When it comes to Alzheimer's disease, scientists usually -- and
understandably -- look to the brain as their first center of attention. Now
researchers at Tel Aviv University say that early clues regarding the
progression of the disease can be found in the brain's metabolism.
In very early stages of the disease, before any symptoms appear,
metabolic processes are already beginning to change in the brain, says PhD
candidate Shiri Stempler of TAU's Sackler Faculty of Medicine. Working with
Profs. Eytan Ruppin and Lior Wolf of TAU's Blavatnik School of Computer
Science, Stempler has developed predictor models that use metabolic information
to pinpoint the progression of Alzheimer's. These models were 90 percent
accurate in predicting the stage of the disease.
Published in the journal Neurobiology of Aging, the
research is the first step towards identifying biomarkers that may ensure
better detection and analysis of the disease at an early stage, all with a
simple blood test. It could also lead to novel therapies. "We hope that by
studying metabolism, and the alterations to metabolism that occur in the very
early stages of the disease, we can find new therapeutic strategies," adds
Stempler.
Interrupting a regulated process
Metabolism describes a set of chemical reactions in cells which
sustain life by controlling processes such as growth and reproduction. It is
also responsible for providing energy to the body. To delve deeper into the
connection between metabolism, brain functioning, and Alzheimer's disease, the
researchers used data collected from the hippocampus region of the brain.
Controlling memory and learning, this region of the brain is damaged as
Alzheimer's progresses.
Based on the number of metabolic genes found in the neurons and
surrounding tissue, they built a predictive model which relates abnormalities
in these genes to the progression of the disease. Out of almost 1500 genes, the
researchers were able to select 50 genes that were the most predictive of
Alzheimer's, says Stempler, noting that in Alzheimer's patients these genes are
either over or under expressed, meaning that there are either too many or too few.
When they compared the findings from these 50 genes among
Alzheimer's patients, healthy patients, and primates (including chimpanzees and
rhesus monkeys), the researchers discovered that in all but the Alzheimer's
group, the number of the specific genes was tightly limited, with little
difference in their number between individuals among each of the species, she
explains. This implies that these genes are significant to normal brain
functioning, and their strict regulation in healthy patients is compromised by
Alzheimer's disease.
Exploring new pathways
Whether metabolic changes are a cause of the disease or merely a
symptom remains a topic for future study. But the discovery of this connection
is encouraging. "The correlation between metabolic gene expression and
cognitive score in Alzheimer's patients is even higher than the correlation we
see in medical literature between beta amyloid plaques -- found in deposits in
the brains of Alzheimer's patients -- and cognitive score, pointing to a strong
association between cognitive decline and an altered metabolism," Stempler
says.
Next the researchers will try to identify biomarkers in the blood
that are associated with these metabolic changes. They may lead to detection
and information about the disease's progression with an easy and non-invasive
blood test. And as their work advances, Stempler hopes to develop therapeutic
strategies that are based around these alterations in the metabolic network to
help Alzheimer's patients, such as medications that can re-introduce strict
regulation over gene expression. They believe that the research is a promising
direction for Alzheimer's research.
Source:
The above story is reprinted from materials provided
byAmerican
Friends of Tel Aviv University.
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Journal Reference:
1. Shiri Stempler, Yedael Y. Waldman, Lior Wolf,
Eytan Ruppin. Hippocampus neuronal metabolic gene expression
outperforms whole tissue data in accurately predicting Alzheimer's disease
progression.Neurobiology of Aging, 2012; 33 (9): 2230.e13 DOI:10.1016/j.neurobiolaging.2012.04.003
Disclaimer: This article is not intended to provide
medical advice, diagnosis or treatment. Views expressed here do not necessarily
reflect those of Eagle Group or its staff.
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