Swiss Researchers Use New Mass Spectrometry Technique to Obtain Protein Data, Create Strategy That Could Lead to Clinical Laboratory Advances in Personalized Medicine
Researchers believe they have begun to crack open a ‘black box’ involving the genomes and diseases of individual patients
Researchers in Switzerland are developing a new way to use mass spectrometry to explain why patients respond differently to specific therapies. The method potentially could become a useful tool for clinical laboratories that want to support the practice of precision medicine.
It is also one more example of how mass spectrometry is being used by researchers to develop new types of diagnostic assays that perform as well as traditional clinical laboratory testing methods, such as chemistry and immunoassay.
Thus, the latest research from the Swiss Federal Institute of Technology in Lausanne (EPFL) and ETH Zurich (ETHZ), will be of interest to pathology laboratory managers and medical laboratory scientists. It combines SWATH-MS (Sequential Window Acquisition of all Theoretical Mass Spectra) with genomics, transcriptomics, and other “omics,” to explain why patients respond differently to specific therapies, and to formulate a personalized strategy for individual treatment.
The groundbreaking systems biology study utilized a new mass spectrometry technique to better understand the role proteins play in metabolizing fat. By connecting the variations in individuals’ genomes to the variations in their proteomes, the Swiss researchers may have overcome a major obstacle to the development of personalized medicine.
Black Box Between Genomes and Disease
The researchers published their study (“Systems Proteomics of Liver Mitochondria Functions”) in the June 10, 2016, issue of Science, a journal of the American Association for the Advancement of Science (AAAS).
“There is a black box between a patient’s genome and [his or her] disease,” stated senior study author Johan Auwerx, MD, PhD, in an EPFL press release. Dr. Auwerx’s lab produced the genomic side of the study. “What we have done here is find a way to fill the black box by obtaining information on the patient’s proteome.”
The researchers obtained protein data from mice using SWATH-MS, which was developed by study co-author Ruedi Aebersold, PhD, and his team at ETHZ. Aebersold also was a co-founder of the Institute of Systems Biology in Seattle. According to the EPFL statement, SWATH-MS “combines the advantages of high-throughput mass spectrometry with reproducibility and consistency,” which enabled the researchers to measure the concentrations of a broad spectrum of liver proteins in the mice.
Genomic Variants Cause Errors in Metabolism
Researchers measured a total of 2,600 different proteins from tissue samples of 40 mice strains, all of which were genetically related to each other. The mice were divided into groups representing each of the 40 strains and fed either a high-fat or low-fat diet. Despite their similar genetic make-up, the mice on the high-fat diet responded differently to diet and exercise. Some developed metabolic disorders such as fatty liver, while others on the same diet and exercise routine did not.
When the researchers combined the physiological data from the mice with their genome, proteome, and transcriptome data—essentially their full set of RNA—they were better able to understand the role proteins play when metabolizing fat and producing energy from it. They identified “genomic variants of mitochondrial enzymes that caused inborn errors in metabolism, and revealed two genes that appear to function in cholesterol metabolism,” noted the Science research article.
Because inherent biological differences between patients cause individuals to respond differently to medication, it has been difficult to develop “standard” treatments for diseases such as diabetes, obesity, fatty liver, and other metabolic disorders, the EPFL news release states.
“Like the mouse strains in this study, each patient with a disease is genetically different. The approach we used in the mouse cohort can now be applied one-for-one in research on human diseases, and particularly for personalized medicine,” Dr. Aebersold stated in the EPFL release, adding that researchers in his group have produced a corresponding database for thousands of human proteins.
Complementary versus Replacement Technologies
The Swiss researchers expect their integrated strategy for exploring and determining the inherent biological differences between individuals to help advance personalized medicine.
“Using mitochondria as a case in point, we show that the integrated analysis of these systems provides more insights into the emergence of the observed phenotypes than any layer can by itself, highlighting the complementarity of a multilayered approach. The increasing implementation of these omics technologies as complements, rather than as replacements, will together move us forward in the integrative analysis of complex traits.” the study concluded.
“The aim here is to be able to customize medical intervention for each patient based on their individual biological makeup,” declared Dr. Auwerx.
Pathologists and clinical laboratory managers should consider these research project to be an intriguing example of how analysis of multiple “omes”—in this case, the genome, the transcriptome, and the proteome—can provide insights into why two individuals respond differently to the same diet and exercise. In turn, this knowledge would be useful for physicians wanting to practice precision medicine with their patients.
—Andrea Downing Peck