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40
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Text
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<a href="http://doi.org/10.1021/acs.jproteome.8b00417" target="_blank" rel="noreferrer noopener">http://doi.org/10.1021/acs.jproteome.8b00417</a>
Pages
3740–3748
Issue
11
Volume
17
Dublin Core
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Title
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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.
Publisher
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Journal of proteome research
Date
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2018
2018-11
Subject
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40S ribosomal proteins; in vivo protein turnover; isotopomer quantification; metabolic labeling; NAFLD; nonlinear least-squares modeling; peak detection and integration; protein half-life; proteome dynamics; UPR
Creator
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Sadygov Rovshan G; Avva Jayant; Rahman Mahbubur; Lee Kwangwon; Ilchenko Sergei; Kasumov Takhar; Borzou Ahmad
Description
An account of the resource
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.
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<a href="http://doi.org/10.1021/acs.jproteome.8b00417" target="_blank" rel="noreferrer noopener">10.1021/acs.jproteome.8b00417</a>
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Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
2018
40S ribosomal proteins
Avva Jayant
Borzou Ahmad
Department of Pharmaceutical Sciences
Ilchenko Sergei
in vivo protein turnover
isotopomer quantification
Journal of proteome research
Kasumov Takhar
Lee Kwangwon
metabolic labeling
NAFLD
NEOMED College of Pharmacy
nonlinear least-squares modeling
peak detection and integration
protein half-life
Proteome dynamics
Rahman Mahbubur
Sadygov Rovshan G
UPR
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1021/acs.jproteorne.8b00417" target="_blank" rel="noreferrer noopener">http://doi.org/10.1021/acs.jproteorne.8b00417</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
3740-3748
Issue
11
Volume
17
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<p>Users with a NEOMED Library login can search for full-text journal articles at the following url: <a href="https://libraryguides.neomed.edu/home">https://libraryguides.neomed.edu/home</a></p>
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
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
Publisher
An entity responsible for making the resource available
Journal of Proteome Research
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-11
Subject
The topic of the resource
40S ribosomal proteins; algorithm; amino-acids; Biochemistry & Molecular Biology; dna; in vivo protein turnover; isotopomer; Mass spectrometry; metabolic labeling; NAFLD; nonlinear least-squares modeling; peak detection and integration; proliferation; protein half-life; proteome dynamics; proteostasis; quantification; rates; respiratory-chain; steatosis; UPR
Creator
An entity primarily responsible for making the resource
Sadygov R G; Avva J; Rahman M; Lee K; Ilchenko S; Kasumov T; Borzou A
Description
An account of the resource
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.
Identifier
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<a href="http://doi.org/10.1021/acs.jproteorne.8b00417" target="_blank" rel="noreferrer noopener">10.1021/acs.jproteorne.8b00417</a>
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Journal Article
2018
40S ribosomal proteins
algorithm
amino-acids
Avva J
Biochemistry & Molecular Biology
Borzou A
Department of Pharmaceutical Sciences
DNA
Ilchenko S
in vivo protein turnover
isotopomer
Journal Article
Journal of proteome research
Kasumov T
Lee K
Mass spectrometry
metabolic labeling
NAFLD
NEOMED College of Pharmacy
nonlinear least-squares modeling
peak detection and integration
proliferation
protein half-life
Proteome dynamics
proteostasis
quantification
Rahman M
rates
respiratory-chain
Sadygov R G
Steatosis
UPR