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Measuring Gene Expression

Measuring Gene Expression

Small variations in our DNA can correlate with differences in the way individuals respond to a medication or in their risk for getting a disease. Often, these variations occur within the DNA letters that make up the protein-coding portion of a gene, influencing how the protein works.

Changing the protein-coding sequence isn't the only way to affect a gene, though. Changing the level of gene expression—thus increasing or decreasing the amount of RNA or protein that is made from it—can influence biological processes just as dramatically.

A major focus in genetic science is to examine how differences in gene expression correlate with drug response or disease risk. Learn more about personalized medicine in Pharmacogenomics


Gene expression and obesity

Obesity study

Let's look at obesity as an example of how gene expression can correlate with disease risk:

Obesity is a major health risk in America that threatens children and adults alike. It can lead to heart disease, high blood pressure, and diabetes, especially as people age. A complex medical condition, obesity is influenced by diet, exercise, metabolism, and genetics.

Shan, age 17, is more than 40 pounds overweight relative to his height. His parents and grandparents are all overweight as well.

Allen is similar to Shan with respect to age, height, diet, and exercise habits, but he is not overweight. Furthermore, no one in Allen's family is overweight.

Both Shan's and Allen's families volunteer to participate in a university study to identify genes that play a role in obesity. How will the researchers approach this question?

Gene expression profiles can be read using DNA microarray technology.


Measuring levels of gene expression

gene expression

Three generations of family members provide cell samples (liver and fat cells) to the researchers. Liver and fat cells were chosen because they are important in metabolism and making fats.

  • The researchers will use an approach called gene expression profiling to identify active and inactive genes in a cell or tissue.
  • Expression profiling then tells the scientist which genes may play a role in obesity.
  • Scientists run similar expression profile studies on all the family members, as well as on those of other study participants.

Once the scientists compare the results from everyone in the study, they have a good idea which genes play a role in obesity. This information can be used in several future applications:

1. Creating diagnostic tests to predict whether a patient has a genetic predisposition to obesity.

  • One test might examine the DNA sequence of a person's obesity-related genes, in order to detect genetic signatures that predict a predisposition to obesity.
  • Another test might examine a tissue sample for abnormal gene expression patterns that indicate a predisposition to obesity.

2. Designing Drugs

  • Designing drugs intended to treat or prevent obesity. This could be done by isolating the protein products of the identified obesity genes, determining their molecular structures and functions, and making drugs to inhibit them.
  • Designing drugs to control expression of obesity genes. These drugs would interact directly with DNA in key cells and tissues to prevent genes that are activated in obesity from being turned on—or, conversely, to prevent genes that are inactivated during obesity from being turned off.
Supported by a Science Education Partnership Award (SEPA) Grant No. R25RR016291 from the National Center for Research Resources, a component of the NIH. The contents provided here are solely the responsibility of the authors and do not necessarily represent the official views of NIH.

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APA format:
Genetic Science Learning Center (2014, June 22) Measuring Gene Expression. Learn.Genetics. Retrieved November 23, 2014, from http://learn.genetics.utah.edu/content/science/expression/
MLA format:
Genetic Science Learning Center. "Measuring Gene Expression." Learn.Genetics 23 November 2014 <http://learn.genetics.utah.edu/content/science/expression/>
Chicago format:
Genetic Science Learning Center, "Measuring Gene Expression," Learn.Genetics, 22 June 2014, <http://learn.genetics.utah.edu/content/science/expression/> (23 November 2014)