Topograph,a Software Platform for Precursor Enrichment Corrected Global Protein Turnover Measurements

Defects in protein turnover have been implicated in a broad range of diseases, but current proteomics methods of measuring protein turnover are limited by the software tools available. Conventional methods require indirect approaches to differentiate newly synthesized protein when synthesized from partially labeled precursor pools. To address this, we have developed Topograph, a software platform which calculates the fraction of peptides that are from newly synthesized proteins and their turnover rates. A unique feature of Topograph is the ability to calculate amino acid precursor pool enrichment levels which allows for accurate calculations when the precursor pool is not fully labeled, and the approach used by Topograph is applicable regardless of the stable isotope label used. We validate the Topograph algorithms using data acquired from a mouse labeling experiment and demonstrate the influence that precursor pool corrections can have on protein turnover measurements.Methods of measuring protein synthesis and degradation using stable or radioactive isotope labels have existed for decades. The isotope label is introduced in the form of a labeled amino acid or amino acid precursor, and the incorporation or removal of that label from protein is used to estimate average protein turnover rates (1, 2). Historically, the amount of stable isotope label incorporated into a protein is measured by enriching for the protein (e.g. affinity chromatography, gel electrophoresis, and other biochemical methods), hydrolyzing the protein to amino acids, derivatizing the amino acids, and measuring the labeled amino acid by gas chromatography-mass spectrometry or gas chromatography-combustion-isotope ratio mass spectrometry (3, 4). More recently, proteomics methods have been developed that measure the labeled amino acid on the peptide level, eliminating the need for a protein enrichment step and enabling the monitoring of many proteins in a single experiment (5).Proteomics approaches to measuring protein turnover rates in mice have been accomplished by the introduction of a ¹⁵N stable isotope label. The labeled diets were created by supplementing a protein-free diet with a ¹⁵N enriched protein source. Price et al. (6) generated ¹⁵N-labeled protein from the alga, Spirulina platensis and Zhang et al. (7) introduced ¹⁵N-label in the form of lysate from the bacterium, Ralstonia eutropha. An advantage of using complete ¹⁵N labeling is the rapid incorporation of ¹⁵N and separation of isotope distributions between labeled and natural isotope abundance peptides, which reduces the need to deconvolute the two distributions. However, current methods require that the dietary protein content be derived from bacterial or alga lysate, a diet that is not normally fed to laboratory mice. As a result, measurements of protein turnover may not reflect conventional mouse model systems because of effects of diet on protein and amino acid metabolism. A more recent work by Claydon et al. (8) demonstrated a stable isotope labeling method by supplementing labeled valine into a standard mouse diet.The complex data generated from these analyses creates a data processing and analysis challenge; exemplified by recent software platforms that have been developed. Guan et al. (9) and Hoopmann et al. (10) demonstrated data analysis pipelines for ¹⁵N labeled SILAM and SILAC experiments. Here we describe the software platform, Topograph, we have developed for the analysis of liquid chromatography-tandem MS (LC-MS/MS) data from samples with isotopic labels. Topograph is able to deconvolute the complex spectra that may result from overlapping isotope distributions, regardless of the isotope label used. More uniquely, Topograph is able to calculate the relative isotope abundance (RIA)¹ of the amino acid precursor pool, which is necessary to correctly determine the amount of newly synthesized peptide and to subsequently calculate peptide and protein turnover rates.