A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling

Deberneh, Henock M.; Abdelrahman, Doaa R.; Verma, Sunil K.; Linares, Jennifer J.; Murton, Andrew J.; Russell, William K.; Kuyumcu-Martinez, Muge N.; Miller, Benjamin F.; Sadygov, Rovshan G.

doi:10.1038/s41597-023-02537-w

Cited by 4 publications

(8 citation statements)

References 35 publications

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“…The violet vertical bars denote the SDs obtained from the variance formula, eq . Figures A and B show the results for +2 charged peptides from the murine liver and kidney, respectively. Except for very small FDRs, the global FDRs computed from PeptideProphet were more conservative compared to the theoretical predictions using the parameters of the null and alternative hypotheses.…”

Section: Resultsmentioning

confidence: 99%

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Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion

Sadygov,

Zhu,

Deberneh

2024

J. Proteome Res.

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Multiple hypothesis testing is an integral component of data analysis for large-scale technologies such as proteomics, transcriptomics, or metabolomics, for which the false discovery rate (FDR) and positive FDR (pFDR) have been accepted as error estimation and control measures. The pFDR is the expectation of false discovery proportion (FDP), which refers to the ratio of the number of null hypotheses to that of all rejected hypotheses. In practice, the expectation of ratio is approximated by the ratio of expectation; however, the conditions for transforming the former into the latter have not been investigated. This work derives exact integral expressions for the expectation (pFDR) and variance of FDP. The widely used approximation (ratio of expectations) is shown to be a particular case (in the limit of a large sample size) of the integral formula for pFDR. A recurrence formula is provided to compute the pFDR for a predefined number of null hypotheses. The variance of FDP was approximated for a practical application in peptide identification using forward and reversed protein sequences. The simulations demonstrate that the integral expression exhibits better accuracy than the approximate formula in the case of a small number of hypotheses. For large sample sizes, the pFDRs obtained by the integral expression and approximation do not differ substantially. Applications to proteomics data sets are included.

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Section: Resultsmentioning

confidence: 99%

“…For tests with data sets obtained by liquid-chromatography coupled to mass spectrometry (LC-MS), we employed murine liver , and kidney data sets. The experiments are described in the original publications.…”

Section: Methodsmentioning

confidence: 99%

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion

Sadygov,

Zhu,

Deberneh

2024

J. Proteome Res.

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“…At each labeling duration, two randomly chosen mice were sacrificed, and dissected livers were used to prepare the samples for LC-MS analysis. ThermoFisher Eclipse Orbitrap mass spectrometer was operated using data-dependent acquisition The performance of the filtering tool was evaluated using a benchmark dataset acquired from a recent work that reported a large-scale LC-MS murine liver proteome study [21]. The dataset contains raw mass spectral data, database search results, and quantification outputs that were obtained from eighteen C57/BL6J male mice liver tissues using an Orbitrap Eclipse mass spectrometer at nine different labeling durations (0,1, 2, 3, 4, 5, 6, 14, and 21 days).…”

Section: Advanced Filters To Facilitate Protein Turnover Rate Analysismentioning

confidence: 99%

“…The remaining mice were IP-injected with 750-960 ul of 99.9% D 2 O that was made isotonic with 0.9 g NaCl w/v. They were immediately given free access to 8% enriched (v/v) deuterated water for variable labeling durations [15,21]. At each labeling duration, two randomly chosen mice were sacrificed, and dissected livers were used to prepare the samples for LC-MS analysis.…”

Section: Advanced Filters To Facilitate Protein Turnover Rate Analysismentioning

confidence: 99%

Flexible Quality Control for Protein Turnover Rates Using d2ome

Deberneh,

Sadygov

2023

IJMS

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Bioinformatics tools are used to estimate in vivo protein turnover rates from the LC-MS data of heavy water labeled samples in high throughput. The quantification includes peak detection and integration in the LC-MS domain of complex input data of the mammalian proteome, which requires the integration of results from different experiments. The existing software tools for the estimation of turnover rate use predefined, built-in, stringent filtering criteria to select well-fitted peptides and determine turnover rates for proteins. The flexible control of filtering and quality measures will help to reduce the effects of fluctuations and interferences to the signals from target peptides while retaining an adequate number of peptides. This work describes an approach for flexible error control and filtering measures implemented in the computational tool d2ome for automating protein turnover rates. The error control measures (based on spectral properties and signal features) reduced the standard deviation and tightened the confidence intervals of the estimated turnover rates.

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“…Heavy water labeling, coupled with mass spectrometry, can be used to trace the synthesis and degradation kinetics of proteins in rodents and in humans [1][2][3][4][5][6][7][8][9][10][11][12][13] . Under continued enrichment of heavy water D 2 O, deuterium (D or 2 H) atoms are incorporated into non-essential amino acids during biosynthesis and metabolism.…”

Section: Introductionmentioning

confidence: 99%

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection

Currie,

Ng,

Pandi

et al. 2024

Preprint

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The synthesis and degradation rates of proteins form an essential component of gene expression control. Heavy water labeling has been used in conjunction with mass spectrometry to measure protein turnover rates, but the optimal analytical approaches to derive turnover rates from the isotopomer patterns of deuterium labeled peptides continue to be a subject of research. Here we describe a method, which comprises a reverse lookup of numerically approximated peptide isotope envelopes, coupled to the selection of optimal isotopomer pairs based on peptide sequence, to calculate the molar fraction of new peptide synthesis in heavy water labeling mass spectrometry experiments. We validated this approach using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes. We then re-analyzed 17 proteome-wide turnover experiments from four mouse organs, and showed that the method increases the coverage of well-fitted peptides in protein turnover experiments by 25–82%. The method is implemented in the Riana software tool for protein turnover analysis, and may avail ongoing efforts to study the synthesis and degradation kinetics of proteins in animals on a proteome-wide scale.What’s newWe describe a reverse lookup method to calculate the molar fraction of new synthesis from numerically approximated peptide isotopomer profiles in heavy water labeling mass spectrometry experiments. Using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes at various proportions, we show that this method provides a straightforward way to calculate the proportion of new proteins in a protein pool from arbitrarily chosen isotopomer ratios. We next analyzed which of the isotopomer pairs within the peptide isotope envelope yielded isotopomer time courses that fit most closely to kinetic models, and found that the identity of the isotopomer pair depends partially on the number of deuterium accessible labeling sites of the peptide. We next derived a strategy to automatically select the isotopomer pairs to calculate turnover rates based on peptide sequence, and showed that this increases the coverage of existing proteome-wide turnover experiments in multiple data sets of the mouse heart, liver, kidney, and skeletal muscle by up to 25–82%.

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A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling

Cited by 4 publications

References 35 publications

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion

Flexible Quality Control for Protein Turnover Rates Using d2ome

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection

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