D2ome, Software for in Vivo Protein Turnover Analysis Using Heavy Water Labeling and LC-MS, Reveals Alterations of Hepatic Proteome Dynamics in a Mouse Model of NAFLD

Rovshan G. Sadygov, Jayant Avva, Mahbubur Rahman, Kwangwon Lee, Sergei Ilchenko, Takhar Kasumov, Ahmad Borzou

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Metabolic labeling with heavy water followed by LC-MS is a high throughput approach to study proteostasis in vivo. Advances in mass spectrometry and sample processing have allowed consistent detection of thousands of proteins at multiple time points. However, freely available automated bioinformatics tools to analyze and extract protein decay rate constants are lacking. Here, we describe d2ome - a robust, automated software solution for in vivo protein turnover analysis. d2ome is highly scalable, uses innovative approaches to nonlinear fitting, implements Grubbs' outlier detection and removal, uses weighted-averaging of replicates, applies a data dependent elution time windowing, and uses mass accuracy in peak detection. Here, we discuss the application of d2ome in a comparative study of protein turnover in the livers of normal vs Western diet-fed LDLR-/- mice (mouse model of nonalcoholic fatty liver disease), which contained 256 LC-MS experiments. The study revealed reduced stability of 40S ribosomal protein subunits in the Western diet-fed mice.

Original languageEnglish (US)
Pages (from-to)3740-3748
Number of pages9
JournalJournal of Proteome Research
Volume17
Issue number11
DOIs
StatePublished - Nov 2 2018

Keywords

  • NAFLD
  • in vivo protein turnover
  • metabolic labeling
  • nonlinear least-squares modeling
  • peak detection and integration
  • protein half-life; UPR; 40S ribosomal proteins; isotopomer quantification
  • proteome dynamics

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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