Doctors can now predict the severity of disease by measuring molecules. An international team of researchers has found a way to diagnose disease and predict patient outcomes simply by measuring extremely small changes in interactions among molecules inside the body. The new technique could offer vastly superior predictions of disease severity in a huge range of conditions with a genetic component, such as Alzheimer's, autism, cancer, cardiovascular disease, diabetes, obesity, schizophrenia, and depression.
Gene mutations that cause disease physically alter the interactions of molecules that cells use to communicate with one another. Until now, scientists have had no easy way to measure the subtle changes in these interaction forces. But researcher J. Julius Zhu, PhD, of the University of Virginia School of Medicine, and his collaborators have developed a method to accurately and efficiently calculate these tiny changes. It's a feat that requires incredible precision: Force is typically measured in newtons--the amount of force needed to accelerate one kilogram of mass one meter per second squared--but Zhu's technique measures on a scale of piconewtons--that is, one trillionth of a newton.
Zhu and colleagues have used the new technique to show that gene mutations responsible for mental-health diseases change molecular interactions by a few piconewtons. These small changes then have a tremendous ripple effect. The researchers found the molecular changes lead to harmful changes in how the cells communicate--and ultimately, in cognitive ability. By measuring the molecular changes, the scientists could predict the resulting cognitive impairment.
Zhu's approach represents a new use for a high-tech scientific instrument called "optical tweezers" that uses a highly focused laser to hold and move microscopic objects. Using the optical tweezers, scientists can measure the force required to break up intermolecular bonds among the signaling molecules inside the body, allowing them gauge the effects of gene mutations in patients.
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|Title Annotation:||The Observatory: NEWS * TRENDS * ANALYSIS|
|Publication:||Medical Laboratory Observer|
|Date:||Nov 1, 2017|
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