Platform for Large-Scale Determination of Biomolecular Turnover Rate
Tech ID: 23601 / UC Case 2013-137-0
Dr. Peipei Ping and colleagues at UCLA have developed a novel, high-throughput method to evaluate the metabolic kinetics of proteins, lipids, nucleic acid and other biomolecules in vivo, including model systems and human. This technology has the potential to identify novel biomarkers for disease prevention, diagnosis, prognosis and determine treatment strategies.
Understanding alterations in biological pathways at the molecular level and quantifying protein turnover rate are fundamental proteomic elements that are critical to identifying pathogenesis of diseases. Current proteomic techniques focus on observing changes in biological systems at the steady-state level, which provides limited information on the dynamic changes occurring in complex biological pathways. Mass spectrometry (MS) has emerged as the preferred method for proteomics during the past decade, mainly due to its high resolution and unparalleled ability to acquire high-content quantitative information about protein dynamics in vivo. The MS-based proteomic market was worth $3.4 billion in 2012, and it is growing at a CAGR of 12.6%. To drive the MS-based proteomics to be easily adopted and routinely applied worldwide, an investigator team from UCLA’s Departments of Physiology and Medicine, Division of Cardiology, have developed a high throughput platform that systematically integrates MS and computational software to understand biomolecular dynamics.
Dr. Peipei Ping and colleagues at UCLA have developed a novel technique to determine the kinetics of proteins and other biomolecules in vivo, including model systems and human. Using metabolic labeling techniques in combination with innovative software analysis, metabolic changes can be measured pre-, during and post- treatment. This level of evaluation presents a unique method to monitor disease and identify key protein changes. The employed labeling techniques have been accepted to be safe for use in humans creating an opportunity to decipher disease pathogenesis and develop novel diagnostic, prognostic and predictive markers.
- Clinical studies monitoring protein turnover responses
- Pre-clinical studies investigating time dependent changes in metabolism
- Development of unique time-based biomarkers
- Provides kinetic not steady-state analysis
- The labeling for measuring protein turnover in vivo is safe for use in humans
- Cost effective
State Of Development
- Functional computational software prototype for murine studies.
- Developing protocol for drosophila and other model systems.
- Developing protocol for healthy human and subjects with congestive heart failure (pre-clinical and clinical) studies.
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