Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress facilitates analysis of stable isotope labeling mass spectrometry experiments for protein turnover quantification (#639)

Edward Lau 1 , Dean E Hammond 2 , Robert J Beynon 3
  1. Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
  2. Cellular and Molecular Physiology, University of Liverpool, Liverpool, United Kingdom
  3. Biochemistry, University of Liverpool, Liverpool, United Kingdom

The turnover rates of proteins are an important proteome parameter that is specific to organisms, tissues, and cellular states, and show promise as a new source of disease markers. Measurement of protein turnover rates may be achieved in intact animal models by stable isotope labeling using various isotope labels including heavy lysine, valine, or water, followed by mass spectrometry analysis of multiple time points. To enable large-scale data analysis, there is an unmet need for software tools which can automate the extraction of relative isotope abundance information from mass spectra and which is broadly applicable to multiple labeling strategies.

We describe (Relative Isotope Abundance Analyzer), an open-source software tool in Python designed to facilitate analysis for multiple isotope labels under a unified workflow. RIAna accepts as inputs mzid or tab-delimited files from database search results, and the corresponding mzML raw data through the Pymzml package. RIAna extracts the intensities of centroided MS1 peaks for each isotopomer for each qualifying peptide within user-defined retention time and mass precision ranges. It then returns the integrated areas-under-curve of the isotopomer chromatograms using the trapezoid method in SciPy. We applied RIAna toward a large dataset on inbred C57BL/6 mice labeled with [2]H2O or [13]C6-lysine (see companion poster by RB and DH). High-resolution mass spectrometry data were acquired on an Orbitrap instrument then searched against UniProt. Peptide mass isotopomers were then integrated over 0.5 min retention time windows at isotopomers 0,1,2,3,4,5 for [2]H2O labeling, and 0,6,12 for [13]C6-lysine labeling. Kinetic curve-fitting was performed using a custom R script with quasi-Newtonian optimization. Our analysis shows that RIAna provides isotopomer intensities and kinetic rate constants comparable to manual analysis but in a fraction of time required, and is applicable to different labeling methods. RIAna is freely available under MIT license on our GitHub repository.