Utilizing Nonequilibrium Isotope Enrichments to Dramatically Increase Turnover Measurement Ranges in Single Biopsy Samples from Humans.

The synthesis of new proteins and the degradation of old proteins in vivo can be quantified in serial samples using metabolic isotope labeling to measure turnover. Because serial biopsies in humans are impractical, we set out to develop a method to calculate the turnover rates of proteins from single human biopsies. This method involved a new metabolic labeling approach and adjustments to the calculations used in previous work to calculate protein turnover. We demonstrate that using a nonequilibrium isotope enrichment strategy avoids the time dependent bias caused by variable lag in label delivery to different tissues observed in traditional metabolic labeling methods. Turnover rates are consistent for the same subject in biopsies from different labeling periods, and turnover rates calculated in this study are consistent with previously reported values. We also demonstrate that by measuring protein turnover we can determine where proteins are synthesized. In human subjects a significant difference in turnover rates differentiated proteins synthesized in the salivary glands versus those imported from the serum. We also provide a data analysis tool, DeuteRater-H, to calculate protein turnover using this nonequilibrium metabolic 2H2O method.

DeuteRater: a tool for quantifying peptide isotope precision and kinetic proteomics.

MOTIVATION: Using mass spectrometry to measure the concentration and turnover of the individual proteins in a proteome, enables the calculation of individual synthesis and degradation rates for each protein. Software to analyze concentration is readily available, but software to analyze turnover is lacking. Data analysis workflows typically don't access the full breadth of information about instrument precision and accuracy that is present in each peptide isotopic envelope measurement. This method utilizes both isotope distribution and changes in neutromer spacing, which benefits the analysis of both concentration and turnover. RESULTS: We have developed a data analysis tool, DeuteRater, to measure protein turnover from metabolic D 2 O labeling. DeuteRater uses theoretical predictions for label-dependent change in isotope abundance and inter-peak (neutromer) spacing within the isotope envelope to calculate protein turnover rate. We have also used these metrics to evaluate the accuracy and precision of peptide measurements and thereby determined the optimal data acquisition parameters of different instruments, as well as the effect of data processing steps. We show that these combined measurements can be used to remove noise and increase confidence in the protein turnover measurement for each protein. AVAILABILITY AND IMPLEMENTATION: Source code and ReadMe for Python 2 and 3 versions of DeuteRater are available at https://github.com/JC-Price/DeuteRater . Data is at https://chorusproject.org/pages/index.html project number 1147. Critical Intermediate calculation files provided as Tables S3 and S4. Software has only been tested on Windows machines. CONTACT: jcprice@chem.byu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.