Computational protocol: Mechanisms of In Vivo Ribosome Maintenance Change in Response to Nutrient Signals*

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Protocol publication

[…] Peptide identifications were made using SpectrumMill B.06 then overlaid onto kinetic acquisitions. SpectrumMill searches were performed against the Uniprot Mouse database (12–2015, with 16,802/51,418 entries searched) with MS1 tolerance ±20ppm and a MS2 tolerance ±50 ppm, cabomidomethylation as a static modification and pyroglutamic acid (n-term) and oxidation as dynamic modifications.Searches were performed using trypsin as a digestion enzyme allowing two missed cleavages at lysine or arginine. A second search with no specific enzyme was performed against a restricted library of identified proteins. Following general recommendations from Agilent, peptides with a score greater than 7 and greater than 60% scored peptide intensity were used for further analysis. False discovery rate was calculated by the built-in algorithms of the Spectrum Mill software and was set at 1% for peptides and proteins. Identified peptides were exported and used to calculate mass isotopomer distributions and extract peptide isotope patterns from MS-only acquisitions (supplemental information). [...] MS-only isotopomer data were extracted based on peptide identification from MSMS acquisition using m/z (± 12 ppm) and retention time alignment (± 0.8 min). Data extraction and analysis were conducted using our DeuteRater () software tool based on previous publications (, ). Briefly, isotope peaks M0-M4 were normalized against the sum of the signal intensity then compared with theoretical calculations based on percentage D2O enrichment to determine fraction deuterium-enriched (new) peptide (as previously described ()). Theoretical calculations were determined using the eMASS algorithm and based on the number of possible deuterium incorporation sites per amino acid (). The theoretical changes in abundance of each isotope peak M0-M4 were compared against experimental changes at each time point in order to determine a time-dependent percentage of newly synthesized peptide reported for each isotope peak. Thus, for each peptide, there are up to five (M0-M3 for peptides below and M0-M4 for peptides above m/z = 2400) semi-independent measurements of the peptide turnover, as previously described (, ). We used the standard deviation between these measurements as a metric of the measurement precision for that peptide. If peptide precision was low (i.e. standard deviation exceeded 0.1) the data point was removed from downstream analysis (Fig. S2). Additional filters were also applied to remove peptides with total relevant intensity below 20,000 counts and a retention time deviation greater than 0.5 min.The median percentage new was calculated at each point, and outliers (defined as greater than 1.4X the median absolute standard deviation) were removed from the calculation of the protein percentage new. All peptide measurements for an individual time point that passed these filters were weighted equally in the calculation of the fraction new protein at that time point. As described previously, the combined fraction new measurements were fit using a nonlinear least squares regression based on first-order kinetic rate equations (). The proteins with high precision data at three or more time points were fit according to first-order rate kinetics. We required three or more labeled time points in order to increase the confidence of the rate constant (Fig. S2). For the regression fit, time point zero was set to 0% new and was given a standard deviation of 0.05 based on the accuracy during long-term performance of this instrument. The standard deviation and confidence interval from these fits were used to compare protein and rRNA in subsequent analysis. Coefficients of variance (standard deviation of the fit over the turnover rate) above 0.2 are considered high confidence fits. [...] There were two biological replicates for each time point in each kinetic pool measurement (assembled/total) of each diet (AL versus DR). Each kinetic rate was determined by up to 10 biological replicates, so no technical replicates were included. A minimum of three time points were required to fit a rate constant because with three time points the rates are well constrained (Fig. S2). As described above, peptide measurements were included if they met the retention time and precision filters. Statistical analysis and graphing was performed using GraphPad Prism and the Numpy software package. GraphPad Prism was used to fit the DNA to a first-order kinetic () and rRNA to a second-order kinetic (, ) as previously described and to calculate 95% confidence intervals. An in-house Python tool termed DeuteRater () was used to calculate the fraction new peptide, fit the protein turnover rates to a single pool model using first-order rate kinetics, and calculate 95% confidence intervals. If rates were outside of the 95% confidence interval of rRNA or another protein they were considered significantly different. […]

Pipeline specifications

Software tools Spectrum Mill, DeuteRater, Numpy
Applications Miscellaneous, MS-based untargeted proteomics
Organisms Mus musculus