"Using a combination of enzyme activity and metabolite profiling, we determined that this protein-whose function was previously unknown-serves as a key regulator of a lipid signaling network that contributes to cancer," said Benjamin F. Cravatt, a Scripps Research professor and a member of its Skaggs Institute for Chemical Biology who led the study. "The heightened expression of KIAA1363 in several cancers indicates that it may be a critical factor in tumorgenesis. In addition, network components, including KIAA1363 itself, might be considered potential diagnostic markers for ovarian cancer."
This experimental method of integrated molecular profiling used in the study should also advance the functional study of metabolic enzymes in any biological system, according to Cravatt.
To date, understanding the roles of uncharacterized enzymes in cell physiology and pathology has remained problematic. Typically, the activities of enzymes have been studied in vitro using purified protein preparations. The outcome of these test-tube studies can be difficult to translate into clear characterizations of the roles that enzymes play in living systems, where these proteins generally operate within larger metabolic networks.
A primary advantage of metabolite profiling in natural biological systems is that it circumvents some of the most time-consuming steps that accompany in vitro enzyme analysis while generating data more directly related to their naturally occurring activities.
"Our hypothesis was that the determination of catalytic activities for enzymes like KIAA1363 could be done directly in living systems through the integrated application of profiling technologies that survey both the enzymatic proteome and its primary biochemical output, the metabolome," Cravatt said.
So, the team drew both on proteomics-the large-scale study of the structure and function of proteins-and metabolomics-the systematic study of cellular processes, specifically their small-molecule metabolite profiles-to begin to decipher the complex metabolic and signaling networks of cancer.
According to the study, one of the primary advantages of the functional proteomic technology employed (activity-based protein profiling) is that it can be used to identify inhibitors for uncharacterized enzymes like KIAA1363. Moreover, because inhibitors are screened against many enzymes in parallel, both potency and selectivity factors are assigned simultaneously.
The development of a selective inhibitor of KIAA1363 was possible due to the availability of an activity-based proteomics probe for this enzyme. Such probes are now available for many enzyme classes that participate in cell metabolism, so Cravatt suggests "a large swath of the enzyme proteome" could be addressed using the study's experimental strategy.
"The success of our study opens the door to assembling the full range of enzymes into both metabolic and signaling networks contributing to complex pathologies like cancer," Cravatt said. "This could lead to the discovery of new markers for diagnosis and targets for treatment."
Keith McKeown | EurekAlert!
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