Common and unique cellular features were found in 6 neurodegenerative diseases

A complex range of neurodegenerative diseases are known to attack different areas of the brain, leading to severe cognitive and motor deficits
.
The combined effects of these (often fatal) diseases take a devastating toll on society
.
The new insights suggest that many of these pains originate from a common set of processes that play out
in different ways as each disease progresses.

In a new study, researchers looked at cellular changes in six different neurodegenerative diseases: amyotrophic lateral sclerosis or Lou Gearay's disease, Alzheimer's disease, Friedreich ataxia, frontotemporal dementia, Huntington's disease, and Parkinson's disease
.
Carol Huseby is a researcher
at the Banner Center for Neurodegenerative Diseases at Arizona State University.

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

Huseby, a researcher at the ASU-Banner Center for Neurodegenerative Diseases, said: "It seems that many neurodegenerative diseases have similar fundamentally dysfunctional cellular processes
.
" "Differences between diseases may be key
to discovering regional cell type vulnerability and therapeutic targets for each disease.
"

The blood samples used in the study came from a publicly available dataset
known as gene expression synthesis.
Each of these six neurodegenerative diseases was studied
.
When a machine learning algorithm combs through thousands of genes, it assembles a set of RNA transcripts, best classifies each disease, and compares
the data to RNA samples from healthy patients' blood.

The selected RNA transcripts revealed 8 common themes for 6 neurodegenerative diseases: transcriptional regulation, degranulation (involved in inflammatory processes), immune response, protein synthesis, cell death or apoptosis, cytoskeletal components, ubiquitination/proteasome (involved in protein degradation), and mitochondrial complexes (supervision of energy use in cells).

The eight cellular dysfunctions identified were associated with
identifiable pathologies in brain features of each disease.

The study also found rare transcripts of each disease, which may represent unexplored disease pathways
.
This disease-specific outlier can be explored
as a potential source of diagnostic biomarkers.

For example, while synaptic loss was a common feature of all six analyzed diseases, transcripts associated with spliceosome regulation phenomena were only detected
in Alzheimer's disease.
(Spliceosomes are protein complexes found in the nucleus that are essential
for normal cell function.
) RNA splicing defects are associated with
disease.
)

The study of blood biomarkers for neurodegenerative diseases, combined with powerful statistical methods using artificial intelligence, opens a new window
into the study of these serious diseases.
Blood sampling from living patients can be easily taken at all stages of health and disease, providing a powerful new tool
for early diagnosis.

According to the United Nations, if all neurodegenerative diseases are taken into account, the number of deaths worldwide could exceed a staggering 1 billion
.
Many of these diseases have a long and relentless course, causing not only severe suffering to patients but also enormous economic burdens
on health-care systems.
There is an urgent need for new methods of early diagnosis, improved treatments and possible prevention
.

However, most neurodegenerative diseases have been difficult to diagnose accurately and are stubbornly resistant to treatment, including Alzheimer's disease (AD), a major cause
of dementia.
While genetic factors do play a role in the development of AD, most cases are thought to be sporadic, meaning the underlying cause is unknown
.
The same is true of the other three diseases highlighted in the study: frontotemporal dementia, frostbite and Parkinson's disease
.
Huntington's disease and Friedrich's ataxia appear to be genetically determined and are said to be familial
.

Signals of neurodegenerative degeneration can be detected
in the central nervous system and peripheral vascular system.
These diseases may also migrate from their place of origin to distant brain regions, where they cause most of the damage
.

The study, which describes RNA clusters or trees selected by the machine learning process, reveals gene expression patterns common to the six neurodegenerative diseases explored in the study, as well as different and disease-dependent expression profiles
.
Machine learning algorithms created thousands of such trees and statistically compared them to select 20 transcripts that most closely matched the known disease pathways in the diseases studied
.

These findings provide clues
about common cellular features that may play a role in initiating neurodegeneration.
The study also raises the puzzling question of how different disease forms ultimately develop from these common factors
.

In RNA transcripts extracted from blood, about 10,000 genes are
expressed.
This machine learning algorithm, known as a random forest, classifies the data and compares
the results to gene expression profiles known to be associated with disease-related biological pathways.

Whole blood screening and intact RNA profiling can overcome the limitations of many other forms of testing, which are often less comprehensive and expensive, highly invasive, and labor-intensive
.
In contrast, diagnosis by whole blood can be done
almost anywhere in the world at low cost.
Blood results can be tracked over time, providing a valuable window
into disease progression.
Such research may also encourage new treatment models
.

The results show a tantalizing possibility: transcriptional changes common to multiple disease types may provide the initial seeds
for later development into each different brain disease.
The mechanisms that lead to the different diseases and symptoms of these co-factors, attacking different regions of the brain, remain a central puzzle
to be solved.

Future research will explore the effects of transcription on neurons other than blood cells, as well as the underlying mechanisms
that lay the groundwork for neurodegenerative diseases to develop and evolve their unique pathologies.