegenerative diseases is known to attack distinct regions of the brain, causing severe cognitive and motor impairment. The combined impact of these (often fatal) diseases has inflicted a devastating toll on society.

New insights suggest that many of these afflictions stem from a constellation of common processes, which unfold in different ways as each disease develops.

In a study published 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 analyze cellular changes in six distinct 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 Neurodegenerative Disease Research Center.

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

“It appears that several neurodegenerative diseases harbor similar fundamental dysfunctional cellular processes,” says Huseby, a researcher at the ASU-Banner Neurodegenerative Disease Research Center.

“Differences between diseases may be the 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 dataset known as the Gene Expression Omnibus. Each of the six neurodegenerative diseases was investigated. As the machine-learning algorithm sifted through thousands of genes, it assembled sets of RNA transcripts that optimally classified each disease, comparing the data with RNA samples from healthy blood from patients.

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

The study also identified unusual transcripts for each disease that may represent unexplored disease pathways. These disease-specific outliers can be exploited 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 spliceosome regulation were detected only in the case of Alzheimer’s disease. (The spliceosome is a protein complex found in the cell nucleus that is essential for proper cell function. Defective RNA splicing is associated with disease.)

The investigation of blood biomarkers for neurodegenerative diseases, along with powerful statistical methods using artificial intelligence, has opened a new window on these serious afflictions. Blood can be easily collected 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 death toll could reach a staggering 1 billion people. The course of many of these diseases is prolonged and unrelenting, causing not only severe suffering for patients but also an enormous economic burden on health care systems.

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

Most neurodegenerative diseases, however, have been difficult to accurately diagnose and 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 that the underlying causes are unclear.

The illustration shows the cell types and regions of the brain 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 RNA groups or trees selected by the machine learning process, which reveals gene expression patterns common to the six neurodegenerative diseases explored in the study, as well as distinct and disease-dependent expression profiles.

Thousands of these trees are created and statistically compared by the machine learning algorithm, to select groupings 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 neurodegeneration processes. The study also raises intriguing questions about how distinct forms of disease develop from these common elements.

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

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

See too

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

Diagnosis using whole blood, in contrast, can be performed at low cost virtually anywhere in the world. Blood results can be tracked over time, providing a valuable window into disease progression. Research of this kind may also encourage new modes of treatment.

The results suggest a tantalizing possibility: transcriptional changes shared by several types of diseases may provide the initial seeds that later develop into each of the distinct brain afflictions. The mechanisms responsible for these common factors germinating to produce diverse diseases and symptomatologies, attacking different regions of the brain, remains a central puzzle to be solved.

Future research will explore the impacts of transcription 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 distinct pathologies.

About this neurology and genetics research news

Author: Press office
Source: University of Arizona
Contact: Press Office – University of Arizona
Image: The image is credited to Shireen Dooling

Original search: Closed access.
“Blood RNA transcripts reveal similar and differential changes in key cellular processes in Alzheimer’s disease and other neurodegenerative diseases” by Carol J. Huseby et al. Alzheimer’s and Dementia


Summary

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

Background

Dysfunctional processes in Alzheimer’s disease and other neurodegenerative diseases lead to neural degeneration in the central and peripheral nervous systems. Research demonstrates that neurodegeneration of any type is a systemic disease that can even start outside the disease-vulnerable region. Neurodegenerative diseases are defined by the vulnerabilities and pathologies that occur in the affected regions.

Method

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

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 affected in the brain and reveal common themes in six neurodegenerative diseases.

Conclusion

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

Tags