Cholesterol is the most well-known member of sterol family of lipids, and it serves as a precursor of many key molecules. Sterols participate in nearly every important cellular function, but are mostly recognized for their causative effect on atherosclerotic plaque.
“We are after the undiscovered roles of sterol metabolites,” says David Russell, Ph.D., professor of molecular genetics, University of Texas Southwestern Medical Center. “Mutations in genes encoding sterol metabolic enzymes lead to serious liver and endocrine problems. And yet, until now we did not possess a robust ability to evaluate all cholesterol precursors and derivatives at once.”
Dr. Russell’s team developed a method to reproducibly detect and analyze >60 sterols in just 100 microliters of blood, plasma, or urine. “To develop this method, we first reviewed the collective scientific wisdom for sterol purification. Next, we optimized each analytical step using dozens of different sterol standards to ensure maximum recovery at each step,” continues Dr. Russell. “The initial method development was lengthy, but now we have a rapid and quantitative method to analyze a majority of sterols in human plasma.”
This analytical method was deployed to evaluate an unbiased population sample, the Dallas Heart Study. The sample consists of “typical” residents of Dallas county aged 18 to 65, who underwent multiple blood tests, diagnostic imaging, and health surveys. The team focused on 20 consistently detected sterols and created sterol profiles for over 3,000 samples. “Our approach combines sterol analytics with SNP genotyping and clinical profiles,” adds Dr. Russell. In collaboration with Merck, the Russell team is searching for correlations between sterol profiles and genetic alleles. If found, such correlations may reveal a new biomarker of disease.
Simultaneously, the team is evaluating samples from patients with clearly defined diseases, such as West Nile Virus, common influenza, and septicemia. The researchers hope that sterol profiles may provide a means to detect an infection before its clinical manifestation.